Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix_import_plot_py3

.gitignore

@@ -28,3 +28,4 @@ third_party/
 build_*
 # clion workspace.
 cmake-build-*
+model_test

CMakeLists.txt

@@ -69,6 +69,7 @@ option(WITH_ANAKIN      "Compile with Anakin library"                   OFF)
 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(WITH_INFERENCE    "Compile fluid inference library"              ON)
+option(ON_INFER         "Turn on inference optimization."               OFF)
 option(WITH_INFERENCE_API_TEST   "Test fluid inference high-level api interface"  OFF)
 option(WITH_SYSTEM_BLAS   "Use system blas library"           OFF)
 option(PY_VERSION       "Compile PaddlePaddle with python3 support"     ${PY_VERSION})
@@ -179,6 +180,7 @@ include(external/eigen)     # download eigen3
 include(external/pybind11)  # download pybind11
 include(external/cares)
 include(external/cub)
+include(external/xxhash)    # download xxhash
 
 if (NOT WIN32)
 # there is no official support of snappystream, warpctc, nccl, cupti in windows
@@ -301,3 +303,8 @@ if(WITH_DOC)
     find_python_module(recommonmark REQUIRED)
     add_subdirectory(doc)
 endif()
+
+if (ON_INFER)
+    message(WARNING "On inference mode, will take place some specific optimization.")
+    add_definitions(-DPADDLE_ON_INFERENCE)
+endif()

Dockerfile

@@ -75,14 +75,14 @@ RUN pip3 install -U wheel && \
     pip3 install -U docopt PyYAML sphinx==1.5.6 && \
     pip3 install sphinx-rtd-theme==0.1.9 recommonmark && \
     easy_install -U pip && \
-    pip install -U wheel && \
+    pip install -U pip setuptools wheel && \
     pip install -U docopt PyYAML sphinx==1.5.6 && \
     pip install sphinx-rtd-theme==0.1.9 recommonmark
 
-RUN pip3 install pre-commit 'ipython==5.3.0' && \
+RUN pip3 install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
     pip3 install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
     pip3 install opencv-python && \
-    pip install pre-commit 'ipython==5.3.0' && \
+    pip install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
     pip install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
     pip install opencv-python
 

benchmark/fluid/args.py

@@ -142,5 +142,10 @@ def parse_args():
         choices=['reduce', 'all_reduce'],
         default='all_reduce',
         help='Specify the reduce strategy, can be reduce, all_reduce')
+    parser.add_argument(
+        '--fuse_broadcast_op',
+        action='store_true',
+        help='If set, would fuse multiple broadcast operators into one fused_broadcast operator.'
+    )
     args = parser.parse_args()
     return args

benchmark/fluid/fluid_benchmark.py

@@ -177,6 +177,7 @@ def train_parallel(train_args, test_args, args, train_prog, test_prog,
     else:
         build_strategy.reduce_strategy = fluid.BuildStrategy(
         ).ReduceStrategy.AllReduce
+    build_strategy.fuse_broadcast_op = args.fuse_broadcast_op
 
     avg_loss = train_args[0]
 
@@ -240,7 +241,6 @@ def train_parallel(train_args, test_args, args, train_prog, test_prog,
 
             if args.use_fake_data or args.use_reader_op:
                 try:
-
                     fetch_ret = exe.run(fetch_list)
                 except fluid.core.EOFException as eof:
                     break

cmake/external/xxhash.cmake

+INCLUDE(ExternalProject)
+
+set(XXHASH_SOURCE_DIR ${THIRD_PARTY_PATH}/xxhash)
+set(XXHASH_INSTALL_DIR ${THIRD_PARTY_PATH}/install/xxhash)
+set(XXHASH_INCLUDE_DIR "${XXHASH_INSTALL_DIR}/include")
+
+IF(WITH_STATIC_LIB)
+  SET(BUILD_CMD make lib)
+ELSE()
+  SET(BUILD_CMD sed -i "s/-Wstrict-prototypes -Wundef/-Wstrict-prototypes -Wundef -fPIC/g" ${XXHASH_SOURCE_DIR}/src/extern_xxhash/Makefile && make lib)
+ENDIF()
+
+ExternalProject_Add(
+    extern_xxhash
+    ${EXTERNAL_PROJECT_LOG_ARGS}
+    GIT_REPOSITORY  "https://github.com/Cyan4973/xxHash"
+    GIT_TAG         "v0.6.5"
+    PREFIX          ${XXHASH_SOURCE_DIR}
+    DOWNLOAD_NAME   "xxhash"
+    UPDATE_COMMAND  ""
+    CONFIGURE_COMMAND ""
+    BUILD_IN_SOURCE 1
+    PATCH_COMMAND
+    BUILD_COMMAND     ${BUILD_CMD}
+    INSTALL_COMMAND   export PREFIX=${XXHASH_INSTALL_DIR}/ && make install
+    TEST_COMMAND      ""
+)
+
+set(XXHASH_LIBRARIES "${XXHASH_INSTALL_DIR}/lib/libxxhash.a")
+INCLUDE_DIRECTORIES(${XXHASH_INCLUDE_DIR})
+
+add_library(xxhash STATIC IMPORTED GLOBAL)
+set_property(TARGET xxhash PROPERTY IMPORTED_LOCATION ${XXHASH_LIBRARIES})
+include_directories(${XXHASH_INCLUDE_DIR})
+add_dependencies(xxhash extern_xxhash)
+
+LIST(APPEND external_project_dependencies xxhash)
+
+IF(WITH_C_API)
+  INSTALL(DIRECTORY ${XXHASH_INCLUDE_DIR} DESTINATION third_party/xxhash)
+  IF(ANDROID)
+    INSTALL(FILES ${XXHASH_LIBRARIES} DESTINATION third_party/xxhash/lib/${ANDROID_ABI})
+  ELSE()
+    INSTALL(FILES ${XXHASH_LIBRARIES} DESTINATION third_party/xxhash/lib)
+  ENDIF()
+ENDIF()

cmake/inference_lib.cmake

@@ -14,6 +14,9 @@
 
 # make package for paddle fluid shared and static library
 function(copy TARGET)
+    if (NOT ON_INFER)
+      message(WARNING "Turn on the ON_INFER flag when building inference_lib only.")
+    endif()
     set(options "")
     set(oneValueArgs "")
     set(multiValueArgs SRCS DSTS DEPS)
@@ -31,7 +34,7 @@ function(copy TARGET)
     foreach(index RANGE ${len})
         list(GET copy_lib_SRCS ${index} src)
         list(GET copy_lib_DSTS ${index} dst)
-        add_custom_command(TARGET ${TARGET} PRE_BUILD 
+        add_custom_command(TARGET ${TARGET} PRE_BUILD
           COMMAND mkdir -p "${dst}"
           COMMAND cp -r "${src}" "${dst}"
           COMMENT "copying ${src} -> ${dst}")
@@ -67,6 +70,13 @@ copy(boost_lib
   DEPS boost
 )
 
+set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/xxhash")
+copy(xxhash_lib
+  SRCS ${XXHASH_INCLUDE_DIR} ${XXHASH_LIBRARIES}
+  DSTS ${dst_dir} ${dst_dir}/lib
+  DEPS xxhash
+)
+
 if(NOT PROTOBUF_FOUND)
     set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/protobuf")
     copy(protobuf_lib
@@ -186,7 +196,7 @@ copy(cmake_cache
   DSTS ${FLUID_INSTALL_DIR})
 
 # This command generates a complete fluid library for both train and inference
-add_custom_target(fluid_lib_dist DEPENDS ${fluid_lib_dist_dep}) 
+add_custom_target(fluid_lib_dist DEPENDS ${fluid_lib_dist_dep})
 
 # Following commands generate a inference-only fluid library
 # third_party, version.txt and CMakeCache.txt are the same position with ${FLUID_INSTALL_DIR}

paddle/fluid/API.spec

@@ -86,7 +86,7 @@ paddle.fluid.layers.reduce_prod ArgSpec(args=['input', 'dim', 'keep_dim', 'name'
 paddle.fluid.layers.sequence_first_step ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.sequence_last_step ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.sequence_slice ArgSpec(args=['input', 'offset', 'length', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.dropout ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed', 'name'], varargs=None, keywords=None, defaults=(False, None, None))
+paddle.fluid.layers.dropout ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed', 'name', 'dropout_implementation'], varargs=None, keywords=None, defaults=(False, None, None, 'downgrade_in_infer'))
 paddle.fluid.layers.split ArgSpec(args=['input', 'num_or_sections', 'dim', 'name'], varargs=None, keywords=None, defaults=(-1, None))
 paddle.fluid.layers.ctc_greedy_decoder ArgSpec(args=['input', 'blank', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.edit_distance ArgSpec(args=['input', 'label', 'normalized', 'ignored_tokens'], varargs=None, keywords=None, defaults=(True, None))
@@ -107,7 +107,7 @@ paddle.fluid.layers.softmax_with_cross_entropy ArgSpec(args=['logits', 'label',
 paddle.fluid.layers.smooth_l1 ArgSpec(args=['x', 'y', 'inside_weight', 'outside_weight', 'sigma'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.layers.one_hot ArgSpec(args=['input', 'depth'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.autoincreased_step_counter ArgSpec(args=['counter_name', 'begin', 'step'], varargs=None, keywords=None, defaults=(None, 1, 1))
-paddle.fluid.layers.reshape ArgSpec(args=['x', 'shape', 'actual_shape', 'act', 'inplace', 'name'], varargs=None, keywords=None, defaults=(None, None, True, None))
+paddle.fluid.layers.reshape ArgSpec(args=['x', 'shape', 'actual_shape', 'act', 'inplace', 'name'], varargs=None, keywords=None, defaults=(None, None, False, None))
 paddle.fluid.layers.squeeze ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.unsqueeze ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.lod_reset ArgSpec(args=['x', 'y', 'target_lod'], varargs=None, keywords=None, defaults=(None, None))
@@ -174,7 +174,9 @@ paddle.fluid.layers.mean ArgSpec(args=['x', 'name'], varargs=None, keywords=None
 paddle.fluid.layers.mul ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None))
 paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.maxout ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.layers.sequence_reverse ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.affine_channel ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None))
+paddle.fluid.layers.hash ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None))
 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))
 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))
 paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
@@ -353,6 +355,8 @@ paddle.fluid.optimizer.ModelAverage.__init__ ArgSpec(args=['self', 'average_wind
 paddle.fluid.optimizer.ModelAverage.apply ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
 paddle.fluid.optimizer.ModelAverage.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.optimizer.ModelAverage.restore ArgSpec(args=['self', 'executor'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.optimizer.LarsMomentumOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'momentum', 'lars_coeff', 'lars_weight_decay', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.0005, None, None))
+paddle.fluid.optimizer.LarsMomentumOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.backward.append_backward ArgSpec(args=['loss', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.regularizer.L1DecayRegularizer.__init__ ArgSpec(args=['self', 'regularization_coeff'], varargs=None, keywords=None, defaults=(0.0,))
 paddle.fluid.regularizer.L2DecayRegularizer.__init__ ArgSpec(args=['self', 'regularization_coeff'], varargs=None, keywords=None, defaults=(0.0,))

paddle/fluid/framework/details/CMakeLists.txt

@@ -16,12 +16,14 @@ if(WITH_GPU)
             dynload_cuda variable_visitor)
     nv_library(reduce_op_handle SRCS reduce_op_handle.cc DEPS op_handle_base variable_visitor scope ddim dynload_cuda)
     nv_library(broadcast_op_handle SRCS broadcast_op_handle.cc DEPS op_handle_base scope ddim memory variable_visitor dynload_cuda)
+    nv_library(fused_broadcast_op_handle SRCS fused_broadcast_op_handle.cc DEPS broadcast_op_handle)
 
 else()
     cc_library(all_reduce_op_handle SRCS all_reduce_op_handle.cc DEPS op_handle_base scope lod_tensor ddim memory
              variable_visitor)
     cc_library(reduce_op_handle SRCS reduce_op_handle.cc DEPS op_handle_base variable_visitor scope ddim)
     cc_library(broadcast_op_handle SRCS broadcast_op_handle.cc DEPS op_handle_base scope ddim memory variable_visitor)
+    cc_library(fused_broadcast_op_handle SRCS fused_broadcast_op_handle.cc DEPS broadcast_op_handle)
 endif()
 
 cc_library(data_balance_op_handle SRCS data_balance_op_handle.cc DEPS op_handle_base scope lod_tensor)
@@ -34,7 +36,7 @@ if(WITH_GPU)
 endif()
 
 cc_library(multi_devices_graph_pass SRCS multi_devices_graph_pass.cc DEPS multi_devices_helper computation_op_handle
-        scale_loss_grad_op_handle rpc_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle)
+        scale_loss_grad_op_handle rpc_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle fused_broadcast_op_handle)
 
 if(WITH_GPU)
   cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS graph framework_proto reference_count_pass)
@@ -58,4 +60,4 @@ cc_library(fast_threaded_ssa_graph_executor SRCS fast_threaded_ssa_graph_executo
 cc_library(build_strategy SRCS build_strategy.cc DEPS
         graph_viz_pass multi_devices_graph_pass
         multi_devices_graph_print_pass multi_devices_graph_check_pass
-        fuse_elewise_add_act_pass)
+        fuse_elewise_add_act_pass multi_batch_merge_pass)

paddle/fluid/framework/details/broadcast_op_handle.cc

@@ -48,16 +48,23 @@ void BroadcastOpHandle::RunImpl() {
     var_scopes.emplace_back(s->FindVar(kLocalExecScopeName)->Get<Scope *>());
   }
 
+  BroadcastOneVar(*in_var_handle, out_var_handles, var_scopes);
+}
+
+void BroadcastOpHandle::BroadcastOneVar(
+    const VarHandle &in_var_handle,
+    const std::vector<VarHandle *> &out_var_handles,
+    const std::vector<const Scope *> &var_scopes) {
   auto *in_var =
-      var_scopes.at(in_var_handle->scope_idx_)->FindVar(in_var_handle->name_);
+      var_scopes.at(in_var_handle.scope_idx_)->FindVar(in_var_handle.name_);
   PADDLE_ENFORCE_NOT_NULL(in_var);
   Tensor &in_tensor = VariableVisitor::GetMutableTensor(in_var);
 
-  InitOutputValue(*in_var_handle, out_var_handles);
+  InitOutputValue(in_var_handle, out_var_handles);
 
   if (platform::is_cpu_place(in_tensor.place())) {
     for (auto *out_var_handle : out_var_handles) {
-      if (out_var_handle->IsTheSameVar(*in_var_handle)) {
+      if (out_var_handle->IsTheSameVar(in_var_handle)) {
         continue;
       }
       auto &out_p = out_var_handle->place_;
@@ -114,12 +121,12 @@ void BroadcastOpHandle::RunImpl() {
         }
       }
 
-      if (!out_handle->IsTheSameVar(*in_var_handle)) {
-        auto out_var = var_scopes.at(in_var_handle->scope_idx_)
+      if (!out_handle->IsTheSameVar(in_var_handle)) {
+        auto out_var = var_scopes.at(in_var_handle.scope_idx_)
                            ->FindVar(out_var_handles[0]->name_);
         paddle::framework::TensorCopy(
-            in_tensor, in_var_handle->place_,
-            *(dev_ctxes_.at(in_var_handle->place_)),
+            in_tensor, in_var_handle.place_,
+            *(dev_ctxes_.at(in_var_handle.place_)),
             &VariableVisitor::GetMutableTensor(out_var));
       }
     });

paddle/fluid/framework/details/broadcast_op_handle.h

@@ -61,7 +61,10 @@ struct BroadcastOpHandle : public OpHandleBase {
  protected:
   void RunImpl() override;
 
- private:
+  void BroadcastOneVar(const VarHandle &in_var_handle,
+                       const std::vector<VarHandle *> &out_var_handles,
+                       const std::vector<const Scope *> &var_scopes);
+
   std::vector<Scope *> local_scopes_;
   std::vector<platform::Place> places_;
 #ifdef PADDLE_WITH_CUDA

paddle/fluid/framework/details/build_strategy.cc

@@ -121,6 +121,7 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
 
 USE_PASS(fuse_elewise_add_act_pass);
 USE_PASS(graph_viz_pass);
+USE_PASS(multi_batch_merge_pass);
 USE_PASS(multi_devices_pass);
 USE_PASS(multi_devices_check_pass);
 USE_PASS(multi_devices_print_pass);

paddle/fluid/framework/details/build_strategy.h

@@ -69,6 +69,8 @@ struct BuildStrategy {
 
   bool enable_data_balance_{false};
 
+  bool fuse_broadcast_op_{false};
+
   // User normally doesn't need to call this API.
   // The PassBuilder allows for more customized insert, remove of passes
   // from python side.

paddle/fluid/framework/details/fused_broadcast_op_handle.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/framework/details/fused_broadcast_op_handle.h"
+#include "paddle/fluid/framework/details/container_cast.h"
+#include "paddle/fluid/framework/details/variable_visitor.h"
+#include "paddle/fluid/platform/profiler.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+void FusedBroadcastOpHandle::RunImpl() {
+  platform::RecordEvent record_event(Name(), dev_ctxes_.begin()->second);
+
+  if (places_.size() == 1UL) return;
+
+  auto in_var_handles = DynamicCast<VarHandle>(inputs_);
+  auto out_var_handles = DynamicCast<VarHandle>(outputs_);
+
+  WaitInputVarGenerated();
+
+  std::vector<const Scope *> var_scopes;
+  for (auto *s : local_scopes_) {
+    var_scopes.emplace_back(s->FindVar(kLocalExecScopeName)->Get<Scope *>());
+  }
+
+  size_t place_num = places_.size();
+  PADDLE_ENFORCE_EQ(in_var_handles.size() * place_num, out_var_handles.size());
+
+  for (size_t i = 0; i < in_var_handles.size(); ++i) {
+    BroadcastOneVar(
+        *in_var_handles[i],
+        std::vector<VarHandle *>(out_var_handles.begin() + i * place_num,
+                                 out_var_handles.begin() + (i + 1) * place_num),
+        var_scopes);
+  }
+}
+
+std::string FusedBroadcastOpHandle::Name() const { return "fused_broadcast"; }
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/fused_broadcast_op_handle.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 <map>
+#include <string>
+#include <vector>
+
+#include "paddle/fluid/framework/details/broadcast_op_handle.h"
+#include "paddle/fluid/framework/details/multi_devices_helper.h"
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/framework/selected_rows.h"
+#include "paddle/fluid/platform/device_context.h"
+
+#ifdef PADDLE_WITH_CUDA
+#include "paddle/fluid/platform/nccl_helper.h"
+#endif
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+struct FusedBroadcastOpHandle : public BroadcastOpHandle {
+ public:
+#ifdef PADDLE_WITH_CUDA
+  FusedBroadcastOpHandle(ir::Node *node,
+                         const std::vector<Scope *> local_scopes,
+                         const std::vector<platform::Place> &places,
+                         const platform::NCCLContextMap *nccl_ctx)
+      : BroadcastOpHandle(node, local_scopes, places, nccl_ctx) {}
+#else
+  FusedBroadcastOpHandle(ir::Node* node, const std::vector<Scope*> local_scopes,
+                         const std::vector<platform::Place>& places)
+      : BroadcastOpHandle(node, local_scopes, places) {}
+#endif
+  std::string Name() const override;
+
+ protected:
+  void RunImpl() override;
+};
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/multi_devices_graph_pass.cc

@@ -21,6 +21,7 @@
 #include "paddle/fluid/framework/details/broadcast_op_handle.h"
 #include "paddle/fluid/framework/details/computation_op_handle.h"
 #include "paddle/fluid/framework/details/data_balance_op_handle.h"
+#include "paddle/fluid/framework/details/fused_broadcast_op_handle.h"
 #include "paddle/fluid/framework/details/multi_devices_graph_pass.h"
 #include "paddle/fluid/framework/details/reduce_op_handle.h"
 #include "paddle/fluid/framework/details/rpc_op_handle.h"
@@ -252,9 +253,9 @@ std::vector<ir::Node *> SortOpsAndDelayOptimizeOp(const ir::Graph &graph) {
   std::vector<ir::Node *> sorted_ret;
   for (size_t i = 0; i < ret.size(); ++i) {
     if (i < last_backward) {
-      if (boost::get<int>(ret[i]->Op()->GetAttr(
-              OpProtoAndCheckerMaker::OpRoleAttrName())) ==
-          static_cast<int>(OpRole::kOptimize)) {
+      if (static_cast<bool>(boost::get<int>(ret[i]->Op()->GetAttr(
+                                OpProtoAndCheckerMaker::OpRoleAttrName())) &
+                            static_cast<int>(OpRole::kOptimize))) {
         optimize_ops.push_back(ret[i]);
       } else {
         sorted_ret.push_back(ret[i]);
@@ -347,7 +348,7 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
           BuildStrategy::GradientScaleStrategy::kCustomized) {
         // TODO(paddle-dev): Why is there no input for this op_handle?
         auto loss_grad_name = node->Op()->OutputArgumentNames()[0];
-        CreateScaleLossGradOp(&result, loss_grad_name);
+        CreateScaleLossGradOp(&result, loss_grad_name, node->outputs[0]);
       }
       // This assumes the backward generating code will ensure IsScaleLossOp
       // is true only for the op that scale the final scalar loss.
@@ -436,10 +437,14 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
   if ((use_gpu &&
        strategy_.reduce_ == BuildStrategy::ReduceStrategy::kReduce) ||
       is_dist_train) {
-    for (size_t dev_id = 0; dev_id < bcast_var_name_set.size(); ++dev_id) {
-      auto &to_bcast_set = bcast_var_name_set[dev_id];
-      for (auto &bcast_name : to_bcast_set) {
-        CreateBroadcastOp(&result, bcast_name, dev_id);
+    if (strategy_.fuse_broadcast_op_) {
+      CreateFusedBroadcastOp(&result, bcast_var_name_set);
+    } else {
+      for (size_t dev_id = 0; dev_id < bcast_var_name_set.size(); ++dev_id) {
+        auto &to_bcast_set = bcast_var_name_set[dev_id];
+        for (auto &bcast_name : to_bcast_set) {
+          CreateBroadcastOp(&result, bcast_name, dev_id);
+        }
       }
     }
   }
@@ -508,6 +513,44 @@ void MultiDevSSAGraphBuilder::CreateBroadcastOp(ir::Graph *result,
   }
 }
 
+void MultiDevSSAGraphBuilder::CreateFusedBroadcastOp(
+    ir::Graph *result,
+    const std::vector<std::unordered_set<std::string>> &bcast_varnames) const {
+#ifdef PADDLE_WITH_CUDA
+  auto *op_handle = new FusedBroadcastOpHandle(
+      result->CreateEmptyNode("fused_broadcast", ir::Node::Type::kOperation),
+      local_scopes_, places_, nccl_ctxs_);
+#else
+  auto *op_handle = new FusedBroadcastOpHandle(
+      result->CreateEmptyNode("fused_broadcast", ir::Node::Type::kOperation),
+      local_scopes_, places_);
+#endif
+  result->Get<GraphOps>(kGraphOps).emplace_back(op_handle);
+
+  for (size_t i = 0; i < places_.size(); ++i) {
+    auto &p = places_[i];
+    SetCommunicationContext(op_handle, p);
+  }
+
+  for (size_t dev_id = 0; dev_id < bcast_varnames.size(); ++dev_id) {
+    for (auto &p_name : bcast_varnames[dev_id]) {
+      auto *in =
+          result->Get<GraphVars>(kGraphVars).at(dev_id).at(p_name).back().get();
+      op_handle->AddInput(in);
+      for (size_t out_dev_id = 0; out_dev_id < places_.size(); ++out_dev_id) {
+        auto &p = places_[out_dev_id];
+        auto &vars =
+            result->Get<GraphVars>(kGraphVars).at(out_dev_id).at(p_name);
+        auto *out_var = new VarHandle(
+            result->CreateEmptyNode(p_name, ir::Node::Type::kVariable),
+            vars.size(), out_dev_id, p_name, p);
+        vars.emplace_back(out_var);
+        op_handle->AddOutput(out_var);
+      }
+    }
+  }
+}
+
 void MultiDevSSAGraphBuilder::CreateComputationalOp(ir::Graph *result,
                                                     ir::Node *node,
                                                     int dev_id) const {
@@ -602,7 +645,8 @@ int MultiDevSSAGraphBuilder::GetVarDeviceID(const ir::Graph &graph,
 }
 
 void MultiDevSSAGraphBuilder::CreateScaleLossGradOp(
-    ir::Graph *result, const std::string &loss_grad_name) const {
+    ir::Graph *result, const std::string &loss_grad_name,
+    ir::Node *out_var_node) const {
   for (size_t i = 0; i < places_.size(); ++i) {
     // Insert ScaleCost OpHandle
     auto *dev_ctx = platform::DeviceContextPool::Instance().Get(places_[i]);
@@ -617,10 +661,8 @@ void MultiDevSSAGraphBuilder::CreateScaleLossGradOp(
     // loss->pending_ops_.emplace_back(op_handle);
     // op_handle->inputs_.emplace_back(loss);
 
-    CreateOpOutput(
-        result, op_handle,
-        result->CreateEmptyNode(loss_grad_name, ir::Node::Type::kVariable),
-        places_[i], i);
+    CreateOpOutput(result, op_handle,
+                   result->CreateVarNode(out_var_node->Var()), places_[i], i);
   }
 }
 

paddle/fluid/framework/details/multi_devices_graph_pass.h

@@ -61,7 +61,8 @@ class MultiDevSSAGraphBuilder : public ir::Pass {
                               size_t num_places) const;
 
   void CreateScaleLossGradOp(ir::Graph *result,
-                             const std::string &loss_grad_name) const;
+                             const std::string &loss_grad_name,
+                             ir::Node *out_var_node) const;
 
   VarHandle *CreateReduceOp(ir::Graph *result, const std::string &og,
                             int dst_dev_id) const;
@@ -78,6 +79,10 @@ class MultiDevSSAGraphBuilder : public ir::Pass {
   void CreateBroadcastOp(ir::Graph *result, const std::string &p_name,
                          size_t src_dev_id) const;
 
+  void CreateFusedBroadcastOp(
+      ir::Graph *result,
+      const std::vector<std::unordered_set<std::string>> &bcast_varnames) const;
+
   bool IsSparseGradient(const std::string &og) const;
 
   size_t GetAppropriateDeviceID(

paddle/fluid/framework/framework.proto

@@ -80,7 +80,6 @@ message OpProto {
     optional bool duplicable = 3 [ default = false ];
     optional bool intermediate = 4 [ default = false ];
     optional bool dispensable = 5 [ default = false ];
-    optional string reuse = 6;
   }
 
   // AttrProto describes the C++ type Attribute.

paddle/fluid/framework/ir/CMakeLists.txt

@@ -36,18 +36,17 @@ pass_library(fc_lstm_fuse_pass inference)
 pass_library(embedding_fc_lstm_fuse_pass inference)
 pass_library(fc_gru_fuse_pass inference)
 pass_library(seq_concat_fc_fuse_pass inference)
+pass_library(multi_batch_merge_pass base)
 pass_library(conv_bn_fuse_pass inference)
 pass_library(seqconv_eltadd_relu_fuse_pass inference)
 if(WITH_MKLDNN)
     pass_library(mkldnn_placement_pass base)
     pass_library(conv_bias_mkldnn_fuse_pass inference)
     pass_library(conv_relu_mkldnn_fuse_pass inference)
+    pass_library(conv_elementwise_add_mkldnn_fuse_pass inference)
 endif()
 
 cc_library(fuse_elewise_add_act_pass SRCS fuse_elewise_add_act_pass.cc DEPS pass graph_pattern_detector )
-if(WITH_MKLDNN)
-  pass_library(conv_elementwise_add_mkldnn_fuse_pass inference)
-endif()
 
 set(GLOB_PASS_LIB ${PASS_LIBRARY} CACHE INTERNAL "Global PASS library")
 

paddle/fluid/framework/ir/graph.cc

@@ -24,79 +24,23 @@ namespace paddle {
 namespace framework {
 namespace ir {
 
-std::vector<std::string> FindDistTrainSendVars(
-    const std::vector<ir::Node *> &nodes) {
-  std::vector<std::string> send_vars;
-  // since parameters are all in block 0,
-  // it's enough to only scan send ops in block 0
-  for (auto &node : nodes) {
-    auto op_vars = node->Op()->InputArgumentNames();
-    send_vars.reserve(send_vars.size() +
-                      std::distance(op_vars.begin(), op_vars.end()));
-    send_vars.insert(send_vars.end(), op_vars.begin(), op_vars.end());
-  }
-  return send_vars;
-}
-
-std::vector<std::string> FindDistTrainRecvVars(
-    const std::vector<ir::Node *> &nodes) {
-  std::vector<std::string> recv_vars;
-  for (auto &node : nodes) {
-    auto op_vars = node->Op()->OutputArgumentNames();
-    recv_vars.reserve(recv_vars.size() +
-                      std::distance(op_vars.begin(), op_vars.end()));
-    recv_vars.insert(recv_vars.end(), op_vars.begin(), op_vars.end());
-  }
-  return recv_vars;
-}
-
-bool IsDistTrainOp(ir::Node *node, const std::vector<std::string> &send_vars,
-                   const std::vector<std::string> &recv_vars) {
-  if (send_vars.size() == 0 || recv_vars.size() == 0) {
-    return false;
-  }
-
-  /**
-   * Check any of opvars contains `.block` and in sendvars
-   */
-  auto checker = [](const std::vector<std::string> &opvars,
-                    const std::vector<std::string> &rpc_vars) -> bool {
-    for (auto &var : opvars) {
-      // a variable name with the suffix `.block` means it's a splited
-      // variable by (DistributeTranspiler)
-      // [python/paddle/fluid/transpiler/distribute_transpiler.py]
-      if (var.find(".block") != std::string::npos &&
-          std::find(rpc_vars.begin(), rpc_vars.end(), var) != rpc_vars.end()) {
-        return true;
-      }
-    }
-    return false;
-  };
-
-  std::vector<std::string> input_var_names;
-  std::vector<std::string> output_var_names;
-  for (ir::Node *input : node->inputs) {
-    input_var_names.push_back(input->Name());
-  }
-  for (ir::Node *output : node->outputs) {
-    output_var_names.push_back(output->Name());
-  }
-
-  return checker(output_var_names, send_vars) ||
-         checker(input_var_names, recv_vars);
-}
-
 Graph::Graph(const ProgramDesc &program) : program_(program) {
   // Make the nodes id start from 0.
   Node::ResetId();
+  auto var_nodes = InitFromProgram(program_);
+  ResolveHazard(var_nodes);
+}
 
+std::map<std::string, std::vector<ir::Node *>> Graph::InitFromProgram(
+    const ProgramDesc &program) {
   VLOG(3) << "block in program:" << program_.Size();
   std::unordered_map<std::string, VarDesc *> all_vars;
+  // var nodes for each var name, will have multiple versions in SSA
+  std::map<std::string, std::vector<ir::Node *>> var_nodes;
   for (auto *var : program.Block(0).AllVars()) {
     all_vars.emplace(var->Name(), var);
   }
 
-  std::map<std::string, std::vector<ir::Node *>> var_nodes;
   for (auto *op : program.Block(0).AllOps()) {
     ir::Node *node = CreateOpNode(op);
     // For input args, reuse the same var name if it was created before.
@@ -134,7 +78,11 @@ Graph::Graph(const ProgramDesc &program) : program_(program) {
       var->inputs.push_back(node);
     }
   }
+  return std::move(var_nodes);
+}
 
+void Graph::ResolveHazard(
+    const std::map<std::string, std::vector<ir::Node *>> &var_nodes) {
   /**
    * We should handle write after read(WAR) and write after write(WAW) here.
    * Because some of the operators of the program can be executed parallelly.
@@ -153,6 +101,7 @@ Graph::Graph(const ProgramDesc &program) : program_(program) {
     auto it_old = versions.rbegin();
     ++it_old;
     for (; it_old != versions.rend(); it_new = it_old, ++it_old) {
+      VLOG(3) << "deal with var: " << (*it_new)->Name();
       ir::Node *write_op =
           (*it_new)->inputs.empty() ? nullptr : (*it_new)->inputs[0];
       const auto &read_ops = (*it_old)->outputs;

paddle/fluid/framework/ir/graph.h

@@ -160,6 +160,12 @@ class Graph {
     return nullptr;
   }
 
+  std::map<std::string, std::vector<ir::Node *>> InitFromProgram(
+      const ProgramDesc &program);
+
+  void ResolveHazard(
+      const std::map<std::string, std::vector<ir::Node *>> &var_nodes);
+
  private:
   // This method takes ownership of `node`.
   ir::Node *AddNode(ir::Node *node) {

paddle/fluid/framework/ir/graph_helper.cc

@@ -120,19 +120,25 @@ size_t GraphNum(const Graph &graph) {
   std::deque<ir::Node *> q_nodes;
   std::vector<std::unordered_set<ir::Node *>> graph_nodes;
   std::unordered_set<ir::Node *> g_nodes;
+  // q_set used to record records in the queue.
+  std::unordered_set<ir::Node *> q_set;
   size_t graph_count = 0;
 
-  auto traverse_nodes = [&visited_nodes,
-                         &q_nodes](const std::vector<ir::Node *> &nodes) {
-    std::copy_if(
-        nodes.begin(), nodes.end(), std::back_inserter(q_nodes),
-        [&visited_nodes](Node *node) { return !visited_nodes.count(node); });
+  auto traverse_nodes = [&visited_nodes, &q_nodes,
+                         &q_set](const std::vector<ir::Node *> &nodes) {
+    for (auto n : nodes) {
+      if (visited_nodes.count(n) == 0 && q_set.count(n) == 0) {
+        q_nodes.push_back(n);
+        q_set.insert(n);
+      }
+    }
   };
 
   while (visited_nodes.size() != nodes.size()) {
     if (!q_nodes.empty()) {
       auto cur_node = q_nodes.front();
       q_nodes.pop_front();
+      q_set.erase(cur_node);
       visited_nodes.insert(cur_node);
       g_nodes.insert(cur_node);
       traverse_nodes(cur_node->inputs);
@@ -146,6 +152,7 @@ size_t GraphNum(const Graph &graph) {
       for (auto &n : nodes) {
         if (visited_nodes.count(n) == 0) {
           q_nodes.push_back(n);
+          q_set.insert(n);
           break;
         }
       }

paddle/fluid/framework/ir/graph_helper_test.cc

@@ -200,15 +200,15 @@ TEST(GraphHelperTest, GraphNum) {
 
   Graph g(prog);
   BuildZeroGraph(&g);
-  ASSERT_EQ(GraphNum(g), 0);
+  ASSERT_EQ(GraphNum(g), 0UL);
 
   Graph g2(prog);
   BuildOneGraph(&g2);
-  ASSERT_EQ(GraphNum(g2), 1);
+  ASSERT_EQ(GraphNum(g2), 1UL);
 
   Graph g3(prog);
   BuildTwoGraphs(&g3);
-  ASSERT_EQ(GraphNum(g3), 2);
+  ASSERT_EQ(GraphNum(g3), 2UL);
 }
 
 }  // namespace ir

paddle/fluid/framework/ir/graph_test.cc

@@ -124,7 +124,7 @@ TEST(GraphTest, Basic) {
       ASSERT_EQ(n->outputs.size(), 0UL);
     }
   }
-  ASSERT_EQ(nodes.size(), 5);
+  ASSERT_EQ(nodes.size(), 5UL);
 }
 
 TEST(GraphTest, WriteAfterRead) {

paddle/fluid/framework/ir/multi_batch_merge_pass.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/framework/ir/multi_batch_merge_pass.h"
+
+#include <map>
+#include <string>
+#include <vector>
+
+#include "paddle/fluid/framework/ir/graph_helper.h"
+#include "paddle/fluid/framework/op_proto_maker.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+static const char kNumRepeats[] = "num_repeats";
+typedef std::unordered_map<std::string, std::vector<ir::Node*>> SSAVarList;
+
+ir::Node* SameNameVar(std::unordered_set<ir::Node*> all, ir::Node* target) {
+  for (auto n : all) {
+    if (target->IsVar() && target->Name() == n->Name()) {
+      return n;
+    }
+  }
+  return nullptr;
+}
+
+VarDesc CopyVarDesc(VarDesc* var_desc) {
+  VarDesc repeated_var(var_desc->Name());
+  // copy other variable attributes
+  if (var_desc->GetType() != proto::VarType::READER) {
+    repeated_var.SetType(var_desc->GetType());
+    repeated_var.SetShape(var_desc->GetShape());
+    repeated_var.SetDataType(var_desc->GetDataType());
+    repeated_var.SetLoDLevel(var_desc->GetLoDLevel());
+    repeated_var.SetPersistable(var_desc->Persistable());
+  } else {
+    // TODO(typhoonzero): copy reader var
+  }
+  return repeated_var;
+}
+
+VarDesc UpdateGradVarDesc(
+    VarDesc* var_desc, int repeat,
+    const std::unordered_set<std::string>& grad_names,
+    const std::unordered_set<std::string>& bn_vars_need_rename) {
+  if (grad_names.find(var_desc->Name()) != grad_names.end() ||
+      bn_vars_need_rename.find(var_desc->Name()) != bn_vars_need_rename.end()) {
+    std::string new_gname =
+        string::Sprintf("%s.repeat.%d", var_desc->Name(), repeat);
+    VarDesc repeated_var = CopyVarDesc(var_desc);
+    repeated_var.SetName(new_gname);
+    VLOG(3) << "update " << var_desc->Name() << " to repeat " << repeat;
+    return repeated_var;
+  }
+  return *var_desc;
+}
+
+std::unique_ptr<Graph> BatchMergePass::ApplyImpl(
+    std::unique_ptr<Graph> graph) const {
+  int num_repeats = Get<const int>(kNumRepeats);
+  std::vector<Node*> forward_backward_ops;
+  std::vector<Node*> optimize_ops;
+  std::vector<Node*> lr_ops;  // ops other than forward/backward/optimize
+  std::unordered_set<std::string> grad_names;
+
+  std::vector<ir::Node*> nodes = TopologySortOperations(*graph);
+  auto origin_nodes = graph->ReleaseNodes();
+  VLOG(3) << "origin nodes count: " << origin_nodes.size();
+  ir::Graph& result = *graph;
+
+  // 1. record op nodes of different roles
+  for (auto node : nodes) {
+    if (node->IsVar()) continue;
+    int op_role = boost::get<int>(node->Op()->GetAttr(
+        framework::OpProtoAndCheckerMaker::OpRoleAttrName()));
+    if ((op_role == static_cast<int>(framework::OpRole::kForward)) ||
+        (op_role & static_cast<int>(framework::OpRole::kBackward)) ||
+        (op_role & static_cast<int>(framework::OpRole::kLoss))) {
+      forward_backward_ops.push_back(node);
+    } else if ((op_role & static_cast<int>(framework::OpRole::kOptimize)) ||
+               (op_role & static_cast<int>(framework::OpRole::kDist)) ||
+               (op_role & static_cast<int>(framework::OpRole::kRPC))) {
+      optimize_ops.push_back(node);
+      auto op_role_var = node->Op()->GetNullableAttr(
+          OpProtoAndCheckerMaker::OpRoleVarAttrName());
+      auto op_role_vars = boost::get<std::vector<std::string>>(op_role_var);
+      for (size_t i = 0; i < op_role_vars.size(); i += 2) {
+        grad_names.insert(op_role_vars[i + 1]);
+      }
+    } else if (op_role & static_cast<int>(framework::OpRole::kLRSched)) {
+      lr_ops.push_back(node);
+    } else {  // NOLINT
+      PADDLE_THROW("Invalid op_role: %d", static_cast<int>(op_role));
+    }
+  }
+
+  // 2. copy forward backward
+  ir::Node* prev_repeat_last_op_node = nullptr;
+  // record origin_grad -> repeated grad list map.
+  std::map<ir::Node*, std::vector<ir::Node*>> grad_repeated_map;
+  std::map<std::string, std::vector<ir::Node*>> created;
+  std::unordered_set<std::string> bn_vars_need_rename;
+  for (int i = 0; i < num_repeats; ++i) {
+    std::unordered_set<ir::Node*> copied;
+    for (size_t node_idx = 0; node_idx < forward_backward_ops.size();
+         ++node_idx) {
+      auto node = forward_backward_ops[node_idx];
+      OpDesc repeated_op(*(node->Op()), node->Op()->Block());
+      // 3. rename grad outputs to current repeat.
+      for (auto outname : repeated_op.OutputArgumentNames()) {
+        if (grad_names.find(outname) != grad_names.end()) {
+          std::string new_gname = string::Sprintf("%s.repeat.%d", outname, i);
+          repeated_op.RenameOutput(outname, new_gname);
+        }
+      }
+      // 3.5 let batch_norm ops use independent vars, note batch_norm_grad do
+      // not need this update
+      if (node->Name() == "batch_norm") {
+        // NOTE: assume bn op created by layers use save var as output mean and
+        // variance
+        std::string new_mean_name =
+            string::Sprintf("%s.repeat.%d", repeated_op.Input("Mean")[0], i);
+        std::string new_var_name = string::Sprintf(
+            "%s.repeat.%d", repeated_op.Input("Variance")[0], i);
+        bn_vars_need_rename.insert(repeated_op.Input("Mean")[0]);
+        bn_vars_need_rename.insert(repeated_op.Input("Variance")[0]);
+        VLOG(3) << "renaming " << repeated_op.Input("Mean")[0] << " to "
+                << new_mean_name;
+        repeated_op.RenameInput(repeated_op.Input("Mean")[0], new_mean_name);
+        repeated_op.RenameInput(repeated_op.Input("Variance")[0], new_var_name);
+        repeated_op.RenameOutput(repeated_op.Output("MeanOut")[0],
+                                 new_mean_name);
+        repeated_op.RenameOutput(repeated_op.Output("VarianceOut")[0],
+                                 new_var_name);
+      }
+
+      // 3.9 do copy
+      auto repeated_node = result.CreateOpNode(&repeated_op);
+      copied.insert(node);
+
+      // 4. add deps between repeats
+      if (node_idx == forward_backward_ops.size() - 1) {
+        prev_repeat_last_op_node = repeated_node;
+      }
+      if (node_idx == 0 && prev_repeat_last_op_node) {
+        auto* depvar = result.CreateControlDepVar();
+        prev_repeat_last_op_node->outputs.push_back(depvar);
+        depvar->inputs.push_back(prev_repeat_last_op_node);
+        repeated_node->inputs.push_back(depvar);
+        depvar->outputs.push_back(repeated_node);
+      }
+
+      for (auto in_node : node->inputs) {
+        if (in_node->IsCtrlVar()) {
+          continue;
+        }
+        ir::Node* var = nullptr;
+        auto updated_var = UpdateGradVarDesc(in_node->Var(), i, grad_names,
+                                             bn_vars_need_rename);
+        // should be initialized by startup, how to initilize tensor in the
+        // scope?
+        if (node->Name() == "batch_norm" &&
+            bn_vars_need_rename.find(in_node->Name()) !=
+                bn_vars_need_rename.end()) {
+          // Create bn mean/variance for each repeat
+          var = result.CreateVarNode(&updated_var);
+          created[updated_var.Name()].push_back(var);
+          copied.insert(in_node);
+          repeated_node->inputs.push_back(var);
+          var->outputs.push_back(repeated_node);
+          continue;
+        }
+
+        // for other ops
+        if (in_node->inputs.empty() && i > 0) {
+          // do not copy head vars (inputs, params) in repeats > 0
+          var = created.at(in_node->Name()).back();
+        } else {
+          if (copied.find(in_node) == copied.end()) {
+            var = result.CreateVarNode(&updated_var);
+            if (grad_names.find(in_node->Var()->Name()) != grad_names.end()) {
+              grad_repeated_map[in_node].push_back(var);
+            }
+            copied.insert(in_node);
+            created[updated_var.Name()].push_back(var);
+          } else {
+            var = created.at(updated_var.Name()).back();
+          }
+        }
+        repeated_node->inputs.push_back(var);
+        var->outputs.push_back(repeated_node);
+      }
+      for (auto out_node : node->outputs) {
+        if (out_node->IsCtrlVar()) {
+          continue;
+        }
+        ir::Node* var = nullptr;
+        auto updated_var = UpdateGradVarDesc(out_node->Var(), i, grad_names,
+                                             bn_vars_need_rename);
+        if (copied.find(out_node) == copied.end()) {
+          var = result.CreateVarNode(&updated_var);
+          if (grad_names.find(out_node->Var()->Name()) != grad_names.end()) {
+            grad_repeated_map[out_node].push_back(var);
+          }
+          copied.insert(out_node);
+          created[updated_var.Name()].push_back(var);
+        } else {
+          var = created.at(updated_var.Name()).back();
+        }
+        repeated_node->outputs.push_back(var);
+        var->inputs.push_back(repeated_node);
+      }
+    }
+  }
+
+  // 5. create GRAD merge op node
+  for (auto kv : grad_repeated_map) {
+    OpDesc sum_op;
+    sum_op.SetType("sum");
+    std::vector<std::string> repeated_grad_names;
+    for (auto r : kv.second) {
+      repeated_grad_names.push_back(r->Var()->Name());
+    }
+    sum_op.SetInput("X", repeated_grad_names);
+    sum_op.SetOutput("Out", {kv.first->Var()->Name()});
+    sum_op.SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
+                   static_cast<int>(OpRole::kBackward));
+    auto sum_op_node = result.CreateOpNode(&sum_op);
+    for (auto r : kv.second) {
+      sum_op_node->inputs.push_back(r);
+      r->outputs.push_back(sum_op_node);
+    }
+    auto sum_out_var_node = result.CreateVarNode(kv.first->Var());
+    sum_op_node->outputs.push_back(sum_out_var_node);
+    sum_out_var_node->inputs.push_back(sum_op_node);
+    created[sum_out_var_node->Name()].push_back(sum_out_var_node);
+
+    OpDesc scale_op;
+    scale_op.SetType("scale");
+    scale_op.SetInput("X", {sum_out_var_node->Var()->Name()});
+    // NOTE: inplace scale.
+    scale_op.SetOutput("Out", {sum_out_var_node->Var()->Name()});
+    scale_op.SetAttr("scale", static_cast<float>(1.0f / num_repeats));
+    scale_op.SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
+                     static_cast<int>(OpRole::kBackward));
+    auto scale_op_node = result.CreateOpNode(&scale_op);
+    scale_op_node->inputs.push_back(sum_out_var_node);
+    sum_out_var_node->outputs.push_back(scale_op_node);
+    auto scale_out_var_node = result.CreateVarNode(sum_out_var_node->Var());
+    scale_op_node->outputs.push_back(scale_out_var_node);
+    scale_out_var_node->inputs.push_back(scale_op_node);
+    created[scale_out_var_node->Name()].push_back(scale_out_var_node);
+  }
+  // 6. add optimize ops
+  {
+    auto copy_node = [&result, &created](ir::Node* node) {
+      auto op_node = result.CreateOpNode(node->Op());
+      // copy op ins/outs
+      // NOTE: for send/recv ops, the OpDesc uses ctrldepvar to describe
+      // dependencies, so create those depvars if OpDesc have in/outs.
+      for (auto in_node : node->inputs) {
+        if (in_node->IsCtrlVar() && !in_node->Var()) {
+          continue;
+        }
+        ir::Node* var = nullptr;
+        if (created.find(in_node->Name()) == created.end()) {
+          var = result.CreateVarNode(in_node->Var());
+          created[in_node->Name()].push_back(var);
+        } else {
+          var = created.at(in_node->Name()).back();
+        }
+        op_node->inputs.push_back(var);
+        var->outputs.push_back(op_node);
+      }
+      for (auto out_node : node->outputs) {
+        if (out_node->IsCtrlVar() && !out_node->Var()) {
+          continue;
+        }
+        auto var = result.CreateVarNode(out_node->Var());
+        created[out_node->Name()].push_back(var);
+        op_node->outputs.push_back(var);
+        var->inputs.push_back(op_node);
+      }
+    };
+    for (auto node : lr_ops) {
+      copy_node(node);
+    }
+    for (auto node : optimize_ops) {
+      copy_node(node);
+    }
+  }
+
+  result.ResolveHazard(created);
+  return graph;
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(multi_batch_merge_pass, paddle::framework::ir::BatchMergePass)
+    .RequirePassAttr(paddle::framework::ir::kNumRepeats);

paddle/fluid/framework/ir/multi_batch_merge_pass.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 "paddle/fluid/framework/ir/fuse_pass_base.h"
+#include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/pass.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+// BatchMergePass is used to copy forward and backward ops for several
+// times to run several batches to simulate large batch size training
+// as if we have more than 1 GPUs.
+// User can define how many batches to run, gradients will be merged
+// through those repeats, and then do optimization using merged gradients.
+// This pass is extremely useful when doing large batch-size distributed
+// sync training, we can simulate even large batch size as if we have more
+// GPUs.
+
+class BatchMergePass : public Pass {
+ public:
+  virtual ~BatchMergePass() {}
+
+ protected:
+  std::unique_ptr<Graph> ApplyImpl(std::unique_ptr<Graph> graph) const override;
+};
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/ir/node.h

@@ -44,6 +44,7 @@ class Node {
     return op_desc_.get();
   }
 
+  // Please don't use this API!
   int id() const { return id_; }
 
   bool IsOp() const { return type_ == Type::kOperation; }
@@ -92,6 +93,7 @@ class Node {
   Node() = delete;
 
   static int count_;
+  // Please don't use this API or make this public.
   static void ResetId() { count_ = 0; }
   DISABLE_COPY_AND_ASSIGN(Node);
 };

paddle/fluid/framework/lod_tensor_array.h

@@ -18,6 +18,82 @@ limitations under the License. */
 
 namespace paddle {
 namespace framework {
+
+// NOTE The vector<LoDTensor> can't be replaced with the class LoDTensorArray
+// directly, because there are many vector<LoDTensor> used accross the project,
+// and some of them are treated as LoDTensorArray.
+#if !defined(PADDLE_ON_INFERENCE)
+
 using LoDTensorArray = std::vector<LoDTensor>;
-}
+
+#else  // !PADDLE_ON_INFERENCE
+
+#pragma message "LoDTensorArray is replaced with the inference one."
+/*
+ * A LoDTensorArray which will not deallocate buffer when resized, fix the data
+ * diff in inference, and more performance friendly in the concurrency
+ * scenerios.
+ */
+class LoDTensorArray {
+ public:
+  LoDTensorArray() = default;
+
+  using iterator = std::vector<LoDTensor>::iterator;
+  using const_iterator = std::vector<LoDTensor>::const_iterator;
+
+  const_iterator begin() const { return array_.begin(); }
+  const_iterator end() const { return array_.begin() + size_; }
+  iterator begin() { return array_.begin(); }
+  iterator end() { return array_.begin() + size_; }
+
+  void push_back(const LoDTensor& x) {
+    if (size_ < array_.size()) {
+      array_[size_++] = x;
+    } else {
+      array_.push_back(x);
+      ++size_;
+    }
+  }
+  void resize(size_t size) {
+    if (array_.size() < size) {
+      array_.resize(size);
+    }
+    size_ = size;
+  }
+
+  void emplace_back() { array_.emplace_back(); }
+
+  void emplace_back(LoDTensor&& x) { array_.emplace_back(std::move(x)); }
+
+  LoDTensor& back() { return array_.back(); }
+
+  size_t space() const { return array_.size(); }
+
+  void reserve(size_t size) {
+    // Naive warning to tell user this array might be to large. The memory and
+    // buffer used by this TensorArray will not be deleted during the training
+    // and inference phase, so attention not to make it expand too long.
+    if (size > 800UL) {
+      LOG(WARNING) << "TensorArray has more than 800 items";
+    }
+    array_.reserve(size);
+  }
+
+  bool empty() const { return size_ == 0UL; }
+  void clear() { size_ = 0UL; }
+
+  LoDTensor& operator[](size_t id) { return array_[id]; }
+  const LoDTensor& operator[](size_t id) const { return array_[id]; }
+  LoDTensor& at(size_t id) { return array_.at(id); }
+  const LoDTensor& at(size_t id) const { return array_.at(id); }
+
+  size_t size() const { return size_; }
+
+ private:
+  size_t size_{0};
+  std::vector<LoDTensor> array_;
+};
+#endif  // !PADDLE_ON_INFERENCE
+
+}  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/mixed_vector.h

@@ -542,6 +542,33 @@ class CPUVector : public std::vector<T, std::allocator<T>> {
     this->reserve(this->size() + size_t(end - begin));
     this->insert(this->end(), begin, end);
   }
+
+  const T *CUDAData(platform::Place place) const {
+    PADDLE_THROW(
+        "Vector::CUDAData() method is not supported in CPU-only version");
+  }
+
+  T *CUDAMutableData(platform::Place place) {
+    PADDLE_THROW(
+        "Vector::CUDAMutableData() method is not supported in CPU-only "
+        "version");
+  }
+
+  const T *Data(platform::Place place) const {
+    PADDLE_ENFORCE(
+        platform::is_cpu_place(place),
+        "Vector::Data() method is not supported when not in CPUPlace");
+    return this->data();
+  }
+
+  T *MutableData(platform::Place place) {
+    PADDLE_ENFORCE(
+        platform::is_cpu_place(place),
+        "Vector::MutableData() method is not supported when not in CPUPlace");
+    return this->data();
+  }
+
+  const void *Handle() const { return static_cast<const void *>(this); }
 };
 
 template <typename T>

paddle/fluid/framework/naive_executor.cc

@@ -146,22 +146,5 @@ void NaiveExecutor::CleanFeedFetchOps() {
   ops_.swap(ops);
 }
 
-void NaiveExecutor::EnableMKLDNN(const ProgramDesc &program) {
-#ifdef PADDLE_WITH_MKLDNN
-  VLOG(3) << "use_mkldnn=True";
-  for (size_t block_id = 0; block_id < program.Size(); ++block_id) {
-    auto *block = const_cast<ProgramDesc &>(program).MutableBlock(block_id);
-    for (auto *op : block->AllOps()) {
-      if (op->HasAttr("use_mkldnn")) {
-        op->SetAttr("use_mkldnn", true);
-      }
-    }
-  }
-#else
-  LOG(WARNING)
-      << "'MKLDNN' is not supported, Please re-compile with WITH_MKLDNN option";
-#endif
-}
-
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/naive_executor.h

@@ -48,8 +48,6 @@ class NaiveExecutor {
 
   void CleanFeedFetchOps();
 
-  void EnableMKLDNN(const ProgramDesc& program);
-
  protected:
   void CreateVariables(const ProgramDesc& desc, Scope* scope, int block_id);
 

paddle/fluid/framework/op_desc.h

@@ -121,10 +121,6 @@ class OpDesc {
 
   BlockDesc *Block() { return this->block_; }
 
-  const BlockDesc &BlockRef() const { return *this->block_; }
-
-  void SetBlock(BlockDesc *block) { this->block_ = block; }
-
  private:
   template <typename MapType>
   static std::vector<typename MapType::key_type> MapKeys(const MapType &map) {

paddle/fluid/framework/op_proto_maker.cc

@@ -21,7 +21,6 @@ namespace framework {
 void OpProtoAndCheckerMaker::Validate() {
   validated_ = true;
   CheckNoDuplicatedInOutAttrs();
-  CheckReuseVars();
 }
 
 OpProtoAndCheckerMaker::VariableBuilder OpProtoAndCheckerMaker::AddInput(
@@ -40,40 +39,6 @@ OpProtoAndCheckerMaker::VariableBuilder OpProtoAndCheckerMaker::AddOutput(
   return OpProtoAndCheckerMaker::VariableBuilder{output};
 }
 
-void OpProtoAndCheckerMaker::Reuse(const std::string& name,
-                                   const std::string& reused_name) {
-  bool found = false;
-  proto::OpProto::Var* var;
-
-  for (auto& var : proto_->inputs()) {
-    if (var.name() == reused_name) {
-      found = true;
-      break;
-    }
-  }
-  PADDLE_ENFORCE(found == true,
-                 "Input/Output name: %s reused_name: %s, one of them is not "
-                 "exists or not matched.",
-                 name, reused_name);
-
-  found = false;
-  for (int i = 0; i < proto_->outputs().size(); ++i) {
-    var = proto_->mutable_outputs()->Mutable(i);
-    if (var->name() == name) {
-      PADDLE_ENFORCE(!var->has_reuse(),
-                     "Output(%s) has been set reused var of %s", name,
-                     var->reuse());
-      found = true;
-      var->set_reuse(reused_name);
-      break;
-    }
-  }
-  PADDLE_ENFORCE(found == true,
-                 "Input/Output name: %s reused_name: %s, one of them is not "
-                 "exists or not matched.",
-                 name, reused_name);
-}
-
 void OpProtoAndCheckerMaker::CheckNoDuplicatedInOutAttrs() {
   std::unordered_set<std::string> names;
   auto checker = [&](const std::string& name) {
@@ -91,24 +56,6 @@ void OpProtoAndCheckerMaker::CheckNoDuplicatedInOutAttrs() {
   }
 }
 
-void OpProtoAndCheckerMaker::CheckReuseVars() {
-  std::unordered_set<std::string> names;
-  for (auto& input : proto_->inputs()) {
-    names.insert(input.name());
-  }
-  auto checker = [&](const std::string& name, const std::string& reused) {
-    PADDLE_ENFORCE(
-        names.count(reused),
-        "Output [%s] reuse Input [%s], but the input is not registered.", name,
-        reused);
-  };
-  for (auto& output : proto_->outputs()) {
-    if (output.has_reuse()) {
-      checker(output.name(), output.reuse());
-    }
-  }
-}
-
 void OpProtoAndCheckerMaker::operator()(proto::OpProto* proto,
                                         OpAttrChecker* attr_checker) {
   proto_ = proto;
@@ -124,6 +71,8 @@ void OpProtoAndCheckerMaker::operator()(proto::OpProto* proto,
            static_cast<int>(OpRole::kLoss) | static_cast<int>(OpRole::kForward),
            static_cast<int>(OpRole::kLoss) |
                static_cast<int>(OpRole::kBackward),
+           static_cast<int>(OpRole::kOptimize) |
+               static_cast<int>(OpRole::kLRSched),
            static_cast<int>(OpRole::kNotSpecified)})
       .SetDefault(static_cast<int>(OpRole::kNotSpecified));
   AddAttr<std::vector<std::string>>(OpRoleVarAttrName(),

paddle/fluid/framework/op_proto_maker.h

@@ -14,25 +14,26 @@ limitations under the License. */
 #pragma once
 
 #include <string>
-#include <unordered_set>
-
 #include "glog/logging.h"
 #include "paddle/fluid/framework/attribute.h"
 #include "paddle/fluid/framework/framework.pb.h"
 namespace paddle {
 namespace framework {
 
+//////////////////////////
+// Don't add more roles to make this too complicated!
+//////////////////////////
 enum class OpRole {
   kForward = 0x0000,
   kBackward = 0x0001,
   kOptimize = 0x0002,
   // RPC role is for send/recv releated op
-  kRPC = 0x0003,
+  kRPC = 0x0004,
   // Dist role is for split_byref/split_selected_rows/concat
   // used for distributed training.
-  kDist = 0x0004,
+  kDist = 0x0008,
   // Tag all learning rate scheduler operators.
-  kLRSched = 0x0005,
+  kLRSched = 0x0010,
 
   kLoss = 0x0100,
   // The default value of op's role. This should be only used for unittests and
@@ -73,11 +74,6 @@ class OpProtoAndCheckerMaker {
       var_->set_dispensable(true);
       return *this;
     }
-
-    VariableBuilder &Reuse(const std::string &name) {
-      var_->set_reuse(name);
-      return *this;
-    }
   };
 
   VariableBuilder AddInput(const std::string &name, const std::string &comment);
@@ -85,8 +81,6 @@ class OpProtoAndCheckerMaker {
   VariableBuilder AddOutput(const std::string &name,
                             const std::string &comment);
 
-  void Reuse(const std::string &name, const std::string &reused_name);
-
   template <typename T>
   TypedAttrChecker<T> &AddAttr(const std::string &name,
                                const std::string &comment,
@@ -105,8 +99,6 @@ class OpProtoAndCheckerMaker {
   void CheckNoDuplicatedInOutAttrs();
   void Validate();
 
-  void CheckReuseVars();
-
   proto::OpProto *proto_;
   OpAttrChecker *op_checker_;
   bool validated_{false};

paddle/fluid/framework/op_proto_maker_test.cc

@@ -47,120 +47,3 @@ TEST(ProtoMaker, DuplicatedInOut) {
   ASSERT_THROW(proto_maker(&op_proto, &op_checker),
                paddle::platform::EnforceNotMet);
 }
-
-class TestInplaceProtoMaker : public paddle::framework::OpProtoAndCheckerMaker {
- public:
-  void Make() {
-    AddInput("X", "input of test op");
-    AddOutput("XOut", "output of test op").Reuse("X");
-  }
-};
-
-class TestInplaceProtoMaker2
-    : public paddle::framework::OpProtoAndCheckerMaker {
- public:
-  void Make() {
-    AddInput("X", "input of test op");
-    AddOutput("XOut", "output of test op").Reuse("X");
-    AddOutput("NoOut", "output of test op").Reuse("NotExists");
-  }
-};
-
-TEST(ProtoMaker, InplaceOutput) {
-  paddle::framework::proto::OpProto op_proto, op_proto2;
-  paddle::framework::OpAttrChecker op_checker;
-  TestInplaceProtoMaker proto_maker;
-  TestInplaceProtoMaker2 proto_maker2;
-
-  proto_maker(&op_proto, &op_checker);
-
-  ASSERT_THROW(proto_maker2(&op_proto2, &op_checker),
-               paddle::platform::EnforceNotMet);
-}
-
-// normal reuse
-class TestReuseProtoMaker : public paddle::framework::OpProtoAndCheckerMaker {
- public:
-  void Make() {
-    AddInput("X", "input of test op");
-    AddInput("Y", "input of test op");
-    AddOutput("Out", "output of test op");
-    AddOutput("XOut", "output of test op");
-    // avoid destructor exception.
-    // Validate();
-    TestReuse();
-  }
-
-  virtual void TestReuse() {}
-};
-
-// test duplicate reuse error
-class TestReuseProtoMaker2 : public TestReuseProtoMaker {
- public:
-  void TestReuse() {
-    Reuse("Out", "X");
-    Reuse("Out", "Y");
-  }
-};
-
-// NotExists Input
-class TestReuseProtoMaker3 : public TestReuseProtoMaker {
- public:
-  void TestReuse() {
-    Reuse("Out", "NotExists");
-    Reuse("XOut", "X");
-  }
-};
-
-// NotExists Output
-class TestReuseProtoMaker4 : public TestReuseProtoMaker {
- public:
-  void TestReuse() { Reuse("NotExists", "X"); }
-};
-
-TEST(ProtoMaker, Reuse) {
-  paddle::framework::proto::OpProto op_proto;
-  paddle::framework::OpAttrChecker op_checker;
-  TestReuseProtoMaker proto_maker;
-  proto_maker(&op_proto, &op_checker);
-}
-
-// NOTE(dzhwinter):
-// There is a Fatal CHECK on base class destructor, which will call abort inside
-// instead of
-// throw an exception. If we throw an exception in Make(), we will trigger the
-// CHECK and terminate the tests.
-//
-// I had tried to replace the default CHECK with a exception, however, it's
-// still not supported by glog.
-// the details:
-// https://github.com/google/glog/issues/249
-// https://github.com/facebookresearch/TensorComprehensions/issues/351
-/*
-TEST(ProtoMaker, ReuseWithException) {
-  paddle::framework::proto::OpProto op_proto2, op_proto3, op_proto4;
-  paddle::framework::OpAttrChecker op_checker;
-  TestReuseProtoMaker2 proto_maker2;
-  TestReuseProtoMaker3 proto_maker3;
-  TestReuseProtoMaker4 proto_maker4;
-  EXPECT_THROW(proto_maker2(&op_proto2, &op_checker),
-               paddle::platform::EnforceNotMet);
-
-  EXPECT_THROW(proto_maker3(&op_proto3, &op_checker),
-               paddle::platform::EnforceNotMet);
-
-  EXPECT_THROW(proto_maker4(&op_proto4, &op_checker),
-               paddle::platform::EnforceNotMet);
-}
-
-void FailureFunction() {
-  throw std::runtime_error("Check failed in destructor.");
-  // return 0;
-}
-
-int main(int argc, char** argv) {
-  testing::InitGoogleTest(&argc, argv);
-  google::InstallFailureFunction(&FailureFunction);
-  return RUN_ALL_TESTS();
-}
-*/

paddle/fluid/framework/parallel_executor.cc

@@ -109,18 +109,9 @@ ParallelExecutor::ParallelExecutor(
   if (member_->local_scopes_.size() != 1 && local_scopes.empty()) {
     BCastParamsToDevices(bcast_vars);
   }
-  // Startup Program has been run. All local scopes has correct parameters.
+// Startup Program has been run. All local scopes has correct parameters.
 
-  // Step 2. Create vars in each scope;
-  std::vector<details::VariableInfo> var_infos;
-  for (auto *var : main_program.Block(0).AllVars()) {
-    var_infos.emplace_back();
-    var_infos.back().name_ = var->Name();
-    var_infos.back().type_ = var->GetType();
-    var_infos.back().persistable_ = var->Persistable();
-  }
-
-// Step 3. Convert main_program to SSA form and dependency graph. Also, insert
+// Step 2. Convert main_program to SSA form and dependency graph. Also, insert
 // ncclOp
 #ifdef PADDLE_WITH_CUDA
   std::unique_ptr<ir::Graph> graph = build_strategy.Apply(
@@ -156,13 +147,22 @@ ParallelExecutor::ParallelExecutor(
                            params, member_->local_scopes_, member_->use_cuda_);
 #endif
 
-  if (VLOG_IS_ON(5)) {
-    // If the loss_var_name is given, the number of graph should be only one.
-    if (loss_var_name.size()) {
-      PADDLE_ENFORCE_EQ(ir::GraphNum(*graph), 1,
-                        "The number of graph should be only one");
+  // Step 3. Create vars in each scope. Passes may also create new vars.
+  //         skip control vars and empty vars
+  std::vector<details::VariableInfo> var_infos;
+  for (auto &node : graph->Nodes()) {
+    if (node->IsVar() && !node->IsCtrlVar() && node->Var()) {
+      var_infos.emplace_back();
+      var_infos.back().name_ = node->Var()->Name();
+      var_infos.back().type_ = node->Var()->GetType();
+      var_infos.back().persistable_ = node->Var()->Persistable();
     }
   }
+  // If the loss_var_name is given, the number of graph should be only one.
+  if (loss_var_name.size()) {
+    PADDLE_ENFORCE_EQ(ir::GraphNum(*graph), 1,
+                      "The number of graph should be only one");
+  }
 
   if (exec_strategy.type_ == ExecutionStrategy::kDefault) {
     member_->executor_.reset(new details::ThreadedSSAGraphExecutor(

paddle/fluid/framework/program_desc_test.cc

@@ -103,7 +103,7 @@ TEST(ProgramDesc, copy_ctor) {
       ASSERT_EQ(1, op->GetBlockAttrId("sub_block"));
       found_sub_block = true;
 
-      ASSERT_EQ(2, op->GetBlocksAttrIds("sub_blocks").size());
+      ASSERT_EQ(2UL, op->GetBlocksAttrIds("sub_blocks").size());
       found_sub_blocks = true;
     }
   }

paddle/fluid/framework/reader_test.cc

@@ -40,7 +40,7 @@ TEST(READER, decorate_chain) {
     auto endpoints = root->GetEndPoints();
     ASSERT_EQ(endpoints.size(), 2U);
     ASSERT_NE(endpoints.count(end_point1.get()), 0UL);
-    ASSERT_NE(endpoints.count(end_point2.get()), 0);
+    ASSERT_NE(endpoints.count(end_point2.get()), 0UL);
   }
 
   {

paddle/fluid/framework/scope.h

@@ -78,6 +78,8 @@ class Scope {
   /// Drop all kids scopes belonged to this scope.
   void DropKids();
 
+  std::list<Scope*>& kids() const { return kids_; }
+
   /// Find if a scope exists in the kid scopes
   bool HasKid(const Scope* scope) const;
 

paddle/fluid/framework/threadpool.cc

@@ -25,7 +25,6 @@ DEFINE_int32(dist_threadpool_size, 0,
 
 namespace paddle {
 namespace framework {
-
 std::unique_ptr<ThreadPool> ThreadPool::threadpool_(nullptr);
 std::once_flag ThreadPool::init_flag_;
 
@@ -47,8 +46,7 @@ void ThreadPool::Init() {
   }
 }
 
-ThreadPool::ThreadPool(int num_threads)
-    : total_threads_(num_threads), idle_threads_(num_threads), running_(true) {
+ThreadPool::ThreadPool(int num_threads) : running_(true) {
   threads_.resize(num_threads);
   for (auto& thread : threads_) {
     // TODO(Yancey1989): binding the thread on the specify CPU number
@@ -59,6 +57,7 @@ ThreadPool::ThreadPool(int num_threads)
 ThreadPool::~ThreadPool() {
   {
     // notify all threads to stop running
+    std::lock_guard<std::mutex> l(mutex_);
     running_ = false;
     scheduled_.notify_all();
   }
@@ -69,36 +68,24 @@ ThreadPool::~ThreadPool() {
   }
 }
 
-void ThreadPool::Wait() {
-  std::unique_lock<std::mutex> lock(mutex_);
-  completed_.wait(lock, [=] { return Done() == true; });
-}
-
 void ThreadPool::TaskLoop() {
-  while (running_) {
+  while (true) {
     std::unique_lock<std::mutex> lock(mutex_);
-    scheduled_.wait(lock, [=] { return !tasks_.empty() || !running_; });
 
-    if (!running_) {
-      break;
+    scheduled_.wait(
+        lock, [this] { return !this->tasks_.empty() || !this->running_; });
+
+    if (!running_ || tasks_.empty()) {
+      return;
     }
+
     // pop a task from the task queue
     auto task = std::move(tasks_.front());
     tasks_.pop();
-
-    --idle_threads_;
     lock.unlock();
 
     // run the task
     task();
-
-    {
-      std::unique_lock<std::mutex> lock(mutex_);
-      ++idle_threads_;
-      if (Done()) {
-        completed_.notify_all();
-      }
-    }
   }
 }
 

paddle/fluid/framework/threadpool.h

@@ -57,15 +57,6 @@ class ThreadPool {
 
   ~ThreadPool();
 
-  // Returns the number of threads created by the constructor.
-  size_t Threads() const { return total_threads_; }
-
-  // Returns the number of currently idle threads.
-  size_t IdleThreads() {
-    std::unique_lock<std::mutex> lock(mutex_);
-    return idle_threads_;
-  }
-
   // Run pushes a function to the task queue and returns a std::future
   // object.  To wait for the completion of the task, call
   // std::future::wait().
@@ -94,25 +85,13 @@ class ThreadPool {
     });
     std::future<std::unique_ptr<platform::EnforceNotMet>> f = task.get_future();
     tasks_.push(std::move(task));
-    lock.unlock();
     scheduled_.notify_one();
     return f;
   }
 
-  // Wait until all the tasks are completed.
-  void Wait();
-
  private:
   DISABLE_COPY_AND_ASSIGN(ThreadPool);
 
-  // If the task queue is empty and avaialbe is equal to the number of
-  // threads, means that all tasks are completed.  Note: this function
-  // is not thread-safe.  Returns true if all tasks are completed.
-  // Note: don't delete the data member total_threads_ and use
-  // threads_.size() instead; because you'd need to lock the mutex
-  // before accessing threads_.
-  bool Done() { return tasks_.empty() && idle_threads_ == total_threads_; }
-
   // The constructor starts threads to run TaskLoop, which retrieves
   // and runs tasks from the queue.
   void TaskLoop();
@@ -125,14 +104,11 @@ class ThreadPool {
   static std::once_flag init_flag_;
 
   std::vector<std::unique_ptr<std::thread>> threads_;
-  const size_t total_threads_;
-  size_t idle_threads_;
 
   std::queue<Task> tasks_;
   std::mutex mutex_;
   bool running_;
   std::condition_variable scheduled_;
-  std::condition_variable completed_;
 };
 
 class ThreadPoolIO : ThreadPool {

paddle/fluid/framework/threadpool_test.cc

@@ -19,10 +19,11 @@ limitations under the License. */
 
 namespace framework = paddle::framework;
 
-void do_sum(framework::ThreadPool* pool, std::atomic<int>* sum, int cnt) {
-  std::vector<std::future<void>> fs;
+void do_sum(std::vector<std::future<void>>* fs, std::mutex* mu,
+            std::atomic<int>* sum, int cnt) {
   for (int i = 0; i < cnt; ++i) {
-    fs.push_back(framework::Async([sum]() { sum->fetch_add(1); }));
+    std::lock_guard<std::mutex> l(*mu);
+    fs->push_back(framework::Async([sum]() { sum->fetch_add(1); }));
   }
 }
 
@@ -40,18 +41,21 @@ TEST(ThreadPool, ConcurrentInit) {
 }
 
 TEST(ThreadPool, ConcurrentRun) {
-  framework::ThreadPool* pool = framework::ThreadPool::GetInstance();
   std::atomic<int> sum(0);
   std::vector<std::thread> threads;
+  std::vector<std::future<void>> fs;
+  std::mutex fs_mu;
   int n = 50;
   // sum = (n * (n + 1)) / 2
   for (int i = 1; i <= n; ++i) {
-    std::thread t(do_sum, pool, &sum, i);
+    std::thread t(do_sum, &fs, &fs_mu, &sum, i);
     threads.push_back(std::move(t));
   }
   for (auto& t : threads) {
     t.join();
   }
-  pool->Wait();
+  for (auto& t : fs) {
+    t.wait();
+  }
   EXPECT_EQ(sum, ((n + 1) * n) / 2);
 }

paddle/fluid/inference/CMakeLists.txt

@@ -30,7 +30,7 @@ if (WITH_GPU AND TENSORRT_FOUND)
 endif()
 
 # Create static library
-cc_library(paddle_fluid DEPS ${fluid_modules} ${STATIC_INFERENCE_APIS} zero_copy_tensor)
+cc_library(paddle_fluid DEPS ${fluid_modules} ${STATIC_INFERENCE_APIS} zero_copy_tensor reset_tensor_array)
 
 if(NOT APPLE)
   # TODO(liuyiqu: Temporarily disable the link flag because it is not support on Mac.
@@ -40,7 +40,7 @@ endif()
 
 # Create shared library
 cc_library(paddle_fluid_shared SHARED SRCS ${SHARED_INFERENCE_SRCS}
-    DEPS ${fluid_modules} paddle_fluid_api)
+    DEPS ${fluid_modules} paddle_fluid_api reset_tensor_array)
 
 set_target_properties(paddle_fluid_shared PROPERTIES OUTPUT_NAME paddle_fluid)
 if(NOT APPLE)

paddle/fluid/inference/analysis/analyzer.cc

@@ -107,6 +107,9 @@ void Analyzer::Run(Argument* argument) {
     passes.push_back("mkldnn_placement_pass");
   }
 #endif
+  // infer_clean_graph_pass should be the first default pass
+  // after mkldnn_placement_pass.
+  passes.push_back("infer_clean_graph_pass");
   for (auto& pass : ir_passes_) {
     if (!disabled_ir_passes_.count(pass)) {
       passes.push_back(pass);

paddle/fluid/inference/analysis/analyzer.h

@@ -67,7 +67,6 @@ class Analyzer : public OrderedRegistry<PassManager> {
   // larger fusion.
   const std::vector<std::string> all_ir_passes_{{
       // Manual update the passes here.
-      "infer_clean_graph_pass",         //
       "attention_lstm_fuse_pass",       //
       "seqconv_eltadd_relu_fuse_pass",  //
       "embedding_fc_lstm_fuse_pass",    //

paddle/fluid/inference/api/CMakeLists.txt

@@ -18,7 +18,8 @@ if(APPLE)
 endif(APPLE)
 
 
-set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager naive_executor ${GLOB_PASS_LIB})
+set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager naive_executor ${GLOB_PASS_LIB}
+        )
 
 if(WITH_GPU AND TENSORRT_FOUND)
     set(inference_deps ${inference_deps} paddle_inference_tensorrt_subgraph_engine analysis_predictor)
@@ -31,10 +32,17 @@ function(inference_api_test TARGET_NAME)
         set(multiValueArgs ARGS)
         cmake_parse_arguments(inference_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
 
-        cc_test(${TARGET_NAME}
-                SRCS ${inference_test_SRC}
-                DEPS "${inference_deps}"
-                ARGS --dirname=${PYTHON_TESTS_DIR}/book/)
+	if (WITH_GPU)
+		cc_test(${TARGET_NAME}
+			SRCS ${inference_test_SRC}
+			DEPS "${inference_deps}"
+			ARGS --dirname=${PYTHON_TESTS_DIR}/book/ --fraction_of_gpu_memory_to_use=0.15)
+        else()
+		cc_test(${TARGET_NAME}
+			SRCS ${inference_test_SRC}
+			DEPS "${inference_deps}"
+			ARGS --dirname=${PYTHON_TESTS_DIR}/book/)
+	endif()
         if(inference_test_ARGS)
             set_tests_properties(${TARGET_NAME}
                     PROPERTIES DEPENDS "${inference_test_ARGS}")
@@ -42,7 +50,8 @@ function(inference_api_test TARGET_NAME)
     endif(WITH_TESTING)
 endfunction(inference_api_test)
 
-cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS lod_tensor scope)
+cc_library(reset_tensor_array SRCS details/reset_tensor_array.cc DEPS lod_tensor scope)
+cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS reset_tensor_array lod_tensor scope)
 cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis naive_executor zero_copy_tensor)
 cc_library(zero_copy_tensor SRCS details/zero_copy_tensor.cc DEPS paddle_inference_api)
 cc_library(zero_copy_tensor_dummy SRCS details/zero_copy_tensor_dummy.cc DEPS paddle_inference_api)

paddle/fluid/inference/api/analysis_predictor.cc

@@ -82,6 +82,7 @@ bool AnalysisPredictor::Init(
 
   // Get the feed_target_names and fetch_target_names
   PrepareFeedFetch();
+
   return true;
 }
 
@@ -109,6 +110,10 @@ bool AnalysisPredictor::Run(const std::vector<PaddleTensor> &inputs,
     return false;
   }
   VLOG(3) << "predict cost: " << timer.toc() << "ms";
+
+  // Fix TensorArray reuse not cleaned bug.
+  tensor_array_batch_cleaner_.CollectTensorArrays(scope_.get());
+  tensor_array_batch_cleaner_.ResetTensorArray();
   return true;
 }
 
@@ -322,6 +327,9 @@ std::unique_ptr<ZeroCopyTensor> AnalysisPredictor::GetOutputTensor(
 
 bool AnalysisPredictor::ZeroCopyRun() {
   executor_->Run();
+  // Fix TensorArray reuse not cleaned bug.
+  tensor_array_batch_cleaner_.CollectTensorArrays(scope_.get());
+  tensor_array_batch_cleaner_.ResetTensorArray();
   return true;
 }
 

paddle/fluid/inference/api/analysis_predictor.h

@@ -18,6 +18,7 @@
 #include "paddle/fluid/framework/naive_executor.h"
 #include "paddle/fluid/inference/analysis/analyzer.h"
 #include "paddle/fluid/inference/api/api_impl.h"
+#include "paddle/fluid/inference/api/details/reset_tensor_array.h"
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
 #include "paddle/fluid/string/printf.h"
 
@@ -88,6 +89,7 @@ class AnalysisPredictor : public PaddlePredictor {
   // Memory buffer for feed inputs. The temporary LoDTensor will cause serious
   // concurrency problems, so cache them.
   std::vector<framework::LoDTensor> feed_tensors_;
+  details::TensorArrayBatchCleaner tensor_array_batch_cleaner_;
 };
 
 }  // namespace paddle

paddle/fluid/inference/api/api_impl.cc

@@ -22,6 +22,7 @@ limitations under the License. */
 
 #include "paddle/fluid/framework/feed_fetch_method.h"
 #include "paddle/fluid/inference/api/api_impl.h"
+#include "paddle/fluid/inference/api/details/reset_tensor_array.h"
 #include "paddle/fluid/inference/api/helper.h"
 #include "paddle/fluid/platform/cpu_helper.h"
 #include "paddle/fluid/platform/profiler.h"
@@ -157,6 +158,10 @@ bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
     return false;
   }
   VLOG(3) << "predict cost: " << timer.toc() << "ms";
+
+  // Fix TensorArray reuse not cleaned bug.
+  tensor_array_batch_cleaner_.CollectTensorArrays(scope_.get());
+  tensor_array_batch_cleaner_.ResetTensorArray();
   return true;
 }
 

paddle/fluid/inference/api/api_impl.h

@@ -26,11 +26,11 @@ limitations under the License. */
 #include <string>
 #include <vector>
 
-#include "paddle/fluid/inference/api/paddle_inference_api.h"
-
 #include "paddle/fluid/framework/ddim.h"
 #include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/lod_tensor_array.h"
 #include "paddle/fluid/framework/naive_executor.h"
+#include "paddle/fluid/inference/api/details/reset_tensor_array.h"
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
 #include "paddle/fluid/inference/io.h"
 #include "paddle/fluid/platform/init.h"
@@ -77,6 +77,7 @@ class NativePaddlePredictor : public PaddlePredictor {
   std::vector<framework::OpDesc *> fetchs_;
   // Do not use unique_ptr, use parent scope to delete
   framework::Scope *sub_scope_{nullptr};
+  details::TensorArrayBatchCleaner tensor_array_batch_cleaner_;
 };
 
 }  // namespace paddle

paddle/fluid/inference/api/demo_ci/CMakeLists.txt

@@ -52,6 +52,7 @@ include_directories("${PADDLE_LIB}")
 include_directories("${PADDLE_LIB}/third_party/install/protobuf/include")
 include_directories("${PADDLE_LIB}/third_party/install/glog/include")
 include_directories("${PADDLE_LIB}/third_party/install/gflags/include")
+include_directories("${PADDLE_LIB}/third_party/install/xxhash/include")
 if (NOT WIN32)
 include_directories("${PADDLE_LIB}/third_party/install/snappy/include")
 include_directories("${PADDLE_LIB}/third_party/install/snappystream/include")
@@ -61,8 +62,8 @@ endif(NOT WIN32)
 include_directories("${PADDLE_LIB}/third_party/boost")
 include_directories("${PADDLE_LIB}/third_party/eigen3")
 
-if (NOT WIN32) 
-  if (USE_TENSORRT AND WITH_GPU) 
+if (NOT WIN32)
+  if (USE_TENSORRT AND WITH_GPU)
       include_directories("${TENSORRT_INCLUDE_DIR}")
       link_directories("${TENSORRT_LIB_DIR}")
   endif()
@@ -77,13 +78,14 @@ endif(NOT WIN32)
 link_directories("${PADDLE_LIB}/third_party/install/protobuf/lib")
 link_directories("${PADDLE_LIB}/third_party/install/glog/lib")
 link_directories("${PADDLE_LIB}/third_party/install/gflags/lib")
+link_directories("${PADDLE_LIB}/third_party/install/xxhash/lib")
 link_directories("${PADDLE_LIB}/paddle/lib")
 
 add_executable(${DEMO_NAME} ${DEMO_NAME}.cc)
 
 if(WITH_MKL)
   include_directories("${PADDLE_LIB}/third_party/install/mklml/include")
-  set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX} 
+  set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX}
                ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5${CMAKE_SHARED_LIBRARY_SUFFIX})
   set(MKLDNN_PATH "${PADDLE_LIB}/third_party/install/mkldnn")
   if(EXISTS ${MKLDNN_PATH})
@@ -107,7 +109,7 @@ if (NOT WIN32)
 set(EXTERNAL_LIB "-lrt -ldl -lpthread")
 set(DEPS ${DEPS}
     ${MATH_LIB} ${MKLDNN_LIB}
-    glog gflags protobuf snappystream snappy z
+    glog gflags protobuf snappystream snappy z xxhash
     ${EXTERNAL_LIB})
 else()
 set(DEPS ${DEPS}
@@ -120,7 +122,7 @@ endif(NOT WIN32)
 
 if(WITH_GPU)
   if(NOT WIN32)
-    if (USE_TENSORRT) 
+    if (USE_TENSORRT)
       set(DEPS ${DEPS} ${TENSORRT_LIB_DIR}/libnvinfer${CMAKE_STATIC_LIBRARY_SUFFIX})
       set(DEPS ${DEPS} ${TENSORRT_LIB_DIR}/libnvinfer_plugin${CMAKE_STATIC_LIBRARY_SUFFIX})
     endif()

paddle/fluid/inference/api/demo_ci/run.sh

@@ -16,12 +16,12 @@ if [ $2 == ON ]; then
 fi
 if [ $3 == ON ]; then
   use_gpu_list='true false'
-else    
+else
   use_gpu_list='false'
 fi
 
 USE_TENSORRT=OFF
-if [ [-d"$TENSORRT_INCLUDE_DIR"] -a [-d"$TENSORRT_LIB_DIR"] ]; then
+if [ -d "$TENSORRT_INCLUDE_DIR" -a -d "$TENSORRT_LIB_DIR" ]; then
   USE_TENSORRT=ON
 fi
 
@@ -60,7 +60,8 @@ for WITH_STATIC_LIB in ON OFF; do
     -DWITH_MKL=$TURN_ON_MKL \
     -DDEMO_NAME=simple_on_word2vec \
     -DWITH_GPU=$TEST_GPU_CPU \
-    -DWITH_STATIC_LIB=$WITH_STATIC_LIB
+    -DWITH_STATIC_LIB=$WITH_STATIC_LIB \
+    -DON_INFER=ON
   make -j
   word2vec_model=${PADDLE_ROOT}'/build/python/paddle/fluid/tests/book/word2vec.inference.model'
   if [ -d $word2vec_model ]; then
@@ -80,10 +81,11 @@ for WITH_STATIC_LIB in ON OFF; do
     -DWITH_MKL=$TURN_ON_MKL \
     -DDEMO_NAME=vis_demo \
     -DWITH_GPU=$TEST_GPU_CPU \
-    -DWITH_STATIC_LIB=$WITH_STATIC_LIB
+    -DWITH_STATIC_LIB=$WITH_STATIC_LIB \
+    -DON_INFER=ON
   make -j
   for use_gpu in $use_gpu_list; do
-    for vis_demo_name in $vis_demo_list; do 
+    for vis_demo_name in $vis_demo_list; do
       ./vis_demo \
         --modeldir=$DATA_DIR/$vis_demo_name/model \
         --data=$DATA_DIR/$vis_demo_name/data.txt \
@@ -95,7 +97,7 @@ for WITH_STATIC_LIB in ON OFF; do
       fi
     done
   done
-  
+
   # --------tensorrt mobilenet------
   if [ $USE_TENSORRT == ON -a $TEST_GPU_CPU == ON ]; then
     rm -rf *
@@ -106,8 +108,9 @@ for WITH_STATIC_LIB in ON OFF; do
       -DWITH_STATIC_LIB=$WITH_STATIC_LIB \
       -DUSE_TENSORRT=$USE_TENSORRT \
       -DTENSORRT_INCLUDE_DIR=$TENSORRT_INCLUDE_DIR \
-      -DTENSORRT_LIB_DIR=$TENSORRT_LIB_DIR
-    make -j 
+      -DTENSORRT_LIB_DIR=$TENSORRT_LIB_DIR \
+      -DON_INFER=ON
+    make -j
     ./trt_mobilenet_demo \
       --modeldir=$DATA_DIR/mobilenet/model \
       --data=$DATA_DIR/mobilenet/data.txt \

paddle/fluid/inference/api/details/reset_tensor_array.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/inference/api/details/reset_tensor_array.h"
+
+namespace paddle {
+namespace details {
+
+// Should be called after the parameters are loaded.
+void TensorArrayBatchCleaner::CollectTensorArrays(framework::Scope *scope) {
+  if (flag_) {
+    for (auto &var_name : scope->LocalVarNames()) {
+      auto *var = scope->FindVar(var_name);
+      // TODO(Superjomn) should avoid the case when a TensorArray is a
+      // parameter.
+      if (var_name == "feed" || var_name == "fetch") continue;
+      if (var->Type() == typeid(framework::LoDTensorArray)) {
+        VLOG(4) << "collect " << var_name;
+        arrays_.push_back(var->GetMutable<framework::LoDTensorArray>());
+      }
+    }
+    for (auto *kid : scope->kids()) {
+      CollectTensorArrays(kid);
+    }
+
+    VLOG(3) << "Collect " << arrays_.size() << " arrays";
+    flag_ = false;
+  }
+}
+
+// Should be called when `Run` finished.
+void TensorArrayBatchCleaner::ResetTensorArray() {
+  for (auto *arr : arrays_) {
+    arr->clear();
+  }
+}
+
+}  // namespace details
+}  // namespace paddle

paddle/fluid/inference/api/details/reset_tensor_array.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 <vector>
+#include "paddle/fluid/framework/lod_tensor_array.h"
+#include "paddle/fluid/framework/scope.h"
+
+namespace paddle {
+namespace details {
+
+// Clean the TensorArray each batch to make the behavior the same with the
+// training phase.
+struct TensorArrayBatchCleaner {
+  // Fix the tensor array not clear in the inference scenarios.
+  void CollectTensorArrays(framework::Scope *scope);
+  void ResetTensorArray();
+
+ private:
+  bool flag_{true};
+  std::vector<framework::LoDTensorArray *> arrays_;
+};
+
+}  // namespace details
+}  // namespace paddle

paddle/fluid/inference/api/helper.h

@@ -160,7 +160,8 @@ static void PrintTime(int batch_size, int repeat, int num_threads, int tid,
                       double latency, int epoch = 1) {
   LOG(INFO) << "====== batch_size: " << batch_size << ", repeat: " << repeat
             << ", threads: " << num_threads << ", thread id: " << tid
-            << ", latency: " << latency << "ms ======";
+            << ", latency: " << latency << "ms, fps: " << 1 / (latency / 1000.f)
+            << " ======";
   if (epoch > 1) {
     int samples = batch_size * epoch;
     LOG(INFO) << "====== sample number: " << samples

paddle/fluid/inference/api/paddle_inference_api.h

@@ -124,7 +124,7 @@ class ZeroCopyTensor {
   std::vector<std::vector<size_t>> lod() const;
 
  protected:
-  ZeroCopyTensor(void* scope) : scope_{scope} {}
+  explicit ZeroCopyTensor(void* scope) : scope_{scope} {}
   void SetName(const std::string& name) { name_ = name; }
   void* FindTensor() const;
 
@@ -259,12 +259,6 @@ struct AnalysisConfig : public NativeConfig {
     kExclude   // Specify the disabled passes in `ir_passes`.
   };
 
-  void SetIncludeMode() {
-    ir_mode = IrPassMode::kInclude;
-    // this pass has to be run at the beginning of all fuse passes
-    ir_passes = {"infer_clean_graph_pass"};
-  }
-
   // Determine whether to perform graph optimization.
   bool enable_ir_optim = true;
   // Manually determine the IR passes to run.

paddle/fluid/inference/tensorrt/convert/pool2d_op.cc

@@ -42,16 +42,22 @@ class Pool2dOpConverter : public OpConverter {
         boost::get<std::vector<int>>(op_desc.GetAttr("strides"));
     std::vector<int> paddings =
         boost::get<std::vector<int>>(op_desc.GetAttr("paddings"));
+    bool ceil_mode = boost::get<bool>(op_desc.GetAttr("ceil_mode"));
 
+    nvinfer1::Dims input_shape = input1->getDimensions();
+    int nbDims = input_shape.nbDims;
     nvinfer1::DimsHW nv_ksize(ksize[0], ksize[1]);
+    nvinfer1::DimsHW nv_strides(strides[0], strides[1]);
+    nvinfer1::DimsHW nv_paddings(paddings[0], paddings[1]);
+
     if (global_pooling == true) {
-      nvinfer1::Dims input_shape = input1->getDimensions();
-      int nbDims = input_shape.nbDims;
       nv_ksize.d[0] = input_shape.d[nbDims - 2];
       nv_ksize.d[1] = input_shape.d[nbDims - 1];
+      nv_strides.h() = 1;
+      nv_strides.w() = 1;
+      nv_paddings.h() = 0;
+      nv_paddings.w() = 0;
     }
-    const nvinfer1::DimsHW nv_strides(strides[0], strides[1]);
-    const nvinfer1::DimsHW nv_paddings(paddings[0], paddings[1]);
 
     PADDLE_ENFORCE_EQ(input1->getDimensions().nbDims, 3UL);
 
@@ -64,6 +70,36 @@ class Pool2dOpConverter : public OpConverter {
       PADDLE_THROW("TensorRT unsupported pooling type!");
     }
 
+    if (ceil_mode) {
+      nvinfer1::DimsHW pre_pad(0, 0);
+      nvinfer1::DimsHW post_pad(0, 0);
+      int input_height = input_shape.d[nbDims - 2];
+      int input_width = input_shape.d[nbDims - 1];
+      int floor_h_output_size =
+          (input_height - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
+      int ceil_h_output_size =
+          (input_height - ksize[0] + 2 * paddings[0] + strides[0] - 1) /
+              strides[0] +
+          1;
+
+      int floor_w_output_size =
+          (input_width - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
+      int ceil_w_output_size =
+          (input_width - ksize[1] + 2 * paddings[1] + strides[1] - 1) /
+              strides[1] +
+          1;
+      if (floor_h_output_size != ceil_h_output_size) {
+        post_pad.h() = strides[0] - 1;
+      }
+
+      if (floor_w_output_size != ceil_w_output_size) {
+        post_pad.w() = strides[1] - 1;
+      }
+      auto* layer = TRT_ENGINE_ADD_LAYER(
+          engine_, Padding, *const_cast<nvinfer1::ITensor*>(input1), pre_pad,
+          post_pad);
+      input1 = layer->getOutput(0);
+    }
     auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Pooling,
                                        *const_cast<nvinfer1::ITensor*>(input1),
                                        nv_pool_type, nv_ksize);

paddle/fluid/inference/tensorrt/convert/test_pool2d_op.cc

@@ -20,18 +20,20 @@ namespace paddle {
 namespace inference {
 namespace tensorrt {
 
-void test_pool2d(bool global_pooling) {
+void test_pool2d(bool global_pooling, bool ceil_mode) {
   framework::Scope scope;
   std::unordered_set<std::string> parameters;
   TRTConvertValidation validator(5, parameters, scope, 1 << 15);
 
   // The ITensor's Dims should not contain the batch size.
   // So, the ITensor's Dims of input and output should be C * H * W.
-  validator.DeclInputVar("pool2d-X", nvinfer1::Dims3(3, 4, 4));
+  validator.DeclInputVar("pool2d-X", nvinfer1::Dims3(3, 13, 14));
   if (global_pooling)
     validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 1, 1));
+  else if (ceil_mode)
+    validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 6, 7));
   else
-    validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 2, 2));
+    validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 6, 6));
 
   // Prepare Op description
   framework::OpDesc desc;
@@ -39,7 +41,7 @@ void test_pool2d(bool global_pooling) {
   desc.SetInput("X", {"pool2d-X"});
   desc.SetOutput("Out", {"pool2d-Out"});
 
-  std::vector<int> ksize({2, 2});
+  std::vector<int> ksize({3, 3});
   std::vector<int> strides({2, 2});
   std::vector<int> paddings({0, 0});
   std::string pooling_t = "max";
@@ -49,6 +51,7 @@ void test_pool2d(bool global_pooling) {
   desc.SetAttr("strides", strides);
   desc.SetAttr("paddings", paddings);
   desc.SetAttr("global_pooling", global_pooling);
+  desc.SetAttr("ceil_mode", ceil_mode);
 
   LOG(INFO) << "set OP";
   validator.SetOp(*desc.Proto());
@@ -57,9 +60,10 @@ void test_pool2d(bool global_pooling) {
   validator.Execute(3);
 }
 
-TEST(Pool2dOpConverter, normal) { test_pool2d(false); }
+TEST(Pool2dOpConverter, normal) { test_pool2d(false, false); }
+TEST(Pool2dOpConverter, test_global_pooling) { test_pool2d(true, false); }
 
-TEST(Pool2dOpConverter, test_global_pooling) { test_pool2d(true); }
+TEST(Pool2dOpConverter, test_ceil_mode) { test_pool2d(false, true); }
 
 }  // namespace tensorrt
 }  // namespace inference

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

@@ -71,7 +71,7 @@ void profile(bool use_mkldnn = false) {
 }
 
 TEST(Analyzer_resnet50, profile) { profile(); }
-#ifndef PADDLE_WITH_MKLDNN
+#ifdef PADDLE_WITH_MKLDNN
 TEST(Analyzer_resnet50, profile_mkldnn) { profile(true /* use_mkldnn */); }
 #endif
 

paddle/fluid/inference/tests/api/analyzer_rnn1_tester.cc

@@ -228,6 +228,7 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
 TEST(Analyzer_rnn1, profile) {
   contrib::AnalysisConfig cfg;
   SetConfig(&cfg);
+  cfg.use_gpu = false;
   std::vector<PaddleTensor> outputs;
 
   std::vector<std::vector<PaddleTensor>> input_slots_all;

paddle/fluid/inference/tests/api/tester_helper.h

@@ -50,7 +50,7 @@ void CompareResult(const std::vector<PaddleTensor> &outputs,
     auto &ref_out = ref_outputs[i];
     size_t size = VecReduceToInt(out.shape);
     size_t ref_size = VecReduceToInt(ref_out.shape);
-    EXPECT_GT(size, 0);
+    EXPECT_GT(size, 0UL);
     EXPECT_EQ(size, ref_size);
     EXPECT_EQ(out.dtype, ref_out.dtype);
     switch (out.dtype) {
@@ -139,6 +139,9 @@ void TestMultiThreadPrediction(
   }
   for (int tid = 0; tid < num_threads; ++tid) {
     threads.emplace_back([&, tid]() {
+#ifdef PADDLE_WITH_MKLDNN
+      platform::set_cur_thread_id(static_cast<int>(tid) + 1);
+#endif
       // Each thread should have local inputs and outputs.
       // The inputs of each thread are all the same.
       std::vector<std::vector<PaddleTensor>> inputs_tid = inputs;

paddle/fluid/operators/CMakeLists.txt

@@ -268,6 +268,7 @@ if (WITH_GPU AND TENSORRT_FOUND)
 else()
     set(DEPS_OPS ${DEPS_OPS} tensorrt_engine_op)
 endif()
+op_library(hash_op DEPS xxhash)
 op_library(clip_by_norm_op DEPS selected_rows_functor selected_rows)
 op_library(sum_op DEPS selected_rows_functor)
 op_library(sgd_op DEPS selected_rows_functor)

paddle/fluid/operators/activation_op.cc

@@ -28,7 +28,7 @@ using paddle::framework::Tensor;
    public:                                                              \
     void Make() override {                                              \
       AddInput("X", "Input of " #OP_NAME " operator");                  \
-      AddOutput("Out", "Output of " #OP_NAME " operator").Reuse("X");   \
+      AddOutput("Out", "Output of " #OP_NAME " operator");              \
       AddAttr<bool>("use_mkldnn",                                       \
                     "(bool, default false) Only used in mkldnn kernel") \
           .SetDefault(false);                                           \

paddle/fluid/operators/adam_op.cc

@@ -92,9 +92,9 @@ class AdamOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("Beta1Pow", "(Tensor) Input beta1 power accumulator");
     AddInput("Beta2Pow", "(Tensor) Input beta2 power accumulator");
 
-    AddOutput("ParamOut", "(Tensor) Output parameter").Reuse("Param");
-    AddOutput("Moment1Out", "(Tensor) Output first moment").Reuse("Moment1");
-    AddOutput("Moment2Out", "(Tensor) Output second moment").Reuse("Moment2");
+    AddOutput("ParamOut", "(Tensor) Output parameter");
+    AddOutput("Moment1Out", "(Tensor) Output first moment");
+    AddOutput("Moment2Out", "(Tensor) Output second moment");
 
     AddAttr<float>("beta1",
                    "(float, default 0.9) "

paddle/fluid/operators/batch_norm_op.cc

@@ -135,15 +135,13 @@ class BatchNormOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("Variance",
              "The global variance (for training) "
              "or estimated Variance (for testing)");
-    AddOutput("Y", "result after normalization").Reuse("X");
+    AddOutput("Y", "result after normalization");
     AddOutput("MeanOut",
               "Share memory with Mean. "
-              "Store the global mean when training")
-        .Reuse("Mean");
+              "Store the global mean when training");
     AddOutput("VarianceOut",
               "Share memory with Variance. "
-              "Store the global Variance when training")
-        .Reuse("Variance");
+              "Store the global Variance when training");
     AddOutput("SavedMean",
               "Mean of the current mini batch, "
               "will apply to output when training")

paddle/fluid/operators/beam_search_decode_op.cc

@@ -79,6 +79,9 @@ struct BeamSearchDecodeFunctor {
   bool tensor_on_gpu_;
   size_t beam_size_;
   int end_id_;
+  // TODO(Superjomn) Here might result serious performance issue in the
+  // concurrency
+  // scenarios.
   const LoDTensorArray& step_ids_origin_;
   const LoDTensorArray& step_scores_origin_;
   LoDTensorArray step_ids_ = LoDTensorArray();

paddle/fluid/operators/conv_op.cc

@@ -130,8 +130,7 @@ void Conv2DOpMaker::Make() {
       .AsDispensable();
   AddOutput("Output",
             "(Tensor) The output tensor of convolution operator. "
-            "The format of output tensor is also NCHW.")
-      .Reuse("Input");
+            "The format of output tensor is also NCHW.");
   AddInput("ResidualData",
            "(Tensor) Tensor with residual data "
            "to which convolution output will be added."
@@ -238,8 +237,7 @@ void Conv3DOpMaker::Make() {
            "input image channels divided by the groups.");
   AddOutput("Output",
             "(Tensor) The output tensor of convolution operator."
-            "The format of output tensor is also NCDHW.")
-      .Reuse("Input");
+            "The format of output tensor is also NCDHW.");
   AddAttr<std::vector<int>>("strides",
                             "(vector<int>, default:{1, 1, 1}), the "
                             "strides(d_stride, h_stride, w_stride) of "

paddle/fluid/operators/detection/generate_proposals_op.cc

@@ -284,7 +284,7 @@ static inline Tensor NMS(const platform::DeviceContext &ctx, Tensor *bbox,
       selected_indices.push_back(idx);
       ++selected_num;
     }
-    sorted_indices.erase(sorted_indices.end());
+    sorted_indices.erase(sorted_indices.end() - 1);
     if (flag && eta < 1 && adaptive_threshold > 0.5) {
       adaptive_threshold *= eta;
     }

paddle/fluid/operators/detection/rpn_target_assign_op.cc

@@ -52,6 +52,9 @@ class RpnTargetAssignOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(
         ctx->HasOutput("TargetBBox"),
         "Output(TargetBBox) of RpnTargetAssignOp should not be null");
+    PADDLE_ENFORCE(
+        ctx->HasOutput("BBoxInsideWeight"),
+        "Output(BBoxInsideWeight) of RpnTargetAssignOp should not be null");
 
     auto anchor_dims = ctx->GetInputDim("Anchor");
     auto gt_boxes_dims = ctx->GetInputDim("GtBoxes");
@@ -68,6 +71,7 @@ class RpnTargetAssignOp : public framework::OperatorWithKernel {
     ctx->SetOutputDim("ScoreIndex", {-1});
     ctx->SetOutputDim("TargetLabel", {-1, 1});
     ctx->SetOutputDim("TargetBBox", {-1, 4});
+    ctx->SetOutputDim("BBoxInsideWeight", {-1, 4});
   }
 
  protected:
@@ -169,6 +173,7 @@ void ScoreAssign(const T* anchor_by_gt_overlap_data,
                  const float rpn_positive_overlap,
                  const float rpn_negative_overlap, std::vector<int>* fg_inds,
                  std::vector<int>* bg_inds, std::vector<int>* tgt_lbl,
+                 std::vector<int>* fg_fake, std::vector<T>* bbox_inside_weight,
                  std::minstd_rand engine, bool use_random) {
   float epsilon = 0.00001;
   int anchor_num = anchor_to_gt_max.dims()[0];
@@ -201,12 +206,12 @@ void ScoreAssign(const T* anchor_by_gt_overlap_data,
   // Reservoir Sampling
   int fg_num = static_cast<int>(rpn_fg_fraction * rpn_batch_size_per_im);
   ReservoirSampling(fg_num, &fg_inds_fake, engine, use_random);
-  fg_num = static_cast<int>(fg_inds_fake.size());
-  for (int64_t i = 0; i < fg_num; ++i) {
+  int fg_fake_num = static_cast<int>(fg_inds_fake.size());
+  for (int64_t i = 0; i < fg_fake_num; ++i) {
     target_label[fg_inds_fake[i]] = 1;
   }
 
-  int bg_num = rpn_batch_size_per_im - fg_num;
+  int bg_num = rpn_batch_size_per_im - fg_fake_num;
   for (int64_t i = 0; i < anchor_num; ++i) {
     if (anchor_to_gt_max_data[i] < rpn_negative_overlap) {
       bg_inds_fake.push_back(i);
@@ -214,12 +219,28 @@ void ScoreAssign(const T* anchor_by_gt_overlap_data,
   }
   ReservoirSampling(bg_num, &bg_inds_fake, engine, use_random);
   bg_num = static_cast<int>(bg_inds_fake.size());
+  int fake_num = 0;
   for (int64_t i = 0; i < bg_num; ++i) {
+    // fg fake found
+    if (target_label[bg_inds_fake[i]] == 1) {
+      fake_num++;
+      fg_fake->emplace_back(fg_inds_fake[0]);
+      for (int j = 0; j < 4; ++j) {
+        bbox_inside_weight->emplace_back(T(0.));
+      }
+    }
     target_label[bg_inds_fake[i]] = 0;
   }
 
+  for (int64_t i = 0; i < (fg_fake_num - fake_num) * 4; ++i) {
+    bbox_inside_weight->emplace_back(T(1.));
+  }
+
   for (int64_t i = 0; i < anchor_num; ++i) {
-    if (target_label[i] == 1) fg_inds->emplace_back(i);
+    if (target_label[i] == 1) {
+      fg_inds->emplace_back(i);
+      fg_fake->emplace_back(i);
+    }
     if (target_label[i] == 0) bg_inds->emplace_back(i);
   }
   fg_num = fg_inds->size();
@@ -248,7 +269,8 @@ std::vector<Tensor> SampleRpnFgBgGt(const platform::CPUDeviceContext& ctx,
   std::vector<int> bg_inds;
   std::vector<int> gt_inds;
   std::vector<int> tgt_lbl;
-
+  std::vector<int> fg_fake;
+  std::vector<T> bbox_inside_weight;
   // Calculate the max IoU between anchors and gt boxes
   // Map from anchor to gt box that has highest overlap
   auto place = ctx.GetPlace();
@@ -275,32 +297,37 @@ std::vector<Tensor> SampleRpnFgBgGt(const platform::CPUDeviceContext& ctx,
   // Follow the Faster RCNN's implementation
   ScoreAssign(anchor_by_gt_overlap_data, anchor_to_gt_max, gt_to_anchor_max,
               rpn_batch_size_per_im, rpn_fg_fraction, rpn_positive_overlap,
-              rpn_negative_overlap, &fg_inds, &bg_inds, &tgt_lbl, engine,
-              use_random);
+              rpn_negative_overlap, &fg_inds, &bg_inds, &tgt_lbl, &fg_fake,
+              &bbox_inside_weight, engine, use_random);
 
   int fg_num = fg_inds.size();
   int bg_num = bg_inds.size();
-  gt_inds.reserve(fg_num);
-  for (int i = 0; i < fg_num; ++i) {
-    gt_inds.emplace_back(argmax[fg_inds[i]]);
+  int fg_fake_num = fg_fake.size();
+  gt_inds.reserve(fg_fake_num);
+  for (int i = 0; i < fg_fake_num; ++i) {
+    gt_inds.emplace_back(argmax[fg_fake[i]]);
   }
-
-  Tensor loc_index_t, score_index_t, tgt_lbl_t, gt_inds_t;
-  int* loc_index_data = loc_index_t.mutable_data<int>({fg_num}, place);
+  Tensor loc_index_t, score_index_t, tgt_lbl_t, gt_inds_t, bbox_inside_weight_t;
+  int* loc_index_data = loc_index_t.mutable_data<int>({fg_fake_num}, place);
   int* score_index_data =
       score_index_t.mutable_data<int>({fg_num + bg_num}, place);
   int* tgt_lbl_data = tgt_lbl_t.mutable_data<int>({fg_num + bg_num}, place);
-  int* gt_inds_data = gt_inds_t.mutable_data<int>({fg_num}, place);
-  std::copy(fg_inds.begin(), fg_inds.end(), loc_index_data);
+  int* gt_inds_data = gt_inds_t.mutable_data<int>({fg_fake_num}, place);
+  T* bbox_inside_weight_data =
+      bbox_inside_weight_t.mutable_data<T>({fg_fake_num, 4}, place);
+  std::copy(fg_fake.begin(), fg_fake.end(), loc_index_data);
   std::copy(fg_inds.begin(), fg_inds.end(), score_index_data);
   std::copy(bg_inds.begin(), bg_inds.end(), score_index_data + fg_num);
   std::copy(tgt_lbl.begin(), tgt_lbl.end(), tgt_lbl_data);
   std::copy(gt_inds.begin(), gt_inds.end(), gt_inds_data);
+  std::copy(bbox_inside_weight.begin(), bbox_inside_weight.end(),
+            bbox_inside_weight_data);
   std::vector<Tensor> loc_score_tgtlbl_gt;
   loc_score_tgtlbl_gt.emplace_back(loc_index_t);
   loc_score_tgtlbl_gt.emplace_back(score_index_t);
   loc_score_tgtlbl_gt.emplace_back(tgt_lbl_t);
   loc_score_tgtlbl_gt.emplace_back(gt_inds_t);
+  loc_score_tgtlbl_gt.emplace_back(bbox_inside_weight_t);
 
   return loc_score_tgtlbl_gt;
 }
@@ -318,6 +345,7 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
     auto* score_index = context.Output<LoDTensor>("ScoreIndex");
     auto* tgt_bbox = context.Output<LoDTensor>("TargetBBox");
     auto* tgt_lbl = context.Output<LoDTensor>("TargetLabel");
+    auto* bbox_inside_weight = context.Output<LoDTensor>("BBoxInsideWeight");
 
     PADDLE_ENFORCE_EQ(gt_boxes->lod().size(), 1UL,
                       "RpnTargetAssignOp gt_boxes needs 1 level of LoD");
@@ -340,7 +368,7 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
     score_index->mutable_data<int>({max_num}, place);
     tgt_bbox->mutable_data<T>({max_num, 4}, place);
     tgt_lbl->mutable_data<int>({max_num, 1}, place);
-
+    bbox_inside_weight->mutable_data<T>({max_num, 4}, place);
     auto& dev_ctx = context.device_context<platform::CPUDeviceContext>();
 
     std::random_device rnd;
@@ -394,6 +422,7 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
       Tensor sampled_score_index = loc_score_tgtlbl_gt[1];
       Tensor sampled_tgtlbl = loc_score_tgtlbl_gt[2];
       Tensor sampled_gt_index = loc_score_tgtlbl_gt[3];
+      Tensor sampled_bbox_inside_weight = loc_score_tgtlbl_gt[4];
 
       int loc_num = sampled_loc_index.dims()[0];
       int score_num = sampled_score_index.dims()[0];
@@ -432,6 +461,8 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
       AppendRpns<int>(score_index, total_score_num, &sampled_score_index_unmap);
       AppendRpns<T>(tgt_bbox, total_loc_num * 4, &sampled_tgt_bbox);
       AppendRpns<int>(tgt_lbl, total_score_num, &sampled_tgtlbl);
+      AppendRpns<T>(bbox_inside_weight, total_loc_num * 4,
+                    &sampled_bbox_inside_weight);
       total_loc_num += loc_num;
 
       total_score_num += score_num;
@@ -448,10 +479,12 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
     score_index->set_lod(loc_score);
     tgt_bbox->set_lod(lod_loc);
     tgt_lbl->set_lod(loc_score);
+    bbox_inside_weight->set_lod(lod_loc);
     loc_index->Resize({total_loc_num});
     score_index->Resize({total_score_num});
     tgt_bbox->Resize({total_loc_num, 4});
     tgt_lbl->Resize({total_score_num, 1});
+    bbox_inside_weight->Resize({total_loc_num, 4});
   }
 };
 
@@ -514,6 +547,9 @@ class RpnTargetAssignOpMaker : public framework::OpProtoAndCheckerMaker {
         "TargetLabel",
         "(Tensor<int>), The target labels of each anchor with shape "
         "[F + B, 1], F and B are sampled foreground and backgroud number.");
+    AddOutput("BBoxInsideWeight",
+              "(Tensor), The bbox inside weight with shape "
+              "[F, 4], F is the sampled foreground number.");
     AddComment(R"DOC(
 This operator can be, for a given set of ground truth bboxes and the
 anchors, to assign classification and regression targets to each prediction.

paddle/fluid/operators/dropout_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/dropout_op.h"
+#include <string>
 
 namespace paddle {
 namespace operators {
@@ -57,6 +58,29 @@ class DropoutOpMaker : public framework::OpProtoAndCheckerMaker {
                   "will be dropped.")
         .SetDefault(false);
     AddAttr<int>("seed", "Dropout random seed.").SetDefault(0);
+    AddAttr<std::string>(
+        "dropout_implementation",
+        "[\"downgrade_in_infer\"|\"upscale_in_train\"]"
+        "There are two kinds of ways to implement dropout"
+        "(the mask below is a tensor have the same shape with input"
+        "the value of mask is 0 or 1, the ratio of 0 is dropout_prob)"
+        "1. downgrade_in_infer(default), downgrade the outcome at inference "
+        "time"
+        "   train: out = input * mask"
+        "   inference: out = input * dropout_prob"
+        "2. upscale_in_train, upscale the outcome at training time, do nothing "
+        "in inference"
+        "   train: out = input * mask / ( 1.0 - dropout_prob )"
+        "   inference: out = input"
+        "   dropout op can be removed from the program. the program will be "
+        "efficient")
+        .SetDefault("downgrade_in_infer")
+        .AddCustomChecker([](const std::string& type) {
+          PADDLE_ENFORCE(
+              type == "downgrade_in_infer" || type == "upscale_in_train",
+              "dropout_implementation can only be downgrade_in_infer or "
+              "upscale_in_train");
+        });
 
     AddComment(R"DOC(
 Dropout Operator.
@@ -104,7 +128,9 @@ REGISTER_OPERATOR(dropout, ops::DropoutOp, ops::DropoutOpMaker,
                   paddle::framework::DefaultGradOpDescMaker<true>);
 REGISTER_OPERATOR(dropout_grad, ops::DropoutOpGrad);
 REGISTER_OP_CPU_KERNEL(
-    dropout, ops::CPUDropoutKernel<paddle::platform::CPUDeviceContext, float>);
+    dropout, ops::CPUDropoutKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::CPUDropoutKernel<paddle::platform::CPUDeviceContext, double>);
 REGISTER_OP_CPU_KERNEL(
     dropout_grad,
-    ops::DropoutGradKernel<paddle::platform::CPUDeviceContext, float>);
+    ops::DropoutGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::DropoutGradKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/dropout_op.cu

@@ -17,6 +17,7 @@ limitations under the License. */
 #include <thrust/iterator/counting_iterator.h>
 #include <thrust/random.h>
 #include <thrust/transform.h>
+#include <string>
 #include "paddle/fluid/operators/dropout_op.h"
 #include "paddle/fluid/platform/float16.h"
 
@@ -26,7 +27,8 @@ namespace operators {
 template <typename T>
 __global__ void RandomGenerator(const size_t n, const int seed,
                                 const float dropout_prob, const T* src,
-                                T* mask_data, T* dst) {
+                                T* mask_data, T* dst,
+                                bool is_upscale_in_train) {
   thrust::minstd_rand rng;
   rng.seed(seed);
   thrust::uniform_real_distribution<float> dist(0, 1);
@@ -47,7 +49,11 @@ __global__ void RandomGenerator(const size_t n, const int seed,
     if (dist(rng) < dropout_prob) {
       mask = static_cast<T>(0);
     } else {
-      mask = static_cast<T>(1);
+      if (is_upscale_in_train) {
+        mask = static_cast<T>(1.0f / (1.0f - dropout_prob));
+      } else {
+        mask = static_cast<T>(1);
+      }
     }
     dest = s * mask;
     mask_data[idx] = mask;
@@ -67,6 +73,8 @@ class GPUDropoutKernel : public framework::OpKernel<T> {
     y->mutable_data<T>(context.GetPlace());
     float dropout_prob = context.Attr<float>("dropout_prob");
 
+    auto dropout_implementation =
+        context.Attr<std::string>("dropout_implementation");
     auto& place = *context.template device_context<Place>().eigen_device();
     if (!context.Attr<bool>("is_test")) {
       auto* mask = context.Output<Tensor>("Mask");
@@ -83,11 +91,16 @@ class GPUDropoutKernel : public framework::OpKernel<T> {
       int grid = (x->numel() + threads - 1) / threads;
       RandomGenerator<
           T><<<grid, threads, 0, context.cuda_device_context().stream()>>>(
-          size, seed, dropout_prob, x_data, mask_data, y_data);
+          size, seed, dropout_prob, x_data, mask_data, y_data,
+          (dropout_implementation == "upscale_in_train"));
     } else {
       auto X = EigenMatrix<T>::Reshape(*x, 1);
       auto Y = EigenMatrix<T>::Reshape(*y, 1);
-      Y.device(place) = X * static_cast<T>(1.0f - dropout_prob);
+      if (dropout_implementation == "upscale_in_train") {
+        Y.device(place) = X;
+      } else {
+        Y.device(place) = X * static_cast<T>(1.0f - dropout_prob);
+      }
     }
   }
 };
@@ -99,6 +112,8 @@ namespace ops = paddle::operators;
 namespace plat = paddle::platform;
 REGISTER_OP_CUDA_KERNEL(
     dropout, ops::GPUDropoutKernel<plat::CUDADeviceContext, float>,
-    ops::GPUDropoutKernel<plat::CUDADeviceContext, plat::float16>);
-REGISTER_OP_CUDA_KERNEL(dropout_grad,
-                        ops::DropoutGradKernel<plat::CUDADeviceContext, float>);
+    ops::GPUDropoutKernel<plat::CUDADeviceContext, plat::float16>,
+    ops::GPUDropoutKernel<plat::CUDADeviceContext, double>);
+REGISTER_OP_CUDA_KERNEL(
+    dropout_grad, ops::DropoutGradKernel<plat::CUDADeviceContext, float>,
+    ops::DropoutGradKernel<plat::CUDADeviceContext, double>);

paddle/fluid/operators/dropout_op.h

@@ -14,6 +14,7 @@ limitations under the License. */
 #pragma once
 
 #include <random>
+#include <string>
 
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
@@ -36,6 +37,8 @@ class CPUDropoutKernel : public framework::OpKernel<T> {
     auto* y_data = y->mutable_data<T>(context.GetPlace());
     float dropout_prob = context.Attr<float>("dropout_prob");
 
+    auto dropout_implementation =
+        context.Attr<std::string>("dropout_implementation");
     if (!context.Attr<bool>("is_test")) {
       auto* mask = context.Output<Tensor>("Mask");
       auto* mask_data = mask->mutable_data<T>(context.GetPlace());
@@ -49,14 +52,20 @@ class CPUDropoutKernel : public framework::OpKernel<T> {
       engine.seed(seed);
 
       std::uniform_real_distribution<float> dist(0, 1);
+
       size_t size = framework::product(mask->dims());
       for (size_t i = 0; i < size; ++i) {
         if (dist(engine) < dropout_prob) {
           mask_data[i] = 0;
           y_data[i] = 0;
         } else {
-          mask_data[i] = 1;
-          y_data[i] = x_data[i];
+          if (dropout_implementation == "upscale_in_train") {
+            mask_data[i] = 1.0f / static_cast<T>(1.0f - dropout_prob);
+            y_data[i] = x_data[i] / static_cast<T>(1.0f - dropout_prob);
+          } else {
+            mask_data[i] = 1;
+            y_data[i] = x_data[i];
+          }
         }
       }
     } else {
@@ -64,7 +73,11 @@ class CPUDropoutKernel : public framework::OpKernel<T> {
       auto Y = EigenMatrix<T>::Reshape(*y, 1);
       auto& place =
           *context.template device_context<DeviceContext>().eigen_device();
-      Y.device(place) = X * (1.0f - dropout_prob);
+      if (dropout_implementation == "upscale_in_train") {
+        Y.device(place) = X;
+      } else {
+        Y.device(place) = X * static_cast<T>(1.0f - dropout_prob);
+      }
     }
   }
 };

paddle/fluid/operators/elementwise_op.h

@@ -80,8 +80,6 @@ class ElementwiseOpMaker : public framework::OpProtoAndCheckerMaker {
   void Make() final {
     AddInput("X", "(Tensor), The first input tensor of elementwise op.");
     AddInput("Y", "(Tensor), The second input tensor of elementwise op.");
-    // AddOutput("SavedShape", "(Tensor), save X, Y shape for grad to save
-    // memory.").AsIntermediate();
     AddOutput("Out", "The output of elementwise op.");
     AddAttr<int>("axis",
                  "(int, default -1). The start dimension index "
@@ -129,13 +127,11 @@ But the output only shares the LoD information with the input $X$.
 
 )DOC",
                                GetName(), GetEquation()));
-    SetReuse();
   }
 
  protected:
   virtual std::string GetName() const = 0;
   virtual std::string GetEquation() const = 0;
-  virtual void SetReuse() {}
 };
 
 class ElementwiseOpGrad : public framework::OperatorWithKernel {
@@ -269,7 +265,6 @@ class ElemwiseGradKernel : public framework::OpKernel<T> {
    protected:                                                          \
     virtual std::string GetName() const { return op_name; }            \
     virtual std::string GetEquation() const { return equation; }       \
-    virtual void SetReuse() { Reuse(__VA_ARGS__); }                    \
   };                                                                   \
   REGISTER_OPERATOR(op_type, ::paddle::operators::ElementwiseOp,       \
                     __ElemwiseOp##op_type##Maker__,                    \

paddle/fluid/operators/fusion_gru_op.cc

@@ -16,10 +16,9 @@ limitations under the License. */
 #include <cstring>  // for memcpy
 #include <string>
 #include "paddle/fluid/operators/math/blas.h"
-#include "paddle/fluid/operators/math/cpu_vec.h"
 #include "paddle/fluid/operators/math/fc_compute.h"
+#include "paddle/fluid/operators/math/jit_kernel.h"
 #include "paddle/fluid/operators/math/sequence2batch.h"
-#include "paddle/fluid/platform/cpu_info.h"
 
 namespace paddle {
 namespace operators {
@@ -174,58 +173,44 @@ class FusionGRUKernel : public framework::OpKernel<T> {
     }
   }
 
-#define INIT_VEC_FUNC                                                     \
-  std::function<void(const int, const T *, T *)> act_gate, act_state;     \
-  std::function<void(const int, const T*, const T*, const T*, T*)> cross; \
-  auto& act_gate_str = ctx.Attr<std::string>("gate_activation");          \
-  auto& act_state_str = ctx.Attr<std::string>("activation");              \
-  if (platform::jit::MayIUse(platform::jit::avx)) {                       \
-    math::VecActivations<T, platform::jit::avx> act_functor;              \
-    act_gate = act_functor(act_gate_str);                                 \
-    act_state = act_functor(act_state_str);                               \
-    cross = math::vec_cross<T, platform::jit::avx>;                       \
-  } else {                                                                \
-    math::VecActivations<T, platform::jit::isa_any> act_functor;          \
-    act_gate = act_functor(act_gate_str);                                 \
-    act_state = act_functor(act_state_str);                               \
-    cross = math::vec_cross<T, platform::jit::isa_any>;                   \
-  }
-
-#define INIT_BASE_INPUT_OUTPUT                        \
-  auto* h0 = ctx.Input<Tensor>("H0");                 \
-  auto* wx = ctx.Input<Tensor>("WeightX");            \
-  auto* wh = ctx.Input<Tensor>("WeightH");            \
-  auto* bias = ctx.Input<Tensor>("Bias");             \
-  auto* xx = ctx.Output<LoDTensor>("XX");             \
-  auto* hidden_out = ctx.Output<LoDTensor>("Hidden"); \
-  bool is_reverse = ctx.Attr<bool>("is_reverse");
-
-#define INIT_BASE_SIZES                  \
-  auto x_dims = x->dims();   /* T x M*/  \
-  auto wh_dims = wh->dims(); /* D x 3D*/ \
-  const int total_T = x_dims[0];         \
-  const int M = x_dims[1];               \
-  const int D = wh_dims[0];              \
-  const int D3 = wh_dims[1];             \
-  const int D2 = D * 2;
+#define INIT_BASE_DEFINES                  \
+  auto* x = ctx.Input<LoDTensor>("X");     \
+  auto* wh = ctx.Input<Tensor>("WeightH"); \
+  auto* xx = ctx.Output<LoDTensor>("XX");  \
+  auto x_lod = x->lod();                   \
+  auto x_dims = x->dims();   /* T x M*/    \
+  auto wh_dims = wh->dims(); /* D x 3D*/   \
+  const int total_T = x_dims[0];           \
+  const int D3 = wh_dims[1]
+
+#define INIT_OTHER_DEFINES                                                     \
+  auto* h0 = ctx.Input<Tensor>("H0");                                          \
+  auto* wx = ctx.Input<Tensor>("WeightX");                                     \
+  auto* bias = ctx.Input<Tensor>("Bias");                                      \
+  auto* hidden_out = ctx.Output<LoDTensor>("Hidden");                          \
+  bool is_reverse = ctx.Attr<bool>("is_reverse");                              \
+  const int M = x_dims[1];                                                     \
+  const int D = wh_dims[0];                                                    \
+  const int D2 = D * 2;                                                        \
+  const auto& ker = math::jitkernel::KernelPool::Instance()                    \
+                        .template Get<math::jitkernel::GRUKernel<T>,           \
+                                      const std::string&, const std::string&>( \
+                            ctx.Attr<std::string>("gate_activation"),          \
+                            ctx.Attr<std::string>("activation"), D);           \
+  const T* x_data = x->data<T>();                                              \
+  const T* wx_data = wx->data<T>();                                            \
+  const T* wh_data = wh->data<T>();                                            \
+  auto place = ctx.GetPlace();                                                 \
+  T* xx_data = xx->mutable_data<T>(place)
 
   void SeqCompute(const framework::ExecutionContext& ctx) const {
     using DeviceContext = paddle::platform::CPUDeviceContext;
-    auto* x = ctx.Input<LoDTensor>("X");
-    INIT_BASE_INPUT_OUTPUT
-    INIT_BASE_SIZES
-    INIT_VEC_FUNC
-
-    auto x_lod = x->lod();
+    INIT_BASE_DEFINES;
+    INIT_OTHER_DEFINES;
     const int N = x_lod[0].size() - 1;
-    const T* x_data = x->data<T>();
     const T* h0_data = h0 ? h0->data<T>() : nullptr;
-    const T* wx_data = wx->data<T>();
-    const T* wh_data = wh->data<T>();
     const T* wh_state_data = wh_data + D * D2;
-    T* xx_data = xx->mutable_data<T>(ctx.GetPlace());
-    T* hidden_out_data = hidden_out->mutable_data<T>(ctx.GetPlace());
-
+    T* hidden_out_data = hidden_out->mutable_data<T>(place);
     auto blas = math::GetBlas<DeviceContext, T>(ctx);
     math::FCCompute<DeviceContext, T>(blas, total_T, D3, M, x_data, wx_data,
                                       xx_data,
@@ -252,14 +237,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
       if (h0_data) {
         prev_hidden_data = h0_data + bid * D;
       } else {
-        // W: {W_update, W_reset; W_state}
-        // update gate
-        act_gate(D, xx_data, xx_data);
-        // state gate
-        act_state(D, xx_data + D2, xx_data + D2);
-        // out = a*b
-        blas.VMUL(D, xx_data, xx_data + D2, hidden_out_data);
-        // save prev
+        ker->ComputeH1(xx_data, hidden_out_data);
         prev_hidden_data = hidden_out_data;
         tstart = 1;
         move_step();
@@ -269,17 +247,12 @@ class FusionGRUKernel : public framework::OpKernel<T> {
         blas.GEMM(CblasNoTrans, CblasNoTrans, 1, D2, D, static_cast<T>(1),
                   prev_hidden_data, D, wh_data, D2, static_cast<T>(1), xx_data,
                   D3);
-        act_gate(D2, xx_data, xx_data);
-        // rt = rt*ht_1 inplace result
-        blas.VMUL(D, prev_hidden_data, xx_data + D, hidden_out_data);
-
+        ker->ComputeHtPart1(xx_data, prev_hidden_data, hidden_out_data);
         // gemm rt * Ws
         blas.GEMM(CblasNoTrans, CblasNoTrans, 1, D, D, static_cast<T>(1),
                   hidden_out_data, D, wh_state_data, D, static_cast<T>(1),
                   xx_data + D2, D3);
-        act_state(D, xx_data + D2, xx_data + D2);
-        // out = zt*ht~ + (1-zt)*ht_1
-        cross(D, xx_data, xx_data + D2, prev_hidden_data, hidden_out_data);
+        ker->ComputeHtPart2(xx_data, prev_hidden_data, hidden_out_data);
         // save prev
         prev_hidden_data = hidden_out_data;
         move_step();
@@ -289,28 +262,19 @@ class FusionGRUKernel : public framework::OpKernel<T> {
 
   void BatchCompute(const framework::ExecutionContext& ctx) const {
     using DeviceContext = paddle::platform::CPUDeviceContext;
-    auto* x = ctx.Input<LoDTensor>("X");
-    INIT_BASE_INPUT_OUTPUT
-    INIT_BASE_SIZES
-    if (x->lod()[0].size() == 2) {
+    INIT_BASE_DEFINES;
+    if (x_lod[0].size() == 2) {
       xx->Resize({total_T, D3});
       SeqCompute(ctx);
       return;
     }
-    INIT_VEC_FUNC
-
+    INIT_OTHER_DEFINES;
     auto* reordered_h0 = ctx.Output<Tensor>("ReorderedH0");
     auto* batched_input = ctx.Output<LoDTensor>("BatchedInput");
     auto* batched_out = ctx.Output<LoDTensor>("BatchedOut");
-
-    const T* x_data = x->data<T>();
-    const T* wx_data = wx->data<T>();
-    const T* wh_data = wh->data<T>();
-    T* xx_data = xx->mutable_data<T>(ctx.GetPlace());
-    T* batched_input_data = batched_input->mutable_data<T>(ctx.GetPlace());
-    T* batched_out_data = batched_out->mutable_data<T>(ctx.GetPlace());
-    hidden_out->mutable_data<T>(ctx.GetPlace());
-
+    T* batched_input_data = batched_input->mutable_data<T>(place);
+    T* batched_out_data = batched_out->mutable_data<T>(place);
+    hidden_out->mutable_data<T>(place);
     auto& dev_ctx = ctx.template device_context<DeviceContext>();
     auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
     math::LoDTensor2BatchFunctor<DeviceContext, T> to_batch;
@@ -336,7 +300,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
     T* prev_hidden_data = nullptr;
     if (h0) {
       // reorder h0
-      T* reordered_h0_data = reordered_h0->mutable_data<T>(ctx.GetPlace());
+      T* reordered_h0_data = reordered_h0->mutable_data<T>(place);
       const T* h0_data = h0->data<T>();
       prev_hidden_data = reordered_h0_data;
       size_t sz = sizeof(T) * D;
@@ -350,12 +314,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
       T* cur_out_data = batched_out_data;
       // W: {W_update, W_reset; W_state}
       for (int i = 0; i < max_bs; ++i) {
-        // update gate
-        act_gate(D, cur_in_data, cur_in_data);
-        // state gate
-        act_state(D, cur_in_data + D2, cur_in_data + D2);
-        // out = a*b
-        blas.VMUL(D, cur_in_data, cur_in_data + D2, cur_out_data);
+        ker->ComputeH1(cur_in_data, cur_out_data);
         // add offset
         cur_in_data += D3;
         cur_out_data += D;
@@ -380,10 +339,8 @@ class FusionGRUKernel : public framework::OpKernel<T> {
       T* cur_out_data = batched_out_data;
       T* cur_prev_hidden_data = prev_hidden_data;
       for (int i = 0; i < cur_bs; ++i) {
-        act_gate(D2, cur_batched_data, cur_batched_data);
-        // rt = rt*ht_1 inplace result
-        blas.VMUL(D, cur_prev_hidden_data, cur_batched_data + D, cur_out_data);
-
+        ker->ComputeHtPart1(cur_batched_data, cur_prev_hidden_data,
+                            cur_out_data);
         cur_batched_data += D3;
         cur_prev_hidden_data += D;
         cur_out_data += D;
@@ -397,12 +354,8 @@ class FusionGRUKernel : public framework::OpKernel<T> {
 
       cur_prev_hidden_data = prev_hidden_data;
       for (int i = 0; i < cur_bs; ++i) {
-        // ht~ = act_state(...)
-        act_state(D, cur_batched_data + D2, cur_batched_data + D2);
-        // out = zt*ht~ + (1-zt)*ht_1
-        cross(D, cur_batched_data, cur_batched_data + D2, cur_prev_hidden_data,
-              cur_out_data);
-
+        ker->ComputeHtPart2(cur_batched_data, cur_prev_hidden_data,
+                            cur_out_data);
         cur_batched_data += D3;
         cur_prev_hidden_data += D;
         cur_out_data += D;
@@ -416,9 +369,8 @@ class FusionGRUKernel : public framework::OpKernel<T> {
     batched_out->set_lod(batched_lod);
     to_seq(dev_ctx, *batched_out, hidden_out);
   }
-#undef INIT_VEC_FUNC
-#undef INIT_BASE_SIZES
-#undef INIT_BASE_INPUT_OUTPUT
+#undef INIT_OTHER_DEFINES
+#undef INIT_BASE_DEFINES
 };
 
 }  // namespace operators

paddle/fluid/operators/hash_op.cc

+/* Copyright (c) 2016 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/hash_op.h"
+#include <string>
+#include <vector>
+
+namespace paddle {
+namespace operators {
+
+class HashOp : public framework::OperatorWithKernel {
+ public:
+  HashOp(const std::string &type, const framework::VariableNameMap &inputs,
+         const framework::VariableNameMap &outputs,
+         const framework::AttributeMap &attrs)
+      : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of HashOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of HashOp should not be null.");
+
+    auto dims = ctx->GetInputDim("X");
+    PADDLE_ENFORCE_EQ(dims.size(), 2UL,
+                      "The input of hash_op's dimensions must be 2");
+    std::vector<int64_t> out_dims;
+    out_dims.reserve(dims.size() + 1);
+    // copy all dims except the last one
+    for (size_t i = 0u; i != dims.size() - 1; ++i) {
+      out_dims.emplace_back(dims[i]);
+    }
+    int num_hash = ctx->Attrs().Get<int>("num_hash");
+    out_dims.emplace_back(num_hash);
+    // keep the last dim to 1
+    out_dims.emplace_back(1);
+
+    ctx->SetOutputDim("Out", framework::make_ddim(out_dims));
+    ctx->ShareLoD("X", /*->*/ "Out");
+  }
+};
+
+class HashOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X", "(Tensor) Input tensor of scale operator.");
+    AddOutput("Out", "(Tensor) Output tensor of scale operator.");
+    AddComment(R"DOC(
+**Hash Operator**
+$$Out = scale * X$$
+)DOC");
+    AddAttr<int>("num_hash", "").SetDefault(1);
+    AddAttr<int>("mod_by", "").SetDefault(100000);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_WITHOUT_GRADIENT(hash, ops::HashOp, ops::HashOpMaker);
+REGISTER_OP_CPU_KERNEL(hash, ops::HashKerel<int>, ops::HashKerel<int64_t>);

paddle/fluid/operators/hash_op.h

+/* Copyright (c) 2016 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
+
+extern "C" {
+#include <xxhash.h>
+}
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+// template <typename DeviceContext, typename T>
+template <typename T>
+class HashKerel : public framework::OpKernel<T> {
+ public:
+  virtual void Compute(const framework::ExecutionContext& context) const {
+    auto* out_t = context.Output<framework::LoDTensor>("Out");
+    auto* in_t = context.Input<framework::LoDTensor>("X");
+    int mod_by = context.Attr<int>("mod_by");
+    int num_hash = context.Attr<int>("num_hash");
+    auto* output = out_t->mutable_data<T>(context.GetPlace());
+
+    auto in_dims = in_t->dims();
+    auto in_lod = in_t->lod();
+    PADDLE_ENFORCE_EQ(
+        static_cast<uint64_t>(in_dims[0]), in_lod[0].back(),
+        "The actual input data's size mismatched with LoD information.");
+
+    auto seq_length = in_dims[0];
+    auto last_dim = in_dims[in_dims.size() - 1];
+    auto* input = in_t->data<T>();
+    for (int idx = 0; idx < seq_length; ++idx) {
+      for (int ihash = 0; ihash != num_hash; ++ihash) {
+        output[idx * num_hash + ihash] =
+            XXH64(input, sizeof(int) * last_dim, ihash) % mod_by;
+      }
+      input += last_dim;
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/lars_momentum_op.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/lars_momentum_op.h"
+#include "paddle/fluid/operators/momentum_op.h"
+
+namespace paddle {
+namespace operators {
+
+class LarsMomentumOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("Param",
+             "(LoDTensor, default LoDTensor<float>) "
+             "Input parameter that has to be updated");
+    AddInput("Grad",
+             "(LoDTensor, default LoDTensor<float>) "
+             "Input gradient of the parameter");
+    AddInput("Velocity",
+             "(LoDTensor, default LoDTensor<float>) "
+             "Input velocity (corresponding to the parameter) "
+             "that has to be updated");
+    AddInput("LearningRate",
+             "(LoDTensor, default LoDTensor<float>) "
+             "Input learning rate");
+
+    AddOutput("ParamOut",
+              "(LoDTensor) This output is updated parameter. "
+              "It shared memory with Input(Param).");
+    AddOutput("VelocityOut",
+              "(LoDTensor) This output is updated velocity. "
+              "It shared memory with Input(Velocity).");
+
+    AddAttr<float>("mu", "(float) Momentum coefficient");
+    AddAttr<float>("lars_coeff", "(float, default 0.001) LARS coefficient.")
+        .SetDefault(0.001);
+    AddAttr<float>("lars_weight_decay",
+                   "(float, default 0.0005) LARS weight decay")
+        .SetDefault(0.0005);
+
+    AddComment(R"DOC(
+Lars Momentum Optimizer.
+
+This optimizer use LARS (https://arxiv.org/abs/1708.03888) to optimize each
+weight using a local learning rate:
+
+$$
+local\_lr = \eta  * 
+    \frac{\left \| param \right \|}{\left \| grad \right \| + \beta *\left \| param \right \|} \\
+velocity = mu * velocity + 
+    local\_lr * (grad + \beta * param) \\
+param = param - velocity. \\
+$$
+
+Note that we use lars_weight_decay here to decay weights, you may need not to
+use L2 regularizers in case of using LARS.
+
+)DOC");
+  }
+};
+
+class LarsMomentumOpVarTypeInference : public framework::VarTypeInference {
+ public:
+  void operator()(const framework::OpDesc &op_desc,
+                  framework::BlockDesc *block) const override {}
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(lars_momentum, ops::MomentumOp, ops::LarsMomentumOpMaker,
+                  paddle::framework::EmptyGradOpMaker,
+                  ops::LarsMomentumOpVarTypeInference);
+REGISTER_OP_CPU_KERNEL(lars_momentum, ops::LarsMomentumOpKernel<float>,
+                       ops::LarsMomentumOpKernel<double>);

paddle/fluid/operators/lars_momentum_op.cu

+/* Copyright (c) 2016 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/framework/op_registry.h"
+#include "paddle/fluid/operators/lars_momentum_op.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+__global__ void MomentumLarsKernel(const T* p, const T* g, const T* v,
+                                   const T* learning_rate, const T mu,
+                                   const int64_t num, const T lars_coeff,
+                                   const T lars_weight_decay, const T* p_norm,
+                                   const T* g_norm, T* p_out, T* v_out) {
+  T lr = learning_rate[0];
+  T local_lr = learning_rate[0];
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < num;
+       i += blockDim.x * gridDim.x) {
+    if (p_norm[0] > 0 && g_norm[0] > 0) {
+      local_lr = lr * lars_coeff * p_norm[0] /
+                 (g_norm[0] + lars_weight_decay * p_norm[0]);
+    }
+    T v_new = v[i] * mu + local_lr * (g[i] + lars_weight_decay * p[i]);
+    v_out[i] = v_new;
+    p_out[i] = p[i] - v_new;
+  }
+}
+
+template <typename DeviceContext, typename T>
+class LarsMomentumOpCUDAKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto param_out = ctx.Output<framework::LoDTensor>("ParamOut");
+    auto velocity_out = ctx.Output<framework::LoDTensor>("VelocityOut");
+    auto param = ctx.Input<framework::LoDTensor>("Param");
+    auto velocity = ctx.Input<framework::LoDTensor>("Velocity");
+    auto grad = ctx.Input<framework::LoDTensor>("Grad");
+    auto learning_rate = ctx.Input<framework::LoDTensor>("LearningRate");
+
+    T* p_out = param_out->mutable_data<T>(ctx.GetPlace());
+    T* v_out = velocity_out->mutable_data<T>(ctx.GetPlace());
+
+    T mu = static_cast<T>(ctx.Attr<float>("mu"));
+    T lars_coeff = ctx.Attr<float>("lars_coeff");
+    T lars_weight_decay = ctx.Attr<float>("lars_weight_decay");
+
+    auto* p = param->data<T>();
+    auto* v = velocity->data<T>();
+    auto* g = grad->data<T>();
+    auto* lr = learning_rate->data<T>();
+
+    int block = 512;
+    int grid = (param->numel() + block - 1) / block;
+
+    auto eigen_p = framework::EigenVector<T>::Flatten(*param);
+    auto eigen_g = framework::EigenVector<T>::Flatten(*grad);
+    // calculate norms using eigein and launch the kernel.
+    framework::Tensor p_norm_t, g_norm_t;
+    p_norm_t.Resize({1});
+    g_norm_t.Resize({1});
+    auto* p_norm_data = p_norm_t.mutable_data<T>(ctx.GetPlace());
+    auto* g_norm_data = g_norm_t.mutable_data<T>(ctx.GetPlace());
+    auto ep_norm = framework::EigenScalar<T>::From(p_norm_t);
+    auto eg_norm = framework::EigenScalar<T>::From(g_norm_t);
+
+    auto* place = ctx.template device_context<DeviceContext>().eigen_device();
+    ep_norm.device(*place) = eigen_p.square().sum().sqrt();
+    eg_norm.device(*place) = eigen_g.square().sum().sqrt();
+    MomentumLarsKernel<<<grid, block, 0, ctx.cuda_device_context().stream()>>>(
+        p, g, v, lr, mu, param->numel(), lars_coeff, lars_weight_decay,
+        p_norm_data, g_norm_data, p_out, v_out);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(
+    lars_momentum,
+    ops::LarsMomentumOpCUDAKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::LarsMomentumOpCUDAKernel<paddle::platform::CUDADeviceContext, double>);

paddle/fluid/operators/lars_momentum_op.h

+/* Copyright (c) 2016 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 {
+
+template <typename T>
+class LarsMomentumOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto param_out = ctx.Output<framework::LoDTensor>("ParamOut");
+    auto velocity_out = ctx.Output<framework::LoDTensor>("VelocityOut");
+    auto param = ctx.Input<framework::LoDTensor>("Param");
+    auto velocity = ctx.Input<framework::LoDTensor>("Velocity");
+    auto learning_rate = ctx.Input<framework::LoDTensor>("LearningRate");
+    auto* grad_var = ctx.InputVar("Grad");
+    // only support dense for now.
+    PADDLE_ENFORCE(grad_var->IsType<framework::LoDTensor>());
+    auto grad = ctx.Input<framework::LoDTensor>("Grad");
+
+    param_out->mutable_data<T>(ctx.GetPlace());
+    velocity_out->mutable_data<T>(ctx.GetPlace());
+
+    T mu = static_cast<T>(ctx.Attr<float>("mu"));
+    T lars_coeff = ctx.Attr<float>("lars_coeff");
+    T lars_weight_decay = ctx.Attr<float>("lars_weight_decay");
+
+    auto p_out = framework::EigenVector<T>::Flatten(*param_out);
+    auto v_out = framework::EigenVector<T>::Flatten(*velocity_out);
+
+    auto p = framework::EigenVector<T>::Flatten(*param);
+    auto v = framework::EigenVector<T>::Flatten(*velocity);
+    auto g = framework::EigenVector<T>::Flatten(*grad);
+    auto* lr = learning_rate->data<T>();
+
+    framework::Tensor p_norm_t, g_norm_t;
+    p_norm_t.Resize({1});
+    g_norm_t.Resize({1});
+    p_norm_t.mutable_data<T>(ctx.GetPlace());
+    g_norm_t.mutable_data<T>(ctx.GetPlace());
+    auto ep_norm = framework::EigenScalar<T>::From(p_norm_t);
+    auto eg_norm = framework::EigenScalar<T>::From(g_norm_t);
+
+    ep_norm = p.square().sum().sqrt();
+    eg_norm = g.square().sum().sqrt();
+    T local_lr = lr[0];
+    if (ep_norm(0) > 0 && eg_norm(0) > 0) {
+      local_lr = lr[0] * lars_coeff * ep_norm(0) /
+                 (eg_norm(0) + lars_weight_decay * ep_norm(0));
+    }
+    v_out = v * mu + local_lr * (g + lars_weight_decay * p);
+    p_out = p - v_out;
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/lookup_table_op.cc

@@ -81,6 +81,12 @@ class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
                      "Otherwise the given value indicates padding the output "
                      "with zeros whenever lookup encounters it in Ids.")
         .SetDefault(kNoPadding);
+    // NOTE(minqiyang): grad_inplace is an temporal attribute,
+    // please do NOT set this attribute in python layer.
+    AddAttr<bool>("grad_inplace",
+                  "(boolean, default false) "
+                  "If the grad op reuse the input's variable.")
+        .SetDefault(false);
     AddComment(R"DOC(
 Lookup Table Operator.
 

paddle/fluid/operators/lookup_table_op.h

@@ -21,6 +21,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/selected_rows.h"
+#include "paddle/fluid/operators/math/blas.h"
 
 namespace paddle {
 namespace operators {
@@ -68,6 +69,7 @@ class LookupTableKernel : public framework::OpKernel<T> {
       const auto *table = table_t.value().data<T>();
       auto *output = output_t->mutable_data<T>(context.GetPlace());
 
+      auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
       for (int64_t i = 0; i < ids_numel; ++i) {
         if (padding_idx != kNoPadding && ids[i] == padding_idx) {
           memset(output + i * row_width, 0, row_width * sizeof(T));
@@ -75,8 +77,8 @@ class LookupTableKernel : public framework::OpKernel<T> {
           PADDLE_ENFORCE_GE(ids[i], 0);
           auto id_index = table_t.Index(ids[i]);
           PADDLE_ENFORCE_GE(id_index, 0, "the input key should be exists.");
-          memcpy(output + i * row_width, table + id_index * row_width,
-                 row_width * sizeof(T));
+          blas.VCOPY(row_width, table + id_index * row_width,
+                     output + i * row_width);
         }
       }
     }
@@ -111,27 +113,37 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
       auto *ids_data = ids->data<int64_t>();
       int64_t ids_num = ids->numel();
 
-      framework::Vector<int64_t> new_rows;
-      new_rows.reserve(ids_num);
-      for (int64_t i = 0; i < ids_num; i++) {
-        new_rows.push_back(ids_data[i]);
-      }
+      std::vector<int64_t> new_rows;
+      new_rows.resize(ids_num);
+      std::memcpy(&new_rows[0], ids_data, ids_num * sizeof(int64_t));
       d_table->set_rows(new_rows);
 
       auto *d_table_value = d_table->mutable_value();
       d_table_value->Resize({ids_num, table_dim[1]});
-      d_table_value->mutable_data<T>(context.GetPlace());
-
-      d_table->set_height(table_dim[0]);
-
-      auto *d_output_data = d_output->data<T>();
-      auto *d_table_data = d_table_value->data<T>();
-
-      auto d_output_dims = d_output->dims();
-      PADDLE_ENFORCE_EQ(
-          d_table_value->dims(),
-          framework::flatten_to_2d(d_output_dims, d_output_dims.size() - 1));
-      memcpy(d_table_data, d_output_data, sizeof(T) * d_output->numel());
+      // FIXME(minqiyang):
+      // memory optimization will NOT reuse Tensor with SelectedRows
+      // so we could just share the tensor here directly.
+      // However, the InferVarType method will infer the output SelectedRows
+      // to Tensor sometimes, which is a bug, so we will add an attribute
+      // here to indicate the inplace and remove this attribute after
+      // the InferVarType's bug was fixed
+      bool grad_inplace = context.Attr<bool>("grad_inplace");
+      if (grad_inplace) {
+        d_table_value->ShareDataWith(*d_output);
+      } else {
+        d_table_value->mutable_data<T>(context.GetPlace());
+
+        d_table->set_height(table_dim[0]);
+
+        auto *d_output_data = d_output->data<T>();
+        auto *d_table_data = d_table_value->data<T>();
+
+        auto d_output_dims = d_output->dims();
+        PADDLE_ENFORCE_EQ(
+            d_table_value->dims(),
+            framework::flatten_to_2d(d_output_dims, d_output_dims.size() - 1));
+        memcpy(d_table_data, d_output_data, sizeof(T) * d_output->numel());
+      }
     } else {
       auto *ids = context.Input<LoDTensor>("Ids");
       auto *d_output = context.Input<LoDTensor>(framework::GradVarName("Out"));

paddle/fluid/operators/math/CMakeLists.txt

@@ -68,6 +68,7 @@ cc_test(selected_rows_functor_test SRCS selected_rows_functor_test.cc DEPS selec
 cc_test(im2col_test SRCS im2col_test.cc DEPS im2col)
 cc_test(vol2col_test SRCS vol2col_test.cc DEPS vol2col)
 cc_test(sequence_padding_test SRCS sequence_padding_test.cc DEPS sequence_padding)
+cc_test(sequence_pooling_test SRCS sequence_pooling_test.cc DEPS sequence_pooling)
 if(WITH_GPU)
     nv_test(math_function_gpu_test SRCS math_function_test.cu DEPS math_function)
     nv_test(selected_rows_functor_gpu_test SRCS selected_rows_functor_test.cu DEPS selected_rows_functor math_function)
@@ -75,6 +76,6 @@ endif()
 cc_test(concat_test SRCS concat_test.cc DEPS concat_and_split)
 cc_test(cpu_vec_test SRCS cpu_vec_test.cc DEPS blas cpu_info)
 cc_library(jit_kernel 
-    SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_lstm.cc
+    SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_rnn.cc
     DEPS cpu_info cblas)
 cc_test(jit_kernel_test SRCS jit_kernel_test.cc DEPS jit_kernel)

paddle/fluid/operators/math/algorithm.h

@@ -39,6 +39,52 @@ HOSTDEVICE inline int64_t BinarySearch(const T *x, int64_t num, const T &val) {
   return -1;
 }
 
+template <typename T>
+HOSTDEVICE inline size_t LowerBound(const T *x, size_t num, const T &val) {
+#ifdef __CUDA_ARCH__
+  // The following code is from
+  // https://en.cppreference.com/w/cpp/algorithm/lower_bound
+  auto *first = x;
+  int64_t count = static_cast<int64_t>(num);
+  while (count > 0) {
+    int64_t step = (count >> 1);
+    auto *it = first + step;
+    if (*it < val) {
+      first = ++it;
+      count -= (step + 1);
+    } else {
+      count = step;
+    }
+  }
+  return static_cast<size_t>(first - x);
+#else
+  return static_cast<size_t>(std::lower_bound(x, x + num, val) - x);
+#endif
+}
+
+template <typename T>
+HOSTDEVICE inline size_t UpperBound(const T *x, size_t num, const T &val) {
+#ifdef __CUDA_ARCH__
+  // The following code is from
+  // https://en.cppreference.com/w/cpp/algorithm/upper_bound
+  auto *first = x;
+  int64_t count = static_cast<int64_t>(num);
+  while (count > 0) {
+    auto step = (count >> 1);
+    auto *it = first + step;
+    if (val < *it) {
+      count = step;
+    } else {
+      first = ++it;
+      count -= (step + 1);
+    }
+  }
+  return static_cast<size_t>(first - x);
+#else
+  return static_cast<size_t>(std::upper_bound(x, x + num, val) - x);
+#endif
+}
+
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/math/jit_kernel.h

@@ -142,6 +142,15 @@ class LSTMKernel : public Kernel {
                            const T *wp_data = nullptr) const = 0;
 };
 
+template <typename T>
+class GRUKernel : public Kernel {
+ public:
+  // compute h1 without h0
+  virtual void ComputeH1(T *gates, T *ht) const = 0;
+  virtual void ComputeHtPart1(T *gates, const T *ht_1, T *ht) const = 0;
+  virtual void ComputeHtPart2(T *gates, const T *ht_1, T *ht) const = 0;
+};
+
 }  // namespace jitkernel
 }  // namespace math
 }  // namespace operators

paddle/fluid/operators/math/jit_kernel_lstm.cc → paddle/fluid/operators/math/jit_kernel_rnn.cc

@@ -136,6 +136,23 @@ static std::shared_ptr<const VActKernel<T>> GetActKernel(
   return nullptr;
 }
 
+#ifdef __AVX__
+template <jit::cpu_isa_t isa>
+static std::unique_ptr<AVXAct> GetAVXAct(const std::string& type) {
+  if (type == "sigmoid") {
+    return std::unique_ptr<AVXAct>(new AVXActImpl<kSigmoid, isa>());
+  } else if (type == "relu") {
+    return std::unique_ptr<AVXAct>(new AVXActImpl<kRelu, isa>());
+  } else if (type == "tanh") {
+    return std::unique_ptr<AVXAct>(new AVXActImpl<kTanh, isa>());
+  } else if (type == "identity" || type == "") {
+    return std::unique_ptr<AVXAct>(new AVXActImpl<kIdentity, isa>());
+  }
+  PADDLE_THROW("Not support type: %s", type);
+  return nullptr;
+}
+#endif
+
 /* LSTM JitKernel */
 template <typename T, jit::cpu_isa_t isa, jit_block>
 class LSTMKernelImpl : public LSTMKernel<T> {
@@ -192,61 +209,49 @@ class LSTMKernelImpl : public LSTMKernel<T> {
 #endif
 };
 
-#define INTRI8_FLOAT(isa)                                                      \
-  template <>                                                                  \
-  LSTMKernelImpl<float, isa, kEQ8>::LSTMKernelImpl(                            \
-      const std::string& act_gate, const std::string& act_cand,                \
-      const std::string& act_cell, int d)                                      \
-      : LSTMKernel<float>() {                                                  \
-    auto GetAVXAct = [&](const std::string& type) -> std::unique_ptr<AVXAct> { \
-      if (type == "sigmoid") {                                                 \
-        return std::unique_ptr<AVXAct>(new AVXActImpl<kSigmoid, isa>());       \
-      } else if (type == "relu") {                                             \
-        return std::unique_ptr<AVXAct>(new AVXActImpl<kRelu, isa>());          \
-      } else if (type == "tanh") {                                             \
-        return std::unique_ptr<AVXAct>(new AVXActImpl<kTanh, isa>());          \
-      } else if (type == "identity" || type == "") {                           \
-        return std::unique_ptr<AVXAct>(new AVXActImpl<kIdentity, isa>());      \
-      }                                                                        \
-      PADDLE_THROW("Not support type: %s", type);                              \
-    };                                                                         \
-    avx_act_gate_ = GetAVXAct(act_gate);                                       \
-    avx_act_cand_ = GetAVXAct(act_cand);                                       \
-    avx_act_cell_ = GetAVXAct(act_cell);                                       \
-  }                                                                            \
-  template <>                                                                  \
-  void LSTMKernelImpl<float, isa, kEQ8>::ComputeCtHt(                          \
-      float* gates, const float* ct_1, float* ct, float* ht,                   \
-      const float* wp_data, float* checked) const {                            \
-    /* gates: W_ch, W_ih, W_fh, W_oh */                                        \
-    __m256 c, i, f, o;                                                         \
-    c = _mm256_loadu_ps(gates);                                                \
-    i = _mm256_loadu_ps(gates + 8);                                            \
-    f = _mm256_loadu_ps(gates + 16);                                           \
-    o = _mm256_loadu_ps(gates + 24);                                           \
-    /* C_t = C_t-1 * fgated + cand_gated * igated*/                            \
-    c = _mm256_mul_ps(avx_act_cand_->Compute(c), avx_act_gate_->Compute(i));   \
-    i = _mm256_loadu_ps(ct_1);                                                 \
-    f = _mm256_mul_ps(i, avx_act_gate_->Compute(f));                           \
-    f = _mm256_add_ps(c, f);                                                   \
-    _mm256_storeu_ps(ct, f);                                                   \
-    /* H_t = act_cell(C_t) * ogated */                                         \
-    o = _mm256_mul_ps(avx_act_cell_->Compute(f), avx_act_gate_->Compute(o));   \
-    _mm256_storeu_ps(ht, o);                                                   \
-  }                                                                            \
-  template <>                                                                  \
-  void LSTMKernelImpl<float, isa, kEQ8>::ComputeC1H1(                          \
-      float* gates, float* ct, float* ht, const float* wp_data) const {        \
-    __m256 c, i, o;                                                            \
-    c = _mm256_loadu_ps(gates);                                                \
-    i = _mm256_loadu_ps(gates + 8);                                            \
-    o = _mm256_loadu_ps(gates + 24);                                           \
-    /* C_t = igated * cgated*/                                                 \
-    c = _mm256_mul_ps(avx_act_gate_->Compute(i), avx_act_cand_->Compute(c));   \
-    _mm256_storeu_ps(ct, c);                                                   \
-    /* H_t = act_cell(C_t) * ogated */                                         \
-    o = _mm256_mul_ps(avx_act_cell_->Compute(c), avx_act_gate_->Compute(o));   \
-    _mm256_storeu_ps(ht, o);                                                   \
+#define INTRI8_FLOAT(isa)                                                    \
+  template <>                                                                \
+  LSTMKernelImpl<float, isa, kEQ8>::LSTMKernelImpl(                          \
+      const std::string& act_gate, const std::string& act_cand,              \
+      const std::string& act_cell, int d)                                    \
+      : LSTMKernel<float>() {                                                \
+    avx_act_gate_ = GetAVXAct<isa>(act_gate);                                \
+    avx_act_cand_ = GetAVXAct<isa>(act_cand);                                \
+    avx_act_cell_ = GetAVXAct<isa>(act_cell);                                \
+  }                                                                          \
+  template <>                                                                \
+  void LSTMKernelImpl<float, isa, kEQ8>::ComputeCtHt(                        \
+      float* gates, const float* ct_1, float* ct, float* ht,                 \
+      const float* wp_data, float* checked) const {                          \
+    /* gates: W_ch, W_ih, W_fh, W_oh */                                      \
+    __m256 c, i, f, o;                                                       \
+    c = _mm256_loadu_ps(gates);                                              \
+    i = _mm256_loadu_ps(gates + 8);                                          \
+    f = _mm256_loadu_ps(gates + 16);                                         \
+    o = _mm256_loadu_ps(gates + 24);                                         \
+    /* C_t = C_t-1 * fgated + cand_gated * igated*/                          \
+    c = _mm256_mul_ps(avx_act_cand_->Compute(c), avx_act_gate_->Compute(i)); \
+    i = _mm256_loadu_ps(ct_1);                                               \
+    f = _mm256_mul_ps(i, avx_act_gate_->Compute(f));                         \
+    f = _mm256_add_ps(c, f);                                                 \
+    _mm256_storeu_ps(ct, f);                                                 \
+    /* H_t = act_cell(C_t) * ogated */                                       \
+    o = _mm256_mul_ps(avx_act_cell_->Compute(f), avx_act_gate_->Compute(o)); \
+    _mm256_storeu_ps(ht, o);                                                 \
+  }                                                                          \
+  template <>                                                                \
+  void LSTMKernelImpl<float, isa, kEQ8>::ComputeC1H1(                        \
+      float* gates, float* ct, float* ht, const float* wp_data) const {      \
+    __m256 c, i, o;                                                          \
+    c = _mm256_loadu_ps(gates);                                              \
+    i = _mm256_loadu_ps(gates + 8);                                          \
+    o = _mm256_loadu_ps(gates + 24);                                         \
+    /* C_t = igated * cgated*/                                               \
+    c = _mm256_mul_ps(avx_act_gate_->Compute(i), avx_act_cand_->Compute(c)); \
+    _mm256_storeu_ps(ct, c);                                                 \
+    /* H_t = act_cell(C_t) * ogated */                                       \
+    o = _mm256_mul_ps(avx_act_cell_->Compute(c), avx_act_gate_->Compute(o)); \
+    _mm256_storeu_ps(ht, o);                                                 \
   }
 
 // TODO(TJ): optimize keq16
@@ -354,6 +359,126 @@ REGISTER_JITKERNEL_ARGS(lstm, LSTMKernel, JITKERNEL_DECLARE_LSTM,
 #undef JITKERNEL_DECLARE_LSTM
 #undef JITKERNEL_KEY_LSTM
 #undef JITKERNEL_NEW_LSTM_IMPL
+
+/* GRU JitKernel */
+template <typename T, jit::cpu_isa_t isa, jit_block>
+class GRUKernelImpl : public GRUKernel<T> {
+ public:
+  explicit GRUKernelImpl(const std::string& act_gate,
+                         const std::string& act_state, int d)
+      : GRUKernel<T>() {
+    d_ = d;
+    d2_ = d * 2;
+    act_gate_d2_ = GetActKernel<T>(act_gate, d2_);
+    act_gate_d_ = GetActKernel<T>(act_gate, d);
+    act_state_d_ = GetActKernel<T>(act_state, d);
+    vmul_d_ = KernelPool::Instance().template Get<VMulKernel<T>>(d);
+  }
+
+  void ComputeH1(T* gates, T* ht) const override {
+    act_gate_d_->Compute(gates, gates);
+    act_state_d_->Compute(gates + d2_, gates + d2_);
+    vmul_d_->Compute(gates, gates + d2_, ht);
+  }
+
+  void ComputeHtPart1(T* gates, const T* ht_1, T* ht) const override {
+    // W: {W_update, W_reset; W_state}
+    act_gate_d2_->Compute(gates, gates);
+    vmul_d_->Compute(ht_1, gates + d_, ht);
+  }
+
+  void ComputeHtPart2(T* gates, const T* ht_1, T* ht) const override {
+    T* y = gates + d2_;
+    act_state_d_->Compute(y, y);
+    // out = zt*ht~ + (1-zt)*ht_1
+    for (int i = 0; i < d_; ++i) {
+      ht[i] = gates[i] * y[i] + (static_cast<T>(1) - gates[i]) * ht_1[i];
+    }
+  }
+
+ private:
+  int d_, d2_;
+  std::shared_ptr<const VActKernel<T>> act_gate_d2_, act_gate_d_, act_state_d_;
+  std::shared_ptr<const VMulKernel<T>> vmul_d_;
+#ifdef __AVX__
+  std::unique_ptr<const AVXAct> avx_act_gate_, avx_act_state_;
+#endif
+};
+
+#define INTRI8_FLOAT(isa)                                                     \
+  template <>                                                                 \
+  GRUKernelImpl<float, isa, kEQ8>::GRUKernelImpl(                             \
+      const std::string& act_gate, const std::string& act_state, int d)       \
+      : GRUKernel<float>() {                                                  \
+    avx_act_gate_ = GetAVXAct<isa>(act_gate);                                 \
+    avx_act_state_ = GetAVXAct<isa>(act_state);                               \
+  }                                                                           \
+  template <>                                                                 \
+  void GRUKernelImpl<float, isa, kEQ8>::ComputeH1(float* gates, float* ht)    \
+      const {                                                                 \
+    __m256 u, s;                                                              \
+    /* W: {W_update, W_reset; W_state} */                                     \
+    u = _mm256_loadu_ps(gates);                                               \
+    s = _mm256_loadu_ps(gates + 16);                                          \
+    s = _mm256_mul_ps(avx_act_gate_->Compute(u), avx_act_state_->Compute(s)); \
+    _mm256_storeu_ps(ht, s);                                                  \
+  }                                                                           \
+  template <>                                                                 \
+  void GRUKernelImpl<float, isa, kEQ8>::ComputeHtPart1(                       \
+      float* gates, const float* ht_1, float* ht) const {                     \
+    /* not exactly equal the any implementation */                            \
+    __m256 r, ht0;                                                            \
+    r = _mm256_loadu_ps(gates + 8);                                           \
+    ht0 = _mm256_loadu_ps(ht_1);                                              \
+    r = _mm256_mul_ps(avx_act_gate_->Compute(r), ht0);                        \
+    _mm256_storeu_ps(ht, r);                                                  \
+  }                                                                           \
+  template <>                                                                 \
+  void GRUKernelImpl<float, isa, kEQ8>::ComputeHtPart2(                       \
+      float* gates, const float* ht_1, float* ht) const {                     \
+    /* not exactly equal the any implementation */                            \
+    __m256 u, s, ht0;                                                         \
+    u = _mm256_loadu_ps(gates);                                               \
+    s = _mm256_loadu_ps(gates + 16);                                          \
+    ht0 = _mm256_loadu_ps(ht_1);                                              \
+    u = avx_act_gate_->Compute(u);                                            \
+    s = _mm256_mul_ps(u, avx_act_state_->Compute(s));                         \
+    u = _mm256_sub_ps(_mm256_set1_ps(1.f), u);                                \
+    u = _mm256_mul_ps(u, ht0);                                                \
+    u = _mm256_add_ps(s, u);                                                  \
+    _mm256_storeu_ps(ht, u);                                                  \
+  }
+
+#ifdef __AVX__
+INTRI8_FLOAT(jit::avx);
+#endif
+#ifdef __AVX2__
+INTRI8_FLOAT(jit::avx2);
+#endif
+#ifdef __AVX512F__
+INTRI8_FLOAT(jit::avx512f);
+#endif
+
+#define JITKERNEL_DECLARE_GRU(ker_class, ker_dtype)                       \
+  template <>                                                             \
+  std::shared_ptr<const GRUKernel<ker_dtype>> KernelPool::Get<            \
+      GRUKernel<ker_dtype>, const std::string&, const std::string&, int>( \
+      const std::string& act_gate, const std::string& act_state, int d)
+
+#define JITKERNEL_KEY_GRU(ker_key, dtype_key) \
+  #ker_key #dtype_key + std::to_string(d) + act_gate + act_state
+
+#define JITKERNEL_NEW_GRU_IMPL(ker, dtype, isa, k) \
+  p = std::dynamic_pointer_cast<ker<dtype>>(       \
+      std::make_shared<ker##Impl<dtype, isa, k>>(act_gate, act_state, d));
+
+REGISTER_JITKERNEL_ARGS(gru, GRUKernel, JITKERNEL_DECLARE_GRU,
+                        JITKERNEL_KEY_GRU, JITKERNEL_NEW_GRU_IMPL);
+
+#undef INTRI8_FLOAT
+#undef JITKERNEL_NEW_GRU_IMPL
+#undef JITKERNEL_KEY_GRU
+#undef JITKERNEL_DECLARE_GRU
 }  // namespace jitkernel
 }  // namespace math
 }  // namespace operators

paddle/fluid/operators/math/sequence_pooling.cc

@@ -157,6 +157,31 @@ class FirstSeqPoolFunctor {
   }
 };
 
+template <typename T>
+class SumSeqPoolGradFunctor {
+ public:
+  void operator()(const platform::CPUDeviceContext& context,
+                  const framework::Tensor& out_grad,
+                  framework::LoDTensor* in_grad) {
+    auto lod = in_grad->lod()[0];
+    int64_t out_w = out_grad.numel() / out_grad.dims()[0];
+    int64_t in_w = in_grad->numel() / in_grad->dims()[0];
+    PADDLE_ENFORCE(in_w == out_w);
+    const T* out_g_data = out_grad.data<T>();
+    T* in_g_data = in_grad->mutable_data<T>(context.GetPlace());
+    auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
+    for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
+      int64_t h = static_cast<int64_t>(lod[i + 1] - lod[i]);
+      int64_t in_offset = lod[i] * in_w;
+      const T* out_pos = out_g_data + i * out_w;
+      T* in_pos = in_g_data + in_offset;
+      for (int r = 0; r != h; ++r) {
+        blas.VCOPY(in_w, out_pos, in_pos + r * in_w);
+      }
+    }
+  }
+};
+
 template <typename T>
 class SequencePoolFunctor<platform::CPUDeviceContext, T> {
  public:
@@ -231,9 +256,15 @@ class SequencePoolGradFunctor<platform::CPUDeviceContext, T> {
       math::SetConstant<platform::CPUDeviceContext, T> functor;
       functor(context, in_grad, 0);
     }
+
+    if (pooltype == "SUM") {
+      math::SumSeqPoolGradFunctor<T> sum_pool_grad;
+      sum_pool_grad(context, out_grad, in_grad);
+      return;
+    }
+
     auto lod = in_grad->lod()[0];
     auto& place = *context.eigen_device();
-    auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
     for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
       auto in_g_t = in_grad->Slice(static_cast<int>(lod[i]),
                                    static_cast<int>(lod[i + 1]));
@@ -247,12 +278,6 @@ class SequencePoolGradFunctor<platform::CPUDeviceContext, T> {
 
       if (pooltype == "AVERAGE") {
         in_g_e.device(place) = (out_g_e / static_cast<T>(h)).broadcast(bcast);
-      } else if (pooltype == "SUM") {
-        const T* out_g_data = out_g_t.data<T>();
-        T* in_g_data = in_g_t.mutable_data<T>(context.GetPlace());
-        for (int r = 0; r != h; ++r) {
-          blas.VCOPY(w, out_g_data, in_g_data + r * w);
-        }
       } else if (pooltype == "SQRT") {
         in_g_e.device(place) =
             (out_g_e / std::sqrt(static_cast<T>(h))).broadcast(bcast);

paddle/fluid/operators/math/sequence_pooling_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/math/sequence_pooling.h"
+#include <gtest/gtest.h>
+#include <vector>
+
+template <typename DeviceContext, typename Place, typename T>
+void TestSequencePoolingSum(const paddle::framework::LoD& lod) {
+  paddle::framework::LoDTensor cpu_out_grad;
+  paddle::framework::LoDTensor cpu_in_grad;
+  paddle::framework::LoDTensor out_grad;
+  paddle::framework::LoDTensor in_grad;
+  const size_t second_dim = 128u;
+
+  // construct out_grad's tensor in cpu
+  const size_t out_first_dim = lod[0].size() - 1;
+  auto out_dims = paddle::framework::make_ddim(
+      {static_cast<int64_t>(out_first_dim), static_cast<int64_t>(second_dim)});
+
+  cpu_out_grad.mutable_data<T>(out_dims, paddle::platform::CPUPlace());
+  for (int64_t i = 0; i < cpu_out_grad.numel(); ++i) {
+    cpu_out_grad.data<T>()[i] = static_cast<T>(i);
+  }
+
+  // copy to dst out_grad
+  auto* place = new Place();
+  DeviceContext* context = new DeviceContext(*place);
+  if (paddle::platform::is_cpu_place(*place)) {
+    out_grad = cpu_out_grad;
+  } else {
+    TensorCopySync(cpu_out_grad, *place, &out_grad);
+  }
+
+  // construct in_grad
+  in_grad.set_lod(lod);
+  auto in_dims = paddle::framework::make_ddim(
+      {static_cast<int64_t>(lod[0].back()), static_cast<int64_t>(second_dim)});
+  in_grad.mutable_data<T>(in_dims, context->GetPlace());
+
+  // check tensor contruction result
+  PADDLE_ENFORCE_EQ(in_grad.dims().size(), out_grad.dims().size());
+  for (int64_t i = 1; i < out_grad.dims().size(); ++i) {
+    PADDLE_ENFORCE_EQ(in_grad.dims()[i], out_grad.dims()[i]);
+  }
+
+  // call functor
+  paddle::operators::math::SequencePoolGradFunctor<DeviceContext, T>()(
+      *context, "SUM", out_grad, &in_grad);
+
+  if (paddle::platform::is_cpu_place(*place)) {
+    cpu_in_grad = in_grad;
+  } else {
+    TensorCopySync(in_grad, paddle::platform::CPUPlace(), &cpu_in_grad);
+    cpu_in_grad.set_lod(in_grad.lod());
+  }
+
+  EXPECT_EQ(in_grad.numel(), lod[0].back() * second_dim);
+  EXPECT_EQ(in_grad.lod(), lod);
+
+  if (paddle::platform::is_cpu_place(*place)) {
+    for (int64_t i = 0; i < in_grad.lod()[0].size() - 1; ++i) {
+      int64_t begin = in_grad.lod()[0][i];
+      int64_t end = in_grad.lod()[0][i + 1];
+      paddle::framework::Tensor tmp = in_grad.Slice(begin, end);
+      for (int64_t j = 0; j != tmp.numel() / second_dim; ++j) {
+        for (int64_t m = 0; m != second_dim; ++m) {
+          EXPECT_EQ(tmp.data<T>()[m + j * second_dim],
+                    out_grad.data<T>()[m + i * second_dim]);
+        }
+      }
+    }
+  } else {
+    for (int64_t i = 0; i < cpu_in_grad.lod()[0].size() - 1; ++i) {
+      int64_t begin = cpu_in_grad.lod()[0][i];
+      int64_t end = cpu_in_grad.lod()[0][i + 1];
+      paddle::framework::Tensor tmp = cpu_in_grad.Slice(begin, end);
+      for (int64_t j = 0; j != tmp.numel() / second_dim; ++j) {
+        for (int64_t m = 0; m != second_dim; ++m) {
+          EXPECT_EQ(tmp.data<T>()[m + j * second_dim],
+                    cpu_out_grad.data<T>()[m + i * second_dim]);
+        }
+      }
+    }
+  }
+
+  delete place;
+  delete context;
+}
+
+TEST(SequencePoolingGrad, CPU_SUM) {
+  paddle::framework::LoD lod1;
+  lod1.push_back(std::vector<size_t>{0, 10});
+  TestSequencePoolingSum<paddle::platform::CPUDeviceContext,
+                         paddle::platform::CPUPlace, float>(lod1);
+
+  paddle::framework::LoD lod2;
+  lod2.push_back(std::vector<size_t>{0, 2, 7, 10});
+  TestSequencePoolingSum<paddle::platform::CPUDeviceContext,
+                         paddle::platform::CPUPlace, float>(lod2);
+}
+
+#ifdef PADDLE_WITH_CUDA
+TEST(SequencePoolingGrad, CUDA_SUM) {
+  paddle::framework::LoD lod1;
+  lod1.push_back(std::vector<size_t>{0, 10});
+  TestSequencePoolingSum<paddle::platform::CUDADeviceContext,
+                         paddle::platform::CUDAPlace, float>(lod1);
+
+  paddle::framework::LoD lod2;
+  lod2.push_back(std::vector<size_t>{0, 2, 7, 10});
+  TestSequencePoolingSum<paddle::platform::CUDADeviceContext,
+                         paddle::platform::CUDAPlace, float>(lod2);
+}
+#endif

paddle/fluid/operators/mean_op.cc

@@ -34,7 +34,7 @@ class MeanOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
     AddInput("X", "(Tensor) The input of mean op");
-    AddOutput("Out", "(Tensor) The output of mean op").Reuse("X");
+    AddOutput("Out", "(Tensor) The output of mean op");
     AddComment(R"DOC(
 Mean Operator calculates the mean of all elements in X.
 

paddle/fluid/operators/momentum_op.cc

@@ -19,54 +19,6 @@ namespace operators {
 
 using Tensor = framework::Tensor;
 
-class MomentumOp : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
- protected:
-  void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("Param"),
-                   "Input(param) of Momentum should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("Grad"),
-                   "Input(grad) of Momentum should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("Velocity"),
-                   "Input(velocity) of Momentum should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("LearningRate"),
-                   "Input(LearningRate) of Momentum should not be null.");
-    PADDLE_ENFORCE(
-        ctx->GetInputsVarType("Param").front() ==
-            framework::proto::VarType::LOD_TENSOR,
-        "The input var's type should be LoDTensor, but the received is %s",
-        ctx->Inputs("Param").front(), ctx->GetInputsVarType("Param").front());
-
-    PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
-                   "Output(ParamOut) of Momentum should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("VelocityOut"),
-                   "Output(VelocityOut) of Momentum should not be null.");
-
-    auto param_dim = ctx->GetInputDim("Param");
-    if (ctx->GetInputsVarType("Grad")[0] ==
-        framework::proto::VarType::LOD_TENSOR) {
-      PADDLE_ENFORCE_EQ(
-          param_dim, ctx->GetInputDim("Grad"),
-          "Param and Grad input of MomentumOp should have the same dimension.");
-      PADDLE_ENFORCE_EQ(
-          param_dim, ctx->GetInputDim("Velocity"),
-          "Param and Velocity of MomentumOp should have the same dimension.");
-    }
-    PADDLE_ENFORCE_EQ(framework::product(ctx->GetInputDim("LearningRate")), 1,
-                      "Learning_rate should be a scalar");
-
-    ctx->SetOutputDim("ParamOut", param_dim);
-    ctx->SetOutputDim("VelocityOut", param_dim);
-  }
-  framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext& ctx) const override {
-    auto input_data_type = framework::GetDataTypeOfVar(ctx.InputVar("Param"));
-    return framework::OpKernelType(input_data_type, ctx.GetPlace());
-  }
-};
-
 class MomentumOpInferVarType : public framework::VarTypeInference {
  public:
   void operator()(const framework::OpDesc& op_desc,

paddle/fluid/operators/momentum_op.h

@@ -28,6 +28,54 @@ using framework::SelectedRows;
 struct NoNesterov;
 struct UseNesterov;
 
+class MomentumOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Param"),
+                   "Input(param) of Momentum should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Grad"),
+                   "Input(grad) of Momentum should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Velocity"),
+                   "Input(velocity) of Momentum should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("LearningRate"),
+                   "Input(LearningRate) of Momentum should not be null.");
+    PADDLE_ENFORCE(
+        ctx->GetInputsVarType("Param").front() ==
+            framework::proto::VarType::LOD_TENSOR,
+        "The input var's type should be LoDTensor, but the received is %s",
+        ctx->Inputs("Param").front(), ctx->GetInputsVarType("Param").front());
+
+    PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
+                   "Output(ParamOut) of Momentum should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("VelocityOut"),
+                   "Output(VelocityOut) of Momentum should not be null.");
+
+    auto param_dim = ctx->GetInputDim("Param");
+    if (ctx->GetInputsVarType("Grad")[0] ==
+        framework::proto::VarType::LOD_TENSOR) {
+      PADDLE_ENFORCE_EQ(
+          param_dim, ctx->GetInputDim("Grad"),
+          "Param and Grad input of MomentumOp should have the same dimension.");
+      PADDLE_ENFORCE_EQ(
+          param_dim, ctx->GetInputDim("Velocity"),
+          "Param and Velocity of MomentumOp should have the same dimension.");
+    }
+    PADDLE_ENFORCE_EQ(framework::product(ctx->GetInputDim("LearningRate")), 1,
+                      "Learning_rate should be a scalar");
+
+    ctx->SetOutputDim("ParamOut", param_dim);
+    ctx->SetOutputDim("VelocityOut", param_dim);
+  }
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    auto input_data_type = framework::GetDataTypeOfVar(ctx.InputVar("Param"));
+    return framework::OpKernelType(input_data_type, ctx.GetPlace());
+  }
+};
+
 template <typename T>
 class CPUDenseMomentumFunctor {
  private:

paddle/fluid/operators/pool_op.cc

@@ -151,8 +151,7 @@ void Pool2dOpMaker::Make() {
             "The format of output tensor is also NCHW, "
             "where N is batch size, C is the number of channels, "
             "H is the height of the feature, "
-            "and W is the width of the feature.")
-      .Reuse("X");
+            "and W is the width of the feature.");
 
   AddAttr<std::string>("pooling_type",
                        "(string), pooling type, can be \"max\" for max-pooling "
@@ -252,8 +251,7 @@ void Pool3dOpMaker::Make() {
             "The format of output tensor is also NCDHW, "
             "where N is batch size, C is "
             "the number of channels, and D, H and W is the depth, height and "
-            "width of the feature, respectively.")
-      .Reuse("X");
+            "width of the feature, respectively.");
 
   AddAttr<std::string>("pooling_type",
                        "(string) Pooling type, can be \"max\" for max-pooling "

paddle/fluid/operators/reader/reader_blocking_queue_test.cc

@@ -237,7 +237,7 @@ TEST(BlockingQueue, speed_test_mode) {
   }
   for (size_t i = 0; i < queue_size; ++i) {
     q2.Receive(&b);
-    EXPECT_EQ(b, 0);
+    EXPECT_EQ(b, 0UL);
   }
   EXPECT_EQ(q2.Size(), queue_size);
 }

paddle/fluid/operators/sequence_reverse_op.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/sequence_reverse_op.h"
+
+namespace ops = paddle::operators;
+
+REGISTER_OPERATOR(sequence_reverse, ops::SequenceReverseOp,
+                  ops::SequenceReverseOpMaker,
+                  ops::SequenceReverseGradOpDescMaker);
+
+REGISTER_OP_CPU_KERNEL(
+    sequence_reverse,
+    ops::SequenceReverseOpKernel<paddle::platform::CPUDeviceContext, uint8_t>,
+    ops::SequenceReverseOpKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::SequenceReverseOpKernel<paddle::platform::CPUDeviceContext, int64_t>,
+    ops::SequenceReverseOpKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::SequenceReverseOpKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/sequence_reverse_op.cu

+// 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/sequence_reverse_op.h"
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_CUDA_KERNEL(
+    sequence_reverse,
+    ops::SequenceReverseOpKernel<paddle::platform::CUDADeviceContext, uint8_t>,
+    ops::SequenceReverseOpKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::SequenceReverseOpKernel<paddle::platform::CUDADeviceContext, int64_t>,
+    ops::SequenceReverseOpKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::SequenceReverseOpKernel<paddle::platform::CUDADeviceContext, double>);

paddle/fluid/operators/sequence_reverse_op.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 "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/math/algorithm.h"
+#include "paddle/fluid/platform/for_range.h"
+
+namespace paddle {
+namespace operators {
+
+class SequenceReverseOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must exist");
+    PADDLE_ENFORCE(ctx->HasOutput("Y"), "Output(Y) must exist");
+
+    auto x_dim = ctx->GetInputDim("X");
+    PADDLE_ENFORCE_GE(x_dim.size(), 2,
+                      "Rank of Input(X) must be not less than 2.");
+
+    ctx->SetOutputDim("Y", x_dim);
+    ctx->ShareLoD("X", "Y");
+  }
+};
+
+class SequenceReverseOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X", "The input LoDTensor of sequence_reverse op.");
+    AddOutput("Y", "The output LoDTensor of sequence_reverse op.");
+    AddComment(R"DOC(
+SequenceReverse Operator.
+
+Reverse each sequence in input X along dim 0.
+
+Assuming X is a LoDTensor with dims [5, 4] and lod [[0, 2, 5]], where:
+
+X.data() = [
+  [1, 2, 3, 4],
+  [5, 6, 7, 8], # the 0-th sequence with length 2
+  [9, 10, 11, 12],
+  [13, 14, 15, 16],
+  [17, 18, 19, 20] # the 1-st sequence with length 3
+]
+
+The output Y would be a LoDTensor sharing the same dims and lod with input X,
+and:
+
+Y.data() = [
+  [5, 6, 7, 8],
+  [1, 2, 3, 4], # the reversed 0-th sequence with length 2
+  [17, 18, 19, 20],
+  [13, 14, 15, 16],
+  [9, 10, 11, 12] # the reversed 1-st sequence with length 3
+]
+
+This Operator is useful to build a reverse dynamic RNN network.
+
+This Operator only supports one-level lod currently.
+    )DOC");
+  }
+};
+
+template <typename T>
+struct SequenceReverseFunctor {
+  SequenceReverseFunctor(const T *x, T *y, const size_t *lod, size_t lod_count,
+                         size_t row_numel)
+      : x_(x), y_(y), lod_(lod), lod_count_(lod_count), row_numel_(row_numel) {}
+
+  HOSTDEVICE void operator()(size_t idx_x) const {
+    auto row_idx_x = idx_x / row_numel_;
+    auto lod_idx = math::UpperBound(lod_, lod_count_, row_idx_x);
+    auto row_idx_y = lod_[lod_idx - 1] + (lod_[lod_idx] - 1 - row_idx_x);
+    auto idx_y = row_idx_y * row_numel_ + idx_x % row_numel_;
+    y_[idx_y] = x_[idx_x];
+  }
+
+  const T *x_;
+  T *y_;
+  const size_t *lod_;
+  size_t lod_count_;
+  size_t row_numel_;
+};
+
+template <typename DeviceContext, typename T>
+class SequenceReverseOpKernel : public framework::OpKernel<T> {
+  using LoDTensor = framework::LoDTensor;
+
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    auto &x = *ctx.Input<LoDTensor>("X");
+    auto *y = ctx.Output<LoDTensor>("Y");
+
+    PADDLE_ENFORCE_EQ(x.lod().size(), 1,
+                      "SequenceReverse Op only support one level lod.");
+
+    auto &dev_ctx = ctx.template device_context<DeviceContext>();
+    const size_t *lod;
+    size_t lod_count = x.lod()[0].size();
+
+#ifdef PADDLE_WITH_CUDA
+    if (platform::is_gpu_place(ctx.GetPlace())) {
+      lod = x.lod()[0].CUDAData(ctx.GetPlace());
+    } else {
+#endif
+      lod = x.lod()[0].data();
+#ifdef PADDLE_WITH_CUDA
+    }
+#endif
+
+    size_t limit = static_cast<size_t>(x.numel());
+    size_t row_numel = static_cast<size_t>(limit / x.dims()[0]);
+    auto *x_data = x.data<T>();
+    auto *y_data = y->mutable_data<T>(ctx.GetPlace());
+
+    PADDLE_ENFORCE_NE(x_data, y_data,
+                      "SequenceReverse Op does not support in-place operation");
+
+    SequenceReverseFunctor<T> functor(x_data, y_data, lod, lod_count,
+                                      row_numel);
+    platform::ForRange<DeviceContext> for_range(dev_ctx, limit);
+    for_range(functor);
+  }
+};
+
+class SequenceReverseGradOpDescMaker : 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("sequence_reverse");
+    op->SetInput("X", OutputGrad("Y"));
+    op->SetOutput("Y", InputGrad("X"));
+    op->SetAttrMap(Attrs());
+    return op;
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/sgd_op.cc

@@ -77,8 +77,7 @@ class SGDOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("Grad", "(Tensor or SelectedRows) Input gradient");
     AddOutput("ParamOut",
               "(Tensor or SelectedRows, same with Param) "
-              "Output parameter, should share the same memory with Param")
-        .Reuse("Param");
+              "Output parameter, should share the same memory with Param");
     AddComment(R"DOC(
 
 SGD operator

paddle/fluid/operators/softmax_cudnn_op.cu.cc

@@ -76,6 +76,8 @@ namespace ops = paddle::operators;
 namespace plat = paddle::platform;
 REGISTER_OP_KERNEL(softmax, CUDNN, plat::CUDAPlace,
                    ops::SoftmaxCUDNNKernel<float>,
+                   ops::SoftmaxCUDNNKernel<double>,
                    ops::SoftmaxCUDNNKernel<plat::float16>);
 REGISTER_OP_KERNEL(softmax_grad, CUDNN, plat::CUDAPlace,
-                   ops::SoftmaxGradCUDNNKernel<float>);
+                   ops::SoftmaxGradCUDNNKernel<float>,
+                   ops::SoftmaxGradCUDNNKernel<double>);

paddle/fluid/operators/softmax_op.cc

@@ -80,8 +80,7 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("X",
              "The input tensor of softmax, "
              "whose last dimension is the input_feature_dimensions.");
-    AddOutput("Out", "The normalized values with the same shape as X.")
-        .Reuse("X");
+    AddOutput("Out", "The normalized values with the same shape as X.");
     AddAttr<bool>(
         "use_cudnn",
         "(bool, default false) Only used in cudnn kernel, need install cudnn")

paddle/fluid/operators/sum_op.cc

@@ -132,7 +132,7 @@ class SumOpMaker : public framework::OpProtoAndCheckerMaker {
   void Make() override {
     AddInput("X", "(vector<Tensor>) The input tensors of sum operator.")
         .AsDuplicable();
-    AddOutput("Out", "(Tensor) The output tensor of sum operator.").Reuse("X");
+    AddOutput("Out", "(Tensor) The output tensor of sum operator.");
     AddAttr<bool>("use_mkldnn",
                   "(bool, default false) Only used in mkldnn kernel")
         .SetDefault(false);

paddle/fluid/operators/top_k_op.cc

@@ -50,7 +50,7 @@ class TopkOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
     AddInput("X", "(Tensor) The input of Topk op");
-    AddOutput("Out", "(Tensor) The output tensor of Topk op").Reuse("X");
+    AddOutput("Out", "(Tensor) The output tensor of Topk op");
     AddOutput("Indices", "(Tensor) The indices of Topk elements of input");
     AddComment(R"DOC(
 Top K operator

paddle/fluid/operators/top_k_op.cu

@@ -262,31 +262,31 @@ __global__ void KeMatrixTopK(T* output, int output_stride, int64_t* indices,
                              const T* src, int lds, int dim, int k,
                              int grid_dim, int num) {
   __shared__ Pair<T> sh_topk[BlockSize];
-  __shared__ int maxid[BlockSize / 2];
   const int tid = threadIdx.x;
   const int warp = threadIdx.x / 32;
 
   const int bid = blockIdx.x;
   for (int i = bid; i < num; i += grid_dim) {
-    output += i * output_stride;
-    indices += i * k;
-
+    int top_num = k;
+    __shared__ int maxid[BlockSize / 2];
+    T* out = output + i * output_stride;
+    int64_t* inds = indices + i * k;
     Pair<T> topk[MaxLength];
     int beam = MaxLength;
     Pair<T> max;
     bool is_empty = false;
     bool firststep = true;
 
-    for (int k = 0; k < MaxLength; k++) {
-      topk[k].set(-INFINITY, -1);
+    for (int j = 0; j < MaxLength; j++) {
+      topk[j].set(-INFINITY, -1);
     }
-    while (k) {
+    while (top_num) {
       ThreadGetTopK<T, MaxLength, BlockSize>(
           topk, &beam, k, src + i * lds, &firststep, &is_empty, &max, dim, tid);
 
       sh_topk[tid] = topk[0];
-      BlockReduce<T, MaxLength, BlockSize>(sh_topk, maxid, topk, &output,
-                                           &indices, &beam, &k, tid, warp);
+      BlockReduce<T, MaxLength, BlockSize>(sh_topk, maxid, topk, &out, &inds,
+                                           &beam, &top_num, tid, warp);
     }
   }
 }
@@ -327,13 +327,15 @@ class TopkOpCUDAKernel : public framework::OpKernel<T> {
     size_t k = static_cast<int>(ctx.Attr<int>("k"));
 
     const T* input_data = input->data<T>();
-
     T* output_data = output->mutable_data<T>(ctx.GetPlace());
     // FIXME(typhoonzero): data is always converted to type T?
     int64_t* indices_data = indices->mutable_data<int64_t>(ctx.GetPlace());
 
-    size_t input_height = input->dims()[0];
-    size_t input_width = input->dims()[1];
+    framework::DDim inputdims = input->dims();
+    const size_t input_height = framework::product(
+        framework::slice_ddim(inputdims, 0, inputdims.size() - 1));
+    const size_t input_width = inputdims[inputdims.size() - 1];
+
     if (k > input_width) k = input_width;
 
     // NOTE: pass lds and dim same to input width.
@@ -342,14 +344,12 @@ class TopkOpCUDAKernel : public framework::OpKernel<T> {
     const int kMaxHeight = 2048;
     int gridx = input_height < kMaxHeight ? input_height : kMaxHeight;
     auto& dev_ctx = ctx.cuda_device_context();
-
     switch (GetDesiredBlockDim(input_width)) {
       FIXED_BLOCK_DIM(
           KeMatrixTopK<T, 5,
                        kBlockDim><<<gridx, kBlockDim, 0, dev_ctx.stream()>>>(
-              output_data, output->dims()[1], indices_data, input_data,
-              input_width, input_width, static_cast<int>(k), gridx,
-              input_height));
+              output_data, k, indices_data, input_data, input_width,
+              input_width, static_cast<int>(k), gridx, input_height));
       default:
         PADDLE_THROW("Error");
     }

paddle/fluid/operators/top_k_op.h

@@ -34,7 +34,6 @@ class TopkKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     // Get the top k elements of each row of input tensor
-    // FIXME: only deal with matrix(2d tensor).
     auto* input = ctx.Input<Tensor>("X");
     auto* output = ctx.Output<Tensor>("Out");
     auto* indices = ctx.Output<Tensor>("Indices");
@@ -44,8 +43,6 @@ class TopkKernel : public framework::OpKernel<T> {
     T* output_data = output->mutable_data<T>(ctx.GetPlace());
     int64_t* indices_data = indices->mutable_data<int64_t>(ctx.GetPlace());
 
-    auto eg_input = EigenMatrix<T>::From(*input);
-
     // reshape input to a flattern matrix(like flat_inner_dims)
     framework::DDim inputdims = input->dims();
     const size_t row = framework::product(
@@ -53,7 +50,7 @@ class TopkKernel : public framework::OpKernel<T> {
     const size_t col = inputdims[inputdims.size() - 1];
     Eigen::DSizes<int, 2> flat2dims(row, col);
     // NOTE: eigen shape doesn't affect paddle tensor.
-    eg_input.reshape(flat2dims);
+    auto eg_input = EigenMatrix<T>::Reshape(*input, inputdims.size() - 1);
 
 #ifdef PADDLE_WITH_MKLML
 #pragma omp parallel for

paddle/fluid/operators/transpose_op.cc

@@ -210,18 +210,21 @@ REGISTER_OPERATOR(transpose, ops::TransposeOp, ops::TransposeOpMaker,
 REGISTER_OPERATOR(transpose_grad, ops::TransposeOpGrad);
 
 REGISTER_OP_CPU_KERNEL(
-    transpose, ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>);
+    transpose, ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::TransposeKernel<paddle::platform::CPUDeviceContext, double>);
 REGISTER_OP_CPU_KERNEL(
     transpose_grad,
-    ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>);
+    ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, double>);
 
 REGISTER_OPERATOR(transpose2, ops::Transpose2Op, ops::Transpose2OpMaker,
                   ops::Transpose2GradMaker);
 REGISTER_OPERATOR(transpose2_grad, ops::Transpose2OpGrad);
 
 REGISTER_OP_CPU_KERNEL(
-    transpose2,
-    ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>);
+    transpose2, ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::TransposeKernel<paddle::platform::CPUDeviceContext, double>);
 REGISTER_OP_CPU_KERNEL(
     transpose2_grad,
-    ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>);
+    ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/transpose_op.cu.cc

@@ -16,15 +16,18 @@ limitations under the License. */
 
 namespace ops = paddle::operators;
 REGISTER_OP_CUDA_KERNEL(
-    transpose,
-    ops::TransposeKernel<paddle::platform::CUDADeviceContext, float>);
+    transpose, ops::TransposeKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::TransposeKernel<paddle::platform::CUDADeviceContext, double>);
 REGISTER_OP_CUDA_KERNEL(
     transpose_grad,
-    ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, float>);
+    ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, double>);
 
 REGISTER_OP_CUDA_KERNEL(
     transpose2,
-    ops::TransposeKernel<paddle::platform::CUDADeviceContext, float>);
+    ops::TransposeKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::TransposeKernel<paddle::platform::CUDADeviceContext, double>);
 REGISTER_OP_CUDA_KERNEL(
     transpose2_grad,
-    ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, float>);
+    ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, double>);

paddle/fluid/platform/device_context.cc

@@ -296,38 +296,73 @@ Place CUDAPinnedDeviceContext::GetPlace() const { return place_; }
 
 #ifdef PADDLE_WITH_MKLDNN
 MKLDNNDeviceContext::MKLDNNDeviceContext(CPUPlace place)
-    : CPUDeviceContext(place), engine_(mkldnn::engine::cpu, 0), p_blobs_() {
-  p_blobs_.reset(new std::unordered_map<std::string, std::shared_ptr<void>>());
+    : CPUDeviceContext(place), engine_(mkldnn::engine::cpu, 0), p_blobmap_() {
+  p_blobmap_.reset(new BlobMap());
+  p_mutex_.reset(new std::mutex());
 }
 
+namespace {
+// Current thread's id.
+thread_local int cur_thread_id = 0;
+}
+
+void set_cur_thread_id(int tid) { cur_thread_id = tid; }
+int get_cur_thread_id(void) { return cur_thread_id; }
+
 void MKLDNNDeviceContext::SetBlob(const std::string& name,
                                   std::shared_ptr<void> data) const {
-  std::unordered_map<std::string, std::shared_ptr<void>>* p;
-  p = p_blobs_.get();
+  BlobMap* pMap = p_blobmap_.get();
+  std::shared_ptr<KeyBlob> pBlob = nullptr;
+
+  int tid = platform::get_cur_thread_id();
 
-  auto it = p->find(name);
+  std::lock_guard<std::mutex> lock(*p_mutex_.get());
 
-  if (it == p->end()) {
-    (*p)[name] = data;  // create new blob
+  // Find KeyBlob for current thread
+  auto map_it = pMap->find(tid);
+
+  if (map_it == pMap->end()) {
+    // 1st time to set blob in current thread
+    pBlob = std::shared_ptr<KeyBlob>(new KeyBlob());
+    (*pMap)[tid] = pBlob;
   } else {
-    it->second = data;  // set data to existing blob
+    pBlob = map_it->second;
   }
 
+  // Find Key in found (or newly created) KeyBlob
+  auto key_it = pBlob->find(name);
+
+  if (key_it == pBlob->end()) {
+    (*pBlob)[name] = data;  // create new blob
+  } else {
+    key_it->second = data;  // set data to existing blob
+  }
+
+  // lock will be automatically released when out of scope
   return;
 }
 
 std::shared_ptr<void> MKLDNNDeviceContext::GetBlob(
     const std::string& name) const {
-  std::unordered_map<std::string, std::shared_ptr<void>>* p;
-  p = p_blobs_.get();
+  BlobMap* pMap = p_blobmap_.get();
+  std::shared_ptr<KeyBlob> pBlob = nullptr;
 
-  auto it = p->find(name);
+  int tid = platform::get_cur_thread_id();
 
-  if (it != p->end()) {
-    return it->second;
-  }
+  std::lock_guard<std::mutex> lock(*p_mutex_.get());
+
+  // Find KeyBlob for current thread firstly
+  auto map_it = pMap->find(tid);
+  if (map_it == pMap->end()) return nullptr;
+  pBlob = map_it->second;
+
+  // Find Blob via name
+  auto key_it = pBlob->find(name);
+
+  if (key_it == pBlob->end()) return nullptr;
 
-  return nullptr;
+  // lock will be automatically released when out of scope
+  return key_it->second;
 }
 
 #endif

paddle/fluid/platform/device_context.h

@@ -176,6 +176,12 @@ struct DefaultDeviceContextType<platform::CUDAPinnedPlace> {
 #endif
 
 #ifdef PADDLE_WITH_MKLDNN
+using KeyBlob = std::unordered_map<std::string, std::shared_ptr<void>>;
+using BlobMap = std::unordered_map<int, std::shared_ptr<KeyBlob>>;
+
+void set_cur_thread_id(int);
+int get_cur_thread_id(void);
+
 class MKLDNNDeviceContext : public CPUDeviceContext {
  public:
   explicit MKLDNNDeviceContext(CPUPlace place);
@@ -191,8 +197,8 @@ class MKLDNNDeviceContext : public CPUDeviceContext {
 
  private:
   mkldnn::engine engine_;
-  std::shared_ptr<std::unordered_map<std::string, std::shared_ptr<void>>>
-      p_blobs_;
+  std::shared_ptr<BlobMap> p_blobmap_;
+  std::shared_ptr<std::mutex> p_mutex_;
 };
 #endif
 

paddle/fluid/pybind/pybind.cc

@@ -645,9 +645,13 @@ All parameter, weight, gradient are variables in Paddle.
 
   py::class_<ir::Pass, std::shared_ptr<ir::Pass>> pass(m, "Pass");
   pass.def(py::init())
-      .def("set_str", [](ir::Pass &self, const std::string &name,
-                         const std::string &attr) {
-        self.Set<std::string>(name, new std::string(attr));
+      .def(
+          "set_str",
+          [](ir::Pass &self, const std::string &name, const std::string &attr) {
+            self.Set<std::string>(name, new std::string(attr));
+          })
+      .def("set_int", [](ir::Pass &self, const std::string &name, int val) {
+        self.Set<const int>(name, new int(val));
       });
 
   py::class_<ir::PassBuilder, std::shared_ptr<ir::PassBuilder>> pb(

paddle/fluid/train/demo/CMakeLists.txt

@@ -15,6 +15,7 @@ include_directories("${PADDLE_LIB}")
 include_directories("${PADDLE_LIB}/third_party/install/protobuf/include")
 include_directories("${PADDLE_LIB}/third_party/install/glog/include")
 include_directories("${PADDLE_LIB}/third_party/install/gflags/include")
+include_directories("${PADDLE_LIB}/third_party/install/xxhash/include")
 include_directories("${PADDLE_LIB}/third_party/install/snappy/include")
 include_directories("${PADDLE_LIB}/third_party/install/snappystream/include")
 include_directories("${PADDLE_LIB}/third_party/install/zlib/include")
@@ -27,6 +28,7 @@ link_directories("${PADDLE_LIB}/third_party/install/snappystream/lib")
 link_directories("${PADDLE_LIB}/third_party/install/protobuf/lib")
 link_directories("${PADDLE_LIB}/third_party/install/glog/lib")
 link_directories("${PADDLE_LIB}/third_party/install/gflags/lib")
+link_directories("${PADDLE_LIB}/third_party/install/xxhash/lib")
 link_directories("${PADDLE_LIB}/third_party/install/zlib/lib")
 
 add_executable(demo_trainer demo_trainer.cc)
@@ -62,5 +64,5 @@ target_link_libraries(demo_trainer
         ${ARCHIVE_END}
         ${MATH_LIB}
         ${MKLDNN_LIB}
-        glog gflags protobuf snappystream snappy z
+        glog gflags protobuf snappystream snappy z xxhash
         ${EXTERNAL_LIB})

paddle/scripts/paddle_build.sh

@@ -95,9 +95,9 @@ function cmake_gen() {
                 exit 1
             fi
         fi
-    else 
+    else
         if [ "$1" != "" ]; then
-            echo "using python abi: $1"     
+            echo "using python abi: $1"
             if [ "$1" == "cp27-cp27m" ]; then
                 export LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs2/lib:${LD_LIBRARY_PATH#/opt/_internal/cpython-2.7.11-ucs4/lib:}
                 export PATH=/opt/python/cp27-cp27m/bin/:${PATH}
@@ -119,7 +119,7 @@ function cmake_gen() {
            fi
         fi
     fi
-    
+
     if [ "$SYSTEM" == "Darwin" ]; then
         WITH_DISTRIBUTE=${WITH_DISTRIBUTE:-ON}
         WITH_AVX=${WITH_AVX:-ON}
@@ -127,7 +127,7 @@ function cmake_gen() {
     else
         INFERENCE_DEMO_INSTALL_DIR=${INFERENCE_DEMO_INSTALL_DIR:-/root/.cache/inference_demo}
     fi
-    
+
     cat <<EOF
     ========================================
     Configuring cmake in /paddle/build ...
@@ -394,8 +394,8 @@ EOF
         export http_proxy=
         export https_proxy=
         # TODO: jiabin need to refine this part when these tests fixed on mac
-        ctest --output-on-failure -j $1     
-        # make install should also be test when unittest 
+        ctest --output-on-failure -j $1
+        # make install should also be test when unittest
         make install -j 8
         pip install --user ${INSTALL_PREFIX:-/paddle/build}/opt/paddle/share/wheels/*.whl
         if [[ ${WITH_FLUID_ONLY:-OFF} == "OFF" ]] ; then
@@ -659,7 +659,7 @@ function gen_fluid_lib() {
     Generating fluid library for train and inference ...
     ========================================
 EOF
-        cmake .. -DWITH_DISTRIBUTE=OFF
+        cmake .. -DWITH_DISTRIBUTE=OFF -DON_INFER=ON
         make -j `nproc` fluid_lib_dist
         make -j `nproc` inference_lib_dist
       fi

python/paddle/dataset/wmt16.py

@@ -78,7 +78,7 @@ def __build_dict(tar_file, dict_size, save_path, lang):
                     six.iteritems(word_dict), key=lambda x: x[1],
                     reverse=True)):
             if idx + 3 == dict_size: break
-            fout.write("%s\n" % (word[0]))
+            fout.write("%s\n" % (cpt.to_bytes(word[0])))
 
 
 def __load_dict(tar_file, dict_size, lang, reverse=False):

python/paddle/fluid/clip.py

@@ -272,7 +272,7 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
                 )
 
         square = grad * grad
-        local_norm_var = layers.cast(layers.reduce_sum(input=square), 'float64')
+        local_norm_var = layers.reduce_sum(input=square)
         context[self.group_name].append(local_norm_var)
 
         self.context = context
@@ -282,7 +282,6 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
         if group_scale_name not in self.context:
             group_norm_var = layers.sums(input=self.context[self.group_name])
             group_norm_var = layers.sqrt(x=group_norm_var)
-            group_norm_var = layers.cast(group_norm_var, 'float32')
             clip_var = self.context[self.group_name + "_clip"]
             group_scale_var = layers.elementwise_div(
                 x=clip_var,
@@ -333,7 +332,8 @@ def append_gradient_clip_ops(param_grads):
     for p, g in param_grads:
         if g is None:
             continue
-        with p.block.program._optimized_guard([p, g]):
+        with p.block.program._optimized_guard(
+            [p, g]), framework.name_scope('append_clip'):
             clip_attr = getattr(p, 'gradient_clip_attr', NullGradientClipAttr())
             if clip_attr is None:
                 clip_attr = NullGradientClipAttr()
@@ -348,7 +348,8 @@ def append_gradient_clip_ops(param_grads):
     for p, g in param_grads:
         if g is None:
             continue
-        with p.block.program._optimized_guard([p, g]):
+        with p.block.program._optimized_guard(
+            [p, g]), framework.name_scope('append_graident_clip'):
             res.append(clip_attr._create_operators(param=p, grad=g))
 
     return res

python/paddle/fluid/evaluator.py

@@ -316,7 +316,7 @@ class DetectionMAP(Evaluator):
         gt_label (Variable): The ground truth label index, which is a LoDTensor
             with shape [N, 1].
         gt_box (Variable): The ground truth bounding box (bbox), which is a
-            LoDTensor with shape [N, 6]. The layout is [xmin, ymin, xmax, ymax].
+            LoDTensor with shape [N, 4]. The layout is [xmin, ymin, xmax, ymax].
         gt_difficult (Variable|None): Whether this ground truth is a difficult
             bounding bbox, which can be a LoDTensor [N, 1] or not set. If None,
             it means all the ground truth labels are not difficult bbox.

python/paddle/fluid/framework.py

@@ -1496,6 +1496,9 @@ class Program(object):
             >>> with program._optimized_guard([p,g]):
             >>>     p = p - 0.001 * g
         """
+        tmp_role = self._current_role
+        tmp_var = self._op_role_var
+
         OpRole = core.op_proto_and_checker_maker.OpRole
         self._current_role = OpRole.Optimize
         self._op_role_var = [
@@ -1503,11 +1506,11 @@ class Program(object):
             for var in param_and_grads
         ]
         yield
-        self._op_role_var = []
-        self._current_role = OpRole.Forward
+        self._op_role_var = tmp_var
+        self._current_role = tmp_role
 
     @contextlib.contextmanager
-    def _lr_schedule_guard(self):
+    def _lr_schedule_guard(self, is_with_opt=False):
         """
         A with guard to set :code:`LRSched` :code:`OpRole` and
         :code:`OpRoleVar` automatically. The :code:`OpRoleVar` is
@@ -1515,6 +1518,10 @@ class Program(object):
 
         Notes: This is a very low level API. Users should not use it directly.
 
+        Args:
+            is_with_opt: Only set to true if these ops a in the middle
+                 of a bunch of optimize ops so that it can be treated
+                 correctly. For example, sgd->lr_op->sgd->lr_op->sgd.
 
         Examples:
 
@@ -1528,6 +1535,8 @@ class Program(object):
 
         OpRole = core.op_proto_and_checker_maker.OpRole
         self._current_role = OpRole.LRSched
+        if is_with_opt:
+            self._current_role = int(OpRole.LRSched) | int(OpRole.Optimize)
         # TODO(typhoonzero): how to set target learning rate var
         self._op_role_var = []
         yield

python/paddle/fluid/layers/detection.py

@@ -116,8 +116,8 @@ def rpn_target_assign(bbox_pred,
     Returns:
         tuple:
                A tuple(predicted_scores, predicted_location, target_label,
-               target_bbox) is returned. The predicted_scores and
-               predicted_location is the predicted result of the RPN.
+               target_bbox, bbox_inside_weight) is returned. The predicted_scores 
+               and predicted_location is the predicted result of the RPN.
                The target_label and target_bbox is the ground truth,
                respectively. The predicted_location is a 2D Tensor with shape
                [F, 4], and the shape of target_bbox is same as the shape of
@@ -126,6 +126,8 @@ def rpn_target_assign(bbox_pred,
                [F + B, 1], and the shape of target_label is same as the shape
                of the predicted_scores, B is the number of the background
                anchors, the F and B is depends on the input of this operator.
+               Bbox_inside_weight represents whether the predicted loc is fake_fg
+               or not and the shape is [F, 4].
 
     Examples:
         .. code-block:: python
@@ -138,7 +140,7 @@ def rpn_target_assign(bbox_pred,
                           append_batch_size=False, dtype='float32')
         gt_boxes = layers.data(name='gt_boxes', shape=[10, 4],
                          append_batch_size=False, dtype='float32')
-        loc_pred, score_pred, loc_target, score_target =
+        loc_pred, score_pred, loc_target, score_target, bbox_inside_weight =
             fluid.layers.rpn_target_assign(bbox_pred=bbox_pred,
                                           cls_logits=cls_logits,
                                           anchor_box=anchor_box,
@@ -152,6 +154,8 @@ def rpn_target_assign(bbox_pred,
     target_label = helper.create_variable_for_type_inference(dtype='int32')
     target_bbox = helper.create_variable_for_type_inference(
         dtype=anchor_box.dtype)
+    bbox_inside_weight = helper.create_variable_for_type_inference(
+        dtype=anchor_box.dtype)
     helper.append_op(
         type="rpn_target_assign",
         inputs={
@@ -164,7 +168,8 @@ def rpn_target_assign(bbox_pred,
             'LocationIndex': loc_index,
             'ScoreIndex': score_index,
             'TargetLabel': target_label,
-            'TargetBBox': target_bbox
+            'TargetBBox': target_bbox,
+            'BBoxInsideWeight': bbox_inside_weight
         },
         attrs={
             'rpn_batch_size_per_im': rpn_batch_size_per_im,
@@ -179,13 +184,14 @@ def rpn_target_assign(bbox_pred,
     score_index.stop_gradient = True
     target_label.stop_gradient = True
     target_bbox.stop_gradient = True
+    bbox_inside_weight.stop_gradient = True
 
     cls_logits = nn.reshape(x=cls_logits, shape=(-1, 1))
     bbox_pred = nn.reshape(x=bbox_pred, shape=(-1, 4))
     predicted_cls_logits = nn.gather(cls_logits, score_index)
     predicted_bbox_pred = nn.gather(bbox_pred, loc_index)
 
-    return predicted_cls_logits, predicted_bbox_pred, target_label, target_bbox
+    return predicted_cls_logits, predicted_bbox_pred, target_label, target_bbox, bbox_inside_weight
 
 
 def detection_output(loc,

python/paddle/fluid/layers/learning_rate_scheduler.py

@@ -27,7 +27,7 @@ from . import nn
 from . import ops
 from . import tensor
 from ..initializer import init_on_cpu
-from ..framework import default_main_program, Parameter, unique_name
+from ..framework import default_main_program, Parameter, unique_name, name_scope
 
 __all__ = [
     'exponential_decay', 'natural_exp_decay', 'inverse_time_decay',
@@ -332,14 +332,16 @@ def append_LARS(params_grads, learning_rate, weight_decay):
             return grad_norm + weight_decay * param_norm
 
     for param, grad in params_grads:
-        param_lr = param.optimize_attr['learning_rate']
-        param_norm = ops.sqrt(nn.reduce_sum(input=ops.square(param)))
-        grad_norm = ops.sqrt(nn.reduce_sum(input=ops.square(grad)))
-        if type(param_lr) == float and param_lr == 1.0:
-            decayed_lr = learning_rate * param_norm \
-                / _balanced_weight(param_norm, grad_norm)
-        else:
-            decayed_lr = learning_rate * param_lr * param_norm \
-                / _balanced_weight(param_norm, grad_norm)
-        # set back param local learning rate
-        param.optimize_attr['learning_rate'] = decayed_lr
+        with param.block.program.optimized_guard(
+            [param, grad]), name_scope("optimizer"):
+            param_lr = param.optimize_attr['learning_rate']
+            param_norm = ops.sqrt(nn.reduce_sum(input=ops.square(param)))
+            grad_norm = ops.sqrt(nn.reduce_sum(input=ops.square(grad)))
+            if type(param_lr) == float and param_lr == 1.0:
+                decayed_lr = learning_rate * param_norm \
+                    / _balanced_weight(param_norm, grad_norm)
+            else:
+                decayed_lr = learning_rate * param_lr * param_norm \
+                    / _balanced_weight(param_norm, grad_norm)
+            # set back param local learning rate
+            param.optimize_attr['learning_rate'] = decayed_lr

python/paddle/fluid/layers/nn.py

@@ -154,7 +154,9 @@ __all__ = [
     'mul',
     'sigmoid_cross_entropy_with_logits',
     'maxout',
+    'sequence_reverse',
     'affine_channel',
+    'hash',
 ]
 
 
@@ -980,7 +982,12 @@ def cos_sim(X, Y):
     return out
 
 
-def dropout(x, dropout_prob, is_test=False, seed=None, name=None):
+def dropout(x,
+            dropout_prob,
+            is_test=False,
+            seed=None,
+            name=None,
+            dropout_implementation="downgrade_in_infer"):
     """
     Computes dropout.
 
@@ -1000,6 +1007,21 @@ def dropout(x, dropout_prob, is_test=False, seed=None, name=None):
                     units will be dropped. DO NOT use a fixed seed in training.
         name (str|None): A name for this layer(optional). If set None, the layer
                          will be named automatically.
+        dropout_implementation(string): ['downgrade_in_infer'(defauld)|'upscale_in_train']
+                                        1. downgrade_in_infer(default), downgrade the outcome at inference
+                                           train: out = input * mask
+                                           inference: out = input * dropout_prob
+                                           (make is a tensor same shape with input, value is 0 or 1
+                                            ratio of 0 is dropout_prob)
+                                        2. upscale_in_train, upscale the outcome at training time
+                                           train: out = input * mask / ( 1.0 - dropout_prob )
+                                           inference: out = input
+                                           (make is a tensor same shape with input, value is 0 or 1
+                                            ratio of 0 is dropout_prob)
+                                           dropout op can be removed from the program. 
+                                           the program will be efficient
+                                        
+
 
     Returns:
         Variable: A tensor variable is the shape with `x`.
@@ -1029,7 +1051,8 @@ def dropout(x, dropout_prob, is_test=False, seed=None, name=None):
             'dropout_prob': dropout_prob,
             'is_test': is_test,
             'fix_seed': seed is not None,
-            'seed': seed if seed is not None else 0
+            'seed': seed if seed is not None else 0,
+            'dropout_implementation': dropout_implementation,
         })
     return out
 
@@ -1969,17 +1992,17 @@ def sequence_slice(input, offset, length, name=None):
     """
     **Sequence Slice Layer**
 
-    The layer crops a subsequence from given sequence with given start 
+    The layer crops a subsequence from given sequence with given start
     offset and subsequence length.
 
     It only supports sequence data (LoDTensor with lod_level equal to 1).
 
     .. code-block:: text
-    
+
 	- Case:
 
             Given the input Variable **input**:
-                
+
                 input.data = [[a1, a2], [b1, b2], [c1, c2], [d1, d2], [e1, e2]],
                 input.lod = [[3, 2]],
                 input.dims = (5, 2),
@@ -1987,16 +2010,16 @@ def sequence_slice(input, offset, length, name=None):
             with offset.data = [[0], [1]] and length.data = [[2], [1]],
 
             the output Variable will be
-                
+
                 out.data = [[a1, a2], [b1, b2], [e1, e2]],
                 out.lod = [[2, 1]],
                 out.dims = (3, 2).
-	
-    NOTE: The first dimension size of **input**, **offset** and **length** 
+
+    NOTE: The first dimension size of **input**, **offset** and **length**
           should be equal. The **offset** should start from 0.
-    
+
     Args:
-        input(Variable): The input Variable which consists of the complete 
+        input(Variable): The input Variable which consists of the complete
                          sequences.
         offset(Variable): The offset to slice each sequence.
         length(Variable): The length of each subsequence.
@@ -2015,7 +2038,7 @@ def sequence_slice(input, offset, length, name=None):
                               dtype='float32', lod_level=1)
              offset = fluid.layers.assign(input=np.array([[0, 1]]).astype("int32"))
              length = fluid.layers.assign(input=np.array([[2, 1]]).astype("int32"))
-             subseqs = fluid.layers.sequence_slice(input=seqs, offset=offset, 
+             subseqs = fluid.layers.sequence_slice(input=seqs, offset=offset,
                                                    length=length)
     """
     helper = LayerHelper("sequence_slice", **locals())
@@ -2398,12 +2421,12 @@ def layer_norm(input,
         param_attr(ParamAttr|None): The parameter attribute for the learnable
             gain :math:`g`. If :attr:`scale` is False, :attr:`param_attr` is
             omitted. If :attr:`scale` is True and :attr:`param_attr` is None,
-            a default :code:`ParamAttr` would be added as scale. The 
-            :attr:`param_attr` is initialized as 1 if it is added. Default None. 
+            a default :code:`ParamAttr` would be added as scale. The
+            :attr:`param_attr` is initialized as 1 if it is added. Default None.
         bias_attr(ParamAttr|None): The parameter attribute for the learnable
             bias :math:`b`. If :attr:`shift` is False, :attr:`bias_attr` is
             omitted. If :attr:`shift` is True and :attr:`param_attr` is None,
-            a default :code:`ParamAttr` would be added as bias. The 
+            a default :code:`ParamAttr` would be added as bias. The
             :attr:`bias_attr` is initialized as 0 if it is added. Default None.
         act(str): Activation to be applied to the output of layer normalizaiton.
                   Default None.
@@ -3021,8 +3044,8 @@ def sequence_unpad(x, length, name=None):
     """
     **Sequence Unpad Layer**
 
-    This layer removes the padding data in the input sequences and convert 
-    them into sequences with actual length as output, identitied by lod 
+    This layer removes the padding data in the input sequences and convert
+    them into sequences with actual length as output, identitied by lod
     information.
 
     .. code-block:: text
@@ -3032,9 +3055,9 @@ def sequence_unpad(x, length, name=None):
 	Given input Variable **x**:
 	    x.data = [[ 1.0,  2.0,  3.0,  4.0,  5.0],
 		      [ 6.0,  7.0,  8.0,  9.0, 10.0],
-		      [11.0, 12.0, 13.0, 14.0, 15.0]], 
-     
-	in which there are 3 sequences padded to length 5, and the acutal length 
+		      [11.0, 12.0, 13.0, 14.0, 15.0]],
+
+	in which there are 3 sequences padded to length 5, and the acutal length
 	specified by input Variable **length**:
 
 	    length.data = [[2], [3], [4]],
@@ -3042,7 +3065,7 @@ def sequence_unpad(x, length, name=None):
 	after unpadding, the output Variable will be:
 
 	    out.data = [[1.0, 2.0, 6.0, 7.0, 8.0, 11.0, 12.0, 13.0, 14.0]]
-	    out.lod = [[2, 3, 4]]      
+	    out.lod = [[2, 3, 4]]
 
     Args:
         x(Variable): Input Variable which contains the padded sequences with
@@ -4844,7 +4867,7 @@ def autoincreased_step_counter(counter_name=None, begin=1, step=1):
     return counter
 
 
-def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
+def reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None):
     """
     Gives a new shape to the input Tensor without changing its data.
 
@@ -4892,15 +4915,22 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
                                 :attr:`shape` specifying shape. That is to
                                 say :attr:`actual_shape` has a higher priority
                                 than :attr:`shape`.
-        act (str): The non-linear activation to be applied to output variable.
-        inplace(bool): If this flag is set true, the output
-                       shares data with input without copying, otherwise
-                       a new output tensor is created
-                       whose data is copied from input x.
+        act (str): The non-linear activation to be applied to the reshaped tensor
+                   variable.
+        inplace(bool): Must use :attr:`False` if :attr:`x` is used in multiple
+                       operators. If this flag is set :attr:`True`, reuse input
+                       :attr:`x` to reshape, which will change the shape of
+                       tensor variable :attr:`x` and might cause errors when
+                       :attr:`x` is used in multiple operators. If :attr:`False`,
+                       preserve the shape :attr:`x` and create a new output tensor
+                       variable whose data is copied from input x but reshaped.
         name (str): The name of this layer. It is optional.
 
     Returns:
-        Variable: The output tensor.
+        Variable: The reshaped tensor variable if :attr:`act` is None. It is a \
+                  new tensor variable if :attr:`inplace` is :attr:`False`, \
+                  otherwise it is :attr:`x`. If :attr:`act` is not None, return \
+                  the activated tensor variable.
 
     Raises:
         TypeError: if actual_shape is neither Variable nor None.
@@ -4911,7 +4941,7 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
             data = fluid.layers.data(
                 name='data', shape=[2, 4, 6], dtype='float32')
             reshaped = fluid.layers.reshape(
-                x=data, shape=[-1, 0, 3, 2], act='tanh', inplace=True)
+                x=data, shape=[-1, 0, 3, 2], inplace=True)
     """
 
     if not (isinstance(shape, list) or isinstance(shape, tuple)):
@@ -4938,7 +4968,8 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
                 "except one unknown dimension.")
 
     helper = LayerHelper("reshape2", **locals())
-    out = helper.create_variable_for_type_inference(dtype=x.dtype)
+    out = x if inplace else helper.create_variable_for_type_inference(
+        dtype=x.dtype)
     x_shape = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(
         type="reshape2",
@@ -5469,9 +5500,9 @@ def roi_align(input,
     Examples:
         .. code-block:: python
 
-            align_out = fluid.layers.roi_align(input=x, 
-                                               rois=rois, 
-                                               pooled_height=7, 
+            align_out = fluid.layers.roi_align(input=x,
+                                               rois=rois,
+                                               pooled_height=7,
                                                pooled_width=7,
                                                spatial_scale=0.5,
                                                sampling_ratio=-1)
@@ -7455,13 +7486,40 @@ def maxout(x, groups, name=None):
     return out
 
 
+@templatedoc()
+def sequence_reverse(x, name=None):
+    """ 
+    ${comment}
+
+    Args:
+        x(${x_type}): ${x_comment}
+        name(basestring|None): Name of the output.
+
+    Returns:
+        out(${y_type}): ${y_comment}
+    """
+    helper = LayerHelper("sequence_reverse", **locals())
+    if name is None:
+        out = helper.create_variable_for_type_inference(dtype=x.dtype)
+    else:
+        out = helper.create_variable(
+            name=name, dtype=x.dtype, persistable=False)
+
+    helper.append_op(
+        type="sequence_reverse",
+        inputs={"X": x},
+        outputs={"Y": out},
+        attrs=dict())
+    return out
+
+
 def affine_channel(x, scale=None, bias=None, data_layout='NCHW', name=None):
     """
     Applies a separate affine transformation to each channel of the input.
     Useful for replacing spatial batch norm with its equivalent fixed
     transformation. The input also can be 2D tensor and applies a affine
     transformation in second dimension.
-    
+
     Args:
         x (Variable): Feature map input can be a 4D tensor with order NCHW
             or NHWC. It also can be a 2D tensor and the affine transformation
@@ -7494,3 +7552,31 @@ def affine_channel(x, scale=None, bias=None, data_layout='NCHW', name=None):
         attrs={"data_layout": data_layout},
         outputs={"Out": out})
     return out
+
+
+def hash(input, hash_size, num_hash=1, name=None):
+    """
+    hash the input
+     Args:
+        input (Variable): The input variable which is a one-hot word.
+        hash_size (int): The space size for hash algorithm.
+        num_hash (int): The times of hash, default 1.
+        name (str, default None): The name of this layer.
+     Returns:
+        Variable: The hash result variable which is a LoDTensor.
+     Examples:
+        .. code-block:: python
+            word_dict = paddle.dataset.imdb.word_dict()
+            x = fluid.layers.data(shape[1], dtype='int32', lod_level=1)
+            out = fluid.layers.hash(input=x, len(word_dict))
+    """
+    helper = LayerHelper('hash', **locals())
+    out = helper.create_variable_for_type_inference(
+        helper.input_dtype(), stop_gradient=True)
+    helper.append_op(
+        type='hash',
+        inputs={'X': input},
+        outputs={'Out': out},
+        attrs={'num_hash': num_hash,
+               'mod_by': hash_size})
+    return out

python/paddle/fluid/metrics.py

@@ -13,8 +13,6 @@
 # limitations under the License.
 """
 Fluid Metrics
-
-The metrics are accomplished via Python natively.
 """
 
 from __future__ import print_function
@@ -24,6 +22,12 @@ import copy
 import warnings
 import six
 
+from .layer_helper import LayerHelper
+from .initializer import Constant
+from . import unique_name
+from .framework import Program, Variable, program_guard
+from . import layers
+
 __all__ = [
     'MetricBase',
     'CompositeMetric',
@@ -474,71 +478,10 @@ class EditDistance(MetricBase):
                 "There is no data in EditDistance Metric. Please check layers.edit_distance output has been added to EditDistance."
             )
         avg_distance = self.total_distance / self.seq_num
-        avg_instance_error = self.instance_error / self.seq_num
+        avg_instance_error = self.instance_error / float(self.seq_num)
         return avg_distance, avg_instance_error
 
 
-class DetectionMAP(MetricBase):
-    """
-    Calculate the detection mean average precision (mAP).
-    mAP is the metric to measure the accuracy of object detectors
-    like Faster R-CNN, SSD, etc.
-    It is the average of the maximum precisions at different recall values.
-    Please get more information from the following articles:
-      https://sanchom.wordpress.com/tag/average-precision/
-
-      https://arxiv.org/abs/1512.02325
-
-    The general steps are as follows:
-
-        1. calculate the true positive and false positive according to the input
-            of detection and labels.
-        2. calculate mAP value, support two versions: '11 point' and 'integral'.
-
-    Examples:
-        .. code-block:: python
-
-            pred = fluid.layers.fc(input=data, size=1000, act="tanh")
-            batch_map = layers.detection_map(
-                input,
-                label,
-                class_num,
-                background_label,
-                overlap_threshold=overlap_threshold,
-                evaluate_difficult=evaluate_difficult,
-                ap_version=ap_version)
-            metric = fluid.metrics.DetectionMAP()
-            for data in train_reader():
-                loss, preds, labels = exe.run(fetch_list=[cost, batch_map])
-                batch_size = data[0]
-                metric.update(value=batch_map, weight=batch_size)
-                numpy_map = metric.eval()
-    """
-
-    def __init__(self, name=None):
-        super(DetectionMAP, self).__init__(name)
-        # the current map value
-        self.value = .0
-        self.weight = .0
-
-    def update(self, value, weight):
-        if not _is_number_or_matrix_(value):
-            raise ValueError(
-                "The 'value' must be a number(int, float) or a numpy ndarray.")
-        if not _is_number_(weight):
-            raise ValueError("The 'weight' must be a number(int, float).")
-        self.value += value
-        self.weight += weight
-
-    def eval(self):
-        if self.weight == 0:
-            raise ValueError(
-                "There is no data in DetectionMAP Metrics. "
-                "Please check layers.detection_map output has added to DetectionMAP."
-            )
-        return self.value / self.weight
-
-
 class Auc(MetricBase):
     """
     Auc metric adapts to the binary classification.
@@ -616,3 +559,179 @@ class Auc(MetricBase):
             idx -= 1
 
         return auc / tot_pos / tot_neg if tot_pos > 0.0 and tot_neg > 0.0 else 0.0
+
+
+class DetectionMAP(object):
+    """
+    Calculate the detection mean average precision (mAP).
+
+    The general steps are as follows:
+    1. calculate the true positive and false positive according to the input
+        of detection and labels.
+    2. calculate mAP value, support two versions: '11 point' and 'integral'.
+
+    Please get more information from the following articles:
+      https://sanchom.wordpress.com/tag/average-precision/
+      https://arxiv.org/abs/1512.02325
+
+    Args:
+        input (Variable): The detection results, which is a LoDTensor with shape
+            [M, 6]. The layout is [label, confidence, xmin, ymin, xmax, ymax].
+        gt_label (Variable): The ground truth label index, which is a LoDTensor
+            with shape [N, 1].
+        gt_box (Variable): The ground truth bounding box (bbox), which is a
+            LoDTensor with shape [N, 4]. The layout is [xmin, ymin, xmax, ymax].
+        gt_difficult (Variable|None): Whether this ground truth is a difficult
+            bounding bbox, which can be a LoDTensor [N, 1] or not set. If None,
+            it means all the ground truth labels are not difficult bbox.
+        class_num (int): The class number.
+        background_label (int): The index of background label, the background
+            label will be ignored. If set to -1, then all categories will be
+            considered, 0 by defalut.
+        overlap_threshold (float): The threshold for deciding true/false
+            positive, 0.5 by defalut.
+        evaluate_difficult (bool): Whether to consider difficult ground truth
+            for evaluation, True by defalut. This argument does not work when
+            gt_difficult is None.
+        ap_version (string): The average precision calculation ways, it must be
+            'integral' or '11point'. Please check
+            https://sanchom.wordpress.com/tag/average-precision/ for details.
+            - 11point: the 11-point interpolated average precision.
+            - integral: the natural integral of the precision-recall curve.
+
+    Examples:
+        .. code-block:: python
+
+            exe = fluid.Executor(place)
+            map_evaluator = fluid.Evaluator.DetectionMAP(input,
+                gt_label, gt_box, gt_difficult)
+            cur_map, accum_map = map_evaluator.get_map_var()
+            fetch = [cost, cur_map, accum_map]
+            for epoch in PASS_NUM:
+                map_evaluator.reset(exe)
+                for data in batches:
+                    loss, cur_map_v, accum_map_v = exe.run(fetch_list=fetch)
+
+        In the above example:
+
+            'cur_map_v' is the mAP of current mini-batch.
+            'accum_map_v' is the accumulative mAP of one pass.
+    """
+
+    def __init__(self,
+                 input,
+                 gt_label,
+                 gt_box,
+                 gt_difficult=None,
+                 class_num=None,
+                 background_label=0,
+                 overlap_threshold=0.5,
+                 evaluate_difficult=True,
+                 ap_version='integral'):
+
+        self.helper = LayerHelper('map_eval')
+        gt_label = layers.cast(x=gt_label, dtype=gt_box.dtype)
+        if gt_difficult:
+            gt_difficult = layers.cast(x=gt_difficult, dtype=gt_box.dtype)
+            label = layers.concat([gt_label, gt_difficult, gt_box], axis=1)
+        else:
+            label = layers.concat([gt_label, gt_box], axis=1)
+
+        # calculate mean average precision (mAP) of current mini-batch
+        map = layers.detection_map(
+            input,
+            label,
+            class_num,
+            background_label,
+            overlap_threshold=overlap_threshold,
+            evaluate_difficult=evaluate_difficult,
+            ap_version=ap_version)
+
+        states = []
+        states.append(
+            self._create_state(
+                dtype='int32', shape=None, suffix='accum_pos_count'))
+        states.append(
+            self._create_state(
+                dtype='float32', shape=None, suffix='accum_true_pos'))
+        states.append(
+            self._create_state(
+                dtype='float32', shape=None, suffix='accum_false_pos'))
+        var = self._create_state(dtype='int32', shape=[1], suffix='has_state')
+        self.helper.set_variable_initializer(
+            var, initializer=Constant(value=int(0)))
+        self.has_state = var
+
+        # calculate accumulative mAP
+        accum_map = layers.detection_map(
+            input,
+            label,
+            class_num,
+            background_label,
+            overlap_threshold=overlap_threshold,
+            evaluate_difficult=evaluate_difficult,
+            has_state=self.has_state,
+            input_states=states,
+            out_states=states,
+            ap_version=ap_version)
+
+        layers.fill_constant(
+            shape=self.has_state.shape,
+            value=1,
+            dtype=self.has_state.dtype,
+            out=self.has_state)
+
+        self.cur_map = map
+        self.accum_map = accum_map
+
+    def _create_state(self, suffix, dtype, shape):
+        """
+        Create state variable.
+        Args:
+            suffix(str): the state suffix.
+            dtype(str|core.VarDesc.VarType): the state data type
+            shape(tuple|list): the shape of state
+        Returns: State variable
+        """
+        state = self.helper.create_variable(
+            name="_".join([unique_name.generate(self.helper.name), suffix]),
+            persistable=True,
+            dtype=dtype,
+            shape=shape)
+        return state
+
+    def get_map_var(self):
+        """
+        Returns: mAP variable of current mini-batch and
+            accumulative mAP variable cross mini-batches.
+        """
+        return self.cur_map, self.accum_map
+
+    def reset(self, executor, reset_program=None):
+        """
+        Reset metric states at the begin of each pass/user specified batch.
+
+        Args:
+            executor(Executor): a executor for executing
+                the reset_program.
+            reset_program(Program|None): a single Program for reset process.
+                If None, will create a Program.
+        """
+
+        def _clone_var_(block, var):
+            assert isinstance(var, Variable)
+            return block.create_var(
+                name=var.name,
+                shape=var.shape,
+                dtype=var.dtype,
+                type=var.type,
+                lod_level=var.lod_level,
+                persistable=var.persistable)
+
+        if reset_program is None:
+            reset_program = Program()
+        with program_guard(main_program=reset_program):
+            var = _clone_var_(reset_program.current_block(), self.has_state)
+            layers.fill_constant(
+                shape=var.shape, value=0, dtype=var.dtype, out=var)
+        executor.run(reset_program)

python/paddle/fluid/optimizer.py

@@ -14,6 +14,7 @@
 
 from __future__ import print_function
 import re
+import sys
 from collections import defaultdict
 from paddle.fluid.framework import Program, Variable, name_scope, default_main_program
 from . import framework
@@ -32,7 +33,8 @@ __all__ = [
     'SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad', 'Ftrl',
     'SGDOptimizer', 'MomentumOptimizer', 'AdagradOptimizer', 'AdamOptimizer',
     'AdamaxOptimizer', 'DecayedAdagradOptimizer', 'RMSPropOptimizer',
-    'FtrlOptimizer', 'Adadelta', 'ModelAverage', 'RMSPropOptimizer'
+    'FtrlOptimizer', 'Adadelta', 'ModelAverage', 'LarsMomentum',
+    'LarsMomentumOptimizer'
 ]
 
 
@@ -105,13 +107,14 @@ class Optimizer(object):
         param = param_and_grad[0]
         param_lr = param.optimize_attr['learning_rate']
         if type(param_lr) == Variable:
-            print("returns updated param lr ", param_lr)
             return param_lr
         else:
             if param_lr == 1.0:
                 return self._global_learning_rate()
             else:
-                with default_main_program()._lr_schedule_guard():
+                with default_main_program()._lr_schedule_guard(
+                        is_with_opt=True), framework.name_scope(
+                            'scale_with_param_lr'):
                     return self._global_learning_rate() * param_lr
 
     def _create_accumulators(self, block, parameters):
@@ -398,6 +401,91 @@ class MomentumOptimizer(Optimizer):
         return momentum_op
 
 
+class LarsMomentumOptimizer(Optimizer):
+    """
+    Momentum optimizer with LARS support
+
+    The update equations are as follows:
+
+    .. math::
+
+        & local\_learning\_rate = learning\_rate * lars\_coeff * \\
+          \\frac{||param||}{||gradient|| + lars\_weight\_decay * ||param||}
+
+        & velocity = mu * velocity + local\_learning\_rate * (gradient + lars\_weight\_decay * param)
+
+        & param = param - velocity
+
+    Args:
+        learning_rate (float|Variable): the learning rate used to update parameters. \
+        Can be a float value or a Variable with one float value as data element.
+        momentum (float): momentum factor
+        lars_coeff (float): defines how much we trust the layer to change its weights.
+        lars_weight_decay (float): weight decay coefficient for decaying using LARS.
+        regularization: A Regularizer, such as
+                        fluid.regularizer.L2DecayRegularizer.
+        name: A optional name prefix.
+        
+
+    Examples:
+        .. code-block:: python
+
+            optimizer = fluid.optimizer.LarsMomentum(learning_rate=0.2, momentum=0.1, lars_weight_decay=0.001)
+            optimizer.minimize(cost)
+    """
+    _velocity_acc_str = "velocity"
+
+    def __init__(self,
+                 learning_rate,
+                 momentum,
+                 lars_coeff=0.001,
+                 lars_weight_decay=0.0005,
+                 regularization=None,
+                 name=None):
+        assert learning_rate is not None
+        assert momentum is not None
+        super(LarsMomentumOptimizer, self).__init__(
+            learning_rate=learning_rate,
+            regularization=regularization,
+            name=name)
+        self.type = "lars_momentum"
+        self._momentum = momentum
+        self._lars_coeff = float(lars_coeff)
+        self._lars_weight_decay = float(lars_weight_decay)
+
+    def _create_accumulators(self, block, parameters):
+        assert isinstance(block, framework.Block)
+
+        for p in parameters:
+            self._add_accumulator(self._velocity_acc_str, p)
+
+    def _append_optimize_op(self, block, param_and_grad):
+        assert isinstance(block, framework.Block)
+
+        velocity_acc = self._get_accumulator(self._velocity_acc_str,
+                                             param_and_grad[0])
+        # create the momentum optimize op
+        momentum_op = block.append_op(
+            type=self.type,
+            inputs={
+                "Param": param_and_grad[0],
+                "Grad": param_and_grad[1],
+                "Velocity": velocity_acc,
+                "LearningRate": self._create_param_lr(param_and_grad)
+            },
+            outputs={
+                "ParamOut": param_and_grad[0],
+                "VelocityOut": velocity_acc
+            },
+            attrs={
+                "mu": self._momentum,
+                "lars_coeff": self._lars_coeff,
+                "lars_weight_decay": self._lars_weight_decay
+            })
+
+        return momentum_op
+
+
 class AdagradOptimizer(Optimizer):
     """
     **Adaptive Gradient Algorithm (Adagrad)**
@@ -602,7 +690,8 @@ class AdamOptimizer(Optimizer):
         for param, grad in param_and_grads:
             if grad is None:
                 continue
-            with param.block.program._optimized_guard([param, grad]):
+            with param.block.program._optimized_guard(
+                [param, grad]), name_scope("optimizer"):
                 beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str,
                                                       param)
                 beta2_pow_acc = self._get_accumulator(self._beta2_pow_acc_str,
@@ -740,7 +829,8 @@ class AdamaxOptimizer(Optimizer):
         for param, grad in parameters_and_grads:
             if grad is None:
                 continue
-            with param.block.program._optimized_guard([param, grad]):
+            with param.block.program._optimized_guard(
+                [param, grad]), name_scope('adamx'):
                 beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str,
                                                       param)
                 main_block.append_op(
@@ -1217,6 +1307,7 @@ DecayedAdagrad = DecayedAdagradOptimizer
 Adadelta = AdadeltaOptimizer
 RMSProp = RMSPropOptimizer
 Ftrl = FtrlOptimizer
+LarsMomentum = LarsMomentumOptimizer
 
 
 class ModelAverage(Optimizer):
@@ -1279,7 +1370,8 @@ class ModelAverage(Optimizer):
         for param, grad in self.params_grads:
             if grad is None:
                 continue
-            with param.block.program._optimized_guard([param, grad]):
+            with param.block.program._optimized_guard(
+                [param, grad]), name_scope('move_average'):
                 self._append_average_accumulate_op(param)
 
         self.apply_program = Program()

python/paddle/fluid/regularizer.py

@@ -47,7 +47,8 @@ def append_regularization_ops(parameters_and_grads, regularization=None):
         if grad is None:
             params_and_grads.append((param, grad))
             continue
-        with param.block.program._optimized_guard([param, grad]):
+        with param.block.program._optimized_guard(
+            [param, grad]), framework.name_scope('regularization'):
             regularization_term = None
             if param.regularizer is not None:
                 # Add variable for regularization term in grad block

python/paddle/fluid/tests/CMakeLists.txt

-if(NOT APPLE)
-  set(PYTHON_TESTS_DIR ${CMAKE_CURRENT_BINARY_DIR} CACHE PATH "python tests directory")
-else()
-  set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests)
-endif(NOT APPLE)
+set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests CACHE INTERNAL "python tests directory")
 
 file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
 string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")

python/paddle/fluid/tests/test_detection.py

@@ -301,7 +301,7 @@ class TestRpnTargetAssign(unittest.TestCase):
                 dtype='float32',
                 lod_level=1,
                 append_batch_size=False)
-            pred_scores, pred_loc, tgt_lbl, tgt_bbox = layers.rpn_target_assign(
+            pred_scores, pred_loc, tgt_lbl, tgt_bbox, bbox_inside_weight = layers.rpn_target_assign(
                 bbox_pred=bbox_pred,
                 cls_logits=cls_logits,
                 anchor_box=anchor_box,
@@ -313,15 +313,18 @@ class TestRpnTargetAssign(unittest.TestCase):
                 rpn_straddle_thresh=0.0,
                 rpn_fg_fraction=0.5,
                 rpn_positive_overlap=0.7,
-                rpn_negative_overlap=0.3)
+                rpn_negative_overlap=0.3,
+                use_random=False)
 
             self.assertIsNotNone(pred_scores)
             self.assertIsNotNone(pred_loc)
             self.assertIsNotNone(tgt_lbl)
             self.assertIsNotNone(tgt_bbox)
+            self.assertIsNotNone(bbox_inside_weight)
             assert pred_scores.shape[1] == 1
             assert pred_loc.shape[1] == 4
             assert pred_loc.shape[1] == tgt_bbox.shape[1]
+            print(str(program))
 
 
 class TestGenerateProposals(unittest.TestCase):

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

@@ -78,9 +78,9 @@ if(WITH_DISTRIBUTE)
         set_tests_properties(test_dist_word2vec PROPERTIES TIMEOUT 200)
         py_test_modules(test_dist_se_resnext MODULES test_dist_se_resnext)
         set_tests_properties(test_dist_se_resnext PROPERTIES TIMEOUT 1000)
-        # TODO: fix this test
-        #py_test_modules(test_dist_transformer MODULES test_dist_transformer)
-        #set_tests_properties(test_dist_transformer PROPERTIES TIMEOUT 1000)
+        # FIXME(typhoonzero): add this back
+	#py_test_modules(test_dist_transformer MODULES test_dist_transformer)
+	#set_tests_properties(test_dist_transformer PROPERTIES TIMEOUT 1000)
     endif(NOT APPLE)
     py_test_modules(test_dist_transpiler MODULES test_dist_transpiler)
 endif()

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

@@ -95,7 +95,7 @@ class TestDistMnist2x2(TestDistRunnerBase):
 
         # Reader
         train_reader = paddle.batch(
-            paddle.dataset.mnist.train(), batch_size=batch_size)
+            paddle.dataset.mnist.test(), batch_size=batch_size)
         test_reader = paddle.batch(
             paddle.dataset.mnist.test(), batch_size=batch_size)
         opt.minimize(avg_cost)

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

+#   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.
+
+from __future__ import print_function
+
+import numpy as np
+import argparse
+import time
+import math
+
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.profiler as profiler
+from paddle.fluid import core
+import unittest
+from multiprocessing import Process
+import os
+import signal
+from functools import reduce
+from test_dist_base import TestDistRunnerBase, runtime_main
+from dist_mnist import cnn_model
+
+DTYPE = "float32"
+
+
+def test_merge_reader(repeat_batch_size=8):
+    orig_reader = paddle.dataset.mnist.test()
+    record_batch = []
+    b = 0
+    for d in orig_reader():
+        if b >= repeat_batch_size:
+            break
+        record_batch.append(d)
+        b += 1
+    while True:
+        for d in record_batch:
+            yield d
+
+
+class TestDistMnist2x2(TestDistRunnerBase):
+    def get_model(self, batch_size=2):
+        # Input data
+        images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype=DTYPE)
+        label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+
+        # Train program
+        predict = cnn_model(images)
+        cost = fluid.layers.cross_entropy(input=predict, label=label)
+        avg_cost = fluid.layers.mean(x=cost)
+
+        # Evaluator
+        batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
+        batch_acc = fluid.layers.accuracy(
+            input=predict, label=label, total=batch_size_tensor)
+
+        inference_program = fluid.default_main_program().clone()
+        # Optimization
+        opt = fluid.optimizer.Momentum(learning_rate=0.001, momentum=0.9)
+
+        # Reader
+        train_reader = paddle.batch(test_merge_reader, batch_size=batch_size)
+        test_reader = paddle.batch(
+            paddle.dataset.mnist.test(), batch_size=batch_size)
+        opt.minimize(avg_cost)
+        return inference_program, avg_cost, train_reader, test_reader, batch_acc, predict
+
+
+if __name__ == "__main__":
+    runtime_main(TestDistMnist2x2)

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

+#   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.
+
+from __future__ import print_function
+
+import numpy as np
+import argparse
+import time
+import math
+
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.profiler as profiler
+from paddle.fluid import core
+import unittest
+from multiprocessing import Process
+import os
+import signal
+from functools import reduce
+from test_dist_base import TestDistRunnerBase, runtime_main
+from dist_mnist import cnn_model
+
+DTYPE = "float32"
+paddle.dataset.mnist.fetch()
+
+# Fix seed for test
+fluid.default_startup_program().random_seed = 1
+fluid.default_main_program().random_seed = 1
+
+
+class TestDistMnist2x2(TestDistRunnerBase):
+    def get_model(self, batch_size=2):
+        # Input data
+        images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype=DTYPE)
+        label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+
+        # Train program
+        predict = cnn_model(images)
+        cost = fluid.layers.cross_entropy(input=predict, label=label)
+        avg_cost = fluid.layers.mean(x=cost)
+
+        # Evaluator
+        batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
+        batch_acc = fluid.layers.accuracy(
+            input=predict, label=label, total=batch_size_tensor)
+
+        inference_program = fluid.default_main_program().clone()
+        # Optimization
+        opt = fluid.optimizer.LarsMomentumOptimizer(
+            learning_rate=0.001, momentum=0.9)
+
+        # Reader
+        train_reader = paddle.batch(
+            paddle.dataset.mnist.test(), batch_size=batch_size)
+        test_reader = paddle.batch(
+            paddle.dataset.mnist.test(), batch_size=batch_size)
+        opt.minimize(avg_cost)
+        return inference_program, avg_cost, train_reader, test_reader, batch_acc, predict
+
+
+if __name__ == "__main__":
+    runtime_main(TestDistMnist2x2)

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

@@ -35,7 +35,7 @@ import paddle
 import paddle.fluid as fluid
 import paddle.fluid.layers as layers
 from paddle.fluid import core
-from test_dist_base import TestDistRunnerBase, runtime_main
+from test_dist_base import TestDistRunnerBase, runtime_main, RUN_STEP
 import paddle.compat as cpt
 from paddle.compat import long_type
 
@@ -562,18 +562,12 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
     for pass_id in six.moves.xrange(TrainTaskConfig.pass_num):
         pass_start_time = time.time()
         for batch_id, data in enumerate(train_data()):
-            if batch_id >= 5:
+            if batch_id >= RUN_STEP:
                 break
 
             feed_list = []
             total_num_token = 0
 
-            #if TrainTaskConfig.local:
-            #    lr_rate = lr_scheduler.update_learning_rate()
-            #for place_id, data_buffer in enumerate(
-            #        split_data(
-            #            data, num_part=dev_count)):
-
             if TrainTaskConfig.local:
                 lr_rate = lr_scheduler.update_learning_rate()
 
@@ -619,12 +613,11 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
             init = True
 
             # Validate and save the model for inference.
-            if batch_id == 0 or batch_id == 4:
-                if TrainTaskConfig.val_file_pattern is not None:
-                    val_avg_cost, val_ppl = test()
-                    print("[%f]" % val_avg_cost)
-                else:
-                    assert (False)
+            if TrainTaskConfig.val_file_pattern is not None:
+                val_avg_cost, val_ppl = test()
+                print("[%f]" % val_avg_cost)
+            else:
+                assert (False)
 
 
 #import transformer_reader as reader
@@ -1166,6 +1159,7 @@ def prepare_encoder(src_word,
             name=pos_enc_param_name,
             trainable=False,
             initializer=fluid.initializer.ConstantInitializer(0.001)))
+    src_pos_enc.stop_gradient = True
     enc_input = src_word_emb + src_pos_enc
     return layers.dropout(
         enc_input,
@@ -1701,7 +1695,7 @@ class DistTransformer2x2(TestDistRunnerBase):
 
     def run_trainer(self, args):
         TrainTaskConfig.use_gpu = args.use_cuda
-        sum_cost, avg_cost, predict, token_num, local_lr_scheduler = get_model(
+        sum_cost, avg_cost, predict, token_num, local_lr_scheduler, test_program = get_model(
             args.is_dist, not args.sync_mode)
 
         if args.is_dist:

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

@@ -26,10 +26,11 @@ import argparse
 import paddle.fluid as fluid
 
 RUN_STEP = 10
+DEFAULT_BATCH_SIZE = 2
 
 
 class TestDistRunnerBase(object):
-    def get_model(self, batch_size=2):
+    def get_model(self, batch_size=DEFAULT_BATCH_SIZE):
         raise NotImplementedError(
             "get_model should be implemented by child classes.")
 
@@ -48,8 +49,7 @@ class TestDistRunnerBase(object):
         return t
 
     def run_pserver(self, args):
-
-        self.get_model(batch_size=2)
+        self.get_model(batch_size=args.batch_size)
         # NOTE: pserver should not call memory optimize
         t = self.get_transpiler(args.trainer_id,
                                 fluid.default_main_program(), args.endpoints,
@@ -65,7 +65,7 @@ class TestDistRunnerBase(object):
 
     def run_trainer(self, args):
         test_program, avg_cost, train_reader, test_reader, batch_acc, predict = \
-            self.get_model(batch_size=2)
+            self.get_model(batch_size=args.batch_size)
 
         if args.mem_opt:
             fluid.memory_optimize(fluid.default_main_program(), skip_grads=True)
@@ -92,6 +92,11 @@ class TestDistRunnerBase(object):
         strategy.allow_op_delay = False
 
         build_stra = fluid.BuildStrategy()
+        if args.batch_merge_repeat > 1:
+            pass_builder = build_stra._create_passes_from_strategy()
+            mypass = pass_builder.insert_pass(
+                len(pass_builder.all_passes()) - 2, "multi_batch_merge_pass")
+            mypass.set_int("num_repeats", args.batch_merge_repeat)
 
         if args.use_reduce:
             build_stra.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
@@ -145,6 +150,9 @@ def runtime_main(test_class):
     parser.add_argument('--use_reduce', action='store_true')
     parser.add_argument(
         '--use_reader_alloc', action='store_true', required=False, default=True)
+    parser.add_argument('--batch_size', required=False, type=int, default=2)
+    parser.add_argument(
+        '--batch_merge_repeat', required=False, type=int, default=1)
 
     args = parser.parse_args()
 
@@ -244,9 +252,18 @@ class TestDistBase(unittest.TestCase):
                                  (e, retry_times))
                 retry_times -= 1
 
-    def _run_local(self, model, envs, check_error_log):
+    def _run_local(self,
+                   model,
+                   envs,
+                   check_error_log=False,
+                   batch_size=DEFAULT_BATCH_SIZE,
+                   batch_merge_repeat=1):
 
         cmd = "%s %s --role trainer" % (self._python_interp, model)
+        if batch_size != DEFAULT_BATCH_SIZE:
+            cmd += " --batch_size %d" % batch_size
+        if batch_merge_repeat > 1:
+            cmd += " --batch_merge_repeat %d" % batch_merge_repeat
 
         if self.__use_cuda:
             cmd += " --use_cuda"

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

@@ -23,9 +23,8 @@ class TestDistCTR2x2(TestDistBase):
         self._sync_mode = True
         self._enforce_place = "CPU"
 
-
-def test_dist_ctr(self):
-    self.check_with_place("dist_ctr.py", delta=1e-7, check_error_log=False)
+    def test_dist_ctr(self):
+        self.check_with_place("dist_ctr.py", delta=1e-7, check_error_log=False)
 
 
 if __name__ == "__main__":

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

@@ -26,6 +26,15 @@ class TestDistMnist2x2(TestDistBase):
         self.check_with_place("dist_mnist.py", delta=1e-5)
 
 
+class TestDistMnist2x2Lars(TestDistBase):
+    def _setup_config(self):
+        self._sync_mode = True
+        self._use_reduce = False
+
+    def test_se_resnext(self):
+        self.check_with_place("dist_mnist_lars.py", delta=1e-5)
+
+
 class TestDistMnist2x2WithMemopt(TestDistBase):
     def _setup_config(self):
         self._sync_mode = True

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

+#   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.
+
+from __future__ import print_function
+import unittest
+from test_dist_base import TestDistBase
+import os
+
+
+class TestDistMnist2x2(TestDistBase):
+    def _setup_config(self):
+        self._sync_mode = True
+        self._use_reduce = False
+
+    def test_dist_train(self):
+        self.check_with_place("dist_mnist_batch_merge.py", delta=1e-5)
+
+    def check_with_place(self,
+                         model_file,
+                         delta=1e-3,
+                         check_error_log=False,
+                         need_envs={}):
+        # TODO(typhoonzero): should auto adapt GPU count on the machine.
+        required_envs = {
+            "PATH": os.getenv("PATH", ""),
+            "PYTHONPATH": os.getenv("PYTHONPATH", ""),
+            "LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""),
+            "FLAGS_fraction_of_gpu_memory_to_use": "0.15",
+            "FLAGS_cudnn_deterministic": "1",
+        }
+
+        required_envs.update(need_envs)
+
+        if check_error_log:
+            required_envs["GLOG_v"] = "7"
+            required_envs["GLOG_logtostderr"] = "1"
+
+        no_merge_losses = self._run_local(
+            model_file,
+            required_envs,
+            check_error_log=check_error_log,
+            batch_size=4)
+
+        batch_merge_losses = self._run_local(
+            model_file,
+            required_envs,
+            check_error_log=check_error_log,
+            batch_size=2,
+            batch_merge_repeat=2)
+        # Ensure both result have values.
+        self.assertGreater(len(no_merge_losses), 1)
+        self.assertEqual(len(no_merge_losses), len(batch_merge_losses))
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -42,7 +42,8 @@ class TestDistSimnetBow2x2DenseAsync(TestDistBase):
         self._sync_mode = False
         self._enforce_place = "CPU"
 
-    def test_simnet_bow(self):
+    #FIXME(typhoonzero): fix async tests later
+    def no_test_simnet_bow(self):
         need_envs = {
             "IS_DISTRIBUTED": '0',
             "IS_SPARSE": '0',

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

@@ -61,7 +61,8 @@ class TestDistTransformer2x2Sync(TestDistBase):
 
     def test_dist_train(self):
         download_files()
-        self.check_with_place("dist_transformer.py", delta=1e-5)
+        self.check_with_place(
+            "dist_transformer.py", delta=1e-5, check_error_log=False)
 
 
 class TestDistTransformer2x2Async(TestDistBase):
@@ -70,7 +71,8 @@ class TestDistTransformer2x2Async(TestDistBase):
 
     def test_dist_train(self):
         download_files()
-        self.check_with_place("dist_transformer.py", delta=1.0)
+        self.check_with_place(
+            "dist_transformer.py", delta=1.0, check_error_log=False)
 
 
 if __name__ == "__main__":

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

@@ -85,6 +85,69 @@ class TestDropoutOp5(OpTest):
         self.check_output()
 
 
+class TestDropoutOp6(TestDropoutOp):
+    def setUp(self):
+        self.op_type = "dropout"
+        self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
+        self.attrs = {
+            'dropout_prob': 1.0,
+            'fix_seed': True,
+            'is_test': False,
+            'dropout_implementation': 'upscale_in_train'
+        }
+        self.outputs = {
+            'Out': np.zeros((32, 64)).astype('float32'),
+            'Mask': np.zeros((32, 64)).astype('float32')
+        }
+
+
+class TestDropoutOp7(TestDropoutOp):
+    def setUp(self):
+        self.op_type = "dropout"
+        self.inputs = {'X': np.random.random((32, 64, 2)).astype("float32")}
+        self.attrs = {
+            'dropout_prob': 0.0,
+            'fix_seed': True,
+            'is_test': False,
+            'dropout_implementation': 'upscale_in_train'
+        }
+        self.outputs = {
+            'Out': self.inputs['X'],
+            'Mask': np.ones((32, 64, 2)).astype('float32')
+        }
+
+
+class TestDropoutOp8(OpTest):
+    def setUp(self):
+        self.op_type = "dropout"
+        self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
+        self.attrs = {
+            'dropout_prob': 0.35,
+            'fix_seed': True,
+            'is_test': True,
+            'dropout_implementation': 'upscale_in_train'
+        }
+        self.outputs = {'Out': self.inputs['X']}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestDropoutOp9(OpTest):
+    def setUp(self):
+        self.op_type = "dropout"
+        self.inputs = {'X': np.random.random((32, 64, 3)).astype("float32")}
+        self.attrs = {
+            'dropout_prob': 0.75,
+            'is_test': True,
+            'dropout_implementation': 'upscale_in_train'
+        }
+        self.outputs = {'Out': self.inputs['X']}
+
+    def test_check_output(self):
+        self.check_output()
+
+
 class TestFP16DropoutOp(OpTest):
     def setUp(self):
         self.op_type = "dropout"

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

@@ -125,6 +125,12 @@ class TestFusionGRUOpMD2(TestFusionGRUOp):
         self.D = 8
 
 
+class TestFusionGRUOpMD3(TestFusionGRUOp):
+    def set_confs(self):
+        self.M = 17
+        self.D = 15
+
+
 class TestFusionGRUOpBS1(TestFusionGRUOp):
     def set_confs(self):
         self.lod = [[3]]

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

+#   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.
+
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestScaleOp(OpTest):
+    def setUp(self):
+        self.op_type = "hash"
+        self.init_test_case()
+        self.inputs = {'X': (self.in_seq, self.lod)}
+        self.attrs = {'num_hash': 4, 'mod_by': 10000}
+        self.outputs = {'Out': (self.out_seq, self.lod)}
+
+    def init_test_case(self):
+        np.random.seed = 1
+        self.in_seq = np.random.randint(0, 10, (30, 1)).astype("int32")
+        self.lod = [[9, 4, 11, 6]]
+        #  self.out_seq = np.ones([30, 4, 1], dtype=np.int32)
+        self.out_seq = [
+            [[9662], [9217], [1129], [8487]], [[9662], [9217], [1129], [8487]],
+            [[8310], [1327], [1654], [4567]], [[6897], [3218], [2013], [1241]],
+            [[9407], [6715], [6949], [8094]], [[8473], [694], [5142], [2479]],
+            [[8310], [1327], [1654], [4567]], [[6897], [3218], [2013], [1241]],
+            [[4372], [9456], [8204], [6695]], [[6897], [3218], [2013], [1241]],
+            [[8473], [694], [5142], [2479]], [[4372], [9456], [8204], [6695]],
+            [[4372], [9456], [8204], [6695]], [[8473], [694], [5142], [2479]],
+            [[9407], [6715], [6949], [8094]], [[9369], [4525], [8935], [9210]],
+            [[4372], [9456], [8204], [6695]], [[4372], [9456], [8204], [6695]],
+            [[9369], [4525], [8935], [9210]], [[6897], [3218], [2013], [1241]],
+            [[9038], [7951], [5953], [8657]], [[9407], [6715], [6949], [8094]],
+            [[9662], [9217], [1129], [8487]], [[9369], [4525], [8935], [9210]],
+            [[9038], [7951], [5953], [8657]], [[9662], [9217], [1129], [8487]],
+            [[9369], [4525], [8935], [9210]], [[1719], [5986], [9919], [3421]],
+            [[4372], [9456], [8204], [6695]], [[9038], [7951], [5953], [8657]]
+        ]
+        self.out_seq = np.array(self.out_seq)
+
+    def test_check_output(self):
+        self.check_output()
+
+
+if __name__ == "__main__":
+    unittest.main()

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

+#   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.
+
+import unittest
+
+import paddle.fluid as fluid
+from paddle.fluid.framework import Program, program_guard
+
+
+class TestMetricsDetectionMap(unittest.TestCase):
+    def test_detection_map(self):
+        program = fluid.Program()
+        with program_guard(program):
+            detect_res = fluid.layers.data(
+                name='detect_res',
+                shape=[10, 6],
+                append_batch_size=False,
+                dtype='float32')
+            label = fluid.layers.data(
+                name='label',
+                shape=[10, 1],
+                append_batch_size=False,
+                dtype='float32')
+            box = fluid.layers.data(
+                name='bbox',
+                shape=[10, 4],
+                append_batch_size=False,
+                dtype='float32')
+            map_eval = fluid.metrics.DetectionMAP(
+                detect_res, label, box, class_num=21)
+            cur_map, accm_map = map_eval.get_map_var()
+            self.assertIsNotNone(cur_map)
+            self.assertIsNotNone(accm_map)
+        print(str(program))
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -90,6 +90,45 @@ class TestMomentumOp2(OpTest):
         self.check_output()
 
 
+class TestLarsMomentumOp(OpTest):
+    def setUp(self):
+        self.op_type = "lars_momentum"
+
+        param = np.random.random((123, 321)).astype("float32")
+        grad = np.random.random((123, 321)).astype("float32")
+        velocity = np.zeros((123, 321)).astype("float32")
+        learning_rate = np.array([0.001]).astype("float32")
+        mu = 0.0001
+        lars_coeff = 0.001
+        lars_weight_decay = 0.0005
+
+        self.inputs = {
+            'Param': param,
+            'Grad': grad,
+            'Velocity': velocity,
+            'LearningRate': learning_rate
+        }
+
+        self.attrs = {
+            'mu': mu,
+            'lars_coeff': lars_coeff,
+            'lars_weight_decay': lars_weight_decay
+        }
+
+        pnorm = np.sqrt(np.square(param).sum())
+        gnorm = np.sqrt(np.square(grad).sum())
+        local_lr = learning_rate * lars_coeff * pnorm / (
+            gnorm + lars_weight_decay * param)
+        velocity_out = mu * velocity + local_lr * (grad + lars_weight_decay *
+                                                   param)
+        param_out = param - velocity_out
+
+        self.outputs = {'ParamOut': param_out, 'VelocityOut': velocity_out}
+
+    def test_check_output(self):
+        self.check_output()
+
+
 class TestSparseMomentumOp(unittest.TestCase):
     def setUp(self):
         self.use_nesterov = False

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

@@ -50,8 +50,10 @@ def rpn_target_assign(anchor_by_gt_overlap,
             fg_inds, size=(len(fg_inds) - num_fg), replace=False)
     else:
         disable_inds = fg_inds[num_fg:]
+
     labels[disable_inds] = -1
     fg_inds = np.where(labels == 1)[0]
+    bbox_inside_weight = np.zeros((len(fg_inds), 4), dtype=np.float32)
 
     num_bg = rpn_batch_size_per_im - np.sum(labels == 1)
     bg_inds = np.where(anchor_to_gt_max < rpn_negative_overlap)[0]
@@ -59,18 +61,27 @@ def rpn_target_assign(anchor_by_gt_overlap,
         enable_inds = bg_inds[np.random.randint(len(bg_inds), size=num_bg)]
     else:
         enable_inds = bg_inds[:num_bg]
+
+    fg_fake_inds = np.array([], np.int32)
+    fg_value = np.array([fg_inds[0]], np.int32)
+    fake_num = 0
+    for bg_id in enable_inds:
+        if bg_id in fg_inds:
+            fake_num += 1
+            fg_fake_inds = np.hstack([fg_fake_inds, fg_value])
     labels[enable_inds] = 0
+
+    bbox_inside_weight[fake_num:, :] = 1
     fg_inds = np.where(labels == 1)[0]
     bg_inds = np.where(labels == 0)[0]
-
-    loc_index = fg_inds
-    score_index = np.hstack((fg_inds, bg_inds))
+    loc_index = np.hstack([fg_fake_inds, fg_inds])
+    score_index = np.hstack([fg_inds, bg_inds])
     labels = labels[score_index]
     assert not np.any(labels == -1), "Wrong labels with -1"
 
-    gt_inds = anchor_to_gt_argmax[fg_inds]
+    gt_inds = anchor_to_gt_argmax[loc_index]
 
-    return loc_index, score_index, labels, gt_inds
+    return loc_index, score_index, labels, gt_inds, bbox_inside_weight
 
 
 def get_anchor(n, c, h, w):
@@ -123,9 +134,12 @@ def rpn_target_assign_in_python(all_anchors,
         gt_boxes_slice = gt_boxes_slice[not_crowd_inds]
         iou = _bbox_overlaps(inside_anchors, gt_boxes_slice)
 
-        loc_inds, score_inds, labels, gt_inds = rpn_target_assign(
-            iou, rpn_batch_size_per_im, rpn_positive_overlap,
-            rpn_negative_overlap, rpn_fg_fraction, use_random)
+        loc_inds, score_inds, labels, gt_inds, bbox_inside_weight = \
+                         rpn_target_assign(iou, rpn_batch_size_per_im,
+                                           rpn_positive_overlap,
+                                           rpn_negative_overlap,
+                                           rpn_fg_fraction,
+                                           use_random)
         # unmap to all anchor 
         loc_inds = inds_inside[loc_inds]
         score_inds = inds_inside[score_inds]
@@ -139,6 +153,7 @@ def rpn_target_assign_in_python(all_anchors,
             score_indexes = score_inds
             tgt_labels = labels
             tgt_bboxes = box_deltas
+            bbox_inside_weights = bbox_inside_weight
         else:
             loc_indexes = np.concatenate(
                 [loc_indexes, loc_inds + i * anchor_num])
@@ -146,8 +161,10 @@ def rpn_target_assign_in_python(all_anchors,
                 [score_indexes, score_inds + i * anchor_num])
             tgt_labels = np.concatenate([tgt_labels, labels])
             tgt_bboxes = np.vstack([tgt_bboxes, box_deltas])
+            bbox_inside_weights = np.vstack([bbox_inside_weights, \
+                                             bbox_inside_weight])
 
-    return loc_indexes, score_indexes, tgt_bboxes, tgt_labels
+    return loc_indexes, score_indexes, tgt_bboxes, tgt_labels, bbox_inside_weights
 
 
 class TestRpnTargetAssignOp(OpTest):
@@ -182,10 +199,12 @@ class TestRpnTargetAssignOp(OpTest):
         rpn_fg_fraction = 0.5
         use_random = False
 
-        loc_index, score_index, tgt_bbox, labels = rpn_target_assign_in_python(
-            all_anchors, gt_boxes, is_crowd, im_info, lod, rpn_straddle_thresh,
-            rpn_batch_size_per_im, rpn_positive_overlap, rpn_negative_overlap,
-            rpn_fg_fraction, use_random)
+        loc_index, score_index, tgt_bbox, labels, bbox_inside_weights = \
+            rpn_target_assign_in_python(all_anchors, gt_boxes, is_crowd,
+                                   im_info, lod, rpn_straddle_thresh,
+                                   rpn_batch_size_per_im, rpn_positive_overlap,
+                                   rpn_negative_overlap,
+                                   rpn_fg_fraction, use_random)
         labels = labels[:, np.newaxis]
 
         self.op_type = "rpn_target_assign"
@@ -207,7 +226,8 @@ class TestRpnTargetAssignOp(OpTest):
             'LocationIndex': loc_index.astype('int32'),
             'ScoreIndex': score_index.astype('int32'),
             'TargetBBox': tgt_bbox.astype('float32'),
-            'TargetLabel': labels.astype('int32')
+            'TargetLabel': labels.astype('int32'),
+            'BBoxInsideWeight': bbox_inside_weights.astype('float32')
         }
 
     def test_check_output(self):

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

+# 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.
+
+import unittest
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from op_test import OpTest
+import numpy as np
+
+
+class TestSequenceReverseBase(OpTest):
+    def initParameters(self):
+        pass
+
+    def setUp(self):
+        self.size = (10, 3, 4)
+        self.lod = [2, 3, 5]
+        self.dtype = 'float32'
+        self.initParameters()
+        self.op_type = 'sequence_reverse'
+        self.x = np.random.random(self.size).astype(self.dtype)
+        self.y = self.get_output()
+
+        self.inputs = {'X': (self.x, [self.lod, ]), }
+        self.outputs = {'Y': (self.y, [self.lod, ]), }
+
+    def get_output(self):
+        tmp_x = np.reshape(self.x, newshape=[self.x.shape[0], -1])
+        tmp_y = np.ndarray(tmp_x.shape).astype(self.dtype)
+        prev_idx = 0
+        for cur_len in self.lod:
+            idx_range = range(prev_idx, prev_idx + cur_len)
+            tmp_y[idx_range, :] = np.flip(tmp_x[idx_range, :], 0)
+            prev_idx += cur_len
+
+        return np.reshape(tmp_y, newshape=self.x.shape).astype(self.dtype)
+
+    def test_output(self):
+        self.check_output(0)
+
+    def test_grad(self):
+        self.check_grad(['X'], 'Y')
+
+
+class TestSequenceReserve1(TestSequenceReverseBase):
+    def initParameters(self):
+        self.size = (12, 10)
+        self.lod = [4, 5, 3]
+
+
+class TestSequenceReverse2(TestSequenceReverseBase):
+    def initParameters(self):
+        self.size = (12, 10)
+        self.lod = [12]
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -21,22 +21,27 @@ from op_test import OpTest
 
 class TestTopkOp(OpTest):
     def setUp(self):
+        self.set_args()
         self.op_type = "top_k"
-        k = 1
-        input = np.random.random((32, 84)).astype("float32")
-        output = np.ndarray((32, k))
-        indices = np.ndarray((32, k)).astype("int64")
+        k = self.top_k
+        input = np.random.random((self.row, k)).astype("float32")
+        output = np.ndarray((self.row, k))
+        indices = np.ndarray((self.row, k)).astype("int64")
 
         self.inputs = {'X': input}
         self.attrs = {'k': k}
 
-        for rowid in range(32):
+        for rowid in range(self.row):
             row = input[rowid]
-            output[rowid] = np.sort(row)[-k:]
-            indices[rowid] = row.argsort()[-k:]
+            output[rowid] = np.sort(row)[::-1][:k]
+            indices[rowid] = row.argsort()[::-1][:k]
 
         self.outputs = {'Out': output, 'Indices': indices}
 
+    def set_args(self):
+        self.row = 32
+        self.top_k = 1
+
     def test_check_output(self):
         self.check_output()
 
@@ -50,14 +55,39 @@ class TestTopkOp3d(OpTest):
         output = np.ndarray((64, k))
         indices = np.ndarray((64, k)).astype("int64")
 
-        # FIXME: should use 'X': input for a 3d input
-        self.inputs = {'X': input_flat_2d}
+        self.inputs = {'X': input}
         self.attrs = {'k': k}
 
         for rowid in range(64):
             row = input_flat_2d[rowid]
-            output[rowid] = np.sort(row)[-k:]
-            indices[rowid] = row.argsort()[-k:]
+            output[rowid] = np.sort(row)[::-1][:k]
+            indices[rowid] = row.argsort()[::-1][:k]
+
+        self.outputs = {
+            'Out': output.reshape((32, 2, k)),
+            'Indices': indices.reshape((32, 2, k))
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestTopkOp2(OpTest):
+    def setUp(self):
+        self.op_type = "top_k"
+        k = 1
+        m = 2056
+        input = np.random.random((m, 84)).astype("float32")
+        output = np.ndarray((m, k))
+        indices = np.ndarray((m, k)).astype("int64")
+
+        self.inputs = {'X': input}
+        self.attrs = {'k': k}
+
+        for rowid in range(m):
+            row = input[rowid]
+            output[rowid] = -np.sort(-row)[:k]
+            indices[rowid] = (-row).argsort()[:k]
 
         self.outputs = {'Out': output, 'Indices': indices}
 
@@ -65,5 +95,17 @@ class TestTopkOp3d(OpTest):
         self.check_output()
 
 
+class TestTopkOp3(TestTopkOp):
+    def set_args(self):
+        self.row = 2056
+        self.top_k = 3
+
+
+class TestTopkOp4(TestTopkOp):
+    def set_args(self):
+        self.row = 40000
+        self.top_k = 1
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -49,6 +49,7 @@ LOOKUP_TABLE_GRAD_TYPE = "lookup_table_grad"
 OP_ROLE_VAR_ATTR_NAME = core.op_proto_and_checker_maker.kOpRoleVarAttrName()
 RPC_OP_ROLE_ATTR_NAME = op_role_attr_name = core.op_proto_and_checker_maker.kOpRoleAttrName(
 )
+OPT_OP_ROLE_ATTR_VALUE = core.op_proto_and_checker_maker.OpRole.Optimize
 RPC_OP_ROLE_ATTR_VALUE = core.op_proto_and_checker_maker.OpRole.RPC
 DIST_OP_ROLE_ATTR_VALUE = core.op_proto_and_checker_maker.OpRole.Dist
 LR_SCHED_OP_ROLE_ATTR_VALUE = core.op_proto_and_checker_maker.OpRole.LRSched
@@ -1430,7 +1431,7 @@ to transpile() call.")
         elif op_type == "adamax":
             if varkey in ["Moment", "InfNorm"]:
                 return param_shape
-        elif op_type == "momentum":
+        elif op_type in ["momentum", "lars_momentum"]:
             if varkey == "Velocity":
                 return param_shape
         elif op_type == "rmsprop":
@@ -1441,6 +1442,10 @@ to transpile() call.")
                 return param_shape
         elif op_type == "sgd":
             pass
+        else:
+            raise ValueError(
+                "Not supported optimizer for distributed training: %s" %
+                op_type)
         return orig_shape
 
     def _get_varname_parts(self, varname):
@@ -1717,8 +1722,10 @@ to transpile() call.")
         lr_ops = []
         block = self.origin_program.global_block()
         for op in block.ops:
-            if int(op.attr(RPC_OP_ROLE_ATTR_NAME)) == int(
-                    LR_SCHED_OP_ROLE_ATTR_VALUE):
+            role_id = int(op.attr(RPC_OP_ROLE_ATTR_NAME))
+            if role_id == int(LR_SCHED_OP_ROLE_ATTR_VALUE) or \
+                role_id == int(LR_SCHED_OP_ROLE_ATTR_VALUE) | \
+                    int(OPT_OP_ROLE_ATTR_VALUE):
                 lr_ops.append(op)
                 log("append lr op: ", op.type)
         return lr_ops