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

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

CMakeLists.txt

@@ -47,33 +47,34 @@ find_package(Threads REQUIRED)
 
 include(simd)
 
-################################ Configurations #######################################
+################################ Exposed Configurations #######################################
 option(WITH_GPU         "Compile PaddlePaddle with NVIDIA GPU"          ${CUDA_FOUND})
-option(WITH_AMD_GPU     "Compile PaddlePaddle with AMD GPU"             OFF)
+option(WITH_DSO         "Compile PaddlePaddle with dynamic linked CUDA" ON)
 option(WITH_AVX         "Compile PaddlePaddle with AVX intrinsics"      ${AVX_FOUND})
+option(WITH_PYTHON      "Compile PaddlePaddle with python interpreter"  ON)
+option(WITH_TESTING     "Compile PaddlePaddle with unit testing"        OFF)
 option(WITH_MKL         "Compile PaddlePaddle with MKL support."        ${AVX_FOUND})
+option(WITH_SYSTEM_BLAS   "Use system blas library"           OFF)
+option(WITH_DISTRIBUTE  "Compile with distributed support"              OFF)
+option(WITH_BRPC_RDMA     "Use brpc rdma as the rpc protocal"           OFF)
+option(ON_INFER         "Turn on inference optimization."               OFF)
+option(WITH_ANAKIN      "Compile with Anakin library"                   OFF)
+################################ Internal Configurations #######################################
+option(WITH_AMD_GPU     "Compile PaddlePaddle with AMD GPU"             OFF)
 option(WITH_NGRAPH      "Compile PaddlePaddle with nGraph support."     OFF)
-option(WITH_DSO         "Compile PaddlePaddle with dynamic linked CUDA" ON)
-option(WITH_TESTING     "Compile PaddlePaddle with unit testing"        OFF)
-option(WITH_PYTHON      "Compile PaddlePaddle with python interpreter"  ON)
 option(WITH_PROFILER    "Compile PaddlePaddle with GPU profiler and gperftools"        OFF)
 option(WITH_JEMALLOC    "Compile PaddlePaddle with jemalloc"            OFF)
 option(WITH_COVERAGE    "Compile PaddlePaddle with code coverage"       OFF)
 option(COVERALLS_UPLOAD "Package code coverage data to coveralls"       OFF)
-option(WITH_DISTRIBUTE  "Compile with distributed support"              OFF)
 option(WITH_PSLIB       "Compile with pslib support"                    OFF)
 option(WITH_CONTRIB     "Compile the third-party contributation"        OFF)
 option(REPLACE_ENFORCE_GLOG "Replace PADDLE_ENFORCE with glog/CHECK for better debug." OFF)
 # TODO(Superjomn) Remove WITH_ANAKIN option if not needed latter.
-option(WITH_ANAKIN      "Compile with Anakin library"                   OFF)
 option(ANAKIN_BUILD_FAT_BIN "Build anakin cuda fat-bin lib for all device plantform, ignored when WITH_ANAKIN=OFF" OFF)
 option(ANAKIN_BUILD_CROSS_PLANTFORM "Build anakin lib for any nvidia device plantform. ignored when WITH_ANAKIN=OFF" ON)
 option(WITH_GRPC     "Use grpc as the default rpc framework"            ${WITH_DISTRIBUTE})
-option(WITH_BRPC_RDMA     "Use brpc rdma as the rpc protocal"           OFF)
-option(ON_INFER         "Turn on inference optimization."               OFF)
 option(WITH_INFERENCE_API_TEST   "Test fluid inference C++ high-level api interface"  OFF)
 option(WITH_HIGH_LEVEL_API_TEST   "Test fluid python high-level api interface"  OFF)
-option(WITH_SYSTEM_BLAS   "Use system blas library"           OFF)
 option(PY_VERSION       "Compile PaddlePaddle with python3 support"     ${PY_VERSION})
 option(WITH_FAST_MATH   "Make use of fast math library, might affect the precision to some extent" ON)
 

paddle/fluid/API.spec

@@ -241,6 +241,7 @@ paddle.fluid.layers.tree_conv (ArgSpec(args=['nodes_vector', 'edge_set', 'output
 paddle.fluid.layers.npair_loss (ArgSpec(args=['anchor', 'positive', 'labels', 'l2_reg'], varargs=None, keywords=None, defaults=(0.002,)), ('document', '46994d10276dd4cb803b4062b5d14329'))
 paddle.fluid.layers.pixel_shuffle (ArgSpec(args=['x', 'upscale_factor'], varargs=None, keywords=None, defaults=None), ('document', '731b21c62a4add60a33bd76d802ffc5c'))
 paddle.fluid.layers.fsp_matrix (ArgSpec(args=['x', 'y'], varargs=None, keywords=None, defaults=None), ('document', 'b76ccca3735bea4a58a0dbf0d77c5393'))
+paddle.fluid.layers.continuous_value_model (ArgSpec(args=['input', 'cvm', 'use_cvm'], varargs=None, keywords=None, defaults=(True,)), ('document', 'a07a44c2bacdcd09c1f5f35a96a0514e'))
 paddle.fluid.layers.data (ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True)), ('document', '33bbd42027d872b3818b3d64ec52e139'))
 paddle.fluid.layers.open_files (ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None)), ('document', 'b1ae2e1cc0750e58726374061ea90ecc'))
 paddle.fluid.layers.read_file (ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None), ('document', 'b0a1c2fc51c27a106da28f3308c41f5e'))
@@ -301,12 +302,12 @@ paddle.fluid.layers.DynamicRNN.static_input (ArgSpec(args=['self', 'x'], varargs
 paddle.fluid.layers.DynamicRNN.step_input (ArgSpec(args=['self', 'x', 'level'], varargs=None, keywords=None, defaults=(0,)), ('document', '7568c5ac7622a10288d3307a94134655'))
 paddle.fluid.layers.DynamicRNN.update_memory (ArgSpec(args=['self', 'ex_mem', 'new_mem'], varargs=None, keywords=None, defaults=None), ('document', '5d83987da13b98363d6a807a52d8024f'))
 paddle.fluid.layers.StaticRNN.__init__ (ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
-paddle.fluid.layers.StaticRNN.memory (ArgSpec(args=['self', 'init', 'shape', 'batch_ref', 'init_value', 'init_batch_dim_idx', 'ref_batch_dim_idx'], varargs=None, keywords=None, defaults=(None, None, None, 0.0, 0, 1)), ('document', 'c24e368e23afac1ed91a78a639d7a9c7'))
-paddle.fluid.layers.StaticRNN.output (ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
-paddle.fluid.layers.StaticRNN.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
-paddle.fluid.layers.StaticRNN.step_input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
-paddle.fluid.layers.StaticRNN.step_output (ArgSpec(args=['self', 'o'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
-paddle.fluid.layers.StaticRNN.update_memory (ArgSpec(args=['self', 'mem', 'var'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.layers.StaticRNN.memory (ArgSpec(args=['self', 'init', 'shape', 'batch_ref', 'init_value', 'init_batch_dim_idx', 'ref_batch_dim_idx'], varargs=None, keywords=None, defaults=(None, None, None, 0.0, 0, 1)), ('document', '72530f299d6451a567cf4a12dc3fb1ff'))
+paddle.fluid.layers.StaticRNN.output (ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None), ('document', 'df6ceab6e6c9bd31e97914d7e7538137'))
+paddle.fluid.layers.StaticRNN.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6d3e0a5d9aa519a9773a36e1620ea9b7'))
+paddle.fluid.layers.StaticRNN.step_input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', '903387ec11f3d0bf46821d31a68cffa5'))
+paddle.fluid.layers.StaticRNN.step_output (ArgSpec(args=['self', 'o'], varargs=None, keywords=None, defaults=None), ('document', '252890d4c3199a7623ab8667e13fd837'))
+paddle.fluid.layers.StaticRNN.update_memory (ArgSpec(args=['self', 'mem', 'var'], varargs=None, keywords=None, defaults=None), ('document', '7a0000520f179f35239956a5ba55119f'))
 paddle.fluid.layers.reorder_lod_tensor_by_rank (ArgSpec(args=['x', 'rank_table'], varargs=None, keywords=None, defaults=None), ('document', '3545f529ef04e8f6ecb76b47fa3df01a'))
 paddle.fluid.layers.Print (ArgSpec(args=['input', 'first_n', 'message', 'summarize', 'print_tensor_name', 'print_tensor_type', 'print_tensor_shape', 'print_tensor_lod', 'print_phase'], varargs=None, keywords=None, defaults=(-1, None, -1, True, True, True, True, 'both')), ('document', '5fef91b0e21c93610785f2b1f7161732'))
 paddle.fluid.layers.is_empty (ArgSpec(args=['x', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', 'bbe578dbb49ad13e15b014e98c22b519'))

paddle/fluid/framework/details/async_ssa_graph_executor.cc

@@ -64,9 +64,12 @@ void ProcessGraph(std::vector<ir::Graph *> graphs, Scope *scope) {
               node->Op()->GetNullableAttr("epmap"));
           auto height_section = boost::get<std::vector<int64_t>>(
               node->Op()->GetNullableAttr("sections"));
+          auto trainer_id =
+              boost::get<int>(node->Op()->GetNullableAttr("trainer_id"));
           send_varname_to_ctx[send_var_name] =
               operators::distributed::RpcContext(send_var_name, send_varnames,
-                                                 epmap, height_section);
+                                                 epmap, height_section,
+                                                 trainer_id);
           VLOG(3) << "find and init an send op: "
                   << send_varname_to_ctx[send_var_name];
         } else if (node->Name() == "recv") {
@@ -75,9 +78,11 @@ void ProcessGraph(std::vector<ir::Graph *> graphs, Scope *scope) {
               node->Op()->GetNullableAttr("recv_varnames"));
           auto epmap = boost::get<std::vector<std::string>>(
               node->Op()->GetNullableAttr("epmap"));
+          auto trainer_id =
+              boost::get<int>(node->Op()->GetNullableAttr("trainer_id"));
           recv_varname_to_ctx[recv_var_name] =
               operators::distributed::RpcContext(recv_var_name, recv_varnames,
-                                                 epmap, {});
+                                                 epmap, {}, trainer_id);
           nodes_to_delete.push_back(node);
           VLOG(3) << "find and remove an recv op: "
                   << recv_varname_to_ctx[recv_var_name];

paddle/fluid/framework/details/build_strategy.cc

@@ -101,8 +101,6 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
                "mode.";
         strategy_.fuse_all_optimizer_ops_ = false;
       } else {
-        VLOG(10) << "Add alloc_continuous_space_for_grad_pass";
-        AppendPass("alloc_continuous_space_for_grad_pass");
         // NOTE: fuse_all_xx_ops will count the number of xx operator first,
         // if the number is zero, fuse_all_reduce_ops will do nothing.
         // Currently, only one type of optimization algorithm can be fused.

paddle/fluid/framework/details/fuse_adam_op_pass.cc

@@ -24,7 +24,7 @@ namespace details {
 const std::string FuseAdamOpPass::GetOpType() const { return "adam"; }
 
 const std::vector<std::string> FuseAdamOpPass::GetAuxiliaryVarNames() const {
-  return {"Param", "Moment1", "Moment2", "Beta1Pow", "Beta2Pow"};
+  return {"Moment1", "Moment2", "Beta1Pow", "Beta2Pow"};
 }
 
 void FuseAdamOpPass::FuseOptimizerOps(
@@ -77,16 +77,16 @@ void FuseAdamOpPass::FuseAdamOps(
   VLOG(10) << "Insert adam to graph ";
   OpDesc adam_desc(adam_ops[0]->Op()->Block());
   adam_desc.SetType("adam");
-  adam_desc.SetInput("Param", {fused_vars_name.at("Param")});
-  adam_desc.SetInput("Grad", {fused_vars_name.at("Grad")});
+  adam_desc.SetInput(kParam, {fused_vars_name.at(kParam)});
+  adam_desc.SetInput(kGrad, {fused_vars_name.at(kGrad)});
   adam_desc.SetInput("Moment1", {fused_vars_name.at("Moment1")});
   adam_desc.SetInput("Moment2", {fused_vars_name.at("Moment2")});
   // TODO(zcd): The LearningRate, Beta1Pow, Beta2Pow should be equal.
-  adam_desc.SetInput("LearningRate", adam_ops[0]->Op()->Input("LearningRate"));
+  adam_desc.SetInput(kLearningRate, adam_ops[0]->Op()->Input(kLearningRate));
   adam_desc.SetInput("Beta1Pow", adam_ops[0]->Op()->Input("Beta1Pow"));
   adam_desc.SetInput("Beta2Pow", adam_ops[0]->Op()->Input("Beta2Pow"));
 
-  adam_desc.SetOutput("ParamOut", {fused_vars_name.at("Param")});
+  adam_desc.SetOutput("ParamOut", {fused_vars_name.at(kParam)});
   adam_desc.SetOutput("Moment1Out", {fused_vars_name.at("Moment1")});
   adam_desc.SetOutput("Moment2Out", {fused_vars_name.at("Moment2")});
   adam_desc.SetAttr("beta1", beta1);

paddle/fluid/framework/details/fuse_optimizer_op_pass.cc

@@ -29,7 +29,9 @@ void FuseOptimizerOpPass::ApplyImpl(ir::Graph *graph) const {
   auto &local_scopes = Get<const std::vector<Scope *>>(kLocalScopes);
 
   const std::string fuse_op_type = GetOpType();
-  const std::vector<std::string> aux_var_names = GetAuxiliaryVarNames();
+  std::vector<std::string> aux_var_names = GetAuxiliaryVarNames();
+  aux_var_names.emplace_back(kParam);
+  aux_var_names.emplace_back(kGrad);
 
   // Step 1: Get the specified op and auxiliary variables.
   std::vector<ir::Node *> topo_nodes = ir::TopologySortOperations(result);
@@ -61,7 +63,7 @@ void FuseOptimizerOpPass::ApplyImpl(ir::Graph *graph) const {
     result.Set(kFusedVars, new FusedVars);
   }
   std::unordered_map<std::string, std::string> fused_vars_name;
-  fused_vars_name.reserve(aux_var_names.size() + 1);
+  fused_vars_name.reserve(aux_var_names.size());
   auto &fused_var_set = result.Get<FusedVars>(kFusedVars);
   const std::string prefix(kFusedVarNamePrefix);
   // NOTE: the fused_var_name should be unique.
@@ -75,39 +77,103 @@ void FuseOptimizerOpPass::ApplyImpl(ir::Graph *graph) const {
   }
 
   // Step 3: Get the fused Gradient's name
-  auto &params_grads = result.Get<ParamsAndGrads>(kParamsAndGrads);
-  if (!result.Has(kFusedGrads)) {
-    PADDLE_THROW(
-        "The alloc_continuous_space_for_grad_pass should be called before this "
-        "pass.");
-  }
-  auto &fused_grad = result.Get<FusedGrads>(kFusedGrads);
-  auto &fused_vars = result.Get<FusedVars>(kFusedVars);
-  auto iter = std::find(fused_vars.begin(), fused_vars.end(), fused_grad);
-  PADDLE_ENFORCE(iter != fused_vars.end(), "Not find the fused_grad.");
-  fused_vars_name.emplace("Grad", fused_grad);
-
-  // Step 4: Sort the parameters and auxiliary variables according
-  // to parameters' name to make variables' name correspond correctly.
-  PADDLE_ENFORCE(result.Has(kParamsAndGrads), "Does't find kParamsAndGrads.");
-  PADDLE_ENFORCE_EQ(params_grads.size(), aux_var_set.begin()->second.size(),
-                    "The size of params_grads and aux_var_set are not equal.");
-  SortParametersAndAuxVars(params_grads, &aux_var_set, &opt_ops);
-
-  // Step 5: Alloc continuous space for Parameters and AuxiliaryVar(e.g.
+  bool grad_fused = false;
+  if (result.Has(kParamsAndGrads)) {
+    auto &params_grads = result.Get<ParamsAndGrads>(kParamsAndGrads);
+    PADDLE_ENFORCE_EQ(
+        params_grads.size(), aux_var_set.at(kGrad).size(),
+        "The number of gradients and optimizer ops is not equal.");
+    std::unordered_set<std::string> opt_grad_set(aux_var_set.at(kGrad).begin(),
+                                                 aux_var_set.at(kGrad).end());
+    size_t same_grad_num = 0;
+    for (auto &p_g : params_grads) {
+      if (opt_grad_set.count(p_g.second)) {
+        ++same_grad_num;
+      }
+    }
+
+    // NOTE(zcd): the gradient of kParamsAndGrads may be different with the
+    // kGrad.
+    if (same_grad_num == aux_var_set.at(kGrad).size()) {
+      if (!result.Has(kFusedGrads)) {
+        PADDLE_THROW(
+            "The alloc_continuous_space_for_grad_pass should be called before "
+            "this pass.");
+      }
+      auto &fused_grad = result.Get<FusedGrads>(kFusedGrads);
+      auto &fused_vars = result.Get<FusedVars>(kFusedVars);
+      auto iter = std::find(fused_vars.begin(), fused_vars.end(), fused_grad);
+      PADDLE_ENFORCE(iter != fused_vars.end(), "Not find the fused_grad.");
+      fused_vars_name[kGrad] = fused_grad;
+
+      // Sort the parameters and auxiliary variables according
+      // to parameters' name to make variables' name correspond correctly.
+      SortParametersAndAuxVars(params_grads, &aux_var_set, &opt_ops);
+      grad_fused = true;
+    }
+  }
+
+  // Step 4: Alloc continuous space for Parameters and AuxiliaryVar(e.g.
   // Moment1, Moment2, Beta1Pow, Beta2Pow) of all the optimizer ops separately.
+  aux_var_names.pop_back();
+  if (!grad_fused) {
+    InitFusedGradsAndAllocSpaceForGrads(
+        places, local_scopes, aux_var_set.at(kParam), aux_var_set.at(kGrad),
+        fused_vars_name.at(kGrad), &result);
+  }
   InitFusedVarsAndAllocSpaceForVars(places, local_scopes, aux_var_names,
                                     aux_var_set, fused_vars_name);
 
-  // Step 6: Fuse optimizer Ops and Scale Ops
+  // Step 5: Fuse optimizer Ops and Scale Ops
   FuseOptimizerOps(aux_var_set, fused_vars_name, opt_ops, &result);
 
-  // Step 7: Remove optimizer Ops
+  // Step 6: Remove optimizer Ops
   for (auto &opt_op : opt_ops) {
     graph->RemoveNode(opt_op);
   }
 }
 
+void FuseOptimizerOpPass::InitFusedGradsAndAllocSpaceForGrads(
+    const std::vector<platform::Place> &places,
+    const std::vector<Scope *> &local_scopes,
+    const std::vector<std::string> &params,
+    const std::vector<std::string> &grads, const std::string &fused_grad_name,
+    ir::Graph *result) const {
+  // Get Var Nodes
+  std::unordered_map<std::string, ir::Node *> vars;
+  for (ir::Node *node : result->Nodes()) {
+    if (node->IsVar() && node->Var()) {
+      // Note: The graph may have the same name node. For example, parameter
+      // is the input of operator and it also is the output of optimizer;
+      vars.emplace(node->Var()->Name(), node);
+    }
+  }
+  // Init Grads
+  for (auto it = local_scopes.rbegin(); it != local_scopes.rend(); ++it) {
+    auto &scope = *it;
+    VLOG(10) << "Init " << fused_grad_name;
+    PADDLE_ENFORCE(scope->FindVar(fused_grad_name) == nullptr,
+                   "%s has existed in scope.", fused_grad_name);
+    scope->Var(fused_grad_name)->GetMutable<LoDTensor>();
+
+    for (auto &grad_var_name : grads) {
+      auto iter = vars.find(grad_var_name);
+      PADDLE_ENFORCE(iter != vars.end());
+      PADDLE_ENFORCE_NOT_NULL(iter->second->Var());
+      PADDLE_ENFORCE_EQ(iter->second->Var()->GetType(),
+                        proto::VarType::LOD_TENSOR);
+      scope->Var(grad_var_name)->GetMutable<LoDTensor>();
+    }
+  }
+  // Define Ops
+  ProgramDesc program_desc;
+  auto *global_block = program_desc.MutableBlock(0);
+  AppendAllocContinuousSpace(params, grads, fused_grad_name, global_block,
+                             false, false);
+  // Run Ops
+  RunInitOps(places, local_scopes, *global_block);
+}
+
 void FuseOptimizerOpPass::InitFusedVarsAndAllocSpaceForVars(
     const std::vector<platform::Place> &places,
     const std::vector<Scope *> &local_scopes,
@@ -115,37 +181,49 @@ void FuseOptimizerOpPass::InitFusedVarsAndAllocSpaceForVars(
     const std::unordered_map<std::string, std::vector<std::string>>
         &aux_var_set,
     const std::unordered_map<std::string, std::string> &fused_vars_name) const {
-  VLOG(10) << "Init FusedVars.";
-  // Alloc parameters and auxiliary vars in the respective scope.
-  size_t idx = local_scopes.size();
-  for (auto iter = local_scopes.rbegin(); iter != local_scopes.rend();
-       ++iter, --idx) {
-    auto &scope = *iter;
-    for (auto &var_name : aux_var_names) {
-      auto fused_var_name = fused_vars_name.at(var_name);
-      VLOG(10) << "Init " << fused_var_name;
-      PADDLE_ENFORCE(scope->FindVar(fused_var_name) == nullptr,
-                     "%s has exist in scope[%d]", fused_var_name, idx);
-      scope->Var(fused_var_name)->GetMutable<LoDTensor>();
-    }
+  // Init Vars
+  for (auto &var_name : aux_var_names) {
+    auto &fused_var_name = fused_vars_name.at(var_name);
+    InitVars(local_scopes, fused_var_name);
   }
-
+  // Define Ops
   ProgramDesc program_desc;
   auto *global_block = program_desc.MutableBlock(0);
   for (auto &var_name : aux_var_names) {
-    AppendAllocContinuousSpace(aux_var_set.at(var_name),
-                               fused_vars_name.at(var_name), true,
-                               global_block);
+    AppendAllocContinuousSpace(
+        aux_var_set.at(var_name), aux_var_set.at(var_name),
+        fused_vars_name.at(var_name), global_block, true);
   }
+  // Run Ops
+  RunInitOps(places, local_scopes, *global_block);
+}
 
+void FuseOptimizerOpPass::RunInitOps(const std::vector<platform::Place> &places,
+                                     const std::vector<Scope *> &local_scopes,
+                                     const BlockDesc &global_block) const {
   for (size_t i = 0; i < local_scopes.size(); ++i) {
-    for (auto &op_desc : global_block->AllOps()) {
+    for (auto &op_desc : global_block.AllOps()) {
       auto op = OpRegistry::CreateOp(*op_desc);
       op->Run(*local_scopes[i], places[i]);
     }
   }
 }
 
+void FuseOptimizerOpPass::InitVars(const std::vector<Scope *> &local_scopes,
+                                   const std::string &fused_var_name) const {
+  VLOG(10) << "Init FusedVars.";
+  // Alloc parameters and auxiliary vars in the respective scope.
+  size_t idx = local_scopes.size();
+  for (auto iter = local_scopes.rbegin(); iter != local_scopes.rend();
+       ++iter, --idx) {
+    auto &scope = *iter;
+    VLOG(10) << "Init " << fused_var_name;
+    PADDLE_ENFORCE(scope->FindVar(fused_var_name) == nullptr,
+                   "%s has exist in scope[%d]", fused_var_name, idx);
+    scope->Var(fused_var_name)->GetMutable<LoDTensor>();
+  }
+}
+
 void FuseOptimizerOpPass::SortParametersAndAuxVars(
     const std::vector<std::pair<std::string, std::string>> &params_grads,
     std::unordered_map<std::string, std::vector<std::string>> *aux_vars_set,
@@ -203,15 +281,16 @@ void FuseOptimizerOpPass::GetSpecifiedOpsAndVars(
 }
 
 void FuseOptimizerOpPass::AppendAllocContinuousSpace(
-    const std::vector<std::string> &args, const std::string &out_arg,
-    bool copy_data, BlockDesc *global_block) const {
+    const std::vector<std::string> &in_args,
+    const std::vector<std::string> &out_args, const std::string &fused_out_arg,
+    BlockDesc *global_block, bool copy_data, bool check_name) const {
   auto op_desc = global_block->AppendOp();
   op_desc->SetType("alloc_continuous_space");
-  op_desc->SetInput("Input", args);
-  op_desc->SetOutput("Output", args);
-  op_desc->SetOutput("FusedOutput", {out_arg});
+  op_desc->SetInput("Input", in_args);
+  op_desc->SetOutput("Output", out_args);
+  op_desc->SetOutput("FusedOutput", {fused_out_arg});
   op_desc->SetAttr("copy_data", copy_data);
-  op_desc->SetAttr("check_name", true);
+  op_desc->SetAttr("check_name", check_name);
 }
 
 void FuseOptimizerOpPass::InserInputAndOutputForOptOps(

paddle/fluid/framework/details/fuse_optimizer_op_pass.h

@@ -27,6 +27,10 @@ namespace paddle {
 namespace framework {
 namespace details {
 
+constexpr char kGrad[] = "Grad";
+constexpr char kParam[] = "Param";
+constexpr char kLearningRate[] = "LearningRate";
+
 class FuseOptimizerOpPass : public ir::Pass {
  protected:
   void ApplyImpl(ir::Graph *graph) const override;
@@ -56,9 +60,18 @@ class FuseOptimizerOpPass : public ir::Pass {
       std::unordered_map<std::string, std::vector<std::string>> *aux_args_name)
       const;
 
-  void AppendAllocContinuousSpace(const std::vector<std::string> &args,
-                                  const std::string &out_arg, bool copy_data,
-                                  BlockDesc *global_block) const;
+  void AppendAllocContinuousSpace(const std::vector<std::string> &in_args,
+                                  const std::vector<std::string> &out_args,
+                                  const std::string &fused_out_arg,
+                                  BlockDesc *global_block, bool copy_data,
+                                  bool check_name = true) const;
+
+  void InitFusedGradsAndAllocSpaceForGrads(
+      const std::vector<platform::Place> &places,
+      const std::vector<Scope *> &local_scopes,
+      const std::vector<std::string> &params,
+      const std::vector<std::string> &grads, const std::string &fused_grad_name,
+      ir::Graph *result) const;
 
   void InitFusedVarsAndAllocSpaceForVars(
       const std::vector<platform::Place> &places,
@@ -68,6 +81,13 @@ class FuseOptimizerOpPass : public ir::Pass {
           &aux_var_set,
       const std::unordered_map<std::string, std::string> &fused_vars_name)
       const;
+
+  void RunInitOps(const std::vector<platform::Place> &places,
+                  const std::vector<Scope *> &local_scopes,
+                  const BlockDesc &global_block) const;
+
+  void InitVars(const std::vector<Scope *> &local_scopes,
+                const std::string &fused_var_name) const;
 };
 
 }  // namespace details

paddle/fluid/framework/details/fuse_sgd_op_pass.cc

@@ -24,7 +24,7 @@ namespace details {
 const std::string FuseSgdOpPass::GetOpType() const { return "sgd"; }
 
 const std::vector<std::string> FuseSgdOpPass::GetAuxiliaryVarNames() const {
-  return {"Param"};
+  return {};
 }
 
 void FuseSgdOpPass::FuseOptimizerOps(
@@ -50,12 +50,12 @@ void FuseSgdOpPass::FuseSgdOps(
   // Add fused scale
   OpDesc Sgd_desc(sgd_ops[0]->Op()->Block());
   Sgd_desc.SetType("sgd");
-  Sgd_desc.SetInput("Param", {fused_vars_name.at("Param")});
-  Sgd_desc.SetInput("Grad", {fused_vars_name.at("Grad")});
-  Sgd_desc.SetOutput("ParamOut", {fused_vars_name.at("Param")});
+  Sgd_desc.SetInput(kParam, {fused_vars_name.at(kParam)});
+  Sgd_desc.SetInput(kGrad, {fused_vars_name.at(kGrad)});
+  Sgd_desc.SetOutput("ParamOut", {fused_vars_name.at(kParam)});
 
   // TODO(zcd): The LearningRate, Beta1Pow, Beta2Pow should be equal.
-  Sgd_desc.SetInput("LearningRate", sgd_ops[0]->Op()->Input("LearningRate"));
+  Sgd_desc.SetInput(kLearningRate, sgd_ops[0]->Op()->Input(kLearningRate));
 
   // NOTE: multi_devices_pass requires that every op should have a role.
   Sgd_desc.SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(), op_role);

paddle/fluid/framework/details/parallel_ssa_graph_executor.cc

@@ -106,7 +106,7 @@ ParallelSSAGraphExecutor::ParallelSSAGraphExecutor(
   VLOG(1) << "set num_threads: " << strategy_.num_threads_
           << " to run the operators of the graph on each device.";
   for (size_t i = 0; i < places.size(); ++i) {
-    executors_.emplace_back(new details::ThreadedSSAGraphExecutor(
+    executors_.emplace_back(new details::FastThreadedSSAGraphExecutor(
         strategy_, local_scopes_, {places_[i]}, graphs_.at(i).get()));
   }
 }

paddle/fluid/framework/details/parallel_ssa_graph_executor.h

@@ -14,12 +14,12 @@
 
 #pragma once
 
+#include <memory>
 #include <string>
 #include <vector>
-
 #include "ThreadPool.h"
+#include "paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h"
 #include "paddle/fluid/framework/details/multi_devices_helper.h"
-#include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
 #include "paddle/fluid/framework/ir/graph.h"
 
 namespace paddle {
@@ -48,7 +48,8 @@ class ParallelSSAGraphExecutor : public SSAGraphExecutor {
   std::vector<platform::Place> places_;
   std::vector<std::unique_ptr<ir::Graph>> graphs_;
 
-  std::vector<std::unique_ptr<details::ThreadedSSAGraphExecutor>> executors_;
+  std::vector<std::unique_ptr<details::FastThreadedSSAGraphExecutor>>
+      executors_;
   ExceptionHolder exception_holder_;
 };
 

paddle/fluid/framework/var_type_inference.h

@@ -45,12 +45,16 @@ class InferVarTypeContext {
 
   virtual bool HasInput(const std::string& name) const {
     PADDLE_ENFORCE_NOT_NULL(op_);
-    return op_->Inputs().count(name) > 0;
+    auto& inputs = op_->Inputs();
+    auto input = inputs.find(name);
+    return input != inputs.end() && !input->second.empty();
   }
 
   virtual bool HasOutput(const std::string& name) const {
     PADDLE_ENFORCE_NOT_NULL(op_);
-    return op_->Outputs().count(name) > 0;
+    auto& outputs = op_->Outputs();
+    auto output = outputs.find(name);
+    return output != outputs.end() && !output->second.empty();
   }
 
   virtual const std::vector<std::string>& Input(const std::string& name) const {

paddle/fluid/inference/api/analysis_predictor.cc

@@ -832,6 +832,45 @@ std::string AnalysisPredictor::GetSerializedProgram() const {
   return inference_program_->Proto()->SerializeAsString();
 }
 
+// Add SaveOptimModel
+void AnalysisPredictor::SaveOptimModel(const std::string &dir) {
+  // save model
+  std::string model_name = dir + "/model";
+  std::ofstream outfile;
+  outfile.open(model_name, std::ios::out | std::ios::binary);
+  std::string inference_prog_desc = GetSerializedProgram();
+  outfile << inference_prog_desc;
+  // save params
+  framework::ProgramDesc save_program;
+  auto *save_block = save_program.MutableBlock(0);
+
+  const framework::ProgramDesc &main_program = program();
+  const framework::BlockDesc &global_block = main_program.Block(0);
+  std::vector<std::string> save_var_list;
+  for (framework::VarDesc *var : global_block.AllVars()) {
+    if (IsPersistable(var)) {
+      framework::VarDesc *new_var = save_block->Var(var->Name());
+      new_var->SetShape(var->GetShape());
+      new_var->SetDataType(var->GetDataType());
+      new_var->SetType(var->GetType());
+      new_var->SetLoDLevel(var->GetLoDLevel());
+      new_var->SetPersistable(true);
+
+      save_var_list.push_back(new_var->Name());
+    }
+  }
+  std::sort(save_var_list.begin(), save_var_list.end());
+  auto *op = save_block->AppendOp();
+  op->SetType("save_combine");
+  op->SetInput("X", save_var_list);
+  op->SetAttr("file_path", dir + "/params");
+  op->CheckAttrs();
+
+  platform::CPUPlace place;
+  framework::Executor exe(place);
+  exe.Run(save_program, scope(), 0, true, true);
+}
+
 template <>
 std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<AnalysisConfig>(
     const AnalysisConfig &config) {

paddle/fluid/inference/api/analysis_predictor.h

@@ -86,6 +86,10 @@ class AnalysisPredictor : public PaddlePredictor {
 
   bool MkldnnQuantize();
 
+  // save program to  model
+  // save parameters to params
+  void SaveOptimModel(const std::string &dir);
+
  protected:
   // For memory optimization.
   bool need_collect_var_shapes_for_memory_optim();

paddle/fluid/inference/api/analysis_predictor_tester.cc

@@ -196,6 +196,9 @@ TEST(AnalysisPredictor, Clone) {
   }
 }
 
+// This function is not released yet, will fail on some machine.
+// TODO(Superjomn) Turn on it latter.
+/*
 TEST(AnalysisPredictor, memory_optim) {
   AnalysisConfig config(FLAGS_dirname);
   config.DisableGpu();
@@ -246,6 +249,7 @@ TEST(AnalysisPredictor, memory_optim) {
 
   inference::CompareResult(output, output1);
 }
+*/
 
 #ifdef PADDLE_WITH_MKLDNN
 class MkldnnQuantizerTest : public testing::Test {

paddle/fluid/inference/tests/api/analyzer_dam_tester.cc

@@ -170,6 +170,15 @@ void SetConfig(AnalysisConfig *cfg) {
   cfg->SwitchIrOptim(true);
 }
 
+void SetOptimConfig(AnalysisConfig *cfg) {
+  std::string optimModelPath =
+      FLAGS_infer_model.substr(0, FLAGS_infer_model.find_last_of("/")) +
+      "/saved_optim_model";
+  cfg->SetModel(optimModelPath + "/model", optimModelPath + "/params");
+  cfg->SwitchIrOptim(true);
+  cfg->SwitchSpecifyInputNames();
+}
+
 void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
   DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
   std::vector<PaddleTensor> input_slots;
@@ -315,5 +324,44 @@ TEST(Analyzer_dam, compare_determine) {
                        input_slots_all);
 }
 
+// Save optim model
+TEST(Analyzer_dam, save_optim_model) {
+  AnalysisConfig cfg;
+  SetConfig(&cfg);
+  std::string optimModelPath =
+      FLAGS_infer_model.substr(0, FLAGS_infer_model.find_last_of("/")) +
+      "/saved_optim_model";
+  mkdir(optimModelPath.c_str(), 0777);
+  auto predictor = CreateTestPredictor(
+      reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
+      FLAGS_use_analysis);
+  (static_cast<AnalysisPredictor *>(predictor.get()))
+      ->SaveOptimModel(optimModelPath);
+}
+
+void CompareOptimAndOrig(const PaddlePredictor::Config *orig_config,
+                         const PaddlePredictor::Config *optim_config,
+                         const std::vector<std::vector<PaddleTensor>> &inputs) {
+  PrintConfig(orig_config, true);
+  PrintConfig(optim_config, true);
+  std::vector<std::vector<PaddleTensor>> orig_outputs, optim_outputs;
+  TestOneThreadPrediction(orig_config, inputs, &orig_outputs, false);
+  TestOneThreadPrediction(optim_config, inputs, &optim_outputs, false);
+  CompareResult(orig_outputs.back(), optim_outputs.back());
+}
+
+TEST(Analyzer_dam, compare_optim_orig) {
+  AnalysisConfig orig_cfg;
+  AnalysisConfig optim_cfg;
+  SetConfig(&orig_cfg);
+  SetOptimConfig(&optim_cfg);
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  CompareOptimAndOrig(
+      reinterpret_cast<const PaddlePredictor::Config *>(&orig_cfg),
+      reinterpret_cast<const PaddlePredictor::Config *>(&optim_cfg),
+      input_slots_all);
+}
+
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/tests/api/analyzer_resnet50_tester.cc

@@ -32,6 +32,17 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
   SetFakeImageInput(inputs, FLAGS_infer_model);
 }
 
+void SetOptimConfig(AnalysisConfig *cfg) {
+  std::string optimModelPath =
+      FLAGS_infer_model.substr(0, FLAGS_infer_model.find_last_of("/")) +
+      "/saved_optim_model";
+  cfg->SetModel(optimModelPath + "/model", optimModelPath + "/params");
+  cfg->DisableGpu();
+  cfg->SwitchIrOptim();
+  cfg->SwitchSpecifyInputNames();
+  cfg->SetCpuMathLibraryNumThreads(FLAGS_paddle_num_threads);
+}
+
 // Easy for profiling independently.
 void profile(bool use_mkldnn = false) {
   AnalysisConfig cfg;
@@ -87,13 +98,51 @@ TEST(Analyzer_resnet50, compare_mkldnn) { compare(true /* use_mkldnn */); }
 TEST(Analyzer_resnet50, compare_determine) {
   AnalysisConfig cfg;
   SetConfig(&cfg);
-
   std::vector<std::vector<PaddleTensor>> input_slots_all;
   SetInput(&input_slots_all);
   CompareDeterministic(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
                        input_slots_all);
 }
 
+// Save optim model
+TEST(Analyzer_resnet50, save_optim_model) {
+  AnalysisConfig cfg;
+  SetConfig(&cfg);
+  std::string optimModelPath =
+      FLAGS_infer_model.substr(0, FLAGS_infer_model.find_last_of("/")) +
+      "/saved_optim_model";
+  mkdir(optimModelPath.c_str(), 0777);
+  auto predictor = CreateTestPredictor(
+      reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
+      FLAGS_use_analysis);
+  (static_cast<AnalysisPredictor *>(predictor.get()))
+      ->SaveOptimModel(optimModelPath);
+}
+
+void CompareOptimAndOrig(const PaddlePredictor::Config *orig_config,
+                         const PaddlePredictor::Config *optim_config,
+                         const std::vector<std::vector<PaddleTensor>> &inputs) {
+  PrintConfig(orig_config, true);
+  PrintConfig(optim_config, true);
+  std::vector<std::vector<PaddleTensor>> orig_outputs, optim_outputs;
+  TestOneThreadPrediction(orig_config, inputs, &orig_outputs, false);
+  TestOneThreadPrediction(optim_config, inputs, &optim_outputs, false);
+  CompareResult(orig_outputs.back(), optim_outputs.back());
+}
+
+TEST(Analyzer_resnet50, compare_optim_orig) {
+  AnalysisConfig orig_cfg;
+  AnalysisConfig optim_cfg;
+  SetConfig(&orig_cfg);
+  SetOptimConfig(&optim_cfg);
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  CompareOptimAndOrig(
+      reinterpret_cast<const PaddlePredictor::Config *>(&orig_cfg),
+      reinterpret_cast<const PaddlePredictor::Config *>(&optim_cfg),
+      input_slots_all);
+}
+
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/op_use_default_grad_op_maker.spec

@@ -18,7 +18,6 @@ gru
 hierarchical_sigmoid
 lrn
 lstm_unit
-lstmp
 max_pool2d_with_index
 max_pool3d_with_index
 maxout
@@ -29,8 +28,6 @@ pool3d
 prelu
 quantize
 rank_loss
-reduce_all
-reduce_any
 reduce_max
 reduce_mean
 reduce_min

paddle/fluid/operators/affine_grid_op.h

@@ -121,9 +121,11 @@ class AffineGridOpKernel : public framework::OpKernel<T> {
     // TODO(wanghaoshuang): Refine batched matrix multiply
     auto blas = math::GetBlas<DeviceContext, T>(ctx);
     for (int i = 0; i < n; ++i) {
-      Tensor sliced_grid = grid.Slice(i, i + 1).Resize({h * w, 3});
+      Tensor sliced_grid = grid.Slice(i, i + 1).Resize(
+          {static_cast<int64_t>(h) * static_cast<int64_t>(w), 3});
       Tensor sliced_theta = theta->Slice(i, i + 1).Resize({2, 3});
-      Tensor sliced_out = output->Slice(i, i + 1).Resize({h * w, 2});
+      Tensor sliced_out = output->Slice(i, i + 1).Resize(
+          {static_cast<int64_t>(h) * static_cast<int64_t>(w), 2});
       blas.MatMul(sliced_grid, false, sliced_theta, true, T(1), &sliced_out,
                   T(0));
     }
@@ -161,8 +163,10 @@ class AffineGridGradOpKernel : public framework::OpKernel<T> {
     // TODO(wanghaoshuang): Refine batched matrix multiply
     auto blas = math::GetBlas<DeviceContext, T>(ctx);
     for (int i = 0; i < n; ++i) {
-      Tensor sliced_grid = grid.Slice(i, i + 1).Resize({h * w, 3});
-      Tensor sliced_out_grad = output_grad->Slice(i, i + 1).Resize({h * w, 2});
+      Tensor sliced_grid = grid.Slice(i, i + 1).Resize(
+          {static_cast<int64_t>(h) * static_cast<int64_t>(w), 3});
+      Tensor sliced_out_grad = output_grad->Slice(i, i + 1).Resize(
+          {static_cast<int64_t>(h) * static_cast<int64_t>(w), 2});
       Tensor sliced_theta_grad = theta_grad->Slice(i, i + 1).Resize({2, 3});
       blas.MatMul(sliced_out_grad, true, sliced_grid, false, T(1),
                   &sliced_theta_grad, T(0));

paddle/fluid/operators/concat_op.cc

@@ -49,7 +49,15 @@ class ConcatOp : public framework::OperatorWithKernel {
     for (size_t i = 1; i < n; i++) {
       for (size_t j = 0; j < in_zero_dims_size; j++) {
         if (j == axis) {
-          out_dims[axis] += ins[i][j];
+          if (ctx->IsRuntime()) {
+            out_dims[axis] += ins[i][j];
+          } else {
+            if (ins[i][j] == -1) {
+              out_dims[axis] = -1;
+            } else {
+              out_dims[axis] += ins[i][j];
+            }
+          }
         } else {
           if (ctx->IsRuntime()) {
             // check all shape in run time

paddle/fluid/operators/conv_shift_op.cc

@@ -36,14 +36,17 @@ class ConvShiftOp : public framework::OperatorWithKernel {
     auto y_dims = ctx->GetInputDim("Y");
     PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
     PADDLE_ENFORCE_EQ(y_dims.size(), 2, "Input(Y)'s rank should be 2.");
-    PADDLE_ENFORCE_EQ(x_dims[0], y_dims[0],
-                      "The 1st dimension of Input(X) and Input(Y) should "
-                      "be equal.");
-    PADDLE_ENFORCE_EQ(y_dims[1] % 2, 1,
-                      "The 2nd dimension of Input(Y) should be odd.");
-    PADDLE_ENFORCE_LE(y_dims[1], x_dims[1],
-                      "The 2nd dimension of Input(Y) should be less than or "
-                      "equal to the 2nd dimension of Input(X).");
+    if (ctx->IsRuntime() || (x_dims[0] > 0 && y_dims[0] > 0))
+      PADDLE_ENFORCE_EQ(x_dims[0], y_dims[0],
+                        "The 1st dimension of Input(X) and Input(Y) should "
+                        "be equal.");
+    if (ctx->IsRuntime() || y_dims[1] > 0)
+      PADDLE_ENFORCE_EQ(y_dims[1] % 2, 1,
+                        "The 2nd dimension of Input(Y) should be odd.");
+    if (ctx->IsRuntime() || (x_dims[1] > 0 && y_dims[1] > 0))
+      PADDLE_ENFORCE_LE(y_dims[1], x_dims[1],
+                        "The 2nd dimension of Input(Y) should be less than or "
+                        "equal to the 2nd dimension of Input(X).");
     ctx->ShareDim("X", /*->*/ "Out");
     ctx->ShareLoD("X", /*->*/ "Out");
   }

paddle/fluid/operators/cvm_op.cc

+/* Copyright (c) 2019 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/fluid/operators/cvm_op.h"
+#include <memory>
+#include "paddle/fluid/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+class CVMOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
+    PADDLE_ENFORCE(ctx->HasInput("CVM"), "Input(CVM) should be not null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Y"), "Output(Y) should be not null.");
+
+    auto x_dims = ctx->GetInputDim("X");
+    auto cvm_dims = ctx->GetInputDim("CVM");
+    PADDLE_ENFORCE_EQ(x_dims.size(), 2UL, "Input(X)'s rank should be 2.");
+    PADDLE_ENFORCE_EQ(cvm_dims.size(), 2UL, "Input(CVM)'s rank should be 2.");
+    PADDLE_ENFORCE_EQ(cvm_dims[1], 2UL,
+                      "The 2nd dimension of "
+                      "Input(CVM) should be 2.");
+
+    if (ctx->Attrs().Get<bool>("use_cvm")) {
+      ctx->SetOutputDim("Y", {x_dims[0], x_dims[1]});
+    } else {
+      ctx->SetOutputDim("Y", {x_dims[0], x_dims[1] - 2});
+    }
+    ctx->ShareLoD("X", /*->*/ "Y");
+  }
+
+ protected:
+  // Explicitly set that the data type of computation kernel of
+  // cvm
+  // is determined by its input "X".
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
+                                   platform::CPUPlace());
+  }
+};
+
+class CVMGradientOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
+    PADDLE_ENFORCE(ctx->HasInput("CVM"), "Input(CVM) should be not null.");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")),
+                   "Input(Y@GRAD) should be not null.");
+    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
+                   "Output(X@GRAD) should be not null.");
+
+    auto x_dims = ctx->GetInputDim("X");
+    auto cvm_dims = ctx->GetInputDim("CVM");
+    auto dy_dims = ctx->GetInputDim(framework::GradVarName("Y"));
+    PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
+    PADDLE_ENFORCE_EQ(dy_dims.size(), 2, "Input(Y@Grad)'s rank should be 2.");
+    PADDLE_ENFORCE_EQ(cvm_dims.size(), 2, "Input(CVM)'s rank should be 2.");
+
+    PADDLE_ENFORCE_EQ(x_dims[0], dy_dims[0],
+                      "The 1st dimension of Input(X) and Input(Y@Grad) should "
+                      "be equal.");
+
+    PADDLE_ENFORCE_EQ(cvm_dims[1], 2,
+                      "When Attr(soft_label) == false, the 2nd dimension of "
+                      "Input(CVM) should be 2.");
+    ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
+    ctx->ShareLoD("X", framework::GradVarName("X"));
+  }
+
+ protected:
+  // Explicitly set that the data type of computation kernel of
+  // cvm
+  // is determined by its input "X".
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
+                                   platform::CPUPlace());
+  }
+};
+
+class CVMOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X",
+             "(LodTensor, default LodTensor<float>), a 2-D tensor with shape "
+             "[N x D],"
+             " where N is the batch size and D is the emebdding dim. ");
+    AddInput("CVM",
+             "(Tensor),  a 2-D Tensor with shape [N x 2], where N is the batch "
+             "size, 2 is show and click.");
+    AddOutput("Y",
+              "(LodTensor, default LodTensor<float>), a 2-D tensor with shape "
+              "[N x K].");
+    AddAttr<bool>("use_cvm", "bool, use cvm or not").SetDefault(true);
+    AddComment(R"DOC(
+CVM Operator.
+
+      We assume that input X is a embedding vector with cvm_feature(show and click), which shape is [N * D] (D is 2(cvm_feature) + embedding dim, N is batch_size)
+      if use_cvm is True, we will log(cvm_feature), and output shape is [N * D].
+      if use_cvm is False, we will remove cvm_feature from input, and output shape is [N * (D - 2)].
+
+)DOC");
+  }
+};
+
+class CVMGradOpDescMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDesc> Apply() const override {
+    std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
+    op->SetType("cvm_grad");
+    op->SetInput("X", Input("X"));
+    op->SetInput("CVM", Input("CVM"));
+    op->SetInput(framework::GradVarName("Y"), OutputGrad("Y"));
+    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(cvm, ops::CVMOp, ops::CVMOpMaker, ops::CVMGradOpDescMaker);
+
+REGISTER_OPERATOR(cvm_grad, ops::CVMGradientOp);
+
+REGISTER_OP_CPU_KERNEL(cvm, ops::CVMOpKernel<float>, ops::CVMOpKernel<double>);
+
+REGISTER_OP_CPU_KERNEL(cvm_grad, ops::CVMGradOpKernel<float>,
+                       ops::CVMGradOpKernel<double>);

paddle/fluid/operators/cvm_op.h

+/* Copyright (c) 2019 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
+
+    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 "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+
+template <typename T>
+class CVMOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    const LoDTensor* x = context.Input<LoDTensor>("X");
+    const T* x_data = x->data<T>();
+    auto lod = x->lod()[0];
+    int64_t item_size = x->numel() / x->dims()[0];
+    int offset = 2;
+    if (!context.Attr<bool>("use_cvm")) {
+      item_size -= offset;
+    }
+    LoDTensor* y = context.Output<LoDTensor>("Y");
+    T* y_data = y->mutable_data<T>(context.GetPlace());
+
+    int seq_num = static_cast<int>(lod.size()) - 1;
+    for (int i = 0; i < seq_num; ++i) {
+      int64_t seq_len = static_cast<int64_t>(lod[i + 1] - lod[i]);
+
+      for (int j = 0; j < seq_len; ++j) {
+        if (context.Attr<bool>("use_cvm")) {
+          std::memcpy(y_data, x_data, item_size * sizeof(T));
+          y_data[0] = log(y_data[0] + 1);
+          y_data[1] = log(y_data[1] + 1) - y_data[0];
+          x_data += item_size;
+          y_data += item_size;
+        } else {
+          std::memcpy(y_data, x_data + offset, item_size * sizeof(T));
+          x_data += item_size + offset;
+          y_data += item_size;
+        }
+      }
+    }
+  }
+};
+
+template <typename T>
+class CVMGradOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    LoDTensor* dx = context.Output<LoDTensor>(framework::GradVarName("X"));
+    T* dx_data = dx->mutable_data<T>(context.GetPlace());
+
+    const Tensor* cvm = context.Input<Tensor>("CVM");
+    const T* cvm_data = cvm->data<T>();
+    int offset = 2;
+    const framework::LoDTensor* dOut =
+        context.Input<framework::LoDTensor>(framework::GradVarName("Y"));
+    const T* dout_data = dOut->data<T>();
+
+    auto lod = dx->lod()[0];
+    int64_t item_size = dx->numel() / dx->dims()[0];
+    if (!context.Attr<bool>("use_cvm")) {
+      item_size -= offset;
+    }
+
+    int seq_num = static_cast<int>(lod.size()) - 1;
+    for (int i = 0; i < seq_num; ++i) {
+      int64_t seq_len = static_cast<int64_t>(lod[i + 1] - lod[i]);
+
+      for (int j = 0; j < seq_len; ++j) {
+        if (context.Attr<bool>("use_cvm")) {
+          std::memcpy(dx_data, dout_data, item_size * sizeof(T));
+          dx_data[0] = cvm_data[0];
+          dx_data[1] = cvm_data[1];
+          dx_data += item_size;
+          dout_data += item_size;
+        } else {
+          std::memcpy(dx_data + offset, dout_data, item_size * sizeof(T));
+          dx_data[0] = cvm_data[0];
+          dx_data[1] = cvm_data[1];
+          dx_data += item_size + offset;
+          dout_data += item_size;
+        }
+      }
+      cvm_data += offset;
+    }
+  }
+};
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/distributed/CMakeLists.txt

@@ -9,6 +9,9 @@ else()
 endif()
 configure_file(send_recv.proto.in ${CMAKE_CURRENT_SOURCE_DIR}/send_recv.proto @ONLY)
 
+cc_library(async_sparse_param_update_recorder SRCS async_sparse_param_update_recorder.cc DEPS enforce simple_threadpool)
+cc_test(async_sparse_param_update_recorder_test SRCS async_sparse_param_update_recorder_test.cc DEPS async_sparse_param_update_recorder)
+
 # FIXME(typhoonzero): use add_subdirectory once we clean the dependency of these files
 set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
 if(WITH_GRPC)
@@ -20,7 +23,7 @@ if(WITH_GRPC)
         collective_client.cc collective_server.cc
         ${GRPC_SRCS}
       PROTO send_recv.proto 
-      DEPS lod_tensor selected_rows_functor memory scope ${GRPC_DEPS})
+      DEPS lod_tensor selected_rows_functor memory scope ${GRPC_DEPS} async_sparse_param_update_recorder)
 
   set_source_files_properties(grpc_serde_test.cc rpc_server_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
   set(RPC_DEPS sendrecvop_rpc ${GRPC_DEPS})

paddle/fluid/operators/distributed/async_sparse_param_update_recorder.cc

+// 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
+//
+//     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/fluid/operators/distributed/async_sparse_param_update_recorder.h"
+
+namespace paddle {
+namespace operators {
+namespace distributed {
+
+std::once_flag AsyncSparseParamUpdateRecorder::init_flag_;
+std::unique_ptr<AsyncSparseParamUpdateRecorder>
+    AsyncSparseParamUpdateRecorder::recorder_(nullptr);
+
+}  // namespace distributed
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/distributed/async_sparse_param_update_recorder.h

+// 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
+//
+//     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 <functional>
+#include <future>  // NOLINT
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+#include <utility>
+#include <vector>
+
+#include <ThreadPool.h>
+
+#include "paddle/fluid/platform/enforce.h"
+
+namespace paddle {
+namespace operators {
+namespace distributed {
+
+class ConcurrentSet {
+ public:
+  ConcurrentSet() : pool_(new ::ThreadPool(1)) {}
+  ~ConcurrentSet() {}
+
+  std::future<void> Update(const std::vector<int64_t>& rows) {
+    auto task = [this, rows] {
+      if (VLOG_IS_ON(3)) {
+        std::ostringstream sstream;
+        sstream << "[";
+        for (auto& id : rows) {
+          sstream << id << ", ";
+        }
+        sstream << "]";
+        VLOG(3) << "update ids -> " << sstream.str();
+      }
+      for (auto row : rows) {
+        set_.insert(row);
+      }
+    };
+    return pool_->enqueue(std::move(task));
+  }
+
+  std::future<void> GetAndClear(std::vector<int64_t>* result) {
+    auto task = [this, &result] {
+      result->clear();
+      for (auto& id : set_) {
+        result->push_back(id);
+      }
+      if (VLOG_IS_ON(3)) {
+        std::ostringstream sstream;
+        sstream << "[";
+        for (auto& id : *result) {
+          sstream << id << ", ";
+        }
+        sstream << "]";
+        VLOG(3) << "result ids size: " << result->size() << " "
+                << sstream.str();
+      }
+      set_.clear();
+    };
+    return pool_->enqueue(std::move(task));
+  }
+
+ private:
+  std::unordered_set<int64_t> set_;
+  std::unique_ptr<::ThreadPool> pool_{nullptr};
+};
+
+class AsyncSparseParamUpdateRecorder {
+  using TrainerToRows = std::vector<std::unique_ptr<ConcurrentSet>>;
+
+ public:
+  AsyncSparseParamUpdateRecorder(
+      int trainer_num,
+      const std::unordered_map<std::string, std::string>& grad_to_param)
+      : trainer_num_(trainer_num), grad_to_param_(grad_to_param) {
+    if (VLOG_IS_ON(3)) {
+      std::ostringstream sstream;
+      sstream << "[";
+      for (auto& item : grad_to_param) {
+        sstream << item.first << ":" << item.second << ", ";
+      }
+      sstream << "]";
+      VLOG(3) << "trainer_num: " << trainer_num
+              << " grad_to_param_: " << sstream.str();
+    }
+    for (auto& iter : grad_to_param) {
+      param_to_grad_[iter.second] = iter.first;
+      auto& param_name = iter.second;
+      param_to_updated_rows_[param_name] = TrainerToRows();
+      auto& trainer_to_rows = param_to_updated_rows_[param_name];
+      for (auto i = 0; i < trainer_num; ++i) {
+        trainer_to_rows.emplace_back(new ConcurrentSet());
+      }
+    }
+  }
+
+  ~AsyncSparseParamUpdateRecorder() = default;
+
+  void Update(const std::string& grad_name,
+              const std::vector<int64_t>& update_rows) {
+    VLOG(3) << "update grad: " << grad_name
+            << " row size: " << update_rows.size();
+    auto& param_name = grad_to_param_.at(grad_name);
+    auto& trainer_to_rows = param_to_updated_rows_.at(param_name);
+
+    std::vector<std::future<void>> fs;
+    for (auto& set : trainer_to_rows) {
+      fs.push_back(set->Update(update_rows));
+    }
+    for (auto& f : fs) {
+      f.wait();
+    }
+  }
+
+  void GetAndClear(const std::string& param_name, int trainer_id,
+                   std::vector<int64_t>* result) {
+    VLOG(3) << "GetAndClear param: " << param_name
+            << " for trainer: " << trainer_id;
+    PADDLE_ENFORCE_LT(trainer_id, trainer_num_);
+    param_to_updated_rows_.at(param_name)[trainer_id]
+        ->GetAndClear(result)
+        .wait();
+  }
+
+  bool HasParam(const std::string& param_name) {
+    return param_to_grad_.find(param_name) != param_to_grad_.end();
+  }
+
+  bool HasGrad(const std::string& grad_name) {
+    return grad_to_param_.find(grad_name) != grad_to_param_.end();
+  }
+
+ private:
+  const int trainer_num_;
+  std::unordered_map<std::string, std::string> grad_to_param_;
+  std::unordered_map<std::string, std::string> param_to_grad_;
+  std::unordered_map<std::string, TrainerToRows> param_to_updated_rows_;
+
+  // init recorder
+ public:
+  static void Init(
+      int trainer_num,
+      const std::unordered_map<std::string, std::string>& grad_to_param) {
+    InitImpl(trainer_num, grad_to_param);
+  }
+
+  static AsyncSparseParamUpdateRecorder* GetInstance() {
+    return recorder_.get();
+  }
+
+ private:
+  // Init is called by GetInstance.
+  static void InitImpl(
+      int trainer_num,
+      const std::unordered_map<std::string, std::string>& grad_to_param) {
+    if (recorder_ == nullptr) {
+      recorder_.reset(
+          new AsyncSparseParamUpdateRecorder(trainer_num, grad_to_param));
+    }
+  }
+
+  static std::once_flag init_flag_;
+  static std::unique_ptr<AsyncSparseParamUpdateRecorder> recorder_;
+};
+
+}  // namespace distributed
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/distributed/async_sparse_param_update_recorder_test.cc

+// 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
+//
+//     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/fluid/operators/distributed/async_sparse_param_update_recorder.h"
+
+#include <algorithm>
+
+#include "gtest/gtest.h"
+
+namespace paddle {
+namespace operators {
+namespace distributed {
+
+TEST(ConcurrentSet, All) {
+  ConcurrentSet concurrent_set;
+  std::vector<int64_t> in1 = {1, 2, 3, 4};
+  std::vector<int64_t> in2 = {2, 3, 5, 6};
+
+  std::vector<std::future<void>> futures;
+  futures.push_back(concurrent_set.Update(in1));
+  futures.push_back(concurrent_set.Update(in2));
+
+  for (auto &f : futures) {
+    f.wait();
+  }
+
+  std::unordered_set<int64_t> in;
+  std::copy(in1.begin(), in1.end(), std::inserter(in, in.begin()));
+  std::copy(in2.begin(), in2.end(), std::inserter(in, in.begin()));
+
+  std::vector<int64_t> ret;
+  concurrent_set.GetAndClear(&ret).wait();
+
+  std::unordered_set<int64_t> out;
+  std::copy(ret.begin(), ret.end(), std::inserter(out, out.begin()));
+
+  EXPECT_EQ(in, out);
+
+  concurrent_set.GetAndClear(&ret).wait();
+  EXPECT_EQ(ret.size(), 0);
+}
+
+TEST(AsyncSparseParamUpdateRecorder, All) {
+  std::unordered_map<std::string, std::string> grad_to_param;
+  grad_to_param["grad1"] = "param1";
+  grad_to_param["grad2"] = "param2";
+
+  int trainer_num = 10;
+
+  AsyncSparseParamUpdateRecorder recorder(trainer_num, grad_to_param);
+  std::vector<int64_t> in1 = {1, 2, 3, 4};
+  std::vector<int64_t> in2 = {2, 3, 5, 6};
+
+  std::unordered_set<int64_t> in;
+  std::copy(in1.begin(), in1.end(), std::inserter(in, in.begin()));
+  std::copy(in2.begin(), in2.end(), std::inserter(in, in.begin()));
+
+  recorder.Update("grad1", in1);
+  recorder.Update("grad1", in2);
+
+  EXPECT_TRUE(recorder.HasParam("param1"));
+  EXPECT_TRUE(recorder.HasParam("param2"));
+  EXPECT_FALSE(recorder.HasParam("param3"));
+
+  EXPECT_TRUE(recorder.HasGrad("grad1"));
+  EXPECT_TRUE(recorder.HasGrad("grad2"));
+  EXPECT_FALSE(recorder.HasGrad("grad3"));
+
+  std::vector<int64_t> ret;
+  EXPECT_ANY_THROW(recorder.GetAndClear("param1", trainer_num, &ret));
+
+  for (int i = 0; i < trainer_num; ++i) {
+    std::vector<int64_t> ret;
+    std::unordered_set<int64_t> out;
+
+    recorder.GetAndClear("param1", i, &ret);
+    std::copy(ret.begin(), ret.end(), std::inserter(out, out.begin()));
+
+    EXPECT_EQ(in, out);
+
+    recorder.GetAndClear("param1", i, &ret);
+    EXPECT_EQ(ret.size(), 0);
+  }
+}
+
+}  // namespace distributed
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/distributed/brpc/brpc_client.cc

@@ -234,6 +234,7 @@ VarHandlePtr BRPCClient::AsyncGetVar(const std::string& ep,
                                      const framework::Scope& scope,
                                      const std::string& var_name,
                                      const std::string& out_var_name,
+                                     const std::string& table_name,
                                      int64_t time_out) {
   return _AsyncGetVar(ep, ctx, scope, var_name, out_var_name, kGetRPC,
                       time_out);

paddle/fluid/operators/distributed/brpc/brpc_client.h

@@ -21,8 +21,10 @@ limitations under the License. */
 #include <functional>
 #include <iostream>
 #include <map>
+#include <memory>
 #include <mutex>  // NOLINT
 #include <string>
+#include <unordered_map>
 #include <vector>
 
 #include "brpc/channel.h"
@@ -66,6 +68,7 @@ class BRPCClient : public RPCClient {
                            const framework::Scope& scope,
                            const std::string& var_name,
                            const std::string& out_var_name,
+                           const std::string& table_name = "",
                            int64_t time_out = FLAGS_rpc_deadline) override;
 
   VarHandlePtr AsyncGetMonomerBarrier(
@@ -107,13 +110,11 @@ class BRPCClient : public RPCClient {
   void SendComplete() override;
 
  private:
-  VarHandlePtr _AsyncGetVar(const std::string& ep,
-                            const platform::DeviceContext& ctx,
-                            const framework::Scope& scope,
-                            const std::string& var_name,
-                            const std::string& out_var_name,
-                            const std::string& method_name,
-                            int64_t time_out = FLAGS_rpc_deadline);
+  VarHandlePtr _AsyncGetVar(
+      const std::string& ep, const platform::DeviceContext& ctx,
+      const framework::Scope& scope, const std::string& var_name,
+      const std::string& out_var_name, const std::string& method_name,
+      const std::string& table_name, int64_t time_out = FLAGS_rpc_deadline);
 
   void Proceed();
   ChannelQueuePtr GetChannel(const std::string& ep);

paddle/fluid/operators/distributed/communicator.cc

@@ -32,6 +32,9 @@ DEFINE_int32(communicator_send_queue_size, 20,
 DEFINE_int32(communicator_max_send_grad_num_before_recv, 20,
              "max grad num to send before recv parameters");
 DEFINE_int32(communicator_thread_pool_size, 5, "thread num to do send or recv");
+DEFINE_int32(communicator_send_wait_times, 5,
+             "times that send thread will wait if merge num does not reach "
+             "max_merge_var_num");
 DEFINE_int32(communicator_max_merge_var_num, 20,
              "max var num to merge and send");
 DEFINE_bool(communicator_fake_rpc, false,
@@ -65,6 +68,8 @@ Communicator::Communicator(const RpcCtxMap &send_varname_to_ctx,
           << FLAGS_communicator_max_send_grad_num_before_recv;
   VLOG(0) << "communicator_thread_pool_size: "
           << FLAGS_communicator_thread_pool_size;
+  VLOG(0) << "communicator_send_wait_times: "
+          << FLAGS_communicator_send_wait_times;
   VLOG(0) << "communicator_max_merge_var_num: "
           << FLAGS_communicator_max_merge_var_num;
   VLOG(0) << "communicator_fake_rpc: " << FLAGS_communicator_fake_rpc;
@@ -101,20 +106,32 @@ void Communicator::SendThread() {
           VLOG(3) << var_name << " merge and send";
           std::vector<std::shared_ptr<Variable>> vars;
           size_t merged_var_num = 0;
-          while (var_queue->Size() > 0 &&
-                 merged_var_num < FLAGS_communicator_max_merge_var_num) {
-            vars.push_back(var_queue->Pop());
-            // only count the send number of the first var
-            if (var_name == send_varname_to_queue_.begin()->first) {
-              grad_num_.fetch_add(1, std::memory_order_relaxed);
+          size_t wait_times = 0;
+          while (merged_var_num < FLAGS_communicator_max_merge_var_num) {
+            if (var_queue->Size() == 0) {
+              VLOG(3) << "wait_times -> " << wait_times;
+              if (wait_times >= FLAGS_communicator_send_wait_times) {
+                break;
+              }
+              std::this_thread::sleep_for(std::chrono::milliseconds(10));
+              wait_times++;
+              continue;
+            } else {
+              wait_times = 0;
+
+              vars.push_back(var_queue->Pop());
+              // only count the send number of the first var
+              if (var_name == send_varname_to_queue_.begin()->first) {
+                grad_num_.fetch_add(1, std::memory_order_relaxed);
+              }
+              merged_var_num++;
             }
-            merged_var_num++;
           }
           auto before_merge = GetCurrentUS();
           MergeVars(var_name, vars, send_scope_.get());
           auto after_merge = GetCurrentUS();
-          VLOG(3) << "merge " << var_name << " use time "
-                  << after_merge - before_merge;
+          VLOG(3) << "merge " << merged_var_num << " " << var_name
+                  << " use time " << after_merge - before_merge;
           auto send_functor = distributed::ParameterSend<float>();
           auto &ctx = send_varname_to_ctx_.at(var_name);
           if (!FLAGS_communicator_fake_rpc) {

paddle/fluid/operators/distributed/communicator.h

@@ -109,7 +109,7 @@ inline void MergeVars(const std::string& var_name,
   auto* out_var = scope->Var(var_name);
   if (var0->IsType<framework::LoDTensor>()) {
     auto dims = var0->Get<framework::LoDTensor>().dims();
-    VLOG(3) << "merge " << var_name << " LoDTensor " << dims;
+    VLOG(3) << "merge " << var_name << " LoDTensor dims " << dims;
 
     // init output tensor
     auto* out_t = out_var->GetMutable<framework::LoDTensor>();

paddle/fluid/operators/distributed/grpc/grpc_client.cc

@@ -128,9 +128,11 @@ VarHandlePtr GRPCClient::AsyncGetVar(const std::string& ep,
                                      const framework::Scope& scope,
                                      const std::string& var_name,
                                      const std::string& out_varname,
+                                     const std::string& table_name,
                                      int64_t time_out) {
   return _AsyncGetVar(ep, ctx, scope, kGetRPC, var_name, out_varname,
-                      "/sendrecv.SendRecvService/GetVariable", time_out);
+                      "/sendrecv.SendRecvService/GetVariable", table_name,
+                      time_out);
 }
 
 VarHandlePtr GRPCClient::AsyncGetVarNoBarrier(
@@ -142,7 +144,7 @@ VarHandlePtr GRPCClient::AsyncGetVarNoBarrier(
 
   return _AsyncGetVar(
       ep, ctx, scope, kGetNoBarrierRPC, var_name_no_barrier, out_varname,
-      "/sendrecv.SendRecvService/GetVariableNoBarrier", time_out);
+      "/sendrecv.SendRecvService/GetVariableNoBarrier", "", time_out);
 }
 
 VarHandlePtr GRPCClient::AsyncGetMonomerVariable(
@@ -150,18 +152,21 @@ VarHandlePtr GRPCClient::AsyncGetMonomerVariable(
     const framework::Scope& scope, const std::string& var_name,
     int64_t time_out) {
   return _AsyncGetVar(ep, ctx, scope, kGetMonomerRPC, var_name, var_name,
-                      "/sendrecv.SendRecvService/GetMonomerVariable", time_out);
+                      "/sendrecv.SendRecvService/GetMonomerVariable", "",
+                      time_out);
 }
 
 VarHandlePtr GRPCClient::_AsyncGetVar(
     const std::string& ep, const platform::DeviceContext& ctx,
     const framework::Scope& scope, const std::string& method,
     const std::string& var_name, const std::string& out_varname,
-    const std::string& rpc_path, int64_t time_out) {
+    const std::string& rpc_path, const std::string& table_name,
+    int64_t time_out) {
   const platform::DeviceContext* p_ctx = &ctx;
   const std::string ep_val = ep;
   const std::string var_name_val = var_name;
   const std::string out_varname_val = out_varname;
+  const std::string table_name_val = table_name;
   const framework::Scope* p_scope = &scope;
   const auto ch = GetChannel(ep_val);
   GetProcessor* s = new GetProcessor(ch);
@@ -169,32 +174,33 @@ VarHandlePtr GRPCClient::_AsyncGetVar(
   VarHandlePtr h(new VarHandle(ep, method, out_varname_val, p_ctx, p_scope));
   s->Prepare(h, time_out);
 
-  framework::AsyncIO(
-      [var_name_val, out_varname_val, s, method, p_ctx, h, rpc_path, this] {
-        // prepare input
-        sendrecv::VariableMessage req;
-        req.set_varname(var_name_val);
-        req.set_out_varname(out_varname_val);
-        req.set_trainer_id(trainer_id_);
-        ::grpc::ByteBuffer buf;
-        RequestToByteBuffer<sendrecv::VariableMessage>(req, &buf);
+  framework::AsyncIO([var_name_val, out_varname_val, table_name_val, s, method,
+                      p_ctx, h, rpc_path, this] {
+    // prepare input
+    sendrecv::VariableMessage req;
+    req.set_varname(var_name_val);
+    req.set_out_varname(out_varname_val);
+    req.set_trainer_id(trainer_id_);
+    req.set_table_name(table_name_val);
+    ::grpc::ByteBuffer buf;
+    RequestToByteBuffer<sendrecv::VariableMessage>(req, &buf);
 
-        VLOG(3) << s->GetVarHandlePtr()->String() << " begin";
+    VLOG(3) << s->GetVarHandlePtr()->String() << " begin";
 
-        // stub context
-        s->response_call_back_ = ProcGetResponse;
+    // stub context
+    s->response_call_back_ = ProcGetResponse;
 
-        platform::RecordRPCEvent record_event(method);
+    platform::RecordRPCEvent record_event(method);
 
-        auto call =
-            s->stub_g_.PrepareUnaryCall(s->context_.get(), rpc_path, buf, &cq_);
-        call->StartCall();
-        call->Finish(&s->reply_, &s->status_, reinterpret_cast<void*>(s));
+    auto call =
+        s->stub_g_.PrepareUnaryCall(s->context_.get(), rpc_path, buf, &cq_);
+    call->StartCall();
+    call->Finish(&s->reply_, &s->status_, reinterpret_cast<void*>(s));
 
-        if (UNLIKELY(platform::IsProfileEnabled())) {
-          h->Wait();
-        }
-      });
+    if (UNLIKELY(platform::IsProfileEnabled())) {
+      h->Wait();
+    }
+  });
 
   req_count_++;
 

paddle/fluid/operators/distributed/grpc/grpc_client.h

@@ -23,9 +23,11 @@ limitations under the License. */
 #include <functional>
 #include <iostream>
 #include <map>
+#include <memory>
 #include <mutex>  // NOLINT
 #include <string>
 #include <thread>  // NOLINT
+#include <unordered_map>
 #include <vector>
 
 #include "grpc++/channel.h"
@@ -187,6 +189,7 @@ class GRPCClient : public RPCClient {
                            const framework::Scope& scope,
                            const std::string& var_name,
                            const std::string& out_varname,
+                           const std::string& table_name = "",
                            int64_t time_out = FLAGS_rpc_deadline) override;
 
   VarHandlePtr AsyncGetVarNoBarrier(
@@ -239,7 +242,8 @@ class GRPCClient : public RPCClient {
       const std::string& ep, const platform::DeviceContext& ctx,
       const framework::Scope& scope, const std::string& method,
       const std::string& var_name, const std::string& out_varname,
-      const std::string& rpc_path, int64_t time_out = FLAGS_rpc_deadline);
+      const std::string& rpc_path, const std::string& table_name = "",
+      int64_t time_out = FLAGS_rpc_deadline);
 
  private:
   grpc::CompletionQueue cq_;

paddle/fluid/operators/distributed/grpc/grpc_server.cc

@@ -137,6 +137,7 @@ class RequestGet final : public RequestBase {
     // proc request.
     std::string varname = request_.varname();
     std::string out_varname = request_.out_varname();
+    std::string table_name = request_.table_name();
     int trainer_id = request_.trainer_id();
 
     VLOG(4) << "RequestGet " << out_varname << " from " << varname;
@@ -145,19 +146,23 @@ class RequestGet final : public RequestBase {
     framework::Variable* invar = nullptr;
     framework::Variable* outvar = nullptr;
 
-    request_handler_->Handle(varname, scope, invar, &outvar, trainer_id,
-                             out_varname);
+    tmp_scope_ = std::move(scope->NewTmpScope());
+    request_handler_->Handle(varname, tmp_scope_.get(), invar, &outvar,
+                             trainer_id, out_varname, table_name);
 
+    VLOG(1) << "before SerializeToByteBuffer";
     if (outvar) {
       SerializeToByteBuffer(out_varname, outvar, *request_handler_->dev_ctx(),
                             &reply_);
     }
+    VLOG(1) << "after SerializeToByteBuffer";
     Finish(reply_, &responder_);
   }
 
  protected:
   sendrecv::VariableMessage request_;
   ::grpc::ByteBuffer reply_;
+  std::unique_ptr<framework::Scope> tmp_scope_;
   ServerAsyncResponseWriter<::grpc::ByteBuffer> responder_;
 };
 

paddle/fluid/operators/distributed/parameter_recv.cc

@@ -42,27 +42,23 @@ using DDim = framework::DDim;
 template <typename T>
 void ParameterRecv<T>::operator()(const RpcContext &rpc_ctx,
                                   const framework::Scope &scope) {
-  VLOG(3) << "ParameterRecv in";
+  VLOG(3) << "ParameterRecv in " << rpc_ctx.var_name;
   std::unique_ptr<framework::Scope> local_scope = scope.NewTmpScope();
 
   platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
   auto &cpu_ctx = *pool.Get(platform::CPUPlace());
 
   distributed::RPCClient *rpc_client =
-      distributed::RPCClient::GetInstance<RPCCLIENT_T>(0);
+      distributed::RPCClient::GetInstance<RPCCLIENT_T>(rpc_ctx.trainer_id);
 
   auto *recv_var = scope.FindVar(rpc_ctx.var_name);
 
-  std::vector<framework::Tensor *> recved_tensors;
-
   // recv all vars to local scope
   if (recv_var->IsType<framework::LoDTensor>()) {
     std::vector<distributed::VarHandlePtr> rets;
     for (size_t i = 0; i < rpc_ctx.splited_var_names.size(); i++) {
       auto &recv_var_name = rpc_ctx.splited_var_names[i];
-      framework::Tensor *t =
-          local_scope->Var(recv_var_name)->GetMutable<framework::LoDTensor>();
-      recved_tensors.push_back(t);
+      local_scope->Var(recv_var_name);
       VLOG(3) << "recv " << recv_var_name << " from " << rpc_ctx.epmap[i];
       rets.push_back(rpc_client->AsyncGetVar(rpc_ctx.epmap[i], cpu_ctx,
                                              *local_scope.get(), recv_var_name,
@@ -78,23 +74,61 @@ void ParameterRecv<T>::operator()(const RpcContext &rpc_ctx,
   // concat recved tensor into one var
   {
     size_t output_offset = 0;
+    size_t row_offset = 0;
     framework::Tensor *recv_tensor =
         recv_var->GetMutable<framework::LoDTensor>();
     auto dev_ctx = paddle::platform::CPUDeviceContext();
     int64_t recv_numel = 0;
-    for (auto *in : recved_tensors) {
-      recv_numel += in->numel();
-      auto in_stride = framework::stride_numel(in->dims());
-      auto out_stride = framework::stride_numel(recv_tensor->dims());
-      StridedNumelCopyWithAxis<T>(
-          dev_ctx, 0, recv_tensor->data<T>() + output_offset, out_stride,
-          in->data<T>(), in_stride, in_stride[0]);
-      output_offset += in_stride[0];
+    for (auto &recv_var_name : rpc_ctx.splited_var_names) {
+      auto *recv_var = local_scope->FindVar(recv_var_name);
+      if (recv_var->IsType<framework::LoDTensor>()) {
+        auto &in = recv_var->Get<framework::LoDTensor>();
+        recv_numel += in.numel();
+        auto in_stride = framework::stride_numel(in.dims());
+        auto out_stride = framework::stride_numel(recv_tensor->dims());
+        StridedNumelCopyWithAxis<T>(
+            dev_ctx, 0, recv_tensor->data<T>() + output_offset, out_stride,
+            in.data<T>(), in_stride, in_stride[0]);
+        output_offset += in_stride[0];
+      } else if (recv_var->IsType<framework::SelectedRows>()) {
+        auto &recv_slr = recv_var->Get<framework::SelectedRows>();
+        auto &recv_dims = recv_tensor->dims();
+        int64_t width = recv_dims[1];
+        recv_numel += recv_slr.height() * width;
+        PADDLE_ENFORCE_EQ(recv_slr.value().dims()[1], width);
+        PADDLE_ENFORCE_EQ(recv_slr.value().dims()[0], recv_slr.rows().size());
+        VLOG(3) << "recv slr " << recv_var_name << " dims "
+                << recv_slr.value().dims();
+        if (VLOG_IS_ON(3)) {
+          std::ostringstream sstream;
+          sstream << "[";
+          for (auto &row_id : recv_slr.rows()) {
+            sstream << row_id << ", ";
+          }
+          sstream << "]";
+          VLOG(3) << "recv_slr size: " << recv_slr.rows().size() << " "
+                  << sstream.str();
+        }
+
+        for (auto i = 0; i < recv_slr.rows().size(); ++i) {
+          auto row_id = recv_slr.rows()[i] + row_offset;
+          PADDLE_ENFORCE_LT(row_id, recv_dims[0]);
+          memcpy(recv_tensor->data<T>() + row_id * width,
+                 recv_slr.value().data<T>() + i * width, sizeof(T) * width);
+        }
+        row_offset += recv_slr.height();
+      } else {
+        PADDLE_THROW("unsupported recieved var type");
+      }
+    }
+    auto numel = recv_tensor->numel();
+    if (recv_numel != numel) {
+      LOG(FATAL) << "recv_numel: " << recv_numel << " acture numel: " << numel;
     }
-    PADDLE_ENFORCE_EQ(recv_numel, recv_tensor->numel());
+    PADDLE_ENFORCE_EQ(recv_numel, numel);
   }
 
-  VLOG(3) << "ParameterRecv out";
+  VLOG(3) << "ParameterRecv out " << rpc_ctx.var_name;
 }
 
 template struct ParameterRecv<float>;

paddle/fluid/operators/distributed/parameter_send.cc

@@ -47,7 +47,7 @@ void ParameterSend<T>::operator()(const RpcContext &rpc_ctx,
   auto &cpu_ctx = *pool.Get(platform::CPUPlace());
 
   distributed::RPCClient *rpc_client =
-      distributed::RPCClient::GetInstance<RPCCLIENT_T>(0);
+      distributed::RPCClient::GetInstance<RPCCLIENT_T>(rpc_ctx.trainer_id);
 
   auto *send_var = scope.FindVar(rpc_ctx.var_name);
   size_t out_num = rpc_ctx.splited_var_names.size();

paddle/fluid/operators/distributed/request_handler.h

@@ -18,7 +18,9 @@
 #include <condition_variable>  // NOLINT
 
 #include <functional>
+#include <memory>
 #include <string>
+#include <unordered_map>
 #include <utility>
 #include <vector>
 
@@ -180,6 +182,10 @@ class RequestHandler {
     grad_to_prepared_ctx_ = g;
   }
 
+  void SetSparseGradToParam(std::unordered_map<std::string, std::string>* g) {
+    sparse_grad_to_param_ = g;
+  }
+
   void SetRPCServer(RPCServer* rpc_server) { rpc_server_ = rpc_server; }
 
   // Get attributes.
@@ -228,6 +234,7 @@ class RequestHandler {
   std::unordered_map<std::string,
                      std::shared_ptr<framework::ExecutorPrepareContext>>*
       grad_to_prepared_ctx_;
+  std::unordered_map<std::string, std::string>* sparse_grad_to_param_;
 
   RPCServer* rpc_server_;
 };

paddle/fluid/operators/distributed/request_handler_impl.cc

@@ -22,6 +22,7 @@
 #include "paddle/fluid/framework/scope.h"
 #include "paddle/fluid/framework/selected_rows.h"
 #include "paddle/fluid/framework/variable_helper.h"
+#include "paddle/fluid/operators/distributed/async_sparse_param_update_recorder.h"
 #include "paddle/fluid/operators/distributed/rpc_server.h"
 #include "paddle/fluid/string/piece.h"
 #include "paddle/fluid/string/printf.h"
@@ -59,6 +60,12 @@ bool RequestSendHandler::Handle(const std::string& varname,
             "async mode should not recv BATCH_BARRIER_MESSAGE or "
             "COMPLETE_MESSAGE");
       }
+      if (AsyncSparseParamUpdateRecorder::GetInstance()->HasGrad(varname)) {
+        auto& grad_slr =
+            scope->FindVar(varname)->Get<framework::SelectedRows>();
+        AsyncSparseParamUpdateRecorder::GetInstance()->Update(varname,
+                                                              grad_slr.rows());
+      }
       executor_->RunPreparedContext((*grad_to_prepared_ctx_)[varname].get(),
                                     scope);
       return true;
@@ -82,8 +89,9 @@ bool RequestGetHandler::Handle(const std::string& varname,
                                const int trainer_id,
                                const std::string& out_var_name,
                                const std::string& table_name) {
-  VLOG(4) << "RequestGetHandler:" << varname
-          << " out_var_name: " << out_var_name;
+  VLOG(3) << "RequestGetHandler:" << varname
+          << " out_var_name: " << out_var_name << " trainer_id: " << trainer_id
+          << " table_name: " << table_name;
 
   if (sync_mode_) {
     if (varname == FETCH_BARRIER_MESSAGE) {
@@ -108,7 +116,42 @@ bool RequestGetHandler::Handle(const std::string& varname,
         VLOG(3) << "copying " << varname << " to " << param_bak_name;
         framework::TensorCopy(t_orig, dev_ctx_->GetPlace(), t);
       }
-      *outvar = scope_->FindVar(varname);
+      if (AsyncSparseParamUpdateRecorder::GetInstance()->HasParam(varname) &&
+          !table_name.empty()) {
+        std::vector<int64_t> updated_rows;
+        AsyncSparseParamUpdateRecorder::GetInstance()->GetAndClear(
+            varname, trainer_id, &updated_rows);
+        if (VLOG_IS_ON(3)) {
+          std::ostringstream sstream;
+          sstream << "[";
+          for (auto& row_id : updated_rows) {
+            sstream << row_id << ", ";
+          }
+          sstream << "]";
+          VLOG(3) << "updated_rows size: " << updated_rows.size() << " "
+                  << sstream.str();
+        }
+        auto& origin_tensor =
+            scope_->FindVar(varname)->Get<framework::LoDTensor>();
+        auto* origin_tensor_data = origin_tensor.data<float>();
+        auto& dims = origin_tensor.dims();
+        *outvar = scope->Var();
+        auto* out_slr = (*outvar)->GetMutable<framework::SelectedRows>();
+        out_slr->set_rows(updated_rows);
+        out_slr->set_height(dims[0]);
+        auto out_dims = framework::make_ddim(
+            {static_cast<int64_t>(updated_rows.size()), dims[1]});
+        auto* data = out_slr->mutable_value()->mutable_data<float>(
+            out_dims, origin_tensor.place());
+        auto width = dims[1];
+        for (auto i = 0; i < updated_rows.size(); ++i) {
+          PADDLE_ENFORCE_LT(updated_rows[i], dims[0]);
+          memcpy(data + i * width, origin_tensor_data + updated_rows[i] * width,
+                 sizeof(float) * width);
+        }
+      } else {
+        *outvar = scope_->FindVar(varname);
+      }
     }
   }
   return true;

paddle/fluid/operators/distributed/rpc_client.h

@@ -15,6 +15,7 @@
 #pragma once
 
 #include <condition_variable>  // NOLINT
+#include <memory>
 #include <string>
 #include "gflags/gflags.h"
 
@@ -44,6 +45,7 @@ class RPCClient {
                                    const framework::Scope& scope,
                                    const std::string& var_name,
                                    const std::string& out_varname,
+                                   const std::string& table_name = "",
                                    int64_t time_out = FLAGS_rpc_deadline) = 0;
 
   virtual VarHandlePtr AsyncGetVarNoBarrier(
@@ -96,6 +98,7 @@ class RPCClient {
   // Init is called by GetInstance.
   template <typename T>
   static void Init(int trainer_id) {
+    VLOG(0) << "init rpc client with trainer_id " << trainer_id;
     trainer_id_ = trainer_id;
     if (rpc_client_.get() == nullptr) {
       rpc_client_.reset(new T());

paddle/fluid/operators/distributed/rpc_common.h

@@ -27,23 +27,26 @@ struct RpcContext {
 
   RpcContext(const std::string &name, const std::vector<std::string> &names,
              const std::vector<std::string> &emap,
-             const std::vector<int64_t> &sections)
+             const std::vector<int64_t> &sections, int id)
       : var_name(name),
         splited_var_names(names),
         epmap(emap),
-        height_sections(sections) {}
+        height_sections(sections),
+        trainer_id(id) {}
 
   RpcContext(const RpcContext &ctx) {
     var_name = ctx.var_name;
     splited_var_names = ctx.splited_var_names;
     epmap = ctx.epmap;
     height_sections = ctx.height_sections;
+    trainer_id = ctx.trainer_id;
   }
 
   std::string var_name;
   std::vector<std::string> splited_var_names;
   std::vector<std::string> epmap;
   std::vector<int64_t> height_sections;
+  int trainer_id;
 };
 
 inline std::ostream &operator<<(std::ostream &os, const RpcContext &rpc_ctx) {

paddle/fluid/operators/distributed_ops/CMakeLists.txt

@@ -2,9 +2,9 @@ include(operators)
 
 set(DISTRIBUTE_DEPS "")
 if(WITH_GRPC)
-    set(DISTRIBUTE_DEPS sendrecvop_rpc parameter_send parameter_recv communicator grpc++_unsecure grpc_unsecure gpr cares zlib protobuf node)
+    set(DISTRIBUTE_DEPS sendrecvop_rpc parameter_send parameter_recv communicator async_sparse_param_update_recorder grpc++_unsecure grpc_unsecure gpr cares zlib protobuf node)
 else()
-    set(DISTRIBUTE_DEPS sendrecvop_rpc parameter_send parameter_recv communicator brpc leveldb snappystream snappy protobuf ssl crypto zlib node)
+    set(DISTRIBUTE_DEPS sendrecvop_rpc parameter_send parameter_recv communicator async_sparse_param_update_recorder brpc leveldb snappystream snappy protobuf ssl crypto zlib node)
     if(WITH_BRPC_RDMA)
         find_library(IBVERBS_LIBRARY NAMES ibverbs)
         ADD_LIBRARY(ibverbs SHARED IMPORTED GLOBAL)

paddle/fluid/operators/distributed_ops/listen_and_serv_op.cc

@@ -24,8 +24,10 @@ limitations under the License. */
 #include "paddle/fluid/operators/distributed/distributed.h"
 #include "paddle/fluid/operators/math/math_function.h"
 
+#include "paddle/fluid/operators/distributed/async_sparse_param_update_recorder.h"
 #include "paddle/fluid/operators/distributed/request_handler_impl.h"
 #include "paddle/fluid/operators/distributed_ops/listen_and_serv_op.h"
+
 #include "paddle/fluid/platform/profiler.h"
 
 DEFINE_int32(rpc_send_thread_num, 12, "number of threads for rpc send");
@@ -292,6 +294,8 @@ static void FillRequestCtx(
     std::unordered_map<std::string,
                        std::shared_ptr<framework::ExecutorPrepareContext>>
         *prefetch_ctx,
+    std::unordered_map<std::string, std::string>
+        *sparse_grad_name_to_param_name,
     std::shared_ptr<framework::ExecutorPrepareContext> checkpoint_ctx,
     distributed::RPCServer *rpc_server) {
   h->SetScope(scope);
@@ -299,6 +303,7 @@ static void FillRequestCtx(
   h->SetExecutor(executor);
   h->SetProgram(program);
   h->SetPrefetchPreparedCtx(prefetch_ctx);
+  h->SetSparseGradToParam(sparse_grad_name_to_param_name);
   h->SetRPCServer(rpc_server);
   h->SetCheckpointNotifyPreparedCtx(checkpoint_ctx);
 }
@@ -414,10 +419,24 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
     prefetch_var_name_to_prepared_ctx[prefetch_var_name] = prefetch_prepared[i];
   }
 
-  auto f =
-      std::bind(FillRequestCtx, std::placeholders::_1, &recv_scope, &dev_ctx,
-                &executor, program, &prefetch_var_name_to_prepared_ctx,
-                ckpt_pre_context, rpc_service_.get());
+  // parse attr of kSparseGradToParam  sparse_grad_name -> param_name
+  std::unordered_map<std::string, std::string> sparse_grad_name_to_param_name;
+  auto sparse_grad_name_to_param_name_str =
+      Attr<std::vector<std::string>>(kSparseGradToParam);
+  for (const auto &sparse_grad_name_and_param_name :
+       sparse_grad_name_to_param_name_str) {
+    std::vector<std::string> pieces;
+    split(sparse_grad_name_and_param_name, ':', &pieces);
+    PADDLE_ENFORCE_EQ(pieces.size(), 2);
+    VLOG(3) << "after split, sparse_grad_name = " << pieces[0]
+            << ", param_name = " << pieces[1];
+    sparse_grad_name_to_param_name[pieces[0]] = pieces[1];
+  }
+
+  auto f = std::bind(
+      FillRequestCtx, std::placeholders::_1, &recv_scope, &dev_ctx, &executor,
+      program, &prefetch_var_name_to_prepared_ctx,
+      &sparse_grad_name_to_param_name, ckpt_pre_context, rpc_service_.get());
 
   f(request_send_handler_.get());
   f(request_get_handler_.get());
@@ -445,6 +464,8 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
     RunSyncLoop(&executor, program, &recv_scope, &dev_ctx,
                 prefetch_block_id_list, checkpoint_block_id);
   } else {
+    distributed::AsyncSparseParamUpdateRecorder::Init(
+        fan_in, sparse_grad_name_to_param_name);
     RunAsyncLoop(&executor, program, &recv_scope);
   }
 }
@@ -475,6 +496,10 @@ class ListenAndServOpMaker : public framework::OpProtoAndCheckerMaker {
     AddAttr<std::vector<std::string>>(kPrefetchVarNameToBlockId,
                                       "prefetch blocks to run on server side.")
         .SetDefault({});
+    AddAttr<std::vector<std::string>>(
+        kSparseGradToParam,
+        "sparse grad name to param name. like: 'emb@Grad:emb'")
+        .SetDefault({});
     AddAttr<int>("Fanin", "How many clients send to this server.")
         .SetDefault(1);
     AddAttr<int>(kCheckpointBlockId,

paddle/fluid/operators/distributed_ops/listen_and_serv_op.h

@@ -16,8 +16,10 @@ limitations under the License. */
 
 #include <stdint.h>
 #include <atomic>
+#include <memory>
 #include <set>
 #include <string>
+#include <unordered_map>
 #include <utility>
 #include <vector>
 
@@ -35,6 +37,7 @@ namespace operators {
 constexpr char kOptimizeBlocks[] = "optimize_blocks";
 constexpr char kPrefetchVarNameToBlockId[] = "prefetch_var_name_to_block_id";
 constexpr char kCheckpointBlockId[] = "checkpint_block_id";
+constexpr char kSparseGradToParam[] = "sparse_grad_to_param";
 
 void RunServer(std::shared_ptr<distributed::RPCServer> service);
 

paddle/fluid/operators/distributed_ops/recv_op.cc

@@ -50,17 +50,18 @@ class RecvOp : public framework::OperatorBase {
 
     platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
     auto &ctx = *pool.Get(place);
+    auto trainer_id = Attr<int>("trainer_id");
 
     distributed::RPCClient *rpc_client =
-        distributed::RPCClient::GetInstance<RPCCLIENT_T>(
-            Attr<int>("trainer_id"));
+        distributed::RPCClient::GetInstance<RPCCLIENT_T>(trainer_id);
 
     std::vector<std::string> recv_varnames =
         Attr<std::vector<std::string>>("recv_varnames");
 
     if (recv_varnames.size() > 0) {
       auto recv_functor = distributed::ParameterRecv<float>();
-      auto rpc_ctx = distributed::RpcContext(outs[0], recv_varnames, epmap, {});
+      auto rpc_ctx = distributed::RpcContext(outs[0], recv_varnames, epmap, {},
+                                             trainer_id);
       recv_functor(rpc_ctx, scope);
     } else {
       if (with_barrier) {

paddle/fluid/operators/distributed_ops/send_op.cc

@@ -42,6 +42,7 @@ class SendOp : public framework::OperatorBase {
 
     auto epmap = Attr<std::vector<std::string>>("epmap");
     int sync_send = Attr<int>("sync_mode");
+    auto trainer_id = Attr<int>("trainer_id");
 
     auto send_varnames = Attr<std::vector<std::string>>("send_varnames");
     auto height_sections = Attr<std::vector<int64_t>>("sections");
@@ -51,7 +52,7 @@ class SendOp : public framework::OperatorBase {
       if (distributed::Communicator::GetInstance() == nullptr) {
         auto send_functor = distributed::ParameterSend<float>();
         auto rpc_ctx = distributed::RpcContext(ins[0], send_varnames, epmap,
-                                               height_sections);
+                                               height_sections, trainer_id);
         send_functor(rpc_ctx, scope, true);
       } else {
         distributed::Communicator::GetInstance()->Send(ins[0], scope);
@@ -62,8 +63,7 @@ class SendOp : public framework::OperatorBase {
       auto& ctx = *pool.Get(place);
 
       distributed::RPCClient* rpc_client =
-          distributed::RPCClient::GetInstance<RPCCLIENT_T>(
-              Attr<int>("trainer_id"));
+          distributed::RPCClient::GetInstance<RPCCLIENT_T>(trainer_id);
 
       std::vector<distributed::VarHandlePtr> rets;
       for (size_t i = 0; i < ins.size(); i++) {

paddle/fluid/operators/distributed_ops/split_byref_op.cc

@@ -31,14 +31,16 @@ class SplitByrefOp : public framework::OperatorWithKernel {
     auto in_dims = ctx->GetInputDim("X");
     auto outs_names = ctx->Outputs("Out");
     size_t num = static_cast<size_t>(ctx->Attrs().Get<int>("num"));
-    std::vector<int> sections = static_cast<std::vector<int>>(
-        ctx->Attrs().Get<std::vector<int>>("sections"));
+    auto sections = ctx->Attrs().Get<std::vector<int>>("sections");
     const size_t outs_number = outs_names.size();
     std::vector<framework::DDim> outs_dims;
     outs_dims.reserve(outs_number);
 
     if (num > 0) {
-      int64_t in_axis_dim = in_dims[0];
+      int64_t in_axis_dim = 0;
+      if (ctx->IsRuntime()) {
+        in_axis_dim = in_dims[0];
+      }
       PADDLE_ENFORCE_EQ(in_axis_dim % num, 0,
                         "tensor split does not result"
                         " in an equal division");

paddle/fluid/operators/fake_quantize_op.cu

@@ -235,11 +235,13 @@ struct FindRangeAbsMaxFunctor<platform::CUDADeviceContext, T> {
 
     int g_find_max;
     memory::Copy(platform::CPUPlace(), &g_find_max, gpu_place, find_max,
-                 sizeof(int), 0);
+                 sizeof(int), ctx.stream());
+    ctx.Wait();
     if (g_find_max) {
       int len;
       memory::Copy(platform::CPUPlace(), &len, gpu_place, out_size_data,
-                   sizeof(int), 0);
+                   sizeof(int), ctx.stream());
+      ctx.Wait();
       FindAbsMaxFunctor<platform::CUDADeviceContext, T>()(ctx, scale_arr, len,
                                                           out_scale_data);
     }
@@ -258,25 +260,26 @@ struct FindMovingAverageAbsMaxFunctor<platform::CUDADeviceContext, T> {
     const auto gpu_place = boost::get<platform::CUDAPlace>(ctx.GetPlace());
 
     T accum;
-    memory::Copy(platform::CPUPlace(), &accum, gpu_place, in_accum.data<T>(),
-                 sizeof(T), 0);
     T state;
-    memory::Copy(platform::CPUPlace(), &state, gpu_place, in_state.data<T>(),
-                 sizeof(T), 0);
     T scale;
+    memory::Copy(platform::CPUPlace(), &accum, gpu_place, in_accum.data<T>(),
+                 sizeof(T), ctx.stream());
+    memory::Copy(platform::CPUPlace(), &state, gpu_place, in_state.data<T>(),
+                 sizeof(T), ctx.stream());
     memory::Copy(platform::CPUPlace(), &scale, gpu_place, cur_scale, sizeof(T),
-                 0);
-
+                 ctx.stream());
+    ctx.Wait();
     state = rate * state + 1;
     accum = rate * accum + scale;
     scale = accum / state;
 
     memory::Copy(gpu_place, out_accum->mutable_data<T>(gpu_place),
-                 platform::CPUPlace(), &accum, sizeof(T), 0);
+                 platform::CPUPlace(), &accum, sizeof(T), ctx.stream());
     memory::Copy(gpu_place, out_state->mutable_data<T>(gpu_place),
-                 platform::CPUPlace(), &state, sizeof(T), 0);
+                 platform::CPUPlace(), &state, sizeof(T), ctx.stream());
     memory::Copy(gpu_place, out_scale->mutable_data<T>(gpu_place),
-                 platform::CPUPlace(), &scale, sizeof(T), 0);
+                 platform::CPUPlace(), &scale, sizeof(T), ctx.stream());
+    ctx.Wait();
   }
 };
 

paddle/fluid/operators/grid_sampler_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/grid_sampler_op.h"
+#include <memory>
 #include "paddle/fluid/framework/op_registry.h"
 #ifdef PADDLE_WITH_CUDA
 #include "paddle/fluid/platform/cudnn_helper.h"
@@ -40,10 +41,12 @@ class GridSampleOp : public framework::OperatorWithKernel {
                    "Input(X) of GridSampleOp should be 4-D Tensor.");
     PADDLE_ENFORCE(grid_dims.size() == 4,
                    "Input(Grid) of GridSampleOp should be 4-D Tensor.");
-    PADDLE_ENFORCE(grid_dims[3] == 2, "Input(Grid) dims[3] should be 2.");
-    PADDLE_ENFORCE_EQ(grid_dims[0], x_dims[0],
-                      "Input(X) and Input(Grid) dims[0] should be equal.");
+    if (ctx->IsRuntime() || grid_dims[3] > 0) {
+      PADDLE_ENFORCE(grid_dims[3] == 2, "Input(Grid) dims[3] should be 2.");
+    }
     if (ctx->IsRuntime()) {
+      PADDLE_ENFORCE_EQ(grid_dims[0], x_dims[0],
+                        "Input(X) and Input(Grid) dims[0] should be equal.");
       PADDLE_ENFORCE_EQ(
           grid_dims[1], x_dims[2],
           "Input(X) dims[2] and Input(Grid) dims[1] should be equal.");

paddle/fluid/operators/hierarchical_sigmoid_op.h

@@ -238,6 +238,8 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
       zero(dev_ctx, w_grad, static_cast<T>(0.0));
       bit_code->MulGradWeight(pre_out_grad, w_grad, in);
     } else {
+      PADDLE_ENFORCE(path != nullptr,
+                     "Sparse mode should not be used without custom tree!");
       framework::Vector<int64_t> real_rows = PathToRows(*path);
       auto* w_grad =
           ctx.Output<framework::SelectedRows>(framework::GradVarName("W"));

paddle/fluid/operators/interpolate_op.cc

@@ -45,9 +45,14 @@ class InterpolateOp : public framework::OperatorWithKernel {
       // round down
       out_h = static_cast<int>(dim_x[2] * scale);
       out_w = static_cast<int>(dim_x[3] * scale);
+      // protect when input shape is -1
+      out_h = out_h > 0 ? out_h : -1;
+      out_w = out_w > 0 ? out_w : -1;
     } else {
       out_h = ctx->Attrs().Get<int>("out_h");
       out_w = ctx->Attrs().Get<int>("out_w");
+      PADDLE_ENFORCE_GT(out_h, 0, "out_h should be greater than 0.");
+      PADDLE_ENFORCE_GT(out_w, 0, "out_w should be greater than 0.");
     }
 
     if (ctx->HasInput("OutSize") && ctx->IsRuntime()) {
@@ -58,6 +63,7 @@ class InterpolateOp : public framework::OperatorWithKernel {
       ctx->ShareLoD("X", "Out");
       return;
     }
+
     std::vector<int64_t> dim_out({dim_x[0], dim_x[1], out_h, out_w});
     ctx->SetOutputDim("Out", framework::make_ddim(dim_out));
   }

paddle/fluid/operators/kldiv_loss_op.cc

@@ -35,8 +35,10 @@ class KLDivLossOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(dim_x.size(), dim_target.size(),
                       "Input(X) rank and Input(Target) rank should be same.");
     for (int i = 0; i < dim_x.size(); i++) {
-      PADDLE_ENFORCE_EQ(dim_x[i], dim_target[i],
-                        "Input(X) and Input(Target) should in same shape.");
+      if (ctx->IsRuntime() || (dim_x[i] > 0 && dim_target[i] > 0)) {
+        PADDLE_ENFORCE_EQ(dim_x[i], dim_target[i],
+                          "Input(X) and Input(Target) should in same shape.");
+      }
     }
 
     auto reduction = ctx->Attrs().Get<std::string>("reduction");

paddle/fluid/operators/lod_reset_op.cc

@@ -30,10 +30,10 @@ class LoDResetOp : public framework::OperatorWithKernel {
 
     if (!ctx->HasInput("Y")) {
       auto level0 = ctx->Attrs().Get<std::vector<int>>("target_lod");
-      PADDLE_ENFORCE_GT(level0.size(), 1,
+      PADDLE_ENFORCE_GT(level0.size(), 0,
                         "If Input(Y) not provided, the target lod should be "
                         "specified by attribute `target_lod`.");
-    } else {
+    } else if (ctx->IsRuntime()) {
       ctx->ShareLoD("Y", "Out");
     }
 
@@ -48,6 +48,23 @@ class LoDResetOp : public framework::OperatorWithKernel {
   }
 };
 
+class LoDResetOpVarTypeInference : public framework::VarTypeInference {
+ public:
+  void operator()(framework::InferVarTypeContext *ctx) const override {
+    auto x_var_name = ctx->Input("X").front();
+    auto out_var_name = ctx->Output("Out").front();
+    if (ctx->HasInput("Y")) {
+      auto y_var_name = ctx->Input("Y").front();
+      auto y_lod_level = std::max(ctx->GetLoDLevel(y_var_name), 1);
+      ctx->SetLoDLevel(out_var_name, y_lod_level);
+    } else {
+      ctx->SetLoDLevel(out_var_name, 1);
+    }
+    ctx->SetDataType(out_var_name, ctx->GetDataType(x_var_name));
+    ctx->SetType(out_var_name, paddle::framework::proto::VarType::LOD_TENSOR);
+  }
+};
+
 class LoDResetOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
@@ -177,9 +194,10 @@ DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(LoDResetGradNoNeedBufferVarInference,
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(lod_reset, ops::LoDResetOp, ops::LoDResetOpMaker,
-                  ops::LoDResetGradDescMaker);
+                  ops::LoDResetGradDescMaker, ops::LoDResetOpVarTypeInference);
 REGISTER_OPERATOR(lod_reset_grad, ops::LoDResetGradOp,
                   ops::LoDResetGradNoNeedBufferVarInference);
+
 REGISTER_OP_CPU_KERNEL(
     lod_reset, ops::LoDResetKernel<paddle::platform::CPUPlace, float>,
     ops::LoDResetKernel<paddle::platform::CPUPlace, double>,

paddle/fluid/operators/lod_reset_op.h

@@ -63,7 +63,7 @@ class LoDResetKernel : public framework::OpKernel<T> {
                       "Target LoD should be a vector end with the "
                       "first dimension of Input(X).");
     for (size_t i = 0; i < level0.size() - 1; ++i) {
-      PADDLE_ENFORCE(level0[i + 1] > level0[i],
+      PADDLE_ENFORCE(level0[i + 1] >= level0[i],
                      "Target LoD should be an ascending vector.");
     }
 

paddle/fluid/operators/lstmp_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/lstmp_op.h"
+#include <memory>
 #include <string>
 
 namespace paddle {
@@ -45,6 +46,7 @@ class LSTMPOp : public framework::OperatorWithKernel {
                    "Output(BatchHidden) of LSTMP operator should not be null.");
 
     auto in_dims = ctx->GetInputDim("Input");
+
     PADDLE_ENFORCE_EQ(in_dims.size(), 2,
                       "Input(X)'s rank of LSTMP operator must be 2.");
 
@@ -269,13 +271,47 @@ Users can choose to use fully-connected operator before LSTMP operator.
   }
 };
 
+class LSTMPGradMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDesc> Apply() const override {
+    auto* grad_op = new framework::OpDesc();
+    grad_op->SetType("lstmp_grad");
+    grad_op->SetInput("Weight", Input("Weight"));
+    grad_op->SetInput("ProjWeight", Input("ProjWeight"));
+    grad_op->SetInput("Bias", Input("Bias"));
+
+    grad_op->SetInput("Projection", Output("Projection"));
+    grad_op->SetInput("Cell", Output("Cell"));
+    grad_op->SetInput("BatchGate", Output("BatchGate"));
+    grad_op->SetInput("BatchCellPreAct", Output("BatchCellPreAct"));
+    grad_op->SetInput("BatchHidden", Output("BatchHidden"));
+    grad_op->SetInput("H0", Input("H0"));
+    grad_op->SetInput("C0", Input("C0"));
+
+    grad_op->SetInput(framework::GradVarName("Projection"),
+                      OutputGrad("Projection"));
+
+    grad_op->SetOutput(framework::GradVarName("Input"), InputGrad("Input"));
+    grad_op->SetOutput(framework::GradVarName("Weight"), InputGrad("Weight"));
+    grad_op->SetOutput(framework::GradVarName("ProjWeight"),
+                       InputGrad("ProjWeight"));
+    grad_op->SetOutput(framework::GradVarName("Bias"), InputGrad("Bias"));
+    grad_op->SetOutput(framework::GradVarName("H0"), InputGrad("H0"));
+    grad_op->SetOutput(framework::GradVarName("C0"), InputGrad("C0"));
+
+    grad_op->SetAttrMap(Attrs());
+    return std::unique_ptr<framework::OpDesc>(grad_op);
+  }
+};
+
 class LSTMPGradOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("Input"),
-                   "Input(Input) of LSTMP operator should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("Projection"),
                    "Input(Projection) of LSTMP operator should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("Cell"),
@@ -298,7 +334,8 @@ class LSTMPGradOp : public framework::OperatorWithKernel {
         ctx->SetOutputDim(g_name, ctx->GetInputDim(name));
     };
 
-    SetOutGradDim("Input");
+    ctx->SetOutputDim(framework::GradVarName("Input"),
+                      ctx->GetInputDim("BatchGate"));
     SetOutGradDim("Weight");
     SetOutGradDim("ProjWeight");
     SetOutGradDim("Bias");
@@ -310,7 +347,8 @@ class LSTMPGradOp : public framework::OperatorWithKernel {
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
     return framework::OpKernelType(
-        ctx.Input<framework::LoDTensor>("Input")->type(), ctx.device_context());
+        ctx.Input<framework::LoDTensor>("BatchGate")->type(),
+        ctx.device_context());
   }
 };
 
@@ -318,8 +356,7 @@ class LSTMPGradOp : public framework::OperatorWithKernel {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OPERATOR(lstmp, ops::LSTMPOp, ops::LSTMPOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
+REGISTER_OPERATOR(lstmp, ops::LSTMPOp, ops::LSTMPOpMaker, ops::LSTMPGradMaker);
 REGISTER_OPERATOR(lstmp_grad, ops::LSTMPGradOp);
 REGISTER_OP_CPU_KERNEL(
     lstmp, ops::LSTMPKernel<paddle::platform::CPUDeviceContext, float>,

paddle/fluid/operators/lstmp_op.h

@@ -267,7 +267,6 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
   }
 
   void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* input = ctx.Input<LoDTensor>("Input");
     auto* weight = ctx.Input<Tensor>("Weight");
     auto* proj_weight = ctx.Input<Tensor>("ProjWeight");
     auto* bias = ctx.Input<Tensor>("Bias");
@@ -323,7 +322,8 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
       ordered_c0_g.mutable_data<T>(c0_g->dims(), ctx.GetPlace());
     }
 
-    auto in_dims = input->dims();
+    // batch_gate dims equal to input dims
+    auto in_dims = batch_gate->dims();
     auto out_dims = cell_out->dims();
     framework::DDim proj_dims({in_dims[0], proj_weight->dims()[1]});
     int frame_size = static_cast<int>(in_dims[1] / 4);

paddle/fluid/operators/math/selected_rows_functor.cc

@@ -296,6 +296,7 @@ struct MergeAdd<platform::CPUDeviceContext, T> {
     auto input_height = has_value_input->height();
     framework::SelectedRows& out = *output;
     std::set<int64_t> merged_row_set;
+    size_t row_num = 0;
     for (auto* input : inputs) {
       if (input->rows().size() == 0) {
         continue;
@@ -305,42 +306,71 @@ struct MergeAdd<platform::CPUDeviceContext, T> {
                         "dimension except for the first one");
       PADDLE_ENFORCE_EQ(input_height, input->height(),
                         "all input should have same height");
+      row_num += input->rows().size();
       merged_row_set.insert(input->rows().begin(), input->rows().end());
     }
-    std::vector<int64_t> merge_rows(merged_row_set.begin(),
-                                    merged_row_set.end());
-    if (sorted_result) {
-      std::sort(merge_rows.begin(), merge_rows.end());
-    }
-    std::unordered_map<int64_t, size_t> rows_to_id;
-    for (size_t i = 0; i < merge_rows.size(); ++i) {
-      rows_to_id[merge_rows[i]] = i;
-    }
-    out.set_rows(merge_rows);
+
     out.set_height(input_height);
     out.mutable_value()->mutable_data<T>(
         framework::make_ddim(
-            {static_cast<int64_t>(merge_rows.size()), input_width}),
+            {static_cast<int64_t>(merged_row_set.size()), input_width}),
         context.GetPlace());
+    auto* out_data = out.mutable_value()->data<T>();
 
-    math::SetConstant<platform::CPUDeviceContext, T> constant_functor;
-    constant_functor(context, out.mutable_value(), 0.0);
+    if (merged_row_set.size() == row_num && !sorted_result) {
+      // no duplicated ids, just concat the result together
+      std::vector<int64_t> merge_rows;
+      merge_rows.reserve(row_num);
+      // concat rows
+      for (auto* in : inputs) {
+        merge_rows.insert(merge_rows.end(), in->rows().begin(),
+                          in->rows().end());
+      }
+      out.set_rows(merge_rows);
+      auto in_place = inputs[0]->place();
+      auto out_place = out.place();
+      int64_t copied_numel = 0;
+      for (auto* in : inputs) {
+        auto* in_data = in->value().data<T>();
+        auto in_numel = in->value().numel();
+        memory::Copy(boost::get<platform::CPUPlace>(out_place),
+                     out_data + copied_numel,
+                     boost::get<platform::CPUPlace>(in_place), in_data,
+                     in_numel * sizeof(T));
+        copied_numel += in_numel;
+      }
+    } else {
+      std::vector<int64_t> merge_rows(merged_row_set.begin(),
+                                      merged_row_set.end());
 
-    auto* out_data = out.mutable_value()->data<T>();
+      if (sorted_result) {
+        std::sort(merge_rows.begin(), merge_rows.end());
+      }
 
-    auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
-    for (auto* input : inputs) {
-      if (input->rows().size() == 0) {
-        continue;
+      out.set_rows(merge_rows);
+
+      math::SetConstant<platform::CPUDeviceContext, T> constant_functor;
+      constant_functor(context, out.mutable_value(), 0.0);
+
+      std::unordered_map<int64_t, size_t> rows_to_id;
+      for (size_t i = 0; i < merge_rows.size(); ++i) {
+        rows_to_id[merge_rows[i]] = i;
       }
-      auto* input_data = input->value().data<T>();
-      auto& input_rows = input->rows();
-
-      for (size_t i = 0; i < input_rows.size(); i++) {
-        size_t out_i = rows_to_id[input_rows[i]];
-        elementwise_add_to<platform::CPUDeviceContext, T>(
-            context, &blas, static_cast<size_t>(input_width),
-            &input_data[i * input_width], &out_data[out_i * input_width]);
+
+      auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
+      for (auto* input : inputs) {
+        if (input->rows().size() == 0) {
+          continue;
+        }
+        auto* input_data = input->value().data<T>();
+        auto& input_rows = input->rows();
+
+        for (size_t i = 0; i < input_rows.size(); i++) {
+          size_t out_i = rows_to_id[input_rows[i]];
+          elementwise_add_to<platform::CPUDeviceContext, T>(
+              context, &blas, static_cast<size_t>(input_width),
+              &input_data[i * input_width], &out_data[out_i * input_width]);
+        }
       }
     }
   }

paddle/fluid/operators/math/selected_rows_functor_test.cc

@@ -13,8 +13,11 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/math/selected_rows_functor.h"
+
+#include <memory>
 #include <vector>
 #include "gtest/gtest.h"
+
 #include "paddle/fluid/operators/math/math_function.h"
 
 TEST(selected_rows_functor, cpu_add) {
@@ -360,6 +363,69 @@ TEST(selected_rows_functor, cpu_merge_add_multi) {
   }
 }
 
+TEST(selected_rows_functor, cpu_merge_add_multi_noduplicated) {
+  paddle::platform::CPUPlace cpu_place;
+  paddle::platform::CPUDeviceContext ctx(cpu_place);
+  paddle::operators::math::SetConstant<paddle::platform::CPUDeviceContext,
+                                       float>
+      set_const;
+
+  int64_t height = 10;
+  int64_t row_numel = 8;
+
+  std::vector<int64_t> rows1{1, 3, 5, 7, 9};
+  std::unique_ptr<paddle::framework::SelectedRows> selected_rows1{
+      new paddle::framework::SelectedRows(rows1, height)};
+  auto* in1_value = selected_rows1->mutable_value();
+  in1_value->mutable_data<float>(
+      paddle::framework::make_ddim(
+          {static_cast<int64_t>(rows1.size()), row_numel}),
+      cpu_place);
+  set_const(ctx, in1_value, 1.0);
+
+  std::vector<int64_t> rows2{0, 2, 4, 6, 8};
+  std::unique_ptr<paddle::framework::SelectedRows> selected_rows2{
+      new paddle::framework::SelectedRows(rows2, height)};
+  auto* in2_value = selected_rows2->mutable_value();
+  in2_value->mutable_data<float>(
+      paddle::framework::make_ddim(
+          {static_cast<int64_t>(rows2.size()), row_numel}),
+      cpu_place);
+  set_const(ctx, in2_value, 2.0);
+
+  std::unique_ptr<paddle::framework::SelectedRows> output{
+      new paddle::framework::SelectedRows()};
+  output->set_height(height);
+  paddle::operators::math::scatter::MergeAdd<paddle::platform::CPUDeviceContext,
+                                             float>
+      merge_add_functor;
+
+  std::vector<const paddle::framework::SelectedRows*> inputs;
+  inputs.push_back(selected_rows1.get());
+  inputs.push_back(selected_rows2.get());
+  merge_add_functor(ctx, inputs, output.get());
+
+  EXPECT_EQ(output->height(), height);
+  EXPECT_EQ(output->value().dims(),
+            paddle::framework::make_ddim({10, row_numel}));
+
+  std::vector<int64_t> ret_rows{1, 3, 5, 7, 9, 0, 2, 4, 6, 8};
+  EXPECT_EQ(output->rows(), ret_rows);
+
+  auto* out_data = output->value().data<float>();
+  for (size_t i = 0; i < ret_rows.size(); ++i) {
+    float data_value = 0;
+    if (i < 5) {
+      data_value = 1.0;
+    } else {
+      data_value = 2.0;
+    }
+    for (size_t j = 0; j < static_cast<size_t>(row_numel); ++j) {
+      EXPECT_EQ(out_data[i * row_numel + j], data_value);
+    }
+  }
+}
+
 TEST(selected_rows_functor, cpu_sum_to) {
   paddle::platform::CPUPlace cpu_place;
   paddle::platform::CPUDeviceContext ctx(cpu_place);

paddle/fluid/operators/merge_lod_tensor_op.cc

@@ -164,7 +164,9 @@ class MergeLoDTensorInferShape : public framework::InferShapeBase {
 
     auto mask_dim = context->GetInputDim("Mask");
     PADDLE_ENFORCE_EQ(mask_dim.size(), 2);
-    PADDLE_ENFORCE_EQ(mask_dim[1], 1);
+    if (context->IsRuntime() || mask_dim[1] > 0) {
+      PADDLE_ENFORCE_EQ(mask_dim[1], 1);
+    }
 
     context->SetOutputDim("Out", context->GetInputDim("InTrue"));
   }

paddle/fluid/operators/mkldnn/batch_norm_mkldnn_op.cc

@@ -39,13 +39,9 @@ struct bn_type_traits {
 
 class BatchNormMKLDNNHandler : public platform::MKLDNNHandler {
  public:
-  BatchNormMKLDNNHandler(
-      std::shared_ptr<batch_norm_fwd::primitive_desc> batch_norm_pd,
-      const platform::MKLDNNDeviceContext &dev_ctx, mkldnn::engine engine,
-      const std::string &base_key)
-      : platform::MKLDNNHandler(dev_ctx, engine, base_key) {
-    batch_norm_pd_ = batch_norm_pd;
-  }
+  BatchNormMKLDNNHandler(const platform::MKLDNNDeviceContext &dev_ctx,
+                         mkldnn::engine engine, const std::string &base_key)
+      : platform::MKLDNNHandler(dev_ctx, engine, base_key) {}
 
   std::shared_ptr<memory> AcquireScaleshiftMemoryFromPrimitive(void *ptr) {
     return this->AcquireMemoryFromPrimitive(
@@ -62,6 +58,26 @@ class BatchNormMKLDNNHandler : public platform::MKLDNNHandler {
         batch_norm_pd_->variance_primitive_desc(), ptr, "@variance_mem_p");
   }
 
+  std::shared_ptr<batch_norm_fwd::primitive_desc>
+  AcquireBatchNormPrimitiveDescriptor(const batch_norm_fwd::desc &bn_fwd_desc,
+                                      const mkldnn::engine &engine) {
+    const std::string key_batch_norm_fwd_pd = key_ + "@bn_fwd_pd";
+    auto batch_norm_pd =
+        std::static_pointer_cast<batch_norm_fwd::primitive_desc>(
+            dev_ctx_.GetBlob(key_batch_norm_fwd_pd));
+
+    if (batch_norm_pd == nullptr) {
+      batch_norm_pd_.reset(
+          new batch_norm_fwd::primitive_desc(bn_fwd_desc, engine));
+      dev_ctx_.SetBlob(key_batch_norm_fwd_pd, batch_norm_pd_);
+    } else {
+      batch_norm_pd_ = batch_norm_pd;
+      is_reusing_ = true;
+    }
+
+    return batch_norm_pd_;
+  }
+
   std::shared_ptr<batch_norm_fwd> AcquireTestTrainingBatchNormFwd(
       std::shared_ptr<memory> src_memory,
       std::shared_ptr<memory> scaleshift_memory,
@@ -213,7 +229,7 @@ class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     const std::string key = BatchNormMKLDNNHandler::GetHash(
         src_tz, epsilon, flags, global_stats, input_format,
         ctx.op().Output("SavedMean"));
-    const std::string key_batch_norm_fwd_pd = key + "@bn_fwd_pd";
+    BatchNormMKLDNNHandler handler(dev_ctx, mkldnn_engine, key);
 
     auto user_src_md = platform::MKLDNNMemDesc(
         {src_tz}, platform::MKLDNNGetDataType<T>(), input_format);
@@ -222,13 +238,9 @@ class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     using bn_fwd_types = bn_type_traits<mkldnn::batch_normalization_forward>;
     auto batch_norm_fwd_desc =
         bn_fwd_types::op_desc{propagation, user_src_md, epsilon, flags};
-    auto batch_norm_fwd_pd = std::make_shared<batch_norm_fwd::primitive_desc>(
-        batch_norm_fwd_desc, mkldnn_engine);
-    // Save conv_pd/src_memory/weights_memory for backward pass
-    dev_ctx.SetBlob(key_batch_norm_fwd_pd, batch_norm_fwd_pd);
 
-    BatchNormMKLDNNHandler handler(batch_norm_fwd_pd, dev_ctx, mkldnn_engine,
-                                   key);
+    auto batch_norm_fwd_pd = handler.AcquireBatchNormPrimitiveDescriptor(
+        batch_norm_fwd_desc, mkldnn_engine);
 
     auto src_memory =
         handler.AcquireSrcMemory(user_src_md, to_void_cast(x_data));

paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc

@@ -144,7 +144,6 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     const std::string key = platform::ConvMKLDNNHandler::GetHash(
         src_tz, weights_tz, strides, paddings, dilations, groups,
         ctx.op().Input("Input") + ctx.op().Input("Filter"));
-    const std::string key_conv_pd = key + "@conv_pd";
 
     std::vector<primitive> pipeline;
 
@@ -183,6 +182,8 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     auto dst_md = platform::MKLDNNMemDesc(
         dst_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
 
+    platform::ConvMKLDNNHandler handler(dev_ctx, mkldnn_engine, key);
+
     // create a conv primitive descriptor and save it for usage in backward
     std::shared_ptr<mkldnn::convolution_forward::primitive_desc> conv_pd;
     auto fwd_prop_kind = is_test ? mkldnn::prop_kind::forward_inference
@@ -191,18 +192,14 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
       bias_tz = paddle::framework::vectorize2int(bias->dims());
       auto bias_md = platform::MKLDNNMemDesc(
           bias_tz, platform::MKLDNNGetDataType<T>(), memory::format::x);
-      conv_pd = ConvFwdPrimitiveDesc(
+      conv_pd = handler.AcquireConvolutionPrimitiveDescriptor(
           src_md, weights_md, bias_md, dst_md, strides, paddings, mkldnn_engine,
           fuse_relu, fuse_residual_conn, fwd_prop_kind);
     } else {
-      conv_pd = ConvFwdPrimitiveDesc(src_md, weights_md, dst_md, strides,
-                                     paddings, mkldnn_engine, fuse_relu,
-                                     fuse_residual_conn, fwd_prop_kind);
+      conv_pd = handler.AcquireConvolutionPrimitiveDescriptor(
+          src_md, weights_md, boost::none, dst_md, strides, paddings,
+          mkldnn_engine, fuse_relu, fuse_residual_conn, fwd_prop_kind);
     }
-    // Save conv_pd/src_memory/weights_memory for backward pass
-    if (!is_test) dev_ctx.SetBlob(key_conv_pd, conv_pd);
-
-    platform::ConvMKLDNNHandler handler(conv_pd, dev_ctx, mkldnn_engine, key);
 
     // create mkldnn memory from input tensors (data/weights)
     auto user_src_memory_p =
@@ -633,31 +630,6 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
   }
 
  private:
-  mkldnn::primitive_attr CreatePostOps(bool fuse_relu,
-                                       bool fuse_residual_conn) const {
-    mkldnn::primitive_attr conv_attr;
-    mkldnn::post_ops post_operations;
-    // Fusion with Elementwise layer relies on adding a sum post-operation with
-    // the scale parameter. It is assumed that when fuse_residual_connection is
-    // true, the output tensor contains the data coming from residual
-    // connection. The result of this post_op is:
-    // Output = scale * Output + Conv_Out.
-    if (fuse_residual_conn) {
-      post_operations.append_sum(1.0f);
-    }
-    // Fusion with ReLU layer is executed through the PostOps feature. Create a
-    // PostOps object and configure it to execute an eltwise relu operation.
-    if (fuse_relu) {
-      constexpr float scale = 1.0f;
-      constexpr float negative_slope = 0.0f;
-      constexpr float placeholder = 0.0f;
-      post_operations.append_eltwise(scale, mkldnn::algorithm::eltwise_relu,
-                                     negative_slope, placeholder);
-    }
-    conv_attr.set_post_ops(post_operations);
-    return conv_attr;
-  }
-
   mkldnn::primitive_attr CreatePostOps(
       bool fuse_relu, bool fuse_residual_conn,
       const std::vector<float> output_shift_scale, float sum_scale) const {
@@ -679,30 +651,6 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     return conv_attr;
   }
 
-  std::unique_ptr<mkldnn::convolution_forward::primitive_desc>
-  ConvFwdPrimitiveDesc(const memory::desc& src, const memory::desc& weights,
-                       const memory::desc& dst, const std::vector<int>& strides,
-                       const std::vector<int>& paddings,
-                       const mkldnn::engine& engine, const bool fuse_relu,
-                       const bool fuse_residual_conn,
-                       mkldnn::prop_kind fwd_prop_kind) const {
-    memory::dims stride_dims = strides;
-    memory::dims padding_dims = paddings;
-
-    auto conv_desc = mkldnn::convolution_forward::desc(
-        fwd_prop_kind, mkldnn::convolution_direct, src, weights, dst,
-        stride_dims, padding_dims, padding_dims, mkldnn::padding_kind::zero);
-
-    mkldnn::primitive_attr conv_attr =
-        CreatePostOps(fuse_relu, fuse_residual_conn);
-
-    auto p_conv_pd = new mkldnn::convolution_forward::primitive_desc(
-        conv_desc, conv_attr, engine);
-
-    return std::unique_ptr<mkldnn::convolution_forward::primitive_desc>(
-        p_conv_pd);
-  }
-
   std::unique_ptr<mkldnn::convolution_forward::primitive_desc>
   ConvFwdPrimitiveDesc(const memory::desc& src, const memory::desc& weights,
                        const memory::desc& dst, const std::vector<int>& strides,
@@ -731,31 +679,6 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
         p_conv_pd);
   }
 
-  std::unique_ptr<mkldnn::convolution_forward::primitive_desc>
-  ConvFwdPrimitiveDesc(const memory::desc& src, const memory::desc& weights,
-                       const memory::desc& bias, const memory::desc& dst,
-                       const std::vector<int>& strides,
-                       const std::vector<int>& paddings,
-                       const mkldnn::engine& engine, const bool fuse_relu,
-                       const bool fuse_residual_conn,
-                       mkldnn::prop_kind fwd_prop_kind) const {
-    memory::dims stride_dims = strides;
-    memory::dims padding_dims = paddings;
-
-    auto conv_desc = mkldnn::convolution_forward::desc(
-        fwd_prop_kind, mkldnn::convolution_direct, src, weights, bias, dst,
-        stride_dims, padding_dims, padding_dims, mkldnn::padding_kind::zero);
-
-    mkldnn::primitive_attr conv_attr =
-        CreatePostOps(fuse_relu, fuse_residual_conn);
-
-    auto p_conv_pd = new mkldnn::convolution_forward::primitive_desc(
-        conv_desc, conv_attr, engine);
-
-    return std::unique_ptr<mkldnn::convolution_forward::primitive_desc>(
-        p_conv_pd);
-  }
-
   std::unique_ptr<mkldnn::convolution_forward::primitive_desc>
   ConvFwdPrimitiveDesc(const memory::desc& src, const memory::desc& weights,
                        const memory::desc& bias, const memory::desc& dst,

paddle/fluid/operators/mkldnn/conv_transpose_mkldnn_op.cc

@@ -12,6 +12,7 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 
+#include "boost/optional.hpp"
 #include "paddle/fluid/framework/data_layout_transform.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/memory/malloc.h"
@@ -124,7 +125,6 @@ class ConvTransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     const std::string key = platform::ConvTransposeMKLDNNHandler::GetHash(
         src_tz, weights_tz, strides, paddings, dilations, groups,
         ctx.op().Output("Output"));
-    const std::string key_conv_transpose_pd = key + "@conv_transpose_pd";
 
     std::vector<mkldnn::primitive> pipeline;
 
@@ -153,6 +153,7 @@ class ConvTransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     auto dst_md = platform::MKLDNNMemDesc(
         dst_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
 
+    platform::ConvTransposeMKLDNNHandler handler(dev_ctx, mkldnn_engine, key);
     // create a deconv(conv transpose) primitive descriptor and save it for
     // usage in backward
     std::shared_ptr<mkldnn::deconvolution_forward::primitive_desc>
@@ -163,19 +164,14 @@ class ConvTransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
       bias_tz = paddle::framework::vectorize2int(bias->dims());
       auto bias_md = platform::MKLDNNMemDesc(
           bias_tz, platform::MKLDNNGetDataType<T>(), mkldnn::memory::format::x);
-      conv_transpose_pd = ConvTransposeFwdPrimitiveDesc(
+      conv_transpose_pd = handler.AcquireConvolutionPrimitiveDescriptor(
           src_md, weights_md, bias_md, dst_md, strides, paddings, mkldnn_engine,
-          fuse_relu, fwd_prop_kind);
+          fuse_relu, false, fwd_prop_kind);
     } else {
-      conv_transpose_pd = ConvTransposeFwdPrimitiveDesc(
-          src_md, weights_md, dst_md, strides, paddings, mkldnn_engine,
-          fuse_relu, fwd_prop_kind);
+      conv_transpose_pd = handler.AcquireConvolutionPrimitiveDescriptor(
+          src_md, weights_md, boost::none, dst_md, strides, paddings,
+          mkldnn_engine, fuse_relu, false, fwd_prop_kind);
     }
-    // Save conv_pd/src_memory/weights_memory for backward pass
-    if (!is_test) dev_ctx.SetBlob(key_conv_transpose_pd, conv_transpose_pd);
-
-    platform::ConvTransposeMKLDNNHandler handler(conv_transpose_pd, dev_ctx,
-                                                 mkldnn_engine, key);
 
     // create mkldnn memory from input tensors (data/weights)
     auto user_src_memory_p = handler.AcquireSrcMemory(
@@ -224,70 +220,6 @@ class ConvTransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     output->set_layout(DataLayout::kMKLDNN);
     output->set_format(platform::GetMKLDNNFormat(*dst_memory_p));
   }
-
- private:
-  mkldnn::primitive_attr CreatePostOps(bool fuse_relu) const {
-    mkldnn::primitive_attr conv_attr;
-    mkldnn::post_ops post_operations;
-    // Fusion with ReLU layer is executed through the PostOps feature. Create a
-    // PostOps object and configure it to execute an eltwise relu operation.
-    if (fuse_relu) {
-      constexpr float scale = 1.0f;
-      constexpr float negative_slope = 0.0f;
-      constexpr float placeholder = 0.0f;
-      post_operations.append_eltwise(scale, mkldnn::algorithm::eltwise_relu,
-                                     negative_slope, placeholder);
-    }
-    conv_attr.set_post_ops(post_operations);
-    return conv_attr;
-  }
-
-  std::unique_ptr<mkldnn::deconvolution_forward::primitive_desc>
-  ConvTransposeFwdPrimitiveDesc(
-      const mkldnn::memory::desc& src, const mkldnn::memory::desc& weights,
-      const mkldnn::memory::desc& dst, const std::vector<int>& strides,
-      const std::vector<int>& paddings, const mkldnn::engine& engine,
-      const bool fuse_relu, mkldnn::prop_kind fwd_prop_kind) const {
-    mkldnn::memory::dims stride_dims = {strides[0], strides[1]};
-    mkldnn::memory::dims padding_dims = {paddings[0], paddings[1]};
-
-    auto deconv_desc = mkldnn::deconvolution_forward::desc(
-        fwd_prop_kind, mkldnn::deconvolution_direct, src, weights, dst,
-        stride_dims, padding_dims, padding_dims, mkldnn::padding_kind::zero);
-
-    mkldnn::primitive_attr deconv_attr = CreatePostOps(fuse_relu);
-
-    auto p_conv_transpose_pd =
-        new mkldnn::deconvolution_forward::primitive_desc(deconv_desc,
-                                                          deconv_attr, engine);
-
-    return std::unique_ptr<mkldnn::deconvolution_forward::primitive_desc>(
-        p_conv_transpose_pd);
-  }
-
-  std::unique_ptr<mkldnn::deconvolution_forward::primitive_desc>
-  ConvTransposeFwdPrimitiveDesc(
-      const mkldnn::memory::desc& src, const mkldnn::memory::desc& weights,
-      const mkldnn::memory::desc& bias, const mkldnn::memory::desc& dst,
-      const std::vector<int>& strides, const std::vector<int>& paddings,
-      const mkldnn::engine& engine, const bool fuse_relu,
-      mkldnn::prop_kind fwd_prop_kind) const {
-    mkldnn::memory::dims stride_dims = {strides[0], strides[1]};
-    mkldnn::memory::dims padding_dims = {paddings[0], paddings[1]};
-
-    auto deconv_desc = mkldnn::deconvolution_forward::desc(
-        fwd_prop_kind, mkldnn::deconvolution_direct, src, weights, bias, dst,
-        stride_dims, padding_dims, padding_dims, mkldnn::padding_kind::zero);
-
-    mkldnn::primitive_attr deconv_attr = CreatePostOps(fuse_relu);
-
-    auto p_conv_transpose_pd =
-        new mkldnn::deconvolution_forward::primitive_desc(deconv_desc,
-                                                          deconv_attr, engine);
-
-    return std::unique_ptr<mkldnn::deconvolution_forward::primitive_desc>(
-        p_conv_transpose_pd);
-  }
 };
 
 }  // namespace operators

paddle/fluid/operators/mkldnn/softmax_mkldnn_op.cc

@@ -34,12 +34,9 @@ using platform::to_void_cast;
 
 class SoftmaxMKLDNNHandler : public platform::MKLDNNHandler {
  public:
-  SoftmaxMKLDNNHandler(
-      std::shared_ptr<mkldnn::softmax_forward::primitive_desc> softmax_pd,
-      const platform::MKLDNNDeviceContext& dev_ctx, mkldnn::engine engine,
-      const std::string& base_key)
-      : platform::MKLDNNHandler(dev_ctx, engine, base_key),
-        softmax_pd_(softmax_pd) {}
+  SoftmaxMKLDNNHandler(const platform::MKLDNNDeviceContext& dev_ctx,
+                       mkldnn::engine engine, const std::string& base_key)
+      : platform::MKLDNNHandler(dev_ctx, engine, base_key) {}
 
   SoftmaxMKLDNNHandler(
       std::shared_ptr<mkldnn::softmax_forward::primitive_desc> softmax_pd,
@@ -54,6 +51,26 @@ class SoftmaxMKLDNNHandler : public platform::MKLDNNHandler {
     key_ += "-BWD";
   }
 
+  std::shared_ptr<softmax_forward::primitive_desc>
+  AcquireSoftmaxPrimitiveDescriptor(const softmax_forward::desc& softmax_desc,
+                                    const mkldnn::engine& engine) {
+    const std::string key_softmax_pd = key_ + "@softmax_pd";
+
+    auto softmax_pd = std::static_pointer_cast<softmax_forward::primitive_desc>(
+        dev_ctx_.GetBlob(key_softmax_pd));
+
+    if (softmax_pd == nullptr) {
+      softmax_pd_.reset(
+          new softmax_forward::primitive_desc(softmax_desc, engine));
+      dev_ctx_.SetBlob(key_softmax_pd, softmax_pd_);
+    } else {
+      softmax_pd_ = softmax_pd;
+      is_reusing_ = true;
+    }
+
+    return softmax_pd_;
+  }
+
   std::shared_ptr<mkldnn::softmax_forward> AcquireSoftmax(
       std::shared_ptr<mkldnn::memory> dst_memory_p,
       std::shared_ptr<mkldnn::memory> src_memory_p) {
@@ -138,19 +155,18 @@ class SoftmaxMKLDNNKernel : public paddle::framework::OpKernel<T> {
     // Generate keys for storing/retriving primitives for this operator
     const std::string key =
         platform::MKLDNNHandler::GetHash(softmax_tz, ctx.op().Output("Out"));
-    const std::string key_softmax_pd = key + "@softmax_pd";
 
+    SoftmaxMKLDNNHandler handler(dev_ctx, mkldnn_engine, key);
     // Currently only NC data format is supported
     auto softmax_md = MKLDNNMemDesc(
         {softmax_tz}, platform::MKLDNNGetDataType<T>(), memory::format::nc);
     // Normalization is made after innermost dimension eg. C out of NC
     auto softmax_desc = softmax_forward::desc(prop_kind::forward_scoring,
                                               softmax_md, 1 /*dim: C*/);
-    auto softmax_pd = std::make_shared<mkldnn::softmax_forward::primitive_desc>(
-        softmax_desc, mkldnn_engine);
-    dev_ctx.SetBlob(key_softmax_pd, softmax_pd);
 
-    SoftmaxMKLDNNHandler handler(softmax_pd, dev_ctx, mkldnn_engine, key);
+    auto softmax_pd =
+        handler.AcquireSoftmaxPrimitiveDescriptor(softmax_desc, mkldnn_engine);
+
     auto softmax_src_memory_p =
         handler.AcquireSrcMemory(softmax_md, to_void_cast<T>(input_data));
     auto softmax_dst_memory_p =

paddle/fluid/operators/pad2d_op.cc

@@ -483,8 +483,10 @@ class Pad2dOp : public framework::OperatorWithKernel {
       PADDLE_ENFORCE_EQ(
           paddings_dim.size(), 1,
           "Size of Input(Paddings)'s dimension should be equal to 1.");
-      PADDLE_ENFORCE_EQ(paddings_dim[0], 4,
-                        "Shape of Input(Paddings) should be equal to [4].");
+      if (ctx->IsRuntime()) {
+        PADDLE_ENFORCE_EQ(paddings_dim[0], 4,
+                          "Shape of Input(Paddings) should be equal to [4].");
+      }
       out_dims[1] = x_dim[1];
       out_dims[2] = x_dim[2];
       out_dims[3] = x_dim[3];
@@ -504,11 +506,7 @@ class Pad2dOp : public framework::OperatorWithKernel {
     }
 
     ctx->SetOutputDim("Out", framework::make_ddim(out_dims));
-    if (out_dims[0] == x_dim[0]) {
-      // Only pass LoD when the first dimension is equal between
-      // output and input.
-      ctx->ShareLoD("X", /*->*/ "Out");
-    }
+    ctx->ShareLoD("X", /*->*/ "Out");
   }
 
  protected:

paddle/fluid/operators/recurrent_op.cc

@@ -23,6 +23,7 @@ constexpr char kInitialStates[] = "initial_states";
 constexpr char kParameters[] = "parameters";
 constexpr char kOutputs[] = "outputs";
 constexpr char kStepScopes[] = "step_scopes";
+constexpr char kHasStates[] = "has_states";
 constexpr char kExStates[] = "ex_states";
 constexpr char kStates[] = "states";
 constexpr char kStepBlock[] = "sub_block";
@@ -241,11 +242,16 @@ class RecurrentOp : public RecurrentBase {
  private:
   void RunImpl(const framework::Scope &scope,
                const platform::Place &place) const override {
+    bool has_state = Attr<bool>(kHasStates);
     auto seq_len = static_cast<size_t>(this->GetSequenceLength(scope));
     VLOG(3) << "Static RNN input sequence length = " << seq_len;
     StepScopes scopes = CreateStepScopes(scope, seq_len);
     auto reverse = Attr<bool>(kReverse);
 
+    // get device context from pool
+    platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
+    auto &dev_ctx = *pool.Get(place);
+
     framework::Executor executor(place);
     auto *block = Attr<framework::BlockDesc *>(kStepBlock);
 
@@ -269,15 +275,17 @@ class RecurrentOp : public RecurrentBase {
             inside->Resize(framework::make_ddim(dims));
           });
 
-      if (i == 0) {
-        // Link initial states  --> ex_states
-        LinkTensor(scope, Inputs(kInitialStates), &cur_scope,
-                   Attr<std::vector<std::string>>(kExStates));
-      } else {
-        auto &ex_scope = scopes.ExScope();
-        // Link ex_scope::state --> cur_scope::ex_state
-        LinkTensor(ex_scope, Attr<std::vector<std::string>>(kStates),
-                   &cur_scope, Attr<std::vector<std::string>>(kExStates));
+      if (has_state) {
+        if (i == 0) {
+          // Link initial states  --> ex_states
+          LinkTensor(scope, Inputs(kInitialStates), &cur_scope,
+                     Attr<std::vector<std::string>>(kExStates));
+        } else {
+          auto &ex_scope = scopes.ExScope();
+          // Link ex_scope::state --> cur_scope::ex_state
+          LinkTensor(ex_scope, Attr<std::vector<std::string>>(kStates),
+                     &cur_scope, Attr<std::vector<std::string>>(kExStates));
+        }
       }
 
       // Every inputs are linked now, execute!
@@ -286,11 +294,6 @@ class RecurrentOp : public RecurrentBase {
                    std::vector<std::string>() /*skip_ref_cnt_vars*/,
                    true /*force_disable_gc*/);
 
-      // get device context from pool
-      platform::DeviceContextPool &pool =
-          platform::DeviceContextPool::Instance();
-      auto &dev_ctx = *pool.Get(place);
-
       // Copy inside::output -> outside::output
       //    outside::output[seq_offset: seq_offset + 1] = inside::output
       this->LinkTensorWithCallback(
@@ -333,13 +336,13 @@ class RecurrentGradOp : public RecurrentBase {
  private:
   void RunImpl(const framework::Scope &scope,
                const platform::Place &place) const override {
-    auto seq_len = static_cast<size_t>(GetSequenceLength(scope));
+    bool has_state = Attr<bool>(kHasStates);
+    const size_t seq_len = static_cast<size_t>(GetSequenceLength(scope));
     StepScopes scopes = CreateStepScopes(scope, seq_len);
     auto reverse = Attr<bool>(kReverse);
 
     framework::Executor executor(place);
     auto *block = Attr<framework::BlockDesc *>(kStepBlock);
-
     auto *program = block->Program();
 
     // get device context from pool
@@ -350,6 +353,7 @@ class RecurrentGradOp : public RecurrentBase {
       size_t seq_offset = reverse ? step_id : seq_len - step_id - 1;
       VLOG(3) << "Recurrent backward operate at the time step " << seq_offset;
       auto &cur_scope = scopes.CurScope();
+
       // Link outside::output_grads --> inside::output_grads
       //   inside::output_grad = outside::output_grad[seq_offset:seq_offset+1]
       LinkTensorWithCallback(
@@ -370,30 +374,32 @@ class RecurrentGradOp : public RecurrentBase {
         VLOG(10) << " RNN output gradients = [" << sout.str() << "]";
       }
 
-      // Link states
-      //   if cur_scope::cur_state_grad in out_grads:
-      //     cur_scope::cur_state_grad += ex_scope::ex_state_grad
-      //   else:
-      //     ex_scope::ex_state_grad --> cur_scope::cur_state_grad
-      if (step_id != 0) {  // not at beginning
-        auto &ex_scope = scopes.ExScope();
-        auto ex_state_grads =
-            GradVarLists(Attr<std::vector<std::string>>(kExStates));
-        auto cur_state_grads =
-            GradVarLists(Attr<std::vector<std::string>>(kStates));
-
-        PADDLE_ENFORCE_EQ(ex_state_grads.size(), cur_state_grads.size());
-        for (size_t i = 0; i < ex_state_grads.size(); ++i) {
-          auto &cur_grad = cur_state_grads[i];
-          auto &ex_grad = ex_state_grads[i];
-          auto &ex_tensor =
-              ex_scope.FindVar(ex_grad)->Get<framework::LoDTensor>();
-
-          VLOG(10) << " RNN link " << cur_grad << " from " << ex_grad;
-          auto *cur_grad_var = cur_scope.Var(cur_grad);
-          auto cur_grad_tensor =
-              cur_grad_var->GetMutable<framework::LoDTensor>();
-          framework::TensorCopy(ex_tensor, place, dev_ctx, cur_grad_tensor);
+      if (has_state) {
+        // Link states
+        //   if cur_scope::cur_state_grad in out_grads:
+        //     cur_scope::cur_state_grad += ex_scope::ex_state_grad
+        //   else:
+        //     ex_scope::ex_state_grad --> cur_scope::cur_state_grad
+        if (step_id != 0) {  // not at beginning
+          auto &ex_scope = scopes.ExScope();
+          auto ex_state_grads =
+              GradVarLists(Attr<std::vector<std::string>>(kExStates));
+          auto cur_state_grads =
+              GradVarLists(Attr<std::vector<std::string>>(kStates));
+
+          PADDLE_ENFORCE_EQ(ex_state_grads.size(), cur_state_grads.size());
+          for (size_t i = 0; i < ex_state_grads.size(); ++i) {
+            auto &cur_grad = cur_state_grads[i];
+            auto &ex_grad = ex_state_grads[i];
+            auto &ex_tensor =
+                ex_scope.FindVar(ex_grad)->Get<framework::LoDTensor>();
+
+            VLOG(10) << " RNN link " << cur_grad << " from " << ex_grad;
+            auto *cur_grad_var = cur_scope.Var(cur_grad);
+            auto cur_grad_tensor =
+                cur_grad_var->GetMutable<framework::LoDTensor>();
+            framework::TensorCopy(ex_tensor, place, dev_ctx, cur_grad_tensor);
+          }
         }
       }
 
@@ -442,8 +448,8 @@ class RecurrentGradOp : public RecurrentBase {
           }
 
           auto new_inside_name = cur_scope.Rename(inside_grad_name);
-          // sum gradient
 
+          // sum gradient
           auto sum_op = framework::OpRegistry::CreateOp(
               "sum", {{"X", {pg_names[param_id], new_inside_name}}},
               {{"Out", {pg_names[param_id]}}},
@@ -475,22 +481,33 @@ class RecurrentGradOp : public RecurrentBase {
           true /*is_backward*/);
       VLOG(5) << "Link outside gradient finished ";
 
-      if (step_id + 1 == seq_len) {  // at_end
-        // copy initialize states gradient from inside to outside
-        LinkTensorWithCallback(
-            cur_scope, GradVarLists(Attr<std::vector<std::string>>(kExStates)),
-            scope, Outputs(kInitStateGrads),
-            [&](const framework::LoDTensor &inside,
-                framework::LoDTensor *outside) {
-              outside->Resize(inside.dims());
-              outside->mutable_data(place, inside.type());
-              framework::TensorCopy(inside, place, dev_ctx, outside);
-            },
-            true /*is_backward*/);
-        VLOG(5) << "Link initialize state gradient finished ";
+      if (has_state) {
+        if (step_id + 1 == seq_len) {  // at_end
+          // copy initialize states gradient from inside to outside
+          LinkTensorWithCallback(
+              cur_scope,
+              GradVarLists(Attr<std::vector<std::string>>(kExStates)), scope,
+              Outputs(kInitStateGrads),
+              [&](const framework::LoDTensor &inside,
+                  framework::LoDTensor *outside) {
+                outside->Resize(inside.dims());
+                outside->mutable_data(place, inside.type());
+                framework::TensorCopy(inside, place, dev_ctx, outside);
+              },
+              true /*is_backward*/);
+          VLOG(5) << "Link initialize state gradient finished ";
+        }
       }
       scopes.Next();
     }
+    // Delete the scope of StepScopes
+    dev_ctx.Wait();
+    auto *var = scope.FindVar(Input(kStepScopes));
+    PADDLE_ENFORCE(var != nullptr);
+    auto step_scopes = var->GetMutable<StepScopeVar>();
+    for (auto *sub_scope : *step_scopes) {
+      const_cast<framework::Scope &>(scope).DeleteScope(sub_scope);
+    }
   }
 
  private:
@@ -541,6 +558,7 @@ class RecurrentOpProtoMaker : public framework::OpProtoAndCheckerMaker {
         .AsDuplicable();
     AddOutput(kStepScopes,
               "StepScopes contain all local variables in each time step.");
+    AddAttr<bool>(kHasStates, "Whether has states.").SetDefault(false);
     AddAttr<std::vector<std::string>>(kExStates,
                                       string::Sprintf(
                                           R"DOC(The ex-state variable names.
@@ -624,20 +642,44 @@ class RecurrentGradOpDescMaker : public framework::SingleGradOpDescMaker {
 class RecurrentGradOpShapeInference : public framework::InferShapeBase {
  public:
   void operator()(framework::InferShapeContext *ctx) const override {
-    std::vector<std::string> input{kInputs, kInitialStates};
     std::vector<std::string> output{kOutputs};
-    for (auto &s : input) {
-      // NOTE(zcd): In some case, some of kInputs doesn't have gradient.
-      PADDLE_ENFORCE(ctx->HasInputs(s));
-    }
-    for (auto &s : output) {
-      PADDLE_ENFORCE(ctx->HasInputs(s));
+
+    // In some case the kInitialStates is empty.
+    // If the kInitialStates is empty, all the states should be empty.
+    if (!ctx->HasInputs(kInitialStates)) {
+      PADDLE_ENFORCE_EQ(
+          ctx->Attrs().Get<std::vector<std::string>>(kExStates).size(), 0,
+          "The Attr(%s) should be empty.", kExStates);
+      PADDLE_ENFORCE_EQ(
+          ctx->Attrs().Get<std::vector<std::string>>(kStates).size(), 0,
+          "The Attr(%s) should be empty.", kStates);
     }
-    for (auto &s : input) {
-      ctx->SetOutputsDim(framework::GradVarName(s), ctx->GetInputsDim(s));
+
+    PADDLE_ENFORCE(ctx->HasInputs(kInputs),
+                   "The input(%s) should not be empty.", kInputs);
+    PADDLE_ENFORCE(ctx->HasInputs(kOutputs),
+                   "The input(%s) should not be empty.", kOutputs);
+
+    // In some case the kInitialStates is empty.
+    if (ctx->HasInputs(kInitialStates)) {
+      PADDLE_ENFORCE(ctx->HasOutputs(framework::GradVarName(kInitialStates)),
+                     "The output of(%s) should not be empty.",
+                     framework::GradVarName(kInitialStates));
+      ctx->SetOutputsDim(framework::GradVarName(kInitialStates),
+                         ctx->GetInputsDim(kInitialStates));
     }
+
+    PADDLE_ENFORCE(ctx->HasOutputs(framework::GradVarName(kInputs)),
+                   "The output of(%s) should not be empty.",
+                   framework::GradVarName(kInputs));
+    ctx->SetOutputsDim(framework::GradVarName(kInputs),
+                       ctx->GetInputsDim(kInputs));
+
+    // In some case the kParameters is empty.
     if (ctx->HasInputs(kParameters)) {
-      PADDLE_ENFORCE(ctx->HasOutputs(framework::GradVarName(kParameters)));
+      PADDLE_ENFORCE(ctx->HasOutputs(framework::GradVarName(kParameters)),
+                     "The output of(%s) should not be empty.",
+                     framework::GradVarName(kParameters));
       ctx->SetOutputsDim(framework::GradVarName(kParameters),
                          ctx->GetInputsDim(kParameters));
     }

paddle/fluid/operators/reduce_ops/reduce_all_op.cc

@@ -14,7 +14,7 @@
 
 #include "paddle/fluid/operators/reduce_ops/reduce_all_op.h"
 
-REGISTER_REDUCE_OP(reduce_all);
+REGISTER_REDUCE_OP_WITHOUT_GRAD(reduce_all);
 REGISTER_OP_CPU_KERNEL(reduce_all,
                        ops::ReduceKernel<paddle::platform::CPUDeviceContext,
                                          bool, ops::AllFunctor>);

paddle/fluid/operators/reduce_ops/reduce_any_op.cc

@@ -14,7 +14,7 @@
 
 #include "paddle/fluid/operators/reduce_ops/reduce_any_op.h"
 
-REGISTER_REDUCE_OP(reduce_any);
+REGISTER_REDUCE_OP_WITHOUT_GRAD(reduce_any);
 REGISTER_OP_CPU_KERNEL(reduce_any,
                        ops::ReduceKernel<paddle::platform::CPUDeviceContext,
                                          bool, ops::AnyFunctor>);

paddle/fluid/operators/reduce_ops/reduce_op.h

@@ -270,3 +270,12 @@ namespace ops = paddle::operators;
   REGISTER_OPERATOR(op_name, ops::ReduceOp, __##op_name##Maker__,        \
                     paddle::framework::DefaultGradOpDescMaker<true>);    \
   REGISTER_OPERATOR(op_name##_grad, ops::ReduceGradOp)
+
+#define REGISTER_REDUCE_OP_WITHOUT_GRAD(op_name)                         \
+  class __##op_name##Maker__ : public ops::ReduceOpMaker {               \
+   protected:                                                            \
+    virtual std::string GetName() const { return #op_name; }             \
+    virtual std::string GetOpType() const { return "Reduce " #op_name; } \
+  };                                                                     \
+  REGISTER_OPERATOR(op_name, ops::ReduceOp, __##op_name##Maker__,        \
+                    paddle::framework::EmptyGradOpMaker);

paddle/fluid/operators/rnn_memory_helper_op.cc

@@ -40,9 +40,12 @@ class RNNMemoryHelperOp : public framework::OperatorBase {
                    "Cannot find out_var in scope, out_var_name is %s",
                    out_name);
 
+    platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
+    auto &dev_ctx = *pool.Get(dev_place);
+
     auto *out_tensor = out_var->GetMutable<framework::LoDTensor>();
     auto &mem_tensor = mem_var->Get<framework::LoDTensor>();
-    framework::TensorCopySync(mem_tensor, dev_place, out_tensor);
+    framework::TensorCopy(mem_tensor, dev_place, dev_ctx, out_tensor);
     out_tensor->set_lod(mem_tensor.lod());
   }
 };
@@ -92,6 +95,9 @@ class RNNMemoryHelperGradOp : public framework::OperatorBase {
                    "Cannot find in_grad_var in scope, name is %s",
                    in_grad_var_name);
 
+    platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
+    auto &dev_ctx = *pool.Get(dev_place);
+
     if (out_grad_var == nullptr) {
       VLOG(5) << "Using fill constant 0 as starting gradient";
       auto in_var_name = Input("X");
@@ -109,7 +115,8 @@ class RNNMemoryHelperGradOp : public framework::OperatorBase {
     } else {
       auto &out_grad_tensor = out_grad_var->Get<framework::LoDTensor>();
       auto *in_grad_tensor = in_grad_var->GetMutable<framework::LoDTensor>();
-      framework::TensorCopySync(out_grad_tensor, dev_place, in_grad_tensor);
+      framework::TensorCopy(out_grad_tensor, dev_place, dev_ctx,
+                            in_grad_tensor);
       in_grad_tensor->set_lod(out_grad_tensor.lod());
     }
   }

paddle/fluid/operators/sample_logits_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/sample_logits_op.h"
+#include <memory>
 #include "paddle/fluid/operators/math/sample_prob.h"
 
 namespace paddle {
@@ -60,6 +61,10 @@ class SampleLogitsOpMaker : public framework::OpProtoAndCheckerMaker {
         "(Tensor, default: Tensor<float>), A 2-D tensor with shape [N, NT + S]."
         "The probabilites of sampled positive and negtive labels.")
         .AsIntermediate();
+    AddOutput("LogitsDim", "Store dim information of Logits for gradient op")
+        .AsIntermediate();
+    AddOutput("LabelsDim", "Store dim information of Logits for gradient op")
+        .AsIntermediate();
     AddOutput("SampledLogits",
               "(Tensor, default: Tensor<float>), A 2-D tensor with shape"
               "[N, NT + S]. The outputs value of sampled logits, which will be"
@@ -121,6 +126,10 @@ class SampleLogitsOp : public framework::OperatorWithKernel {
                    "Output(SampledLogits) should be not null.");
     PADDLE_ENFORCE(ctx->HasOutput("SampledLabels"),
                    "Output(SampledLabels) should be not null.");
+    PADDLE_ENFORCE(ctx->HasOutput("LogitsDim"),
+                   "Output(LogitsDim) should be not null.");
+    PADDLE_ENFORCE(ctx->HasOutput("LabelsDim"),
+                   "Output(LabelsDim) should be not null.");
 
     auto logits_dims = ctx->GetInputDim("Logits");
     auto labels_dims = ctx->GetInputDim("Labels");
@@ -137,6 +146,15 @@ class SampleLogitsOp : public framework::OperatorWithKernel {
     ctx->SetOutputDim("Probabilities", {logits_dims[0], num_sampled_classes});
     ctx->SetOutputDim("SampledLogits", {logits_dims[0], num_sampled_classes});
     ctx->SetOutputDim("SampledLabels", {logits_dims[0], labels_dims[1]});
+
+    // append 0 to shape variable to avoid optimized by memory optimize pass
+    auto logits_dim_vec = framework::vectorize(logits_dims);
+    logits_dim_vec.push_back(0);
+    ctx->SetOutputDim("LogitsDim", framework::make_ddim(logits_dim_vec));
+
+    auto labels_dim_vec = framework::vectorize(labels_dims);
+    labels_dim_vec.push_back(0);
+    ctx->SetOutputDim("LabelsDim", framework::make_ddim(labels_dim_vec));
   }
 
  protected:
@@ -155,28 +173,27 @@ class SampleLogitsOpGrad : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("Logits"),
-                   "Input(Logits) should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("Labels"),
-                   "Input(Labels) should be not null.");
+    PADDLE_ENFORCE(ctx->HasInput("LogitsDim"),
+                   "Input(LogitsDim) should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("LabelsDim"),
+                   "Input(LabelsDim) should be not null.");
     PADDLE_ENFORCE(ctx->HasInput("Samples"),
                    "Input(Samples) should be not null.");
-    PADDLE_ENFORCE(ctx->HasInput("SampledLogits"),
-                   "Input(SampledLogits) should be not null.");
     PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("SampledLogits")),
                    "Input(SampledLogits@Grad) should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("Logits")),
                    "Output(Logits@Grad) should be not null.");
 
-    auto logit_dims = ctx->GetInputDim("Logits");
-    auto label_dims = ctx->GetInputDim("Labels");
-    PADDLE_ENFORCE_EQ(label_dims.size(), 2UL,
+    auto logits_dims = ctx->GetInputDim("LogitsDim");
+    logits_dims = framework::DDim(logits_dims.Get(), logits_dims.size() - 1);
+    auto labels_dims = ctx->GetInputDim("LabelsDim");
+    labels_dims = framework::DDim(labels_dims.Get(), labels_dims.size() - 1);
+    PADDLE_ENFORCE_EQ(labels_dims.size(), 2UL,
                       "The label should be a 2-D tensor.");
-    PADDLE_ENFORCE_EQ(logit_dims.size(), 2UL,
+    PADDLE_ENFORCE_EQ(logits_dims.size(), 2UL,
                       "The logits should be a 2-D tensor.");
 
-    ctx->SetOutputDim(framework::GradVarName("Logits"),
-                      ctx->GetInputDim("Logits"));
+    ctx->SetOutputDim(framework::GradVarName("Logits"), logits_dims);
   }
 
  protected:
@@ -199,10 +216,9 @@ class SampleLogitsGradMaker : public framework::SingleGradOpDescMaker {
   std::unique_ptr<framework::OpDesc> Apply() const override {
     auto* grad_op = new framework::OpDesc();
     grad_op->SetType("sample_logits_grad");
-    grad_op->SetInput("Logits", Input("Logits"));
-    grad_op->SetInput("Labels", Input("Labels"));
+    grad_op->SetInput("LogitsDim", Output("LogitsDim"));
+    grad_op->SetInput("LabelsDim", Output("LabelsDim"));
     grad_op->SetInput("Samples", Output("Samples"));
-    grad_op->SetInput("SampledLogits", Output("SampledLogits"));
     grad_op->SetInput(framework::GradVarName("SampledLogits"),
                       OutputGrad("SampledLogits"));
     grad_op->SetOutput(framework::GradVarName("Logits"), InputGrad("Logits"));

paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op.cc

@@ -34,15 +34,22 @@ class SigmoidCrossEntropyWithLogitsOp : public framework::OperatorWithKernel {
 
     auto x_dims = ctx->GetInputDim("X");
     auto labels_dims = ctx->GetInputDim("Label");
-    PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
-    PADDLE_ENFORCE_EQ(labels_dims.size(), 2,
-                      "Input(Label)'s rank should be 2.");
-    PADDLE_ENFORCE_EQ(x_dims[0], labels_dims[0],
-                      "The 1st dimension of Input(X) and Input(Label) should "
-                      "be equal.");
-    PADDLE_ENFORCE_EQ(x_dims[1], labels_dims[1],
-                      "The 2nd dimension of Input(X) and Input(Label) should "
-                      "be equal.");
+
+    int rank = x_dims.size();
+    PADDLE_ENFORCE_EQ(rank, labels_dims.size(),
+                      "Input(X) and Input(Label) shall have the same rank.");
+    bool check = true;
+    if ((!ctx->IsRuntime()) && (framework::product(x_dims) <= 0 ||
+                                framework::product(labels_dims) <= 0)) {
+      check = false;
+    }
+
+    if (check) {
+      PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank),
+                        framework::slice_ddim(labels_dims, 0, rank),
+                        "Input(X) and Input(Label) shall have the same shape "
+                        "except the last dimension.");
+    }
 
     ctx->ShareDim("X", /*->*/ "Out");
     ctx->ShareLoD("X", /*->*/ "Out");
@@ -65,23 +72,24 @@ class SigmoidCrossEntropyWithLogitsGradOp
     auto x_dims = ctx->GetInputDim("X");
     auto labels_dims = ctx->GetInputDim("Label");
     auto dout_dims = ctx->GetInputDim(framework::GradVarName("Out"));
-    PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
-    PADDLE_ENFORCE_EQ(labels_dims.size(), 2,
-                      "Input(Label)'s rank should be 2.");
-    PADDLE_ENFORCE_EQ(dout_dims.size(), 2,
-                      "Input(Out@Grad)'s rank should be 2.");
-    PADDLE_ENFORCE_EQ(x_dims[0], labels_dims[0],
-                      "The 1st dimension of Input(X) and Input(Label) should "
-                      "be equal.");
-    PADDLE_ENFORCE_EQ(x_dims[1], labels_dims[1],
-                      "The 2nd dimension of Input(X) and Input(Label) should "
-                      "be equal.");
-    PADDLE_ENFORCE_EQ(x_dims[0], dout_dims[0],
-                      "The 1st dimension of Input(X) and Input(Out@Grad) "
-                      "should be equal.");
-    PADDLE_ENFORCE_EQ(x_dims[1], dout_dims[1],
-                      "The 2nd dimension of Input(X) and Input(Out@Grad) "
-                      "should be equal.");
+
+    int rank = x_dims.size();
+    bool check = true;
+    if ((!ctx->IsRuntime()) && (framework::product(x_dims) <= 0 ||
+                                framework::product(labels_dims) <= 0)) {
+      check = false;
+    }
+
+    if (check) {
+      PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank),
+                        framework::slice_ddim(labels_dims, 0, rank),
+                        "Input(X) and Input(Label) shall have the same shape.");
+
+      PADDLE_ENFORCE_EQ(
+          framework::slice_ddim(x_dims, 0, rank),
+          framework::slice_ddim(dout_dims, 0, rank),
+          "Input(X) and Input(Out@Grad) shall have the same shape.");
+    }
 
     ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
   }

paddle/fluid/operators/spectral_norm_op.cc

@@ -56,13 +56,19 @@ class SpectralNormOp : public framework::OperatorWithKernel {
     }
     auto dim_u = ctx->GetInputDim("U");
     auto dim_v = ctx->GetInputDim("V");
-    PADDLE_ENFORCE_EQ(dim_u[0], h,
-                      "Input(U) dims[0] should be equal to "
-                      "Input(Weight) dims[Attr(dim)]");
-    PADDLE_ENFORCE_EQ(
-        dim_v[0], w,
-        "Input(V) dims[0] should be equal to "
-        "the product of Input(Weight) dims except dims[Attr(dim)]");
+
+    if (ctx->IsRuntime() || (dim_u[0] > 0 && h > 0)) {
+      PADDLE_ENFORCE_EQ(dim_u[0], h,
+                        "Input(U) dims[0] should be equal to "
+                        "Input(Weight) dims[Attr(dim)]");
+    }
+
+    if (ctx->IsRuntime() || (dim_v[0] > 0 && w > 0)) {
+      PADDLE_ENFORCE_EQ(
+          dim_v[0], w,
+          "Input(V) dims[0] should be equal to "
+          "the product of Input(Weight) dims except dims[Attr(dim)]");
+    }
 
     ctx->SetOutputDim("Out", dim_weight);
     ctx->ShareLoD("Weight", /*->*/ "Out");

paddle/fluid/operators/split_op.cc

@@ -39,14 +39,22 @@ class SplitOp : public framework::OperatorWithKernel {
 
     if (num > 0) {
       int64_t in_axis_dim = in_dims[axis];
-      PADDLE_ENFORCE_EQ(in_axis_dim % num, 0,
-                        "tensor split does not result"
-                        " in an equal division");
-      size_t out_axis_dim = in_axis_dim / num;
-      for (size_t i = 0; i < outs_number; ++i) {
-        auto dim = in_dims;
-        dim[axis] = out_axis_dim;
-        outs_dims.push_back(dim);
+      if (ctx->IsRuntime() || in_axis_dim > 0) {
+        PADDLE_ENFORCE_EQ(in_axis_dim % num, 0,
+                          "tensor split does not result"
+                          " in an equal division");
+        size_t out_axis_dim = in_axis_dim / num;
+        for (size_t i = 0; i < outs_number; ++i) {
+          auto dim = in_dims;
+          dim[axis] = out_axis_dim;
+          outs_dims.push_back(dim);
+        }
+      } else {
+        for (size_t i = 0; i < outs_number; ++i) {
+          auto dim = in_dims;
+          dim[axis] = -1;
+          outs_dims.push_back(dim);
+        }
       }
     } else if (sections.size() > 0) {
       PADDLE_ENFORCE_EQ(sections.size(), outs_number,

paddle/fluid/operators/sum_op.cc

@@ -65,7 +65,21 @@ class SumOp : public framework::OperatorWithKernel {
       if (framework::product(in_dim) == 0) {
         in_dim = x_dim;
       } else {
-        PADDLE_ENFORCE_EQ(in_dim, x_dim, "Input tensors must have same shape");
+        if (ctx->IsRuntime()) {
+          PADDLE_ENFORCE_EQ(in_dim, x_dim,
+                            "Input tensors must have same shape");
+        } else {
+          PADDLE_ENFORCE_EQ(in_dim.size(), x_dim.size(),
+                            "Input tensors must have same shape size");
+          // if in_dim or x_dim has -1, not check equal
+          for (int i = 0; i < x_dim.size(); ++i) {
+            if (x_dim[i] == -1 || in_dim[i] == -1) {
+              continue;
+            }
+            PADDLE_ENFORCE_EQ(in_dim[i], x_dim[i],
+                              "Input tensors must have same shape if not -1");
+          }
+        }
       }
     }
     ctx->SetOutputDim("Out", in_dim);

paddle/fluid/platform/lodtensor_printer.cc

@@ -52,16 +52,26 @@ void PrintVar(framework::Scope* scope, const std::string& var_name,
     return;
   }
 
-#define PrintLoDTensorCallback(cpp_type, proto_type)             \
-  do {                                                           \
-    if (tensor->type() == proto_type) {                          \
-      print_lod_tensor<cpp_type>(var_name, *tensor, print_info); \
-      return;                                                    \
-    }                                                            \
+  framework::LoDTensor printed_tensor;
+  printed_tensor.set_lod(tensor->lod());
+  printed_tensor.Resize(tensor->dims());
+  if (platform::is_cpu_place(tensor->place())) {
+    printed_tensor.ShareDataWith(*tensor);
+  } else {
+    platform::CPUPlace place;
+    framework::TensorCopy(*tensor, place, &printed_tensor);
+  }
+
+#define PrintLoDTensorCallback(cpp_type, proto_type)                    \
+  do {                                                                  \
+    if (tensor->type() == proto_type) {                                 \
+      print_lod_tensor<cpp_type>(var_name, printed_tensor, print_info); \
+      return;                                                           \
+    }                                                                   \
   } while (0)
 
   _ForEachDataType_(PrintLoDTensorCallback);
-  VLOG(1) << "PrintVar: unrecognized data type:" << tensor->type();
+  VLOG(1) << "PrintVar: unrecognized data type:" << printed_tensor.type();
 }
 
 }  // end namespace platform

paddle/fluid/platform/mkldnn_reuse.h

@@ -16,6 +16,7 @@ limitations under the License. */
 #include <memory>
 #include <string>
 #include <vector>
+#include "boost/optional.hpp"
 #include "paddle/fluid/framework/data_layout_transform.h"
 #include "paddle/fluid/framework/operator.h"
 #include "paddle/fluid/platform/mkldnn_helper.h"
@@ -395,9 +396,28 @@ class TransposeMKLDNNHandler : public MKLDNNHandler {
   std::vector<int> logical_axis_;
 };
 
+template <typename T>
+struct convolutional_algorithm;
+
+template <>
+struct convolutional_algorithm<mkldnn::convolution_forward> {
+  static constexpr mkldnn::algorithm T = mkldnn::algorithm::convolution_direct;
+};
+
+template <>
+struct convolutional_algorithm<mkldnn::deconvolution_forward> {
+  static constexpr mkldnn::algorithm T =
+      mkldnn::algorithm::deconvolution_direct;
+};
+
 template <class forward_t, class backward_data_t, class backward_weights_t>
 class ConvMKLDNNTemplateHandler : public MKLDNNHandler {
  public:
+  ConvMKLDNNTemplateHandler(const platform::MKLDNNDeviceContext& dev_ctx,
+                            mkldnn::engine engine, const std::string& base_key)
+      : platform::MKLDNNHandler(dev_ctx, engine, base_key) {}
+
+  // TODO(jczaja): remove after conv int8 is adapted
   ConvMKLDNNTemplateHandler(
       std::shared_ptr<typename forward_t::primitive_desc> conv_pd,
       const platform::MKLDNNDeviceContext& dev_ctx, mkldnn::engine engine,
@@ -542,6 +562,73 @@ class ConvMKLDNNTemplateHandler : public MKLDNNHandler {
                                scale_data, mask);
   }
 
+  mkldnn::primitive_attr CreatePostOps(bool fuse_relu,
+                                       bool fuse_residual_conn = false) const {
+    mkldnn::primitive_attr conv_attr;
+    mkldnn::post_ops post_operations;
+    // Fusion with Elementwise layer relies on adding a sum post-operation with
+    // the scale parameter. It is assumed that when fuse_residual_connection is
+    // true, the output tensor contains the data coming from residual
+    // connection. The result of this post_op is:
+    // Output = scale * Output + Conv_Out.
+    if (fuse_residual_conn) {
+      post_operations.append_sum(1.0f);
+    }
+    // Fusion with ReLU layer is executed through the PostOps feature. Create a
+    // PostOps object and configure it to execute an eltwise relu operation.
+    if (fuse_relu) {
+      constexpr float scale = 1.0f;
+      constexpr float negative_slope = 0.0f;
+      constexpr float placeholder = 0.0f;
+      post_operations.append_eltwise(scale, mkldnn::algorithm::eltwise_relu,
+                                     negative_slope, placeholder);
+    }
+    conv_attr.set_post_ops(post_operations);
+    return conv_attr;
+  }
+
+  std::shared_ptr<typename forward_t::primitive_desc>
+  AcquireConvolutionPrimitiveDescriptor(
+      const mkldnn::memory::desc& src, const mkldnn::memory::desc& weights,
+      boost::optional<const mkldnn::memory::desc&> bias,
+      const mkldnn::memory::desc& dst, const std::vector<int>& strides,
+      const std::vector<int>& paddings, const mkldnn::engine& engine,
+      const bool fuse_relu, const bool fuse_residual_conn,
+      mkldnn::prop_kind fwd_prop_kind) {
+    const std::string key_conv_pd = key_ + "@conv_pd";
+
+    auto conv_pd = std::static_pointer_cast<typename forward_t::primitive_desc>(
+        dev_ctx_.GetBlob(key_conv_pd));
+
+    if (conv_pd == nullptr) {
+      mkldnn::memory::dims stride_dims = strides;
+      mkldnn::memory::dims padding_dims = paddings;
+
+      auto conv_desc =
+          bias ? typename forward_t::desc(
+                     fwd_prop_kind, convolutional_algorithm<forward_t>::T, src,
+                     weights, *bias, dst, stride_dims, padding_dims,
+                     padding_dims, mkldnn::padding_kind::zero)
+               : typename forward_t::desc(
+                     fwd_prop_kind, convolutional_algorithm<forward_t>::T, src,
+                     weights, dst, stride_dims, padding_dims, padding_dims,
+                     mkldnn::padding_kind::zero);
+
+      mkldnn::primitive_attr conv_attr =
+          CreatePostOps(fuse_relu, fuse_residual_conn);
+
+      conv_pd_.reset(
+          new typename forward_t::primitive_desc(conv_desc, conv_attr, engine));
+      // Save conv_pd/src_memory/weights_memory for backward pass
+      dev_ctx_.SetBlob(key_conv_pd, conv_pd_);
+    } else {
+      conv_pd_ = conv_pd;
+      is_reusing_ = true;
+    }
+
+    return conv_pd_;
+  }
+
   std::shared_ptr<forward_t> AcquireConvolution(
       std::shared_ptr<mkldnn::memory> src_memory_p,
       std::shared_ptr<mkldnn::memory> weights_memory_p,

python/paddle/fluid/__init__.py

@@ -175,6 +175,7 @@ def __bootstrap__():
         read_env_flags.append('communicator_thread_pool_size')
         read_env_flags.append('communicator_max_merge_var_num')
         read_env_flags.append('communicator_fake_rpc')
+        read_env_flags.append('communicator_send_wait_times')
         if core.is_compiled_with_brpc():
             read_env_flags.append('max_body_size')
             #set brpc max body size

python/paddle/fluid/contrib/tests/test_calibration.py

@@ -147,10 +147,11 @@ class TestCalibrationForResnet50(unittest.TestCase):
                                                    self.data_cache_folder)
         os.system(cmd)
 
-        self.batch_size = 1
-        self.sample_iterations = 50
+        self.batch_size = 1 if os.environ.get('DATASET') == 'full' else 50
+        self.sample_iterations = 50 if os.environ.get(
+            'DATASET') == 'full' else 1
         self.infer_iterations = 50000 if os.environ.get(
-            'DATASET') == 'full' else 50
+            'DATASET') == 'full' else 1
 
     def cache_unzipping(self, target_folder, zip_path):
         if not os.path.exists(target_folder):
@@ -279,15 +280,15 @@ class TestCalibrationForResnet50(unittest.TestCase):
     def test_calibration(self):
         self.download_model()
         print("Start FP32 inference for {0} on {1} images ...").format(
-            self.model, self.infer_iterations)
+            self.model, self.infer_iterations * self.batch_size)
         (fp32_throughput, fp32_latency,
          fp32_acc1) = self.run_program(self.model_cache_folder + "/model")
         print("Start INT8 calibration for {0} on {1} images ...").format(
-            self.model, self.sample_iterations)
+            self.model, self.sample_iterations * self.batch_size)
         self.run_program(
             self.model_cache_folder + "/model", True, algo=self.algo)
         print("Start INT8 inference for {0} on {1} images ...").format(
-            self.model, self.infer_iterations)
+            self.model, self.infer_iterations * self.batch_size)
         (int8_throughput, int8_latency,
          int8_acc1) = self.run_program("calibration_out")
         delta_value = fp32_acc1 - int8_acc1

python/paddle/fluid/device_worker.py

@@ -26,8 +26,8 @@ class DeviceWorker(object):
         """
         Init.
         """
-        self.program_ = None
-        self.infer_ = None
+        self._program = None
+        self._infer = None
 
     def _set_infer(self, infer=False):
         """
@@ -36,7 +36,7 @@ class DeviceWorker(object):
         Args:
             infer(bool): whether to do inference
         """
-        self.infer_ = infer
+        self._infer = infer
 
     def _set_fleet_desc(self, fleet_desc):
         """
@@ -45,7 +45,7 @@ class DeviceWorker(object):
         Args:
             fleet_desc(PSParameter): pslib.PSParameter object
         """
-        self.fleet_desc_ = fleet_desc
+        self._fleet_desc = fleet_desc
 
     def _set_program(self, program):
         """
@@ -54,7 +54,7 @@ class DeviceWorker(object):
         Args:
             program(Program): a Program object
         """
-        self.program_ = program
+        self._program = program
 
     def _gen_worker_desc(self, trainer_desc):
         """
@@ -88,7 +88,7 @@ class Hogwild(DeviceWorker):
             trainer_desc(TrainerDesc): a TrainerDesc object
         """
         trainer_desc.device_worker_name = "HogwildWorker"
-        if self.infer_:
+        if self._infer:
             # just ignore feed op for inference model
             trainer_desc.hogwild_param.skip_ops.extend(["feed"])
 
@@ -113,11 +113,11 @@ class DownpourSGD(DeviceWorker):
             trainer_desc(TrainerDesc): a TrainerDesc object
         """
         dense_table_set = set()
-        program_id = str(id(self.program_))
-        if self.program_ == None:
+        program_id = str(id(self._program))
+        if self._program == None:
             print("program of current device worker is not configured")
             exit(-1)
-        opt_info = self.program_._fleet_opt
+        opt_info = self._program._fleet_opt
         program_configs = opt_info["program_configs"]
         downpour = trainer_desc.downpour_param
 
@@ -140,7 +140,7 @@ class DownpourSGD(DeviceWorker):
         trainer_desc.device_worker_name = "DownpourWorker"
         pull_thread = trainer_desc.pull_dense_param
         pull_thread.device_num = trainer_desc.thread_num
-        for i in self.fleet_desc_.trainer_param.dense_table:
+        for i in self._fleet_desc.trainer_param.dense_table:
             if i.table_id in dense_table_set:
                 dense_table = pull_thread.dense_table.add()
                 dense_table.dense_value_name.extend(i.dense_variable_name)
@@ -148,29 +148,29 @@ class DownpourSGD(DeviceWorker):
                     i.table_id
         sparse_table = downpour.sparse_table.add()
         sparse_table.table_id = \
-                    self.fleet_desc_.trainer_param.sparse_table[0].table_id
+                    self._fleet_desc.trainer_param.sparse_table[0].table_id
         sparse_table.sparse_key_name.extend(
-            self.fleet_desc_.trainer_param.sparse_table[0].slot_key)
+            self._fleet_desc.trainer_param.sparse_table[0].slot_key)
         sparse_table.sparse_value_name.extend(
-            self.fleet_desc_.trainer_param.sparse_table[0].slot_value)
+            self._fleet_desc.trainer_param.sparse_table[0].slot_value)
         sparse_table.sparse_grad_name.extend(
-            self.fleet_desc_.trainer_param.sparse_table[0].slot_gradient)
+            self._fleet_desc.trainer_param.sparse_table[0].slot_gradient)
         sparse_table.emb_dim = \
-                    self.fleet_desc_.server_param.downpour_server_param.downpour_table_param[
+                    self._fleet_desc.server_param.downpour_server_param.downpour_table_param[
                         0].accessor.fea_dim - 2
         sparse_table.fea_dim = sparse_table.emb_dim + 2
         # TODO(guru4elephant): hard code here, need to improve
         sparse_table.label_var_name = "click"
 
-        for i in self.fleet_desc_.trainer_param.dense_table:
+        for i in self._fleet_desc.trainer_param.dense_table:
             if i.table_id in dense_table_set:
                 dense_table = downpour.dense_table.add()
                 dense_table.table_id = i.table_id
                 dense_table.dense_value_name.extend(i.dense_variable_name)
                 dense_table.dense_grad_name.extend(
                     i.dense_gradient_variable_name)
-                downpour.skip_ops.extend(self.fleet_desc_.trainer_param.skip_op)
-        if self.infer_:
+                downpour.skip_ops.extend(self._fleet_desc.trainer_param.skip_op)
+        if self._infer:
             downpour.push_dense = False
             downpour.push_sparse = False
 

python/paddle/fluid/incubate/fleet/base/role_maker.py

@@ -23,10 +23,10 @@ class RoleMakerBase(object):
     """
 
     def __init__(self):
-        self.role_maker_name_ = ""
-        self.trainer_endpoints_ = []
-        self.pserver_endpoints_ = []
-        self.role_is_generated_ = False
+        self._role_maker_name = ""
+        self._trainer_endpoints = []
+        self._pserver_endpoints = []
+        self._role_is_generated = False
 
     def _is_worker(self):
         """
@@ -45,20 +45,20 @@ class RoleMakerBase(object):
         return get local ip
         """
         import socket
-        self.ip_ = socket.gethostbyname(socket.gethostname())
-        return self.ip_
+        self._ip = socket.gethostbyname(socket.gethostname())
+        return self._ip
 
     def _get_trainer_endpoints(self):
         """
         return trainer endpoints
         """
-        return self.trainer_endpoints_
+        return self._trainer_endpoints
 
     def _get_pserver_endpoints(self):
         """
         return pserver endpoints
         """
-        return self.pserver_endpoints_
+        return self._pserver_endpoints
 
     def _generate_role(self):
         """
@@ -76,59 +76,59 @@ class MPIRoleMaker(RoleMakerBase):
     def __init__(self):
         super(MPIRoleMaker, self).__init__()
         from mpi4py import MPI
-        self.comm_ = MPI.COMM_WORLD
+        self._comm = MPI.COMM_WORLD
         self.MPI = MPI
-        self.ips_ = None
+        self._ips = None
 
     def _get_rank(self):
         """
         return rank
         """
-        self.rank_ = self.comm_.Get_rank()
-        return self.rank_
+        self._rank = self._comm.Get_rank()
+        return self._rank
 
     def _get_size(self):
         """
         return size
         """
-        self.size_ = self.comm_.Get_size()
-        return self.size_
+        self._size = self._comm.Get_size()
+        return self._size
 
     def _all_gather(self, obj):
         """
         all_gather(obj) will call MPI's allgather function
         """
         self._barrier_all()
-        return self.comm_.allgather(obj)
+        return self._comm.allgather(obj)
 
     def _worker_gather(self, obj):
         """
         worker_gather(obj) will call MPI's allgather function
         """
         if self._is_worker():
-            self.node_type_comm_.barrier()
-            return self.node_type_comm_.allgather(obj)
+            self._node_type_comm.barrier()
+            return self._node_type_comm.allgather(obj)
         return None
 
     def _barrier_all(self):
         """
         barrier_all() will call MPI's barrier_all function
         """
-        self.comm_.barrier()
+        self._comm.barrier()
 
     def _get_ips(self):
         """
         collect current distributed job's ip list
         """
-        if self.ips_ == None:
-            self.ips_ = self.comm_.allgather(self._get_local_ip())
-        return self.ips_
+        if self._ips == None:
+            self._ips = self._comm.allgather(self._get_local_ip())
+        return self._ips
 
     def _finalize(self):
         """
         finalize the current MPI instance.
         """
-        self.comm_.finalize()
+        self._comm.finalize()
 
 
 class MPISymetricRoleMaker(MPIRoleMaker):
@@ -140,11 +140,11 @@ class MPISymetricRoleMaker(MPIRoleMaker):
 
     def __init__(self):
         super(MPISymetricRoleMaker, self).__init__()
-        self.node_type_ = None
-        self.proc_per_node_ = 2
+        self._node_type = None
+        self._proc_per_node = 2
 
     def _check_role_generation(self):
-        if not self.role_is_generated_:
+        if not self._role_is_generated:
             sys.stderr.write("generate_role() should be called first")
             sys.exit(-1)
             return False
@@ -163,7 +163,7 @@ class MPISymetricRoleMaker(MPIRoleMaker):
         return whether current process is worker assigned by role maker
         """
         if self._check_role_generation():
-            return self.node_type_ == 1
+            return self._node_type == 1
         return False
 
     def _is_server(self):
@@ -171,7 +171,7 @@ class MPISymetricRoleMaker(MPIRoleMaker):
         return whether current process is server assigned by role maker
         """
         if self._check_role_generation():
-            return self.node_type_ == 0
+            return self._node_type == 0
         return False
 
     def _worker_num(self):
@@ -197,7 +197,7 @@ class MPISymetricRoleMaker(MPIRoleMaker):
         return the index of worker
         """
         if self._check_role_generation():
-            return self.rank_ / self.proc_per_node_
+            return self._rank / self._proc_per_node
         return 0
 
     def _server_index(self):
@@ -205,7 +205,7 @@ class MPISymetricRoleMaker(MPIRoleMaker):
         return the index of server
         """
         if self._check_role_generation():
-            return self.rank_ / self.proc_per_node_
+            return self._rank / self._proc_per_node
         return 0
 
     def _barrier_worker(self):
@@ -214,7 +214,7 @@ class MPISymetricRoleMaker(MPIRoleMaker):
         """
         if self._check_role_generation():
             if self._is_worker():
-                self.node_type_comm_.barrier()
+                self._node_type_comm.barrier()
 
     def _barrier_server(self):
         """
@@ -222,20 +222,20 @@ class MPISymetricRoleMaker(MPIRoleMaker):
         """
         if self._check_role_generation():
             if self._is_server():
-                self.node_type_comm_.barrier()
+                self._node_type_comm.barrier()
 
     def _generate_role(self):
         """
         generate currently process's role
         """
-        if not self.role_is_generated_:
+        if not self._role_is_generated:
             # TODO(guru4elephant): only allow to be called once
-            self.trainer_endpoints_ = self._get_ips()
-            self.pserver_endpoints_ = self._get_ips()
+            self._trainer_endpoints = self._get_ips()
+            self._pserver_endpoints = self._get_ips()
 
-            if 0 == self._get_rank() % self.proc_per_node_ % 2:
-                self.node_type_ = 0
+            if 0 == self._get_rank() % self._proc_per_node % 2:
+                self._node_type = 0
             else:
-                self.node_type_ = 1
-            self.node_type_comm_ = self.comm_.Split(self.node_type_)
-            self.role_is_generated_ = True
+                self._node_type = 1
+            self._node_type_comm = self._comm.Split(self._node_type)
+            self._role_is_generated = True

python/paddle/fluid/incubate/fleet/parameter_server/__init__.py

@@ -64,9 +64,9 @@ class Fleet(object):
 
     def __init__(self):
         self._opt_info = None  # for fleet only
-        self.role_maker_ = None
-        self.local_ip_ = 0
-        self.is_initialized_ = False
+        self._role_maker = None
+        self._local_ip = 0
+        self._is_initialized = False
 
     def init(self):
         # TODO(guru4elephant)
@@ -78,22 +78,22 @@ class Fleet(object):
             current node's role, e.g. worker, server, etc.
         """
         if not self.is_initialized_:
-            self.role_maker_ = MPISymetricRoleMaker()
-            self.role_maker_._generate_role()
+            self._role_maker = MPISymetricRoleMaker()
+            self._role_maker._generate_role()
             self._fleet_ptr = fluid.core.Fleet()
-            self.is_initialized_ = True
+            self._is_initialized = True
 
     def stop(self):
         """
         stop(): will be called after a user finishes his/her training task. Fleet instance will be
             destroyed when stop() is called.
         """
-        self.role_maker_._barrier_worker()
-        if self.role_maker_._is_first_worker():
+        self._role_maker._barrier_worker()
+        if self._role_maker._is_first_worker():
             self._fleet_ptr.stop_server()
-        self.role_maker_._barrier_worker()
-        self.role_maker_._barrier_all()
-        self.role_maker_._finalize()
+        self._role_maker._barrier_worker()
+        self._role_maker._barrier_all()
+        self._role_maker._finalize()
 
     def init_pserver(self):
         """
@@ -110,15 +110,15 @@ class Fleet(object):
                 sys.exit(-1)
             self._fleet_ptr.init_server(self._dist_desc_str,
                                         self.role_maker_._get_rank())
-            self.local_ip_ = self._fleet_ptr.run_server()
+            self._local_ip = self._fleet_ptr.run_server()
             # barrier_all for init_server
-            self.role_maker_._barrier_all()
-            self.all_ips_ = self.role_maker_._all_gather(self.local_ip_)
+            self._role_maker._barrier_all()
+            self._all_ips = self._role_maker._all_gather(self.local_ip_)
 
-            self._fleet_ptr.gather_servers(self.all_ips_,
-                                           self.role_maker_._get_size())
+            self._fleet_ptr.gather_servers(self._all_ips,
+                                           self._role_maker._get_size())
             # barrier_all for init_worker, wait all workers start
-            self.role_maker_._barrier_all()
+            self._role_maker._barrier_all()
         else:
             print("You should run DistributedOptimizer.minimize() first")
             sys.exit(-1)
@@ -151,21 +151,21 @@ class Fleet(object):
                 print("You should run DistributedOptimizer.minimize() first")
                 sys.exit(-1)
             # barrier_all for init_server, wait for server starts
-            self.role_maker_._barrier_all()
-            self.all_ips_ = self.role_maker_._all_gather(self.local_ip_)
-            self._fleet_ptr.init_worker(self._dist_desc_str, self.all_ips_,
-                                        self.role_maker_._get_size(),
-                                        self.role_maker_._get_rank())
+            self._role_maker._barrier_all()
+            self._all_ips = self._role_maker._all_gather(self.local_ip_)
+            self._fleet_ptr.init_worker(self._dist_desc_str, self._all_ips,
+                                        self._role_maker._get_size(),
+                                        self._role_maker._get_rank())
             # barrier_all for init_worker
-            self.role_maker_._barrier_all()
+            self._role_maker._barrier_all()
             # prepare for client to client communication
             info = self._fleet_ptr.get_clients_info()
-            all_info = self.role_maker_._worker_gather(info[0])
+            all_info = self._role_maker._worker_gather(info[0])
             self._fleet_ptr.gather_clients(all_info)
             self._fleet_ptr.create_client2client_connection()
             # barrier for init model
-            self.role_maker_._barrier_worker()
-            if self.role_maker_._is_first_worker():
+            self._role_maker._barrier_worker()
+            if self._role_maker._is_first_worker():
                 tables = self._dist_desc.trainer_param.dense_table
                 for prog, scope in zip(programs, scopes):
                     prog_id = str(id(prog))
@@ -192,7 +192,7 @@ class Fleet(object):
                                                    int(table.table_id),
                                                    var_name_list)
             # barrier for init model done
-            self.role_maker_._barrier_worker()
+            self._role_maker._barrier_worker()
         else:
             print("You should run DistributedOptimizer.minimize() first")
             sys.exit(-1)
@@ -201,39 +201,39 @@ class Fleet(object):
         """
         return the number of current job's worker num
         """
-        return self.role_maker_._worker_num()
+        return self._role_maker._worker_num()
 
     def get_server_num(self):
         """
         return the number of current job's server num
         """
-        return self.role_maker_._server_num()
+        return self._role_maker._server_num()
 
     def get_worker_index(self):
         """
         return the mpi rank of current worker
         """
-        return self.role_maker_._worker_index()
+        return self._role_maker._worker_index()
 
     def is_worker(self):
         """
         return whether current node is a worker
         """
-        return self.role_maker_._is_worker()
+        return self._role_maker._is_worker()
 
     def is_server(self):
         """
         return whether current node is pserver
         """
-        return self.role_maker_._is_server()
+        return self._role_maker._is_server()
 
     def init_pserver_model(self):
         """
         init pserver model called from pserver
         """
-        if self.role_maker_._is_first_worker():
+        if self._role_maker._is_first_worker():
             self._fleet_ptr.init_model()
-        self.role_maker_._barrier_worker()
+        self._role_maker._barrier_worker()
 
     def save_pserver_model(self, save_path):
         """

python/paddle/fluid/incubate/fleet/parameter_server/node.py

@@ -42,13 +42,13 @@ class DownpourServer(Server):
     """
 
     def __init__(self):
-        self.server_ = pslib.ServerParameter()
-        self.server_.downpour_server_param.service_param.start_server_port = 0
-        self.server_.downpour_server_param.service_param.server_class = "DownpourBrpcPsServer"
-        self.server_.downpour_server_param.service_param.client_class = "DownpourBrpcPsClient"
-        self.server_.downpour_server_param.service_param.service_class = "DownpourPsService"
-        self.server_.downpour_server_param.service_param.start_server_port = 0
-        self.server_.downpour_server_param.service_param.server_thread_num = 12
+        self._server = pslib.ServerParameter()
+        self._server.downpour_server_param.service_param.start_server_port = 0
+        self._server.downpour_server_param.service_param.server_class = "DownpourBrpcPsServer"
+        self._server.downpour_server_param.service_param.client_class = "DownpourBrpcPsClient"
+        self._server.downpour_server_param.service_param.service_class = "DownpourPsService"
+        self._server.downpour_server_param.service_param.start_server_port = 0
+        self._server.downpour_server_param.service_param.server_thread_num = 12
 
     def add_sparse_table(self, table_id, learning_rate, slot_key_vars,
                          slot_value_var):
@@ -62,7 +62,7 @@ class DownpourServer(Server):
         Returns:
             return None 
         """
-        table = self.server_.downpour_server_param.downpour_table_param.add()
+        table = self._server.downpour_server_param.downpour_table_param.add()
         table.table_id = table_id
         table.table_class = "DownpourSparseTable"
         table.type = pslib.PS_SPARSE_TABLE
@@ -123,7 +123,7 @@ class DownpourServer(Server):
         Returns:
             return None 
         """
-        table = self.server_.downpour_server_param.downpour_table_param.add()
+        table = self._server.downpour_server_param.downpour_table_param.add()
         table.table_id = table_id
         table.table_class = "DownpourDenseTable"
         table.type = pslib.PS_DENSE_TABLE
@@ -140,7 +140,7 @@ class DownpourServer(Server):
         """
         Return downpour server program_desc
         """
-        return self.server_
+        return self._server
 
 
 class DownpourWorker(Worker):
@@ -155,7 +155,7 @@ class DownpourWorker(Worker):
 
     def __init__(self, window):
         self.window = window
-        self.worker_ = pslib.DownpourTrainerParameter()
+        self._worker = pslib.DownpourTrainerParameter()
 
     def add_sparse_table(self, table_id, learning_rate, slot_key_vars,
                          slot_value_vars):
@@ -187,7 +187,7 @@ class DownpourWorker(Worker):
         Returns:
             return None 
         """
-        table = self.worker_.dense_table.add()
+        table = self._worker.dense_table.add()
         table.table_id = table_id
         table.dense_variable_name.extend(
             filter(lambda x: x.find("embedding") == -1,
@@ -200,4 +200,4 @@ class DownpourWorker(Worker):
         """
         Return downpour worker program_desc
         """
-        return self.worker_
+        return self._worker

python/paddle/fluid/incubate/fleet/parameter_server/optimizer_factory.py

@@ -24,9 +24,9 @@ from .node import DownpourWorker, DownpourServer
 
 class DistributedOptimizerImplBase(object):
     def __init__(self, optimizer):
-        self.optimizer_ = optimizer
-        self.learning_rate_ = optimizer._learning_rate
-        self.regularization_ = optimizer.regularization
+        self._optimizer = optimizer
+        self._learning_rate = optimizer._learning_rate
+        self._regularization = optimizer.regularization
 
     def minimize(self,
                  losses,
@@ -41,7 +41,7 @@ class DistributedAdam(DistributedOptimizerImplBase):
         # todo(guru4elephant): add more optimizers here as argument
         # todo(guru4elephant): make learning_rate as a variable
         super(DistributedAdam, self).__init__(optimizer)
-        self.window_ = 1
+        self._window = 1
         self.type = "downpour"
         self.data_norm_name = [
             ".batch_size", ".batch_square_sum", ".batch_sum",
@@ -79,9 +79,9 @@ class DistributedAdam(DistributedOptimizerImplBase):
         server = DownpourServer()
         worker = DownpourWorker(self.window_)
         sparse_table_index = 0
-        server.add_sparse_table(sparse_table_index, self.learning_rate_,
+        server.add_sparse_table(sparse_table_index, self._learning_rate,
                                 prefetch_slots, prefetch_slots_emb)
-        worker.add_sparse_table(sparse_table_index, self.learning_rate_,
+        worker.add_sparse_table(sparse_table_index, self._learning_rate,
                                 prefetch_slots, prefetch_slots_emb)
         dense_table_index = 1
         program_configs = {}
@@ -124,9 +124,9 @@ class DistributedAdam(DistributedOptimizerImplBase):
                         data_norm_grads.append(i[1])
                 if not is_data_norm_data:
                     grads.append(i[1])
-            server.add_dense_table(dense_table_index, self.learning_rate_,
+            server.add_dense_table(dense_table_index, self._learning_rate,
                                    params, grads)
-            worker.add_dense_table(dense_table_index, self.learning_rate_,
+            worker.add_dense_table(dense_table_index, self._learning_rate,
                                    params, grads)
             program_configs[program_id]["pull_dense"] = [dense_table_index]
             program_configs[program_id]["push_dense"] = [dense_table_index]
@@ -135,9 +135,9 @@ class DistributedAdam(DistributedOptimizerImplBase):
             if len(data_norm_params) != 0 and len(data_norm_grads) != 0:
                 dense_table_index += 1
                 server.add_data_norm_table(dense_table_index,
-                                           self.learning_rate_,
+                                           self._learning_rate,
                                            data_norm_params, data_norm_grads)
-                worker.add_dense_table(dense_table_index, self.learning_rate_,
+                worker.add_dense_table(dense_table_index, self._learning_rate,
                                        data_norm_params, data_norm_grads)
                 #program_config.pull_dense_table_id.extend([dense_table_index])
                 #program_config.push_dense_table_id.extend([dense_table_index])

python/paddle/fluid/layers/control_flow.py

@@ -267,8 +267,44 @@ class StaticRNN(object):
     """
     StaticRNN class.
 
-    StaticRNN class is used to create a StaticRNN. The RNN will have its
-    own parameters like inputs, outputs, memories, status and length.
+    The StaticRNN can process a batch of sequence data. The length of each
+    sample sequence must be equal. The StaticRNN will have its own parameters
+    like inputs, outputs, memories. **Note that the first dimension of inputs
+    represents sequence length, and all the sequence length of inputs must be
+    the same. And the meaning of each axis of input and output are the same.**
+
+    Examples:
+        >>> import paddle.fluid as fluid
+        >>> import paddle.fluid.layers as layers
+        >>>
+        >>> vocab_size, hidden_size=10000, 200
+        >>> x = layers.data(name="x", shape=[-1, 1, 1], dtype='int64')
+        >>> x_emb = layers.embedding(
+        >>>         input=x,
+        >>>         size=[vocab_size, hidden_size],
+        >>>         dtype='float32',
+        >>>         is_sparse=False)
+        >>> x_emb = layers.transpose(x_emb, perm=[1, 0, 2])
+        >>>
+        >>> rnn = fluid.layers.StaticRNN()
+        >>> with rnn.step():
+        >>>    word = rnn.step_input(x_emb)
+        >>>    prev = rnn.memory(shape=[-1, hidden_size], batch_ref = word)
+        >>>    hidden = fluid.layers.fc(input=[word, prev], size=hidden_size, act='relu')
+        >>>    rnn.update_memory(prev, hidden)  # set prev to hidden
+        >>>    rnn.step_output(hidden)
+        >>>
+        >>> result = rnn()
+
+    The StaticRNN will unfold sequence into time steps. Users need to define
+    how to process each time step during the :code:`with` step.
+
+    The :code:`memory` is used as a staging data cross time step. The initial
+    value of memory can be a variable that is filled with a constant value or
+    a specified variable.
+
+    The StaticRNN can mark multiple variables as its output. Use `rnn()` to
+    get the output sequence.
     """
     BEFORE_RNN_BLOCK = 0
     IN_RNN_BLOCK = 1
@@ -284,6 +320,9 @@ class StaticRNN(object):
         self.seq_len = None
 
     def step(self):
+        """
+        The block for user to define operators in RNN.
+        """
         return BlockGuardWithCompletion(self)
 
     def _assert_in_rnn_block_(self, method):
@@ -298,13 +337,28 @@ class StaticRNN(object):
                init_batch_dim_idx=0,
                ref_batch_dim_idx=1):
         """
+        Create a memory variable for static rnn.
+
+        If the :code:`init` is not None, :code:`memory` will be initialized by
+        this Variable. If the :code:`init` is None, :code:`shape` and :code:`batch_ref`
+        must be set, and this function will initialize a :code:`init` Variable.
+
         Args:
-            init: boot memory, if not set, a shape, batch_ref must be provided
-            shape: shape of the boot memory
-            batch_ref: batch size reference variable
-            init_value: the init value of boot memory
-            init_batch_dim_idx: the index of batch size in init's dimension
-            ref_batch_dim_idx: the index of batch size in batch_ref's dimension
+            init(Variable|None): The initialized variable. If it is not set,
+                :code:`shape` and :code:`batch_ref` must be provided.
+                Default: None.
+            shape(list|tuple): The shape of the boot memory. NOTE the shape
+                does not contain batch_size. Default: None.
+            batch_ref(Variable|None): The batch size reference Variable.
+                Default: None.
+            init_value(float): the init value of boot memory. Default: 0.0.
+            init_batch_dim_idx(int): the batch_size axis of the
+                :code:`init` Variable. Default: 0.
+            ref_batch_dim_idx(int): the batch_size axis of the
+                :code:`batch_ref` Variable. Default: 1.
+
+        Returns:
+            The memory variable.
         """
         self._assert_in_rnn_block_('memory')
         if init is None:
@@ -343,6 +397,16 @@ class StaticRNN(object):
             return pre_mem
 
     def step_input(self, x):
+        """
+        Mark a sequence as a StaticRNN input.
+
+        Args:
+            x(Variable): The input sequence, the shape of x
+                should be [seq_len, ...].
+
+        Returns:
+            The current time step in the input sequence.
+        """
         self._assert_in_rnn_block_('step_input')
         if not isinstance(x, Variable):
             raise TypeError("step input takes a Variable")
@@ -357,6 +421,15 @@ class StaticRNN(object):
         return ipt
 
     def step_output(self, o):
+        """
+        Mark a sequence as a StaticRNN output.
+
+        Args:
+            o(Variable): The output sequence.
+
+        Returns:
+            None.
+        """
         self._assert_in_rnn_block_('step_output')
         if not isinstance(o, Variable):
             raise TypeError("step output takes a Variable")
@@ -376,10 +449,30 @@ class StaticRNN(object):
         self.outputs.append(out_var)
 
     def output(self, *outputs):
+        """
+        Mark the StaticRNN output variables.
+
+        Args:
+            outputs: The output Variables.
+
+        Returns:
+            None
+        """
         for each in outputs:
             self.step_output(each)
 
     def update_memory(self, mem, var):
+        """
+        Update the memory from ex_mem to new_mem. NOTE that the shape and data
+        type of :code:`ex_mem` and :code:`new_mem` must be same.
+
+        Args:
+            mem(Variable): the memory variable.
+            var(Variable): the plain variable generated in RNN block.
+
+        Returns:
+            None
+        """
         if not isinstance(mem, Variable) or not isinstance(var, Variable):
             raise TypeError("update memory should take variables")
         self.memories[mem.name].mem = var
@@ -419,6 +512,9 @@ class StaticRNN(object):
         for m in self.memories:
             local_inputs.add(m)
 
+        # NOTE(zcd): the params have two categories of variables.
+        #   - the variables that are the out of StaticRnn.
+        #   - the variables that are the parameters of some layers, for example, conv2d.
         params = list()
         for op in rnn_block.ops:
             assert isinstance(op, Operator)
@@ -435,17 +531,19 @@ class StaticRNN(object):
         inlinks = [parent_block.var(i.name) for i in self.inputs]
         outlinks = self.outputs
 
+        # NOTE(zcd): the states maybe empty in some case.
         boot_memories = []
         pre_memories = []
         memories = []
         for _, mem in six.iteritems(self.memories):
             boot_memories.append(mem.init)
             pre_memories.append(mem.pre_mem.name)
+            assert mem.mem is not None, "%s should be updated in every step." % (
+                mem.init.name)
             mem_var = rnn_block.var(mem.mem.name)
             assert isinstance(mem_var, Variable)
             new_mem = self.helper.create_variable_for_type_inference(
                 dtype=mem_var.dtype)
-
             rnn_block.append_op(
                 type='rnn_memory_helper',
                 inputs={'X': [mem_var]},
@@ -464,6 +562,7 @@ class StaticRNN(object):
             outputs={'outputs': outlinks,
                      'step_scopes': [step_scope]},
             attrs={
+                'has_states': len(pre_memories) > 0,
                 'ex_states': pre_memories,
                 'states': memories,
                 'sub_block': rnn_block

python/paddle/fluid/layers/nn.py

@@ -196,6 +196,7 @@ __all__ = [
     'npair_loss',
     'pixel_shuffle',
     'fsp_matrix',
+    'continuous_value_model',
 ]
 
 kIgnoreIndex = -100
@@ -5720,12 +5721,21 @@ def hsigmoid(input,
         raise ValueError(
             "num_classes must not be less than 2 with default tree")
 
+    if (not is_custom) and (is_sparse):
+        print("Sparse mode should not be used without custom tree")
+        is_sparse = False
+
+    if (not is_custom) and ((path_table is not None) or
+                            (path_code is not None)):
+        raise ValueError(
+            "only num_classes should be passed without custom tree")
+
     if (is_custom) and (path_code is None):
-        raise ValueError("path_code should not be None with costum tree")
+        raise ValueError("path_code should not be None with custom tree")
     elif (is_custom) and (path_table is None):
-        raise ValueError("path_table should not be None with costum tree")
+        raise ValueError("path_table should not be None with custom tree")
     elif (is_custom) and (num_classes is None):
-        raise ValueError("num_classes should not be None with costum tree")
+        raise ValueError("num_classes should not be None with custom tree")
     else:
         pass
 
@@ -6268,6 +6278,8 @@ def sampled_softmax_with_cross_entropy(logits,
     sampled_label = helper.create_variable_for_type_inference(dtype='int64')
     sampled_softlabel = helper.create_variable_for_type_inference(
         dtype=logits.dtype)
+    logits_dim = helper.create_variable_for_type_inference(dtype=logits.dtype)
+    labels_dim = helper.create_variable_for_type_inference(dtype=label.type)
 
     helper.append_op(
         type='sample_logits',
@@ -6281,7 +6293,9 @@ def sampled_softmax_with_cross_entropy(logits,
             'Samples': samples,
             'Probabilities': probabilities,
             'SampledLabels': sampled_label,
-            'SampledLogits': sampled_logits
+            'SampledLogits': sampled_logits,
+            'LogitsDim': logits_dim,
+            'LabelsDim': labels_dim
         },
         attrs={
             'use_customized_samples': use_customized_samples,
@@ -11202,3 +11216,54 @@ def fsp_matrix(x, y):
         input_param_name='x'))
     helper.append_op(type='fsp', inputs={'X': x, 'Y': y}, outputs={'Out': out})
     return out
+
+
+def continuous_value_model(input, cvm, use_cvm=True):
+    """
+
+    **continuous_value_model layers**
+
+    continuous value model(cvm). Now, it only considers show and click value in CTR project.
+    We assume that input is an embedding vector with cvm_feature, whose shape is [N * D] (D is 2 + embedding dim).
+    If use_cvm is True, it will log(cvm_feature), and output shape is [N * D].
+    If use_cvm is False, it will remove cvm_feature from input, and output shape is [N * (D - 2)].
+    
+    This layer accepts a tensor named input which is ID after embedded(lod level is 1), cvm is a show_click info.
+
+    Args:
+
+        input (Variable): a 2-D LodTensor with shape [N x D], where N is the batch size, D is 2 + the embedding dim. lod level = 1.
+        cvm (Variable):   a 2-D Tensor with shape [N x 2], where N is the batch size, 2 is show and click.
+        use_cvm  (bool):  use cvm or not. if use cvm, the output dim is the same as input
+                          if don't use cvm, the output dim is input dim - 2(remove show and click)
+                          (cvm op is a customized op, which input is a sequence has embedd_with_cvm default, so we need an op named cvm to decided whever use it or not.)
+
+    Returns:
+
+        Variable: A 2-D LodTensor with shape [N x D], if use cvm, D is equal to input dim, if don't use cvm, D is equal to input dim - 2. 
+
+    Examples:
+
+        .. code-block:: python
+
+          input = fluid.layers.data(name="input", shape=[-1, 1], lod_level=1, append_batch_size=False, dtype="int64")#, stop_gradient=False)
+          label = fluid.layers.data(name="label", shape=[-1, 1], append_batch_size=False, dtype="int64")
+          embed = fluid.layers.embedding(
+                            input=input,
+                            size=[100, 11],
+                            dtype='float32')
+          ones = fluid.layers.fill_constant_batch_size_like(input=label, shape=[-1, 1], dtype="int64", value=1)
+          show_clk = fluid.layers.cast(fluid.layers.concat([ones, label], axis=1), dtype='float32')
+          show_clk.stop_gradient = True
+          input_with_cvm = fluid.layers.continuous_value_model(embed, show_clk, True)
+
+    """
+    helper = LayerHelper('cvm', **locals())
+    out = helper.create_variable(dtype=input.dtype)
+    helper.append_op(
+        type='cvm',
+        inputs={'X': [input],
+                'CVM': [cvm]},
+        outputs={'Y': [out]},
+        attrs={"use_cvm": use_cvm})
+    return out

python/paddle/fluid/optimizer.py

@@ -275,15 +275,26 @@ class Optimizer(object):
         self._create_global_learning_rate()
 
         optimize_ops = []
-        for param_and_grad in parameters_and_grads:
-            if param_and_grad[1] is None:
-                continue
-            with param_and_grad[0].block.program._optimized_guard(
-                    param_and_grad), name_scope("optimizer"):
-                if param_and_grad[0].trainable is True:
-                    optimize_op = self._append_optimize_op(global_block,
-                                                           param_and_grad)
-                    optimize_ops.append(optimize_op)
+        if framework.in_dygraph_mode():
+            for param_and_grad in parameters_and_grads:
+                if param_and_grad[1] is None:
+                    continue
+                with param_and_grad[0].block.program._optimized_guard(
+                        param_and_grad):
+                    if param_and_grad[0].trainable is True:
+                        optimize_op = self._append_optimize_op(global_block,
+                                                               param_and_grad)
+                        optimize_ops.append(optimize_op)
+        else:
+            for param_and_grad in parameters_and_grads:
+                if param_and_grad[1] is None:
+                    continue
+                with param_and_grad[0].block.program._optimized_guard(
+                        param_and_grad), name_scope("optimizer"):
+                    if param_and_grad[0].trainable is True:
+                        optimize_op = self._append_optimize_op(global_block,
+                                                               param_and_grad)
+                        optimize_ops.append(optimize_op)
 
         # Get custom finish ops for subclasses
         # FIXME: Need to fix this once we figure out how to handle dependencies

python/paddle/fluid/tests/book/high-level-api/cifar10_small_test_set.py

@@ -88,3 +88,19 @@ def train10(batch_size=None):
         paddle.dataset.common.download(CIFAR10_URL, 'cifar', CIFAR10_MD5),
         'data_batch',
         batch_size=batch_size)
+
+
+def test10(batch_size=None):
+    """
+    CIFAR-10 test set creator.
+
+    It returns a reader creator, each sample in the reader is image pixels in
+    [0, 1] and label in [0, 9].
+
+    :return: Test reader creator.
+    :rtype: callable
+    """
+    return reader_creator(
+        paddle.dataset.common.download(CIFAR10_URL, 'cifar', CIFAR10_MD5),
+        'test_batch',
+        batch_size=batch_size)

python/paddle/fluid/tests/book/high-level-api/test_image_classification_vgg_new_api.py

@@ -89,9 +89,11 @@ def train(use_cuda, train_program, parallel, params_dirname):
             cifar10_small_test_set.train10(batch_size=10), buf_size=128 * 10),
         batch_size=BATCH_SIZE,
         drop_last=False)
-
+    # Use only part of the test set data validation program
     test_reader = paddle.batch(
-        paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE, drop_last=False)
+        cifar10_small_test_set.test10(BATCH_SIZE),
+        batch_size=BATCH_SIZE,
+        drop_last=False)
 
     def event_handler(event):
         if isinstance(event, EndStepEvent):

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

@@ -25,7 +25,6 @@ endif()
 list(REMOVE_ITEM TEST_OPS test_seq_concat_op) # FIXME(helin): https://github.com/PaddlePaddle/Paddle/issues/8290
 list(REMOVE_ITEM TEST_OPS test_modified_huber_loss_op) # FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/5184
 list(REMOVE_ITEM TEST_OPS test_lstm_unit_op) # # FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/5185
-list(REMOVE_ITEM TEST_OPS test_recurrent_op) # FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/6152
 list(REMOVE_ITEM TEST_OPS test_cond_op) # FIXME(qijun): https://github.com/PaddlePaddle/Paddle/issues/5101#issuecomment-339814957
 
 list(REMOVE_ITEM TEST_OPS op_test) # op_test is a helper python file, not a test

python/paddle/fluid/tests/unittests/test_cvm_op.py

+#   Copyright (c) 2019 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
+#
+#     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 numpy as np
+from math import log
+from math import exp
+from op_test import OpTest
+import unittest
+
+
+class TestCVMOp(OpTest):
+    """
+        Test cvm op with discrete one-hot labels.
+    """
+
+    def setUp(self):
+        self.op_type = "cvm"
+        batch_size = 4
+        dims = 11
+        lod = [[1]]
+        self.inputs = {
+            'X': (np.random.uniform(0, 1, [1, dims]).astype("float32"), lod),
+            'CVM': np.array([[0.6, 0.4]]).astype("float32"),
+        }
+        self.attrs = {'use_cvm': False}
+        out = []
+        for index, emb in enumerate(self.inputs["X"][0]):
+            out.append(emb[2:])
+        self.outputs = {'Y': (np.array(out), lod)}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/test_dyn_rnn.py

@@ -24,15 +24,15 @@ from paddle.fluid.layers.control_flow import max_sequence_len
 from paddle.fluid.layers.control_flow import lod_tensor_to_array
 from paddle.fluid.layers.control_flow import array_to_lod_tensor
 from paddle.fluid.layers.control_flow import shrink_memory
+from fake_reader import fake_imdb_reader
 
 
 class TestDynRNN(unittest.TestCase):
     def setUp(self):
-        self.word_dict = paddle.dataset.imdb.word_dict()
+        self.word_dict_len = 5147
         self.BATCH_SIZE = 2
-        self.train_data = paddle.batch(
-            paddle.dataset.imdb.train(self.word_dict),
-            batch_size=self.BATCH_SIZE)
+        reader = fake_imdb_reader(self.word_dict_len, self.BATCH_SIZE * 100)
+        self.train_data = paddle.batch(reader, batch_size=self.BATCH_SIZE)
 
     def test_plain_while_op(self):
         main_program = fluid.Program()
@@ -42,7 +42,7 @@ class TestDynRNN(unittest.TestCase):
             sentence = fluid.layers.data(
                 name='word', shape=[1], dtype='int64', lod_level=1)
             sent_emb = fluid.layers.embedding(
-                input=sentence, size=[len(self.word_dict), 32], dtype='float32')
+                input=sentence, size=[self.word_dict_len, 32], dtype='float32')
 
             label = fluid.layers.data(name='label', shape=[1], dtype='float32')
 
@@ -109,7 +109,7 @@ class TestDynRNN(unittest.TestCase):
             sentence = fluid.layers.data(
                 name='word', shape=[1], dtype='int64', lod_level=1)
             sent_emb = fluid.layers.embedding(
-                input=sentence, size=[len(self.word_dict), 32], dtype='float32')
+                input=sentence, size=[self.word_dict_len, 32], dtype='float32')
 
             rnn = fluid.layers.DynamicRNN()
 

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

@@ -1759,10 +1759,20 @@ class TestBook(LayerTest):
     def test_lod_reset(self):
         # TODO(minqiyang): dygraph do not support lod now
         with self.static_graph():
+            # case 1
             x = layers.data(name='x', shape=[10], dtype='float32')
             y = layers.data(
                 name='y', shape=[10, 20], dtype='float32', lod_level=2)
-            return (layers.lod_reset(x=x, y=y))
+            z = layers.lod_reset(x=x, y=y)
+            self.assertTrue(z.lod_level == 2)
+            # case 2
+            lod_tensor_in = layers.data(name='lod_in', shape=[1], dtype='int64')
+            z = layers.lod_reset(x=x, y=lod_tensor_in)
+            self.assertTrue(z.lod_level == 1)
+            # case 3
+            z = layers.lod_reset(x=x, target_lod=[1, 2, 3])
+            self.assertTrue(z.lod_level == 1)
+            return z
 
     def test_affine_grid(self):
         with self.static_graph():

python/paddle/fluid/tests/unittests/test_parallel_executor_transformer.py

@@ -65,7 +65,9 @@ class ModelHyperParams(object):
     # number of head used in multi-head attention.
     n_head = 8
     # number of sub-layers to be stacked in the encoder and decoder.
-    n_layer = 6
+    # NOTE(zcd): the origin number of layer is 6, to make this unit test faster,
+    # we should reduce the layer number to 4.
+    n_layer = 4
     # dropout rate used by all dropout layers.
     dropout = 0.1
 
@@ -175,7 +177,7 @@ class TestTransformer(TestParallelExecutorBase):
             self.check_network_convergence(transformer, use_cuda=True)
             self.check_network_convergence(
                 transformer, use_cuda=True, enable_sequential_execution=True)
-        self.check_network_convergence(transformer, use_cuda=False, iter=5)
+        self.check_network_convergence(transformer, use_cuda=False, iter=2)
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/test_recurrent_op.py

@@ -15,7 +15,7 @@
 from __future__ import print_function
 
 import unittest
-
+import paddle.fluid as fluid
 import paddle.fluid.layers as layers
 from paddle.fluid.framework import Program, grad_var_name
 from paddle.fluid.executor import Executor
@@ -115,10 +115,6 @@ class RecurrentOpTest1(unittest.TestCase):
     def setup_program(self):
         self.main_program = Program()
         self.startup_program = Program()
-        self.p_info = {
-            "main_program": self.main_program,
-            "startup_program": self.startup_program
-        }
         self.place = core.CPUPlace()
 
     def setUp(self):
@@ -129,33 +125,29 @@ class RecurrentOpTest1(unittest.TestCase):
         self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
         self.py_rnn = PySimpleRNN1(self.input_shape, self.output_shape)
 
-        self.output = layers.mean(self.create_rnn_op(), **self.p_info)
+        with fluid.program_guard(self.main_program, self.startup_program):
+            self.output = layers.mean(self.create_rnn_op())
 
     def create_rnn_op(self):
         x = layers.data(
             shape=[self.sent_len, self.batch_size, self.input_dim],
             dtype='float32',
             name='x',
-            append_batch_size=False,
-            **self.p_info)
+            append_batch_size=False)
         x.stop_gradient = False
         h_boot = layers.data(
-            shape=[self.input_dim],
-            dtype='float32',
-            name='h_boot',
-            **self.p_info)
+            shape=[self.input_dim], dtype='float32', name='h_boot')
         h_boot.stop_gradient = False
 
-        rnn = layers.StaticRNN(main_program=self.main_program)
+        rnn = layers.StaticRNN()
         with rnn.step():
             h_pre = rnn.memory(init=h_boot)
             x_t = rnn.step_input(x)
 
             h = layers.scale(
                 x=layers.elementwise_add(
-                    x=h_pre, y=x_t, **self.p_info),
-                scale=self.py_rnn.scale,
-                **self.p_info)
+                    x=h_pre, y=x_t),
+                scale=self.py_rnn.scale)
 
             rnn.update_memory(h_pre, h)
             rnn.output(h)
@@ -193,7 +185,8 @@ class RecurrentOpTest1(unittest.TestCase):
     def test_backward(self):
         self.check_forward()
 
-        append_backward(self.output)
+        with fluid.program_guard(self.main_program, self.startup_program):
+            append_backward(self.output)
 
         ana_grad = [np.array(x) for x in self.backward()]
 
@@ -205,12 +198,8 @@ class RecurrentOpTest1(unittest.TestCase):
                     num_grad[idx], ana_grad[idx], rtol=0.1).all())
 
     def check_forward(self):
-        print('test recurrent op forward')
         pd_output = self.forward()
         py_output = self.py_rnn.forward()
-        print('pd_output', pd_output)
-        print
-        print('py_output', py_output)
         self.assertEqual(pd_output.shape, py_output.shape)
         self.assertTrue(np.isclose(pd_output, py_output, rtol=0.1).all())
 
@@ -263,24 +252,21 @@ class RecurrentOpTest2(RecurrentOpTest1):
         self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
         self.py_rnn = PySimpleRNN2(self.input_shape, self.output_shape)
 
-        self.output = layers.mean(self.create_rnn_op(), **self.p_info)
+        with fluid.program_guard(self.main_program, self.startup_program):
+            self.output = layers.mean(self.create_rnn_op())
 
     def create_rnn_op(self):
         x = layers.data(
             shape=[self.sent_len, self.batch_size, self.input_dim],
             dtype='float32',
             name='x',
-            append_batch_size=False,
-            **self.p_info)
+            append_batch_size=False)
         x.stop_gradient = False
         h_boot = layers.data(
-            shape=[self.input_dim],
-            dtype='float32',
-            name='h_boot',
-            **self.p_info)
+            shape=[self.input_dim], dtype='float32', name='h_boot')
         h_boot.stop_gradient = False
 
-        rnn = layers.StaticRNN(main_program=self.main_program)
+        rnn = layers.StaticRNN()
         with rnn.step():
             h_pre = rnn.memory(init=h_boot)
             x_t = rnn.step_input(x)
@@ -288,18 +274,13 @@ class RecurrentOpTest2(RecurrentOpTest1):
             temp_l = layers.fc(input=x_t,
                                size=self.input_dim,
                                param_attr='W',
-                               bias_attr=False,
-                               **self.p_info)
+                               bias_attr=False)
             temp_r = layers.fc(input=h_pre,
                                size=self.input_dim,
                                param_attr='U',
-                               bias_attr=False,
-                               **self.p_info)
+                               bias_attr=False)
 
-            h = layers.sigmoid(
-                x=layers.elementwise_add(
-                    x=temp_l, y=temp_r, **self.p_info),
-                **self.p_info)
+            h = layers.sigmoid(x=layers.elementwise_add(x=temp_l, y=temp_r))
 
             rnn.update_memory(h_pre, h)
             rnn.output(h)
@@ -362,40 +343,38 @@ class RecurrentOpMultipleMemoryTest(RecurrentOpTest1):
         self.py_rnn = RecurrentOpMultipleMemoryTest.PySimpleRNN3(
             self.input_shape, self.output_shape)
 
-        self.output = layers.mean(self.create_rnn_op(), **self.p_info)
+        with fluid.program_guard(self.main_program, self.startup_program):
+            self.output = layers.mean(self.create_rnn_op())
 
     def create_rnn_op(self):
         x = layers.data(
             shape=[self.sent_len, self.batch_size, self.input_dim],
             dtype='float32',
             name='x',
-            append_batch_size=False,
-            **self.p_info)
+            append_batch_size=False)
         x.stop_gradient = False
         h_boot1 = layers.data(
             shape=[self.batch_size, self.input_dim],
             dtype='float32',
             name='h_boot1',
-            append_batch_size=False,
-            **self.p_info)
+            append_batch_size=False)
         h_boot1.stop_gradient = False
         h_boot2 = layers.data(
             shape=[self.batch_size, self.input_dim],
             dtype='float32',
             name='h_boot2',
-            append_batch_size=False,
-            **self.p_info)
+            append_batch_size=False)
         h_boot2.stop_gradient = False
 
-        rnn = layers.StaticRNN(main_program=self.main_program)
+        rnn = layers.StaticRNN()
         with rnn.step():
             h_pre1 = rnn.memory(init=h_boot1)
             h_pre2 = rnn.memory(init=h_boot2)
             x_t = rnn.step_input(x)
 
-            mem1 = layers.scale(x=h_pre1, scale=1.0, **self.p_info)
-            mem2 = layers.scale(x=h_pre2, scale=1.0, **self.p_info)
-            out = layers.sums(input=[mem1, x_t, mem2], **self.p_info)
+            mem1 = layers.scale(x=h_pre1, scale=1.0)
+            mem2 = layers.scale(x=h_pre2, scale=1.0)
+            out = layers.sums(input=[mem1, x_t, mem2])
 
             rnn.update_memory(h_pre1, mem1)
             rnn.update_memory(h_pre2, mem2)
@@ -446,23 +425,23 @@ class RecurrentOpNoMemBootTest(RecurrentOpTest1):
         self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
         self.py_rnn = RecurrentOpNoMemBootTest.PySimpleRNN4(self.input_shape,
                                                             self.output_shape)
-        self.output = layers.mean(self.create_rnn_op(), **self.p_info)
-        print(self.main_program)
+
+        with fluid.program_guard(self.main_program, self.startup_program):
+            self.output = layers.mean(self.create_rnn_op())
 
     def create_rnn_op(self):
         x = layers.data(
             shape=[self.sent_len, self.batch_size, self.input_dim],
             dtype='float32',
             name='x',
-            append_batch_size=False,
-            **self.p_info)
+            append_batch_size=False)
         x.stop_gradient = False
 
-        rnn = layers.StaticRNN(main_program=self.main_program)
+        rnn = layers.StaticRNN()
         with rnn.step():
             mem_pre = rnn.memory(shape=[-1, self.input_dim], batch_ref=x)
             x_t = rnn.step_input(x)
-            mem = layers.elementwise_add(x=mem_pre, y=x_t, **self.p_info)
+            mem = layers.elementwise_add(x=mem_pre, y=x_t)
             rnn.update_memory(mem_pre, mem)
             rnn.output(mem)
 

python/paddle/fluid/tests/unittests/test_sigmoid_cross_entropy_with_logits_op.py

@@ -149,5 +149,98 @@ class TestSigmoidCrossEntropyWithNorm(OpTest):
         self.check_grad(['X'], 'Out')
 
 
+class TestSigmoidCrossEntropyWithLogitsOp5(OpTest):
+    """Test sigmoid_cross_entropy_with_logit_op with probabalistic label
+    """
+
+    def setUp(self):
+        self.op_type = "sigmoid_cross_entropy_with_logits"
+        batch_size = [10, 10]
+        num_classes = 20
+        self.inputs = {
+            'X': logit(
+                np.random.uniform(0, 1, tuple(batch_size + [num_classes]))
+                .astype("float32")),
+            'Label': np.random.uniform(0, 1, tuple(batch_size + [num_classes]))
+            .astype("float32")
+        }
+
+        # Fw Pass is implemented as elementwise sigmoid followed by
+        # elementwise logistic loss
+        # Label * -log(sigmoid(X)) + (1 - label) * -log(1 - sigmoid(X))
+        sigmoid_X = expit(self.inputs['X'])
+        term1 = self.inputs['Label'] * np.log(sigmoid_X)
+        term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
+        self.outputs = {'Out': -term1 - term2}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+class TestSigmoidCrossEntropyWithNorm2(OpTest):
+    def setUp(self):
+        self.op_type = "sigmoid_cross_entropy_with_logits"
+        batch_size = [10, 10]
+        num_classes = 20
+        ignore_index = -1
+        self.inputs = {
+            'X': logit(
+                np.random.uniform(0, 1, tuple(batch_size + [num_classes]))
+                .astype("float32")),
+            'Label': np.random.randint(-1, 2, tuple(batch_size + [num_classes]))
+            .astype("float32")
+        }
+        self.attrs = {'ignore_index': ignore_index, 'normalize': True}
+        sigmoid_X = expit(self.inputs['X'])
+        term1 = self.inputs['Label'] * np.log(sigmoid_X)
+        term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
+        out = -term1 - term2
+        out[np.where(self.inputs['Label'] == ignore_index)] = 0
+        if self.attrs['normalize']:
+            out = out / float(
+                np.where(self.inputs['Label'] != ignore_index)[0].size)
+        self.outputs = {'Out': out}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+class TestSigmoidCrossEntropyWithLogitsOp6(OpTest):
+    """Test sigmoid_cross_entropy_with_logit_op with binary label
+    """
+
+    def setUp(self):
+        self.op_type = "sigmoid_cross_entropy_with_logits"
+        batch_size = [10, 10]
+        num_classes = 20
+        self.inputs = {
+            'X': logit(
+                np.random.uniform(0, 1, tuple(batch_size + [num_classes]))
+                .astype("float32")),
+            'Label': np.random.randint(0, 2, tuple(batch_size + [num_classes]))
+            .astype("float32")
+        }
+
+        # Fw Pass is implemented as elementwise sigmoid followed by
+        # elementwise logistic loss
+        # Label * -log(sigmoid(X)) + (1 - label) * -log(1 - sigmoid(X))
+        sigmoid_X = expit(self.inputs['X'])
+        term1 = self.inputs['Label'] * np.log(sigmoid_X)
+        term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
+        self.outputs = {'Out': -term1 - term2}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/trainer_desc.py

@@ -28,10 +28,10 @@ class TrainerDesc(object):
         import multiprocessing as mp
         # set default thread num == cpu count
         self.proto_desc.thread_num = mp.cpu_count()
-        self.fleet_desc_ = None
-        self.device_worker_ = None
-        self.program_ = None
-        self.infer_ = False
+        self._fleet_desc = None
+        self._device_worker = None
+        self._program = None
+        self._infer = False
 
     def _set_fetch_var_and_info(self, fetch_vars, fetch_info, print_period):
         for i, v in enumerate(fetch_vars):
@@ -47,19 +47,19 @@ class TrainerDesc(object):
         self.proto_desc.thread_num = thread_num
 
     def _set_device_worker(self, device_worker):
-        self.device_worker_ = device_worker
+        self._device_worker = device_worker
 
     def _set_infer(self, infer):
-        self.infer_ = infer
+        self._infer = infer
 
     def _set_fleet_desc(self, fleet_desc):
-        self.fleet_desc_ = fleet_desc
+        self._fleet_desc = fleet_desc
 
     def _gen_trainer_desc(self):
         pass
 
     def _set_program(self, program):
-        self.program_ = program
+        self._program = program
 
     def _desc(self):
         from google.protobuf import text_format
@@ -73,13 +73,13 @@ class MultiTrainer(TrainerDesc):
 
     def _set_program(self, program):
         super(MultiTrainer, self)._set_program(program)
-        self.program_ = program
+        self._program = program
 
     def _gen_trainer_desc(self):
         super(MultiTrainer, self)._gen_trainer_desc()
         self.proto_desc.class_name = "MultiTrainer"
-        self.device_worker_._set_infer(self.infer_)
-        self.device_worker_._gen_worker_desc(self.proto_desc)
+        self._device_worker._set_infer(self._infer)
+        self._device_worker._gen_worker_desc(self.proto_desc)
 
 
 class DistMultiTrainer(TrainerDesc):
@@ -89,13 +89,13 @@ class DistMultiTrainer(TrainerDesc):
 
     def _set_program(self, program):
         super(DistMultiTrainer, self)._set_program(program)
-        self.program_ = program
+        self._program = program
 
     def _gen_trainer_desc(self):
         super(DistMultiTrainer, self)._gen_trainer_desc()
         self.proto_desc.class_name = "DistMultiTrainer"
-        if self.program_ == None:
+        if self._program == None:
             raise RuntimeError("None Program")
-        self.device_worker_._set_infer(self.infer_)
-        self.device_worker_._set_program(self.program_)
-        self.device_worker_._gen_worker_desc(self.proto_desc)
+        self._device_worker._set_infer(self._infer)
+        self._device_worker._set_program(self._program)
+        self._device_worker._gen_worker_desc(self.proto_desc)

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -658,6 +658,7 @@ class DistributeTranspiler(object):
                 outputs={"Out": splited_var},
                 attrs={
                     "epmap": eps,
+                    "trainer_id": self.trainer_id,
                     RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
                 })
 
@@ -669,6 +670,7 @@ class DistributeTranspiler(object):
             outputs={"Out": fetch_barrier_out},
             attrs={
                 "endpoints": self.pserver_endpoints,
+                "trainer_id": self.trainer_id,
                 RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
             })
 
@@ -791,11 +793,15 @@ class DistributeTranspiler(object):
 
         global_ops = []
 
+        # sparse grad name to param name
+        sparse_grad_to_param = []
+
         def __append_optimize_op__(op, block, grad_to_block_id, merged_var,
                                    lr_ops):
             if self._is_optimizer_op(op):
                 self._append_pserver_ops(block, op, endpoint, grad_to_block_id,
-                                         self.origin_program, merged_var)
+                                         self.origin_program, merged_var,
+                                         sparse_grad_to_param)
             elif op not in lr_ops:
                 self._append_pserver_non_opt_ops(block, op)
 
@@ -911,6 +917,7 @@ class DistributeTranspiler(object):
             "Fanin": self.trainer_num,
             "sync_mode": self.sync_mode,
             "grad_to_block_id": grad_to_block_id,
+            "sparse_grad_to_param": sparse_grad_to_param,
         }
 
         if self.has_distributed_lookup_table:
@@ -1779,7 +1786,8 @@ class DistributeTranspiler(object):
         return o4
 
     def _append_pserver_ops(self, optimize_block, opt_op, endpoint,
-                            grad_to_block_id, origin_program, merged_var):
+                            grad_to_block_id, origin_program, merged_var,
+                            sparse_grad_to_param):
         program = optimize_block.program
         pserver_block = program.global_block()
         new_inputs = collections.OrderedDict()
@@ -1863,6 +1871,12 @@ class DistributeTranspiler(object):
             outputs=outputs,
             attrs=opt_op.all_attrs())
 
+        # record sparse grad to param name
+        if new_inputs["Grad"].type == core.VarDesc.VarType.SELECTED_ROWS:
+            sparse_grad_to_param.append(
+                str(new_inputs["Grad"].name) + ":" + str(new_inputs["Param"]
+                                                         .name))
+
     def _get_pserver_grad_param_var(self, var, var_dict):
         """
         Return pserver side grad/param variable, return None