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

test=develop

CMakeLists.txt

@@ -214,6 +214,7 @@ if (NOT WIN32)
 # there is no official support of warpctc, nccl, cupti in windows
 include(external/warpctc)   # download, build, install warpctc
 include(cupti)
+include(external/gzstream)
 endif (NOT WIN32)
 
 if(WITH_DISTRIBUTE)

cmake/external/gzstream.cmake

+# 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.
+#
+
+IF(MOBILE_INFERENCE)
+    return()
+ENDIF()
+
+include (ExternalProject)
+
+# NOTE: gzstream is needed when linking with ctr reader.
+
+SET(GZSTREAM_SOURCES_DIR ${THIRD_PARTY_PATH}/gzstream)
+SET(GZSTREAM_INSTALL_DIR ${THIRD_PARTY_PATH}/install/gzstream)
+SET(GZSTREAM_INCLUDE_DIR "${GZSTREAM_INSTALL_DIR}/include/" CACHE PATH "gzstream include directory." FORCE)
+
+ExternalProject_Add(
+        extern_gzstream
+        GIT_REPOSITORY "https://github.com/jacquesqiao/gzstream.git"
+        GIT_TAG ""
+        PREFIX          ${GZSTREAM_SOURCES_DIR}
+        UPDATE_COMMAND  ""
+        CONFIGURE_COMMAND ""
+        BUILD_IN_SOURCE 1
+        BUILD_COMMAND   make -j8
+        INSTALL_COMMAND mkdir -p ${GZSTREAM_INSTALL_DIR}/lib/ && mkdir -p ${GZSTREAM_INSTALL_DIR}/include/
+        && cp ${GZSTREAM_SOURCES_DIR}/src/extern_gzstream/libgzstream.a ${GZSTREAM_INSTALL_DIR}/lib
+        && cp -r ${GZSTREAM_SOURCES_DIR}/src/extern_gzstream/gzstream.h ${GZSTREAM_INSTALL_DIR}/include
+)
+
+ADD_LIBRARY(gzstream STATIC IMPORTED GLOBAL)
+SET_PROPERTY(TARGET gzstream PROPERTY IMPORTED_LOCATION
+        "${GZSTREAM_INSTALL_DIR}/lib/libgzstream.a")
+
+include_directories(${GZSTREAM_INCLUDE_DIR})
+ADD_DEPENDENCIES(gzstream extern_gzstream zlib)

cmake/inference_lib.cmake

@@ -186,8 +186,7 @@ set(module "inference")
 copy(inference_lib DEPS ${inference_deps}
   SRCS ${src_dir}/${module}/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/inference/libpaddle_fluid.*
        ${src_dir}/${module}/api/paddle_*.h
-       ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/paddle_inference_pass.h
-  DSTS ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module}
+  DSTS ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module}
         )
 
 set(module "platform")

paddle/fluid/API.spec

@@ -97,8 +97,8 @@ paddle.fluid.layers.warpctc ArgSpec(args=['input', 'label', 'blank', 'norm_by_ti
 paddle.fluid.layers.sequence_reshape ArgSpec(args=['input', 'new_dim'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.transpose ArgSpec(args=['x', 'perm', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.im2sequence ArgSpec(args=['input', 'filter_size', 'stride', 'padding', 'input_image_size', 'out_stride', 'name'], varargs=None, keywords=None, defaults=(1, 1, 0, None, 1, None))
-paddle.fluid.layers.nce ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'name', 'sampler', 'custom_dist', 'seed'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 'uniform', None, 0))
-paddle.fluid.layers.hsigmoid ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
+paddle.fluid.layers.nce ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'name', 'sampler', 'custom_dist', 'seed', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 'uniform', None, 0, False))
+paddle.fluid.layers.hsigmoid ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr', 'name', 'path_table', 'path_code', 'is_custom', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, False, False))
 paddle.fluid.layers.beam_search ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 'scores', 'beam_size', 'end_id', 'level', 'name'], varargs=None, keywords=None, defaults=(0, None))
 paddle.fluid.layers.row_conv ArgSpec(args=['input', 'future_context_size', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.layers.multiplex ArgSpec(args=['inputs', 'index'], varargs=None, keywords=None, defaults=None)

paddle/fluid/framework/details/CMakeLists.txt

@@ -39,11 +39,12 @@ if (WITH_GPU)
 endif()
 
 cc_library(sequential_execution_pass SRCS sequential_execution_pass.cc DEPS graph graph_helper pass)
+cc_library(all_reduce_deps_pass SRCS all_reduce_deps_pass.cc DEPS graph graph_helper pass)
 
 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 fused_broadcast_op_handle)
 
-set(SSA_GRAPH_EXECUTOR_DEPS graph framework_proto sequential_execution_pass modify_op_lock_and_record_event_pass) 
+set(SSA_GRAPH_EXECUTOR_DEPS graph framework_proto sequential_execution_pass modify_op_lock_and_record_event_pass all_reduce_deps_pass) 
 if (WITH_GPU)
   list(APPEND SSA_GRAPH_EXECUTOR_DEPS reference_count_pass)
 endif()

paddle/fluid/framework/details/all_reduce_deps_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 <algorithm>
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+#include <vector>
+
+#include "paddle/fluid/framework/details/all_reduce_deps_pass.h"
+#include "paddle/fluid/framework/details/all_reduce_op_handle.h"
+#include "paddle/fluid/framework/details/multi_devices_helper.h"
+#include "paddle/fluid/framework/details/op_graph_view.h"
+#include "paddle/fluid/framework/details/var_handle.h"
+#include "paddle/fluid/framework/ir/graph_helper.h"
+#include "paddle/fluid/framework/op_proto_maker.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+static constexpr char kAllOpDescs[] = "all_op_descs";
+
+VarHandle* GetValidInput(const OpHandleBase* a) {
+  for (auto p : a->Inputs()) {
+    VarHandle* b = dynamic_cast<VarHandle*>(p);
+    if (b) {
+      return b;
+    }
+  }
+
+  return nullptr;
+}
+
+std::unique_ptr<ir::Graph> AllReduceDepsPass::ApplyImpl(
+    std::unique_ptr<ir::Graph> graph) const {
+  auto graph_ops = ir::FilterByNodeWrapper<OpHandleBase>(*graph);
+
+  // get vars order
+  int order = 0;
+  std::unordered_map<std::string, int> vars;
+  // TODO(gongwb): use graph topology sort to find the order of operators.
+  //               Note that must assert topology sort is stable
+  auto& ops = Get<const std::vector<OpDesc*>>(kAllOpDescs);
+  for (auto* op_desc : ops) {
+    auto outputs = op_desc->Outputs();
+    for (auto& o_it : outputs) {
+      for (auto& v : o_it.second) {  // values
+        vars[v] = order;
+      }
+    }
+    order++;
+  }
+
+  std::vector<OpHandleBase*> dist_ops;
+  // get allreduce ops.
+  for (auto& op : graph_ops) {
+    // FIXME(gongwb):add broad cast.
+    if (op->Name() == "all_reduce" || op->Name() == "reduce") {
+      dist_ops.push_back(op);
+    }
+  }
+
+  VLOG(10) << "dist_ops size:" << dist_ops.size() << std::endl;
+
+  std::sort(dist_ops.begin(), dist_ops.end(), [&](OpHandleBase* op1,
+                                                  OpHandleBase* op2) {
+    VarHandle* i0 = dynamic_cast<VarHandle*>(GetValidInput(op1));
+    VarHandle* i1 = dynamic_cast<VarHandle*>(GetValidInput(op2));
+
+    PADDLE_ENFORCE(i0 != nullptr && i1 != nullptr, "%s convert to %s error",
+                   op1->DebugString(), op2->DebugString());
+
+    auto l_it = vars.find(i0->name_);
+    auto r_it = vars.find(i1->name_);
+
+    if (l_it->second < r_it->second) return true;
+
+    if (l_it->second == r_it->second) {
+      return i0->name_ < i1->name_;
+    }
+
+    return false;
+  });
+
+  // add dependency.
+  auto& sorted_ops = dist_ops;
+  for (size_t i = 1; i < sorted_ops.size(); ++i) {
+    auto* dep_var = new DummyVarHandle(graph->CreateControlDepVar());
+
+    auto* pre_op = sorted_ops[i - 1];
+    auto* op = sorted_ops[i];
+
+    pre_op->AddOutput(dep_var);
+    op->AddInput(dep_var);
+    graph->Get<GraphDepVars>(kGraphDepVars).emplace(dep_var);
+
+    VLOG(10) << "add all_reduce sequential dependencies between " << pre_op
+             << " and " << op;
+
+    VLOG(10) << "pre_op:" << pre_op->DebugString()
+             << ", op:" << op->DebugString();
+  }
+
+  return graph;
+}
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(all_reduce_deps_pass,
+              paddle::framework::details::AllReduceDepsPass)
+    .RequirePassAttr(paddle::framework::details::kAllOpDescs);

paddle/fluid/framework/details/all_reduce_deps_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/graph.h"
+#include "paddle/fluid/framework/ir/pass.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+// TODO(gongwb): overlap allreduce with backward computation.
+class AllReduceDepsPass : public ir::Pass {
+ protected:
+  std::unique_ptr<ir::Graph> ApplyImpl(
+      std::unique_ptr<ir::Graph> graph) const override;
+};
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/build_strategy.cc

@@ -16,6 +16,7 @@ limitations under the License. */
 
 #include "paddle/fluid/framework/details/multi_devices_graph_check_pass.h"
 #include "paddle/fluid/framework/details/multi_devices_graph_print_pass.h"
+#include "paddle/fluid/framework/details/reduce_op_handle.h"
 #include "paddle/fluid/framework/details/sequential_execution_pass.h"
 #include "paddle/fluid/framework/ir/graph.h"
 #include "paddle/fluid/framework/ir/graph_viz_pass.h"
@@ -24,6 +25,10 @@ namespace paddle {
 namespace framework {
 namespace details {
 
+static inline bool SeqOnlyAllReduceOps(const BuildStrategy &strategy) {
+  return (!strategy.enable_sequential_execution_ && strategy.num_trainers_ > 1);
+}
+
 class ParallelExecutorPassBuilder : public ir::PassBuilder {
  public:
   explicit ParallelExecutorPassBuilder(const BuildStrategy &strategy)
@@ -70,6 +75,10 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
     // Verify that the graph is correct for multi-device executor.
     AppendPass("multi_devices_check_pass");
 
+    if (SeqOnlyAllReduceOps(strategy)) {
+      AppendPass("all_reduce_deps_pass");
+    }
+
     if (strategy_.remove_unnecessary_lock_) {
       AppendPass("modify_op_lock_and_record_event_pass");
     }
@@ -124,6 +133,17 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
       pass->SetNotOwned<platform::NCCLContextMap>("nccl_ctxs", nctx);
 #endif
     } else if (pass->Type() == "sequential_execution_pass") {
+      VLOG(1) << "set enable_sequential_execution:"
+              << enable_sequential_execution_;
+
+      pass->Erase(kAllOpDescs);
+      pass->Set<const std::vector<OpDesc *>>(
+          kAllOpDescs,
+          new std::vector<OpDesc *>(main_program.Block(0).AllOps()));
+    } else if (pass->Type() == "all_reduce_deps_pass") {
+      VLOG(1) << "SeqOnlyAllReduceOps:" << SeqOnlyAllReduceOps(*this)
+              << ", num_trainers:" << num_trainers_;
+
       pass->Erase(kAllOpDescs);
       pass->Set<const std::vector<OpDesc *>>(
           kAllOpDescs,
@@ -144,4 +164,5 @@ USE_PASS(multi_devices_pass);
 USE_PASS(multi_devices_check_pass);
 USE_PASS(multi_devices_print_pass);
 USE_PASS(sequential_execution_pass);
+USE_PASS(all_reduce_deps_pass);
 USE_PASS(modify_op_lock_and_record_event_pass);

paddle/fluid/framework/details/build_strategy.h

@@ -73,6 +73,7 @@ struct BuildStrategy {
 
   bool fuse_broadcast_op_{false};
 
+  int num_trainers_{1};
   bool remove_unnecessary_lock_{false};
 
   // NOTE:

paddle/fluid/framework/operator.h

@@ -71,7 +71,7 @@ class OperatorBase;
 class ExecutionContext;
 
 /**
- * OperatorBase has the basic element that Net will call to do computation.
+ * OperatorBase has the basic elements that Net will call to do computation.
  * Only CreateOperator from OpRegistry will new Operator directly. User
  * should always construct a proto message OpDesc and call
  * OpRegistry::CreateOp(op_desc) to get an Operator instance.

paddle/fluid/framework/parallel_executor.cc

@@ -20,7 +20,7 @@ limitations under the License. */
 
 #include "paddle/fluid/framework/ir/graph.h"
 
-#ifdef PADDLE_WITH_CUDA
+#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
 #include "paddle/fluid/platform/nccl_helper.h"
 #endif
 
@@ -54,7 +54,7 @@ class ParallelExecutorPrivate {
   Scope *global_scope_;  // not owned
   std::unique_ptr<details::SSAGraphExecutor> executor_;
 
-#ifdef PADDLE_WITH_CUDA
+#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
   std::unique_ptr<platform::NCCLContextMap> nccl_ctxs_;
 #endif
   bool own_local_scope_;
@@ -104,7 +104,7 @@ ParallelExecutor::ParallelExecutor(
 
   if (member_->use_cuda_) {
 // Bcast Parameters to all GPUs
-#ifdef PADDLE_WITH_CUDA
+#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
     auto *nccl_id_var = scope->FindVar(NCCL_ID_VARNAME);
     ncclUniqueId *nccl_id = nullptr;
     if (nccl_id_var != nullptr) {
@@ -124,7 +124,7 @@ ParallelExecutor::ParallelExecutor(
 
 // Step 2. Convert main_program to SSA form and dependency graph. Also, insert
 // ncclOp
-#ifdef PADDLE_WITH_CUDA
+#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
   std::unique_ptr<ir::Graph> graph = build_strategy.Apply(
       main_program, member_->places_, loss_var_name, params,
       member_->local_scopes_, member_->use_cuda_, member_->nccl_ctxs_.get());
@@ -213,7 +213,7 @@ void ParallelExecutor::BCastParamsToDevices(
     }
     auto &dims = main_tensor.dims();
     if (paddle::platform::is_gpu_place(main_tensor.place())) {
-#ifdef PADDLE_WITH_CUDA
+#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
       std::vector<void *> buffers;
       size_t numel = main_tensor.numel();
       ncclDataType_t data_type = platform::ToNCCLDataType(main_tensor.type());

paddle/fluid/framework/selected_rows.h

@@ -120,8 +120,22 @@ class SelectedRows {
    */
   int64_t AutoGrownIndex(int64_t key, bool auto_grown, bool is_test = false);
 
-  void SyncIndex();
+  /*
+   * @brief Get the index of the key from id_to_index_ map.
+   */
+  inline int64_t GetIndexFromId(int64_t key) {
+    auto iter = id_to_index_.find(key);
+    if (iter == id_to_index_.end()) {
+      return -1;
+    } else {
+      return iter->second;
+    }
+  }
 
+  void SyncIndex();
+  /*
+   * @brief Get complete Dims before
+   */
   DDim GetCompleteDims() const {
     std::vector<int64_t> dims = vectorize(value_->dims());
     dims[0] = height_;
@@ -133,9 +147,10 @@ class SelectedRows {
   // SelectedRows are simply concated when adding together. Until a
   // SelectedRows add a Tensor, will the duplicate rows be handled.
   Vector<int64_t> rows_;
-  std::unordered_map<int64_t, int64_t> id_to_index_;
+  std::unordered_map<int64_t, int64_t>
+      id_to_index_;  // should not be used when rows_ has duplicate member
   std::unique_ptr<Tensor> value_{nullptr};
-  int64_t height_;
+  int64_t height_;  // height indicates the underline tensor's height
   std::unique_ptr<RWLock> rwlock_{nullptr};
 };
 

paddle/fluid/framework/transfer_scope_cache.cc

@@ -17,28 +17,16 @@
 namespace paddle {
 namespace framework {
 
-// Holds all the transfer scope across the process.
 std::unordered_map<size_t, Scope*>& global_transfer_data_cache() {
-  typedef std::unordered_map<size_t, Scope*> map_t;
-  thread_local std::unique_ptr<map_t> x(new map_t);
+  thread_local auto* x = new std::unordered_map<size_t, Scope*>;
   return *x;
 }
 
-// Holds all the transfer scope for this thread.
 std::unordered_set<Scope*>& global_transfer_scope_cache() {
-  typedef std::unordered_set<Scope*> set_t;
-  thread_local std::unique_ptr<set_t> x(new set_t);
+  thread_local auto* x = new std::unordered_set<Scope*>;
   return *x;
 }
 
-// Try to create a transfer scope. If one cached scope has match the
-// requirement, just return that one.
-// Inputs:
-// @type0: the source kernel type.
-// @type1: the target kernel type.
-// @scope: the execution scope of this op.
-// Returns: A scope used to hold the transfer data across the different kernel
-// type.
 Scope* TryCreateTransferScope(OpKernelType type0, OpKernelType type1,
                               const Scope* scope) {
   Scope* new_scope{nullptr};
@@ -58,5 +46,27 @@ Scope* TryCreateTransferScope(OpKernelType type0, OpKernelType type1,
   return new_scope;
 }
 
+void RemoveKidsFromTransferScopeCache(Scope* scope) {
+  auto it = global_transfer_scope_cache().find(scope);
+  if (it != global_transfer_scope_cache().end()) {
+    global_transfer_scope_cache().erase(it);
+  }
+  for (auto* s : scope->kids()) {
+    auto it = global_transfer_scope_cache().find(s);
+    if (it != global_transfer_scope_cache().end()) {
+      global_transfer_scope_cache().erase(it);
+    }
+  }
+
+  // remove global transfer data cache
+  auto& cache = global_transfer_data_cache();
+  for (auto it = cache.begin(); it != cache.end();) {
+    if (it->second == scope)
+      it = cache.erase(it);
+    else
+      it++;
+  }
+}
+
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/inference/analysis/CMakeLists.txt

@@ -35,4 +35,5 @@ function(inference_analysis_test TARGET)
   endif()
 endfunction(inference_analysis_test)
 
-inference_analysis_test(test_analyzer SRCS analyzer_tester.cc EXTRA_DEPS reset_tensor_array paddle_inference_api)
+inference_analysis_test(test_analyzer SRCS analyzer_tester.cc
+    EXTRA_DEPS reset_tensor_array paddle_inference_api)

paddle/fluid/inference/analysis/analyzer_tester.cc

@@ -76,7 +76,8 @@ void TestWord2vecPrediction(const std::string& model_path) {
                      0.000932706};
   const size_t num_elements = outputs.front().data.length() / sizeof(float);
   // The outputs' buffers are in CPU memory.
-  for (size_t i = 0; i < std::min((size_t)5UL, num_elements); i++) {
+  for (size_t i = 0; i < std::min(static_cast<size_t>(5UL), num_elements);
+       i++) {
     LOG(INFO) << "data: "
               << static_cast<float*>(outputs.front().data.data())[i];
     PADDLE_ENFORCE(static_cast<float*>(outputs.front().data.data())[i],

paddle/fluid/inference/api/analysis_predictor.cc

@@ -284,6 +284,7 @@ bool AnalysisPredictor::GetFetch(std::vector<PaddleTensor> *outputs,
         framework::GetFetchVariable(*scope, "fetch", idx);
     auto type = fetch.type();
     auto output = &(outputs->at(i));
+    output->name = fetchs_[idx]->Input("X")[0];
     if (type == typeid(float)) {
       GetFetchOne<float>(fetch, output);
       output->dtype = PaddleDType::FLOAT32;

paddle/fluid/inference/api/analysis_predictor.h

@@ -109,7 +109,7 @@ class AnalysisPredictor : public PaddlePredictor {
   std::map<std::string, size_t> feed_names_;
   std::vector<framework::OpDesc *> fetchs_;
   // Memory buffer for feed inputs. The temporary LoDTensor will cause serious
-  // concurrency problems, so cache them.
+  // concurrency problems, wrong results and memory leak, so cache them.
   std::vector<framework::LoDTensor> feed_tensors_;
   details::TensorArrayBatchCleaner tensor_array_batch_cleaner_;
 

paddle/fluid/inference/api/api_impl.cc

@@ -185,8 +185,12 @@ bool NativePaddlePredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
                << inputs.size();
     return false;
   }
+
+  // Cache the inputs memory for better concurrency performance.
+  feed_tensors_.resize(inputs.size());
+
   for (size_t i = 0; i < inputs.size(); ++i) {
-    framework::LoDTensor input;
+    auto &input = feed_tensors_[i];
     framework::DDim ddim = framework::make_ddim(inputs[i].shape);
     void *input_ptr;
     if (inputs[i].dtype == PaddleDType::INT64) {
@@ -261,6 +265,7 @@ bool NativePaddlePredictor::GetFetch(std::vector<PaddleTensor> *outputs,
         framework::GetFetchVariable(*scope, "fetch", idx);
     auto type = fetch.type();
     auto output = &(outputs->at(i));
+    output->name = fetchs_[idx]->Input("X")[0];
     if (type == typeid(float)) {
       GetFetchOne<float>(fetch, output);
       output->dtype = PaddleDType::FLOAT32;

paddle/fluid/inference/api/api_impl.h

@@ -69,6 +69,9 @@ class NativePaddlePredictor : public PaddlePredictor {
   std::vector<framework::OpDesc *> feeds_;
   std::map<std::string, size_t> feed_names_;
   std::vector<framework::OpDesc *> fetchs_;
+  // Memory buffer for feed inputs. The temporary LoDTensor will cause serious
+  // concurrency problems, wrong results and memory leak, so cache them.
+  std::vector<framework::LoDTensor> feed_tensors_;
   // Do not use unique_ptr, use parent scope to delete
   framework::Scope *sub_scope_{nullptr};
   details::TensorArrayBatchCleaner tensor_array_batch_cleaner_;

paddle/fluid/memory/allocation/best_fit_allocator_test.cc

@@ -99,9 +99,8 @@ TEST(BestFitAllocator, test_concurrent_cpu_allocation) {
 
   LockedAllocator locked_allocator(std::move(best_fit_allocator));
 
-  auto th_main = [&] {
-    std::random_device dev;
-    std::default_random_engine engine(dev());
+  auto th_main = [&](std::random_device::result_type seed) {
+    std::default_random_engine engine(seed);
     std::uniform_int_distribution<size_t> dist(1U, 1024U);
 
     for (size_t i = 0; i < 128; ++i) {
@@ -125,7 +124,8 @@ TEST(BestFitAllocator, test_concurrent_cpu_allocation) {
   {
     std::vector<std::thread> threads;
     for (size_t i = 0; i < 1024; ++i) {
-      threads.emplace_back(th_main);
+      std::random_device dev;
+      threads.emplace_back(th_main, dev());
     }
     for (auto& th : threads) {
       th.join();

paddle/fluid/memory/allocation/best_fit_allocator_test.cu

@@ -41,9 +41,8 @@ TEST(BestFitAllocator, concurrent_cuda) {
   LockedAllocator concurrent_allocator(
       std::unique_ptr<Allocator>(new BestFitAllocator(cuda_allocation.get())));
 
-  auto th_main = [&] {
-    std::random_device dev;
-    std::default_random_engine engine(dev());
+  auto th_main = [&](std::random_device::result_type seed) {
+    std::default_random_engine engine(seed);
     std::uniform_int_distribution<size_t> dist(1U, 1024U);
     platform::CUDAPlace gpu(0);
     platform::CUDADeviceContext dev_ctx(gpu);
@@ -75,7 +74,8 @@ TEST(BestFitAllocator, concurrent_cuda) {
   {
     std::vector<std::thread> threads;
     for (size_t i = 0; i < 1024; ++i) {
-      threads.emplace_back(th_main);
+      std::random_device dev;
+      threads.emplace_back(th_main, dev());
     }
     for (auto& th : threads) {
       th.join();

paddle/fluid/memory/detail/system_allocator.cc

@@ -86,7 +86,11 @@ void CPUAllocator::Free(void* p, size_t size, size_t index) {
     munlock(p, size);
 #endif
   }
+#ifdef _WIN32
+  _aligned_free(p);
+#else
   free(p);
+#endif
 }
 
 bool CPUAllocator::UseGpu() const { return false; }

paddle/fluid/operators/activation_op.cc

@@ -149,6 +149,13 @@ $out = \max(x, 0)$
 
 )DOC";
 
+UNUSED constexpr char GeluDoc[] = R"DOC(
+Gelu Activation Operator.
+
+$out = \\frac{1 + erf(\\frac{x}{\\sqrt{2}})}{2} x$
+
+)DOC";
+
 UNUSED constexpr char TanhDoc[] = R"DOC(
 Tanh Activation Operator.
 
@@ -472,6 +479,7 @@ REGISTER_ACTIVATION_OP_MAKER(Sigmoid, SigmoidDoc);
 REGISTER_ACTIVATION_OP_MAKER(LogSigmoid, LogSigmoidDoc);
 REGISTER_ACTIVATION_OP_MAKER(Exp, ExpDoc);
 REGISTER_ACTIVATION_OP_MAKER(Relu, ReluDoc);
+REGISTER_ACTIVATION_OP_MAKER(Gelu, GeluDoc);
 REGISTER_ACTIVATION_OP_MAKER(Tanh, TanhDoc);
 REGISTER_ACTIVATION_OP_MAKER(TanhShrink, TanhShrinkDoc);
 REGISTER_ACTIVATION_OP_MAKER(Sqrt, SqrtDoc);
@@ -489,6 +497,7 @@ REGISTER_ACTIVATION_OP_MAKER(Softsign, SoftsignDoc);
 
 REGISTER_ACTIVATION_OP_GRAD_MAKER(Sigmoid, sigmoid);
 REGISTER_ACTIVATION_OP_GRAD_MAKER(Relu, relu);
+REGISTER_ACTIVATION_OP_GRAD_MAKER(Gelu, gelu);
 REGISTER_ACTIVATION_OP_GRAD_MAKER(Exp, exp);
 REGISTER_ACTIVATION_OP_GRAD_MAKER(Tanh, tanh);
 REGISTER_ACTIVATION_OP_GRAD_MAKER(Ceil, ceil);
@@ -525,6 +534,7 @@ namespace ops = paddle::operators;
   __macro(Round, round);             \
   __macro(Log, log);                 \
   __macro(Square, square);           \
+  __macro(Gelu, gelu);               \
   __macro(BRelu, brelu);             \
   __macro(Pow, pow);                 \
   __macro(STanh, stanh);             \

paddle/fluid/operators/activation_op.h

@@ -16,6 +16,11 @@ limitations under the License. */
 #include <utility>
 #include <vector>
 
+#include <cmath>
+#ifndef _USE_MATH_DEFINES
+#define _USE_MATH_DEFINES
+#endif
+
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/detail/safe_ref.h"
@@ -212,6 +217,31 @@ struct ReluGradFunctor : public BaseActivationFunctor<T> {
   }
 };
 
+// gelu(x) = 0.5 * x *  (1 + erf(x / sqrt(2)))
+template <typename T>
+struct GeluFunctor : public BaseActivationFunctor<T> {
+  template <typename Device, typename X, typename Out>
+  void operator()(Device d, X x, Out out) const {
+    auto temp =
+        ((x * static_cast<T>(M_SQRT1_2)).erf()).template cast<T>().eval();
+    out.device(d) = x * static_cast<T>(0.5) * (static_cast<T>(1) + temp);
+  }
+};
+
+template <typename T>
+struct GeluGradFunctor : BaseActivationFunctor<T> {
+  bool Inplace() const { return IsInplace("gelu"); }
+  template <typename Device, typename X, typename Out, typename dOut,
+            typename dX>
+  void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
+    auto temp = (static_cast<T>(0.5 * M_2_SQRTPI * M_SQRT1_2) * x *
+                 ((-static_cast<T>(0.5) * x.square()).exp()))
+                    .template cast<T>()
+                    .eval();
+    dx.device(d) = dout * (out / x + temp);
+  }
+};
+
 // tanh(x) = (exp(x) - exp(-x)) / (exp(x) + exp(-x))
 template <typename T>
 struct TanhFunctor : public BaseActivationFunctor<T> {
@@ -877,6 +907,7 @@ struct SwishGradFunctor : public BaseActivationFunctor<T> {
   __macro(logsigmoid, LogSigmoidFunctor, LogSigmoidGradFunctor);     \
   __macro(exp, ExpFunctor, ExpGradFunctor);                          \
   __macro(relu, ReluFunctor, ReluGradFunctor);                       \
+  __macro(gelu, GeluFunctor, GeluGradFunctor);                       \
   __macro(tanh, TanhFunctor, TanhGradFunctor);                       \
   __macro(softshrink, SoftShrinkFunctor, SoftShrinkGradFunctor);     \
   __macro(sqrt, SqrtFunctor, SqrtGradFunctor);                       \

paddle/fluid/operators/bilinear_tensor_product_op.h

@@ -70,7 +70,7 @@ class BilinearTensorProductKernel : public framework::OpKernel<T> {
     if (bias) {
       auto bias_vec = EigenMatrix<T>::From(*bias);
       Eigen::DSizes<int, 2> bcast(batch_size, 1);
-      output_mat.device(place) = bias_vec.broadcast(bcast) + output_mat;
+      output_mat.device(place) = bias_vec.broadcast(bcast).eval() + output_mat;
     }
   }
 };
@@ -99,13 +99,13 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> {
     auto d_out_mat = EigenMatrix<T>::From(*d_out);
     auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
     auto& dev_ctx = ctx.template device_context<DeviceContext>();
-    // Create the intermediate variable to caculate the Output(Y@Grad).
+    // Create the intermediate variable to calculate the Output(Y@Grad).
     Tensor x_scale;
     x_scale.mutable_data<T>(framework::make_ddim({batch_size, x_dim}),
                             ctx.GetPlace());
     auto x_scale_mat = EigenMatrix<T>::From(x_scale);
 
-    // Create the intermediate variable to caculate the Output(X@Grad).
+    // Create the intermediate variable to calculate the Output(X@Grad).
     Tensor y_scale;
     y_scale.mutable_data<T>(framework::make_ddim({batch_size, y_dim}),
                             ctx.GetPlace());
@@ -113,65 +113,64 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> {
 
     math::SetConstant<DeviceContext, T> set_zero;
 
-    // Set Output(X@Grad) be zero.
     if (d_x) {
       d_x->mutable_data<T>(ctx.GetPlace());
       set_zero(dev_ctx, d_x, static_cast<T>(0));
     }
 
-    // Set Output(Y@Grad) be zero.
     if (d_y) {
       d_y->mutable_data<T>(ctx.GetPlace());
       set_zero(dev_ctx, d_y, static_cast<T>(0));
     }
 
+    if (d_weight) {
+      d_weight->mutable_data<T>(ctx.GetPlace());
+    }
+
     auto blas = math::GetBlas<DeviceContext, T>(ctx);
 
     // Caculate the Output(X@Grad) and Output(Y@Grad).
-    if (d_x || d_y) {
+    if (d_x || d_y || d_weight) {
       Eigen::DSizes<int, 2> bcast_for_x(1, y_dim);
       Eigen::DSizes<int, 2> bcast_for_y(1, x_dim);
+      Eigen::DSizes<int, 2> bcast_for_weight(1, x_dim);
+
       for (int i = 0; i < out_dim; ++i) {
         Tensor weight_i = weight->Slice(i, i + 1).Resize(
             framework::make_ddim({x_dim, y_dim}));
         auto output_vec = d_out_mat.chip(i, 1);
+
         if (d_x) {
           y_scale_mat.device(place) =
               output_vec.reshape(Eigen::DSizes<int, 2>(batch_size, 1))
-                  .broadcast(bcast_for_x) *
+                  .broadcast(bcast_for_x)
+                  .eval() *
               y_mat;
           blas.GEMM(CblasNoTrans, CblasTrans, batch_size, x_dim, y_dim, 1,
                     y_scale.data<T>(), weight_i.data<T>(), 1, d_x->data<T>());
         }
-        if (d_y) {
-          x_scale_mat.device(place) =
+
+        if (d_y || d_weight) {
+          auto output_vec_y =
               output_vec.reshape(Eigen::DSizes<int, 2>(batch_size, 1))
-                  .broadcast(bcast_for_y) *
-              x_mat;
-          blas.GEMM(CblasNoTrans, CblasNoTrans, batch_size, y_dim, x_dim, 1,
-                    x_scale.data<T>(), weight_i.data<T>(), 1, d_y->data<T>());
+                  .broadcast(bcast_for_y)
+                  .eval();
+          x_scale_mat.device(place) = output_vec_y * x_mat;
+          if (d_y) {
+            blas.GEMM(CblasNoTrans, CblasNoTrans, batch_size, y_dim, x_dim, 1,
+                      x_scale.data<T>(), weight_i.data<T>(), 1, d_y->data<T>());
+          }
+          if (d_weight) {
+            Tensor d_weight_i = d_weight->Slice(i, i + 1).Resize(
+                framework::make_ddim({x_dim, y_dim}));
+            blas.GEMM(CblasTrans, CblasNoTrans, x_dim, y_dim, batch_size, 1,
+                      x_scale.data<T>(), y->data<T>(), 0, d_weight_i.data<T>());
+          }
         }
       }
     }
 
-    // Caculate the gradient of Input(Weight).
-    if (d_weight) {
-      d_weight->mutable_data<T>(ctx.GetPlace());
-      Eigen::DSizes<int, 2> bcast_for_weight(1, x_dim);
-      for (int i = 0; i < out_dim; ++i) {
-        Tensor d_weight_i = d_weight->Slice(i, i + 1).Resize(
-            framework::make_ddim({x_dim, y_dim}));
-        auto output_vec = d_out_mat.chip(i, 1);
-        x_scale_mat.device(place) =
-            output_vec.reshape(Eigen::DSizes<int, 2>(batch_size, 1))
-                .broadcast(bcast_for_weight) *
-            x_mat;
-        blas.GEMM(CblasTrans, CblasNoTrans, x_dim, y_dim, batch_size, 1,
-                  x_scale.data<T>(), y->data<T>(), 0, d_weight_i.data<T>());
-      }
-    }
-
-    // Caculate the gradient of Input(Bias).
+    // calculate the gradient of Input(Bias).
     if (d_bias) {
       d_bias->mutable_data<T>(ctx.GetPlace());
       auto d_bias_mat = framework::EigenVector<T>::Flatten(*d_bias);

paddle/fluid/operators/dropout_op.cc

@@ -120,6 +120,7 @@ class DropoutOpGrad : public framework::OperatorWithKernel {
                       "Dimensions of Input(X) and Mask must be the same.");
 
     ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
+    ctx->ShareLoD("X", /*->*/ framework::GradVarName("X"));
   }
 };
 

paddle/fluid/operators/dropout_op_test.cc

@@ -12,7 +12,9 @@ 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. */
 
+#ifndef _WIN32
 #include <unistd.h>
+#endif
 
 #include <string>
 #include <thread>  // NOLINT

paddle/fluid/operators/elementwise/elementwise_mul_mkldnn_op.cc

@@ -19,36 +19,21 @@ limitations under the License. */
 #include "paddle/fluid/platform/mkldnn_helper.h"
 
 #include "paddle/fluid/operators/math/jit_kernel.h"
-#include "xbyak.h"
-#include "xbyak_util.h"
+#include "xbyak/xbyak.h"
+#include "xbyak/xbyak_util.h"
 
 namespace paddle {
 namespace operators {
 
 using framework::DataLayout;
 using mkldnn::memory;
-
-static mkldnn::memory::format StringToMKLDNNFormat(std::string& format) {
-  std::transform(format.begin(), format.end(), format.begin(), ::tolower);
-
-  if (!format.compare("nchw")) {
-    return memory::format::nchw;
-  } else if (!format.compare("nchw16c")) {
-    return memory::format::nChw16c;
-  } else if (!format.compare("nchw8c")) {
-    return memory::format::nChw8c;
-  } else if (!format.compare("nhwc")) {
-    return memory::format::nhwc;
-  } else {
-    return memory::format::any;
-  }
-}
+using platform::StringToMKLDNNFormat;
 
 static void UpdateDataFormat(const framework::ExecutionContext& ctx,
                              framework::Tensor* tensor, const char* attribute) {
   if (ctx.op().HasAttr(attribute)) {
     auto format_as_string = ctx.Attr<std::string>(attribute);
-    auto format = StringToMKLDNNFormat(format_as_string);
+    auto format = StringToMKLDNNFormat(&format_as_string);
     if (format != memory::format::any) {
       tensor->set_format(format);
     }
@@ -93,8 +78,8 @@ class ElementwiseMulMKLDNNKernel : public framework::OpKernel<T> {
     auto y_dims_untrimmed = y->dims();
     auto x_int_dims = paddle::framework::vectorize2int(x_dims);
 
-    UpdateDataFormat(ctx, (Tensor*)x, "x_data_format");
-    UpdateDataFormat(ctx, (Tensor*)y, "y_data_format");
+    UpdateDataFormat(ctx, const_cast<Tensor*>(x), "x_data_format");
+    UpdateDataFormat(ctx, const_cast<Tensor*>(y), "y_data_format");
 
     Xbyak::util::Cpu cpu;
     const bool is_avx512_enabled = cpu.has(Xbyak::util::Cpu::tAVX512F);
@@ -156,10 +141,10 @@ class ElementwiseMulMKLDNNKernel : public framework::OpKernel<T> {
         auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
         const auto& mkldnn_engine = dev_ctx.GetEngine();
         if (!(is_x_nchw || is_x_nc))
-          ReorderInput<T>((Tensor*)x, ctx.GetPlace(), mkldnn_engine,
+          ReorderInput<T>(const_cast<Tensor*>(x), ctx.GetPlace(), mkldnn_engine,
                           x->dims().size() == 4);
         if (!(is_y_nchw || is_y_nc))
-          ReorderInput<T>((Tensor*)y, ctx.GetPlace(), mkldnn_engine,
+          ReorderInput<T>(const_cast<Tensor*>(y), ctx.GetPlace(), mkldnn_engine,
                           y->dims().size() == 4);
       }
 

paddle/fluid/operators/hierarchical_sigmoid_op.cc

@@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/hierarchical_sigmoid_op.h"
+#include <string>
 #include <vector>
-
 namespace paddle {
 namespace operators {
 
@@ -70,13 +70,14 @@ class HierarchicalSigmoidOp : public framework::OperatorWithKernel {
     const int64_t batch_size = ctx->GetInputDim("X")[0];
     std::vector<int64_t> output_shape({batch_size, 1});
     ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
+    ctx->ShareLoD("X", /*->*/ "Out");
   }
 
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
     return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
+        framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
         ctx.GetPlace());
   }
 };
@@ -86,27 +87,40 @@ class HierarchicalSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
     AddInput("X",
-             "(Tensor, required) The input tensor with shape [N, D], "
+             "(LoDTensor, required) The input tensor with shape [N, D], "
              "where N is the size of mini-batch, and D is the feature size.");
     AddInput("W",
-             "(Tensor, required), The parameters of hierarchical "
+             "(LoDTensor, required), The parameters of hierarchical "
              "sigmoid operator, each of them is a 2-D tensor, the shape is"
-             "[num_classes - 1, D].");
+             "[K, D]. Which K is the num of non-leaf node in Path Tree");
     AddInput("Label",
-             "(Tensor, required), The labels of training data. It's a"
+             "(LoDTensor, required), The labels of training data. It's a"
              "tensor with shape [N, 1].");
+    AddInput("PTable",
+             "(LoDTensor, optional), The Path Table from root to current word"
+             "it should have shape like [N, L], L is the length of the Path")
+        .AsDispensable();
+    AddInput(
+        "PathCode",
+        "(LoDTensor, optional), The Code on each Node of the Path from root "
+        "to current word"
+        "it should have shape like [N, L], L is the length of the Path")
+        .AsDispensable();
     AddInput("Bias",
-             "(Tensor, optional), The bias is a tensor with shape"
-             "[1, num_classes - 1].");
-    AddOutput("Out",
-              "(Tensor, required) The output of hierarchical sigmoid operator."
-              "The shape is [N, 1].");
+             "(LoDTensor, optional), The bias is a tensor with shape or "
+             "[num_classes, 1]"
+             "[num_classes - 1, 1].")
+        .AsDispensable();
+    AddOutput(
+        "Out",
+        "(LoDTensor, required) The output of hierarchical sigmoid operator."
+        "The shape is [N, 1].");
     AddOutput("PreOut",
-              "(Tensor, required) A intermedia 2-D tensor with shape "
+              "(LoDTensor, required) A intermedia 2-D tensor with shape "
               "[batch_size, code_length], where code_length represents the "
               "maximum path length from root to leaf nodes.")
         .AsIntermediate();
-    AddAttr<AttrType>("num_classes", "(int, required), The number of classes")
+    AddAttr<AttrType>("num_classes", "(int, optional), The number of classes")
         .SetDefault(2);
     AddComment(R"DOC(
 The hierarchical sigmoid operator organize the classes into a binary tree.
@@ -115,6 +129,10 @@ belonging to the right branch. This idea is from
 "F. Morin, Y. Bengio (AISTATS 05):
 Hierarchical Probabilistic Neural Network Language Model."
       )DOC");
+    AddAttr<bool>("is_sparse",
+                  "(boolean, default false) "
+                  "Sparse update.")
+        .SetDefault(false);
   }
 };
 
@@ -124,16 +142,21 @@ class HierarchicalSigmoidGradOp : public framework::OperatorWithKernel {
   void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("W"), "Input(W) should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
+                   "Input(Out@Grad) should not be null");
     PADDLE_ENFORCE(ctx->HasInput("PreOut"),
                    "Input(Preout) should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("W")),
-                   "Output(W@Grad should not be null.)");
-    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")));
-    if (ctx->HasOutput(framework::GradVarName("Bias"))) {
-      ctx->SetOutputDim(framework::GradVarName("Bias"),
-                        ctx->GetInputDim("Bias"));
+                   "Output(W@Grad should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
+                   "Output(X@Grad should not be null.");
+    if (!ctx->Attrs().Get<bool>("is_sparse")) {
+      if (ctx->HasOutput(framework::GradVarName("Bias"))) {
+        ctx->SetOutputDim(framework::GradVarName("Bias"),
+                          ctx->GetInputDim("Bias"));
+      }
+      ctx->SetOutputDim(framework::GradVarName("W"), ctx->GetInputDim("W"));
     }
-    ctx->SetOutputDim(framework::GradVarName("W"), ctx->GetInputDim("W"));
     ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
   }
 
@@ -141,11 +164,55 @@ class HierarchicalSigmoidGradOp : public framework::OperatorWithKernel {
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
     return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
+        framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
         ctx.GetPlace());
   }
 };
 
+class HierarchicalSigmoidGradOpGradVarTypeInference
+    : public framework::VarTypeInference {
+ public:
+  void operator()(const framework::OpDesc& op_desc,
+                  framework::BlockDesc* block) const override {
+    auto w_grad_var_name = op_desc.Output(framework::GradVarName("W")).front();
+    auto bias_grad_var_name_vec =
+        op_desc.Output(framework::GradVarName("Bias"));
+    std::string bias_grad_var_name;
+    bool hasBias = false;
+    if (bias_grad_var_name_vec.size()) {
+      hasBias = true;
+      bias_grad_var_name =
+          op_desc.Output(framework::GradVarName("Bias")).front();
+    }
+    auto attr = op_desc.GetAttr("is_sparse");
+    bool is_sparse = boost::get<bool>(attr);
+    if (is_sparse) {
+      VLOG(30) << "hierarchical_sigmoid_grad op " << framework::GradVarName("W")
+               << " is set to SelectedRows";
+      block->Var(w_grad_var_name)
+          ->SetType(framework::proto::VarType::SELECTED_ROWS);
+      if (hasBias) {
+        VLOG(30) << "hierarchical_sigmoid_grad op "
+                 << framework::GradVarName("Bias") << " is set to SelectedRows";
+        block->Var(bias_grad_var_name)
+            ->SetType(framework::proto::VarType::SELECTED_ROWS);
+      }
+    } else {
+      VLOG(30) << "hierarchical_sigmoid_grad op " << framework::GradVarName("W")
+               << " is set to LoDTensor";
+      block->Var(w_grad_var_name)
+          ->SetType(framework::proto::VarType::LOD_TENSOR);
+      if (hasBias) {
+        VLOG(30) << "hierarchical_sigmoid_grad op "
+                 << framework::GradVarName("Bias") << " is set to LoDTensor";
+        block->Var(bias_grad_var_name)
+            ->SetType(framework::proto::VarType::LOD_TENSOR);
+      }
+    }
+    block->Var(w_grad_var_name)->SetDataType(block->Var("W")->GetDataType());
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
@@ -153,7 +220,8 @@ namespace ops = paddle::operators;
 REGISTER_OPERATOR(hierarchical_sigmoid, ops::HierarchicalSigmoidOp,
                   ops::HierarchicalSigmoidOpMaker<int>,
                   paddle::framework::DefaultGradOpDescMaker<true>);
-REGISTER_OPERATOR(hierarchical_sigmoid_grad, ops::HierarchicalSigmoidGradOp);
+REGISTER_OPERATOR(hierarchical_sigmoid_grad, ops::HierarchicalSigmoidGradOp,
+                  ops::HierarchicalSigmoidGradOpGradVarTypeInference);
 REGISTER_OP_CPU_KERNEL(
     hierarchical_sigmoid,
     ops::HierarchicalSigmoidOpKernel<paddle::platform::CPUDeviceContext, float>,

paddle/fluid/operators/hierarchical_sigmoid_op.h

@@ -14,12 +14,16 @@ limitations under the License. */
 
 #pragma once
 #include <iostream>
+#include <set>
 #include <vector>
+#include "paddle/fluid/framework/mixed_vector.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/clip_op.h"
+#include "paddle/fluid/operators/detail/safe_ref.h"
 #include "paddle/fluid/operators/math/math_function.h"
 #include "paddle/fluid/operators/math/matrix_bit_code.h"
 #include "paddle/fluid/platform/transform.h"
+
 namespace paddle {
 namespace operators {
 
@@ -28,20 +32,38 @@ template <typename T, int MajorType = Eigen::RowMajor,
 using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
 using platform::Transform;
 
+static std::vector<int64_t> PathToRows(const framework::LoDTensor& path) {
+  std::set<int64_t> rows;
+  for (int64_t i = 0; i < path.numel(); ++i) {
+    int64_t row = path.data<int64_t>()[i];
+    if (row < 0) {
+      continue;
+    }
+    rows.emplace(row);
+  }
+  return std::vector<int64_t>(rows.begin(), rows.end());
+}
 template <typename DeviceContext, typename T>
 class HierarchicalSigmoidOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* in = ctx.Input<framework::Tensor>("X");
-    auto* w = ctx.Input<framework::Tensor>("W");
-    auto* label = ctx.Input<framework::Tensor>("Label");
-    auto* bias = ctx.Input<framework::Tensor>("Bias");
-    auto* out = ctx.Output<framework::Tensor>("Out");
-    auto* pre_out = ctx.Output<framework::Tensor>("PreOut");
+    auto& in = detail::Ref(ctx.Input<framework::LoDTensor>("X"));
+    auto& w = detail::Ref(ctx.Input<framework::LoDTensor>("W"));
+    auto* path = ctx.Input<framework::LoDTensor>("PTable");
+    auto* code = ctx.Input<framework::LoDTensor>("PathCode");
+    auto& label = detail::Ref(ctx.Input<framework::LoDTensor>("Label"));
+    auto* bias = ctx.Input<framework::LoDTensor>("Bias");
+    auto* out = ctx.Output<framework::LoDTensor>("Out");
+    auto* pre_out = ctx.Output<framework::LoDTensor>("PreOut");
     size_t num_classes = static_cast<size_t>(ctx.Attr<int>("num_classes"));
-    int64_t code_length = math::FindLastSet(num_classes - 1);
-    int64_t batch_size = in->dims()[0];
-    framework::Tensor sum;
+    bool is_custom = false;
+    if (path) {
+      is_custom = true;
+    }
+    int64_t code_length =
+        path ? path->dims()[1] : math::FindLastSet(num_classes - 1);
+    int64_t batch_size = in.dims()[0];
+    framework::LoDTensor sum;
     auto& dev_ctx = ctx.template device_context<DeviceContext>();
     auto* pre_out_data = pre_out->mutable_data<T>(
         framework::make_ddim({batch_size, code_length}), ctx.GetPlace());
@@ -52,7 +74,15 @@ class HierarchicalSigmoidOpKernel : public framework::OpKernel<T> {
     zero(dev_ctx, pre_out, static_cast<T>(0.0));
     auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
     math::RowwiseSum<DeviceContext, T> row_sum;
-    math::MatrixBitCodeFunctor<T> bit_code(num_classes, label->data<int64_t>());
+
+    std::unique_ptr<math::MatrixBitCodeFunctor<T>> bit_code;
+    if (!is_custom) {
+      bit_code.reset(new math::MatrixBitCodeFunctor<T>(num_classes,
+                                                       label.data<int64_t>()));
+    } else {
+      bit_code.reset(new math::MatrixBitCodeFunctor<T>(*path, *code,
+                                                       label.data<int64_t>()));
+    }
 
     std::vector<int64_t> sum_dims({batch_size, 1UL});
     sum.mutable_data<T>(framework::make_ddim(sum_dims), ctx.GetPlace());
@@ -60,15 +90,15 @@ class HierarchicalSigmoidOpKernel : public framework::OpKernel<T> {
     out->mutable_data<T>(ctx.GetPlace());
     auto out_mat = framework::EigenVector<T>::Flatten(*out);
     if (bias) {
-      bit_code.Add(pre_out, *bias);
+      bit_code->Add(*bias, pre_out);
     }
-    bit_code.Mul(pre_out, *w, *in);
+    bit_code->Mul(pre_out, w, in);
     // clip to [-40, 40]
     Transform<DeviceContext> trans;
     trans(ctx.template device_context<DeviceContext>(), pre_out_data,
           pre_out_data + pre_out->numel(), pre_out_data,
           ClipFunctor<T>(static_cast<T>(-40.0), static_cast<T>(40.0)));
-    bit_code.Sum(*pre_out, out, static_cast<T>(-1));
+    bit_code->Sum(*pre_out, out, static_cast<T>(-1));
     // use softrelu to calculate cross entropy
     pre_out_mat.device(place) = (static_cast<T>(1.0) + pre_out_mat.exp()).log();
     row_sum(dev_ctx, *pre_out, &sum);
@@ -84,50 +114,103 @@ template <typename DeviceContext, typename T>
 class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* in = ctx.Input<framework::Tensor>("X");
-    auto* w = ctx.Input<framework::Tensor>("W");
-    auto* in_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
-    auto* w_grad = ctx.Output<framework::Tensor>(framework::GradVarName("W"));
-    auto* bias_grad =
-        ctx.Output<framework::Tensor>(framework::GradVarName("Bias"));
-    auto* label = ctx.Input<framework::Tensor>("Label");
-    auto* pre_out = ctx.Input<framework::Tensor>("PreOut");
-    auto* out_grad =
-        ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
-    framework::Tensor pre_out_grad;
-
-    pre_out_grad.mutable_data<T>(pre_out->dims(), ctx.GetPlace());
-    in_grad->mutable_data<T>(ctx.GetPlace());
-    w_grad->mutable_data<T>(ctx.GetPlace());
+    auto& in = detail::Ref(ctx.Input<framework::LoDTensor>("X"));
+    auto& w = detail::Ref(ctx.Input<framework::LoDTensor>("W"));
+    auto* path = ctx.Input<framework::LoDTensor>("PTable");
+    auto* code = ctx.Input<framework::LoDTensor>("PathCode");
+    auto* bias = ctx.Input<framework::LoDTensor>("Bias");
+    auto* in_grad =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
+    bool is_sparse = ctx.Attr<bool>("is_sparse");
     auto& dev_ctx = ctx.template device_context<DeviceContext>();
     math::SetConstant<DeviceContext, T> zero;
+    auto& label = detail::Ref(ctx.Input<framework::LoDTensor>("Label"));
+    auto& pre_out = detail::Ref(ctx.Input<framework::LoDTensor>("PreOut"));
+    auto& out_grad = detail::Ref(
+        ctx.Input<framework::LoDTensor>(framework::GradVarName("Out")));
+    framework::LoDTensor pre_out_grad;
+
+    pre_out_grad.mutable_data<T>(pre_out.dims(), ctx.GetPlace());
+    in_grad->mutable_data<T>(ctx.GetPlace());
     zero(dev_ctx, in_grad, static_cast<T>(0.0));
-    zero(dev_ctx, w_grad, static_cast<T>(0.0));
 
     size_t num_classes = static_cast<size_t>(ctx.Attr<int>("num_classes"));
-    math::MatrixBitCodeFunctor<T> bit_code(num_classes, label->data<int64_t>());
+
+    bool is_custom = false;
+    if (path) {
+      is_custom = true;
+    }
+
+    std::unique_ptr<math::MatrixBitCodeFunctor<T>> bit_code;
+    if (!is_custom) {
+      bit_code.reset(new math::MatrixBitCodeFunctor<T>(num_classes,
+                                                       label.data<int64_t>()));
+    } else {
+      bit_code.reset(new math::MatrixBitCodeFunctor<T>(*path, *code,
+                                                       label.data<int64_t>()));
+    }
 
     auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
-    auto pre_out_mat = EigenMatrix<T>::From(*pre_out);
+    auto pre_out_mat = EigenMatrix<T>::From(pre_out);
     auto pre_out_grad_mat = EigenMatrix<T>::From(pre_out_grad);
-    auto out_grad_mat = EigenMatrix<T>::From(*out_grad);
+    auto out_grad_mat = EigenMatrix<T>::From(out_grad);
+
     Eigen::array<int, 2> bcast{1, static_cast<int>(pre_out_grad.dims()[1])};
 
     // softrelu derivative
     pre_out_grad_mat.device(place) =
         static_cast<T>(1.0) - static_cast<T>(1.0) / pre_out_mat.exp();
-    bit_code.Sub(&pre_out_grad);  // the gradient of clip(w * x + b)
+    bit_code->Sub(&pre_out_grad);  // the gradient of clip(w * x + b)
     pre_out_grad_mat.device(place) =
         pre_out_grad_mat * out_grad_mat.broadcast(bcast);
     // TODO(guosheng): multiply pre_out_grad with subgradient of clipping to
     // be consistent with the clipping in forward.
-    if (bias_grad) {
-      bias_grad->mutable_data<T>(ctx.GetPlace());
-      zero(dev_ctx, bias_grad, static_cast<T>(0.0));
-      bit_code.AddGrad(pre_out_grad, bias_grad);
+
+    if (!is_sparse) {
+      auto* bias_grad =
+          ctx.Output<framework::LoDTensor>(framework::GradVarName("Bias"));
+      if (bias_grad) {
+        bias_grad->mutable_data<T>(ctx.GetPlace());
+        zero(dev_ctx, bias_grad, static_cast<T>(0.0));
+        bit_code->AddGrad(pre_out_grad, bias_grad);
+      }
+      auto* w_grad =
+          ctx.Output<framework::LoDTensor>(framework::GradVarName("W"));
+      w_grad->mutable_data<T>(ctx.GetPlace());
+      zero(dev_ctx, w_grad, static_cast<T>(0.0));
+      bit_code->MulGradWeight(pre_out_grad, w_grad, in);
+    } else {
+      framework::Vector<int64_t> real_rows = PathToRows(*path);
+      auto* w_grad =
+          ctx.Output<framework::SelectedRows>(framework::GradVarName("W"));
+      w_grad->set_rows(real_rows);
+      // Build a map of id -> row_index to speed up finding the index of one id
+      w_grad->SyncIndex();
+      w_grad->set_height(w.dims()[0]);
+      auto* w_grad_value = w_grad->mutable_value();
+      framework::DDim temp_dim(w.dims());
+      set(temp_dim, 0, real_rows.size());
+
+      w_grad_value->mutable_data<T>(temp_dim, ctx.GetPlace());
+      zero(dev_ctx, w_grad_value, static_cast<T>(0.0));
+      auto* bias_grad =
+          ctx.Output<framework::SelectedRows>(framework::GradVarName("Bias"));
+      if (bias_grad) {
+        bias_grad->set_rows(real_rows);
+        // build ids -> rows index map
+        bias_grad->SyncIndex();
+        bias_grad->set_height(bias->dims()[0]);
+        auto* bias_grad_value = bias_grad->mutable_value();
+        std::vector<int64_t> dims = {static_cast<int64_t>(real_rows.size()),
+                                     bias->dims()[1]};
+        bias_grad_value->mutable_data<T>(framework::make_ddim(dims),
+                                         ctx.GetPlace());
+        zero(dev_ctx, bias_grad_value, static_cast<T>(0.0));
+        bit_code->AddGrad(pre_out_grad, bias_grad);
+      }
+      bit_code->MulGradWeight(pre_out_grad, w_grad, in);
     }
-    bit_code.MulGradWeight(pre_out_grad, w_grad, *in);
-    bit_code.MulGradError(pre_out_grad, *w, in_grad);
+    bit_code->MulGradError(pre_out_grad, w, in_grad);
   }
 };
 

paddle/fluid/operators/interpolate_op.cc

@@ -76,11 +76,12 @@ class InterpolateOpMaker : public framework::OpProtoAndCheckerMaker {
 
     AddAttr<int>("out_h", "output height of interpolate op.");
     AddAttr<int>("out_w", "output width of interpolate op.");
-    AddAttr<std::string>(
-        "interp_method",
-        "(string), interpolation method, can be \"bilinear\" for "
-        "bilinear interpolation and \"nearest\" for nearest "
-        "neighbor interpolation.");
+    AddAttr<std::string>("interp_method",
+                         "(string, default \"bilinear\"), interpolation "
+                         "method, can be \"bilinear\" for "
+                         "bilinear interpolation and \"nearest\" for nearest "
+                         "neighbor interpolation.")
+        .SetDefault("bilinear");
     AddComment(R"DOC(
           This operator samples input X to given output shape by using specified
           interpolation method, the interpolation methods can be \"nearest\"
@@ -132,11 +133,19 @@ class InterpolateOpGrad : public framework::OperatorWithKernel {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OPERATOR(interpolate, ops::InterpolateOp, ops::InterpolateOpMaker,
+REGISTER_OPERATOR(bilinear_interp, ops::InterpolateOp, ops::InterpolateOpMaker,
                   paddle::framework::DefaultGradOpDescMaker<true>);
-REGISTER_OPERATOR(interpolate_grad, ops::InterpolateOpGrad);
-REGISTER_OP_CPU_KERNEL(interpolate, ops::InterpolateKernel<float>,
+REGISTER_OPERATOR(bilinear_interp_grad, ops::InterpolateOpGrad);
+REGISTER_OPERATOR(nearest_interp, ops::InterpolateOp, ops::InterpolateOpMaker,
+                  paddle::framework::DefaultGradOpDescMaker<true>);
+REGISTER_OPERATOR(nearest_interp_grad, ops::InterpolateOpGrad);
+REGISTER_OP_CPU_KERNEL(bilinear_interp, ops::InterpolateKernel<float>,
+                       ops::InterpolateKernel<double>,
+                       ops::InterpolateKernel<uint8_t>);
+REGISTER_OP_CPU_KERNEL(bilinear_interp_grad, ops::InterpolateGradKernel<float>,
+                       ops::InterpolateGradKernel<double>);
+REGISTER_OP_CPU_KERNEL(nearest_interp, ops::InterpolateKernel<float>,
                        ops::InterpolateKernel<double>,
                        ops::InterpolateKernel<uint8_t>);
-REGISTER_OP_CPU_KERNEL(interpolate_grad, ops::InterpolateGradKernel<float>,
+REGISTER_OP_CPU_KERNEL(nearest_interp_grad, ops::InterpolateGradKernel<float>,
                        ops::InterpolateGradKernel<double>);

paddle/fluid/operators/interpolate_op.cu

@@ -284,9 +284,15 @@ class InterpolateGradOpCUDAKernel : public framework::OpKernel<T> {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP_CUDA_KERNEL(interpolate, ops::InterpolateOpCUDAKernel<float>,
+REGISTER_OP_CUDA_KERNEL(bilinear_interp, ops::InterpolateOpCUDAKernel<float>,
                         ops::InterpolateOpCUDAKernel<double>,
                         ops::InterpolateOpCUDAKernel<int>);
-REGISTER_OP_CUDA_KERNEL(interpolate_grad,
+REGISTER_OP_CUDA_KERNEL(bilinear_interp_grad,
+                        ops::InterpolateGradOpCUDAKernel<float>,
+                        ops::InterpolateGradOpCUDAKernel<double>);
+REGISTER_OP_CUDA_KERNEL(nearest_interp, ops::InterpolateOpCUDAKernel<float>,
+                        ops::InterpolateOpCUDAKernel<double>,
+                        ops::InterpolateOpCUDAKernel<int>);
+REGISTER_OP_CUDA_KERNEL(nearest_interp_grad,
                         ops::InterpolateGradOpCUDAKernel<float>,
                         ops::InterpolateGradOpCUDAKernel<double>);

paddle/fluid/operators/math/matrix_bit_code.cc

@@ -19,16 +19,15 @@ namespace operators {
 namespace math {
 
 template <typename T>
-void MatrixBitCodeFunctor<T>::Add(framework::Tensor* tmat,
-                                  const framework::Tensor& vec) {
-  SimpleCodeTable code_table(num_classes_);
+void MatrixBitCodeFunctor<T>::Add(const framework::Tensor& vec,
+                                  framework::Tensor* tmat) {
   size_t batch_size = tmat->dims()[0];
   size_t width = tmat->dims()[1];
   for (size_t i = 0; i < batch_size; ++i) {
-    auto code = code_table(static_cast<size_t>(ids_[i]));
-    int code_length = code.get_length();
+    auto code = code_table_->get_code(i);
+    int code_length = code->get_length();
     for (int j = 0; j < code_length; ++j) {
-      size_t index = code.calc_index(j);
+      size_t index = code->calc_index(j);
       tmat->data<T>()[i * width + j] += vec.data<T>()[index];
     }
   }
@@ -37,31 +36,46 @@ void MatrixBitCodeFunctor<T>::Add(framework::Tensor* tmat,
 template <typename T>
 void MatrixBitCodeFunctor<T>::AddGrad(const framework::Tensor& tmat,
                                       framework::Tensor* vec) {
-  SimpleCodeTable code_table(num_classes_);
   size_t batch_size = tmat.dims()[0];
   size_t width = tmat.dims()[1];
   for (size_t i = 0; i < batch_size; ++i) {
-    auto code = code_table(static_cast<size_t>(ids_[i]));
-    int code_length = code.get_length();
+    auto code = code_table_->get_code(i);
+    int code_length = code->get_length();
     for (int j = 0; j < code_length; ++j) {
-      size_t index = code.calc_index(j);
+      size_t index = code->calc_index(j);
       vec->data<T>()[index] += tmat.data<T>()[i * width + j];
     }
   }
 }
 
+template <typename T>
+void MatrixBitCodeFunctor<T>::AddGrad(const framework::Tensor& tmat,
+                                      framework::SelectedRows* vec) {
+  size_t batch_size = tmat.dims()[0];
+  size_t width = tmat.dims()[1];
+  for (size_t i = 0; i < batch_size; ++i) {
+    auto code = code_table_->get_code(i);
+    int code_length = code->get_length();
+    for (int j = 0; j < code_length; ++j) {
+      size_t index = code->calc_index(j);
+      int64_t row_index = vec->GetIndexFromId(static_cast<int64_t>(index));
+      vec->mutable_value()->data<T>()[row_index] +=
+          tmat.data<T>()[i * width + j];
+    }
+  }
+}
+
 template <typename T>
 void MatrixBitCodeFunctor<T>::Sum(const framework::Tensor& tmat,
                                   framework::Tensor* sum, T scale_sum) {
-  SimpleCodeTable code_table(num_classes_);
   size_t num_samples = tmat.dims()[0];
   size_t o_width = tmat.dims()[1];
   for (size_t i = 0; i < num_samples; ++i) {
     T sm = static_cast<T>(0.0);
-    auto code = code_table(static_cast<size_t>(ids_[i]));
-    int code_length = code.get_length();
+    auto code = code_table_->get_code(i);
+    int code_length = code->get_length();
     for (int j = 0; j < code_length; ++j) {
-      if (code.calc_bit(j)) {
+      if (code->calc_bit(j)) {
         // calc_bit starts from right most bit, while data in tmat[i] is in the
         // reverse order.
         sm += tmat.data<T>()[i * o_width + j];
@@ -75,7 +89,6 @@ template <typename T>
 void MatrixBitCodeFunctor<T>::Mul(framework::Tensor* tmat,
                                   const framework::Tensor& weight,
                                   const framework::Tensor& input) {
-  SimpleCodeTable code_table(num_classes_);
   size_t num_samples = tmat->dims()[0];
   size_t tmat_width = tmat->dims()[1];
   size_t input_width = input.dims()[1];
@@ -84,10 +97,10 @@ void MatrixBitCodeFunctor<T>::Mul(framework::Tensor* tmat,
   auto weight_value = weight.data<T>();
   auto input_value = input.data<T>();
   for (size_t i = 0; i < num_samples; ++i) {
-    auto code = code_table(static_cast<size_t>(ids_[i]));
-    int code_length = code.get_length();
+    auto code = code_table_->get_code(i);
+    int code_length = code->get_length();
     for (int j = 0; j < code_length; ++j) {
-      size_t index = code.calc_index(j);
+      size_t index = code->calc_index(j);
       T sum = static_cast<T>(0.0);
       for (size_t k = 0; k < input_width; ++k) {
         sum += weight_value[weight_width * index + k] *
@@ -102,7 +115,6 @@ template <typename T>
 void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::Tensor& tmat,
                                             framework::Tensor* weight,
                                             const framework::Tensor& input) {
-  SimpleCodeTable code_table(num_classes_);
   size_t num_samples = tmat.dims()[0];
   size_t input_width = input.dims()[1];
   size_t tmat_width = tmat.dims()[1];
@@ -111,10 +123,10 @@ void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::Tensor& tmat,
   auto weight_value = weight->data<T>();
   auto input_value = input.data<T>();
   for (size_t i = 0; i < num_samples; ++i) {
-    auto code = code_table(static_cast<size_t>(ids_[i]));
-    int code_length = code.get_length();
+    auto code = code_table_->get_code(i);
+    int code_length = code->get_length();
     for (int j = 0; j < code_length; ++j) {
-      size_t index = code.calc_index(j);
+      size_t index = code->calc_index(j);
 
       for (size_t k = 0; k < input_width; ++k) {
         weight_value[weight_width * index + k] +=
@@ -124,11 +136,35 @@ void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::Tensor& tmat,
   }
 }
 
+template <typename T>
+void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::Tensor& tmat,
+                                            framework::SelectedRows* weight,
+                                            const framework::Tensor& input) {
+  size_t num_samples = tmat.dims()[0];
+  size_t input_width = input.dims()[1];
+  size_t tmat_width = tmat.dims()[1];
+  size_t weight_width = weight->value().dims()[1];
+  auto tmat_value = tmat.data<T>();
+  auto weight_value = weight->mutable_value()->data<T>();
+  auto input_value = input.data<T>();
+  for (size_t i = 0; i < num_samples; ++i) {
+    auto code = code_table_->get_code(i);
+    int code_length = code->get_length();
+    for (int j = 0; j < code_length; ++j) {
+      size_t index = code->calc_index(j);
+      for (size_t k = 0; k < input_width; ++k) {
+        int64_t row_index = weight->GetIndexFromId(static_cast<int64_t>(index));
+        weight_value[row_index * weight_width + k] +=
+            tmat_value[i * tmat_width + j] * input_value[input_width * i + k];
+      }
+    }
+  }
+}
+
 template <typename T>
 void MatrixBitCodeFunctor<T>::MulGradError(const framework::Tensor& tmat,
                                            const framework::Tensor& weight,
                                            framework::Tensor* input) {
-  SimpleCodeTable code_table(num_classes_);
   size_t num_samples = tmat.dims()[0];
   size_t tmat_width = tmat.dims()[1];
   size_t input_width = input->dims()[1];
@@ -138,10 +174,10 @@ void MatrixBitCodeFunctor<T>::MulGradError(const framework::Tensor& tmat,
   auto input_value = input->data<T>();
 
   for (size_t i = 0; i < num_samples; ++i) {
-    auto code = code_table(static_cast<size_t>(ids_[i]));
-    int code_length = code.get_length();
+    auto code = code_table_->get_code(i);
+    int code_length = code->get_length();
     for (int j = 0; j < code_length; ++j) {
-      size_t index = code.calc_index(j);
+      size_t index = code->calc_index(j);
 
       for (size_t k = 0; k < input_width; ++k) {
         input_value[input_width * i + k] +=
@@ -154,14 +190,13 @@ void MatrixBitCodeFunctor<T>::MulGradError(const framework::Tensor& tmat,
 
 template <typename T>
 void MatrixBitCodeFunctor<T>::Sub(framework::Tensor* tmat) {
-  SimpleCodeTable code_table(num_classes_);
   size_t num_samples = tmat->dims()[0];
   size_t o_width = tmat->dims()[1];
   for (size_t i = 0; i < num_samples; ++i) {
-    auto code = code_table(static_cast<size_t>(ids_[i]));
-    int code_length = code.get_length();
+    auto code = code_table_->get_code(i);
+    int code_length = code->get_length();
     for (int j = 0; j < code_length; ++j) {
-      if (code.calc_bit(j)) {
+      if (code->calc_bit(j)) {
         tmat->data<T>()[i * o_width + j] -= 1;
       }
     }

paddle/fluid/operators/math/matrix_bit_code.h

@@ -14,6 +14,8 @@ limitations under the License. */
 
 #pragma once
 #include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/selected_rows.h"
 #include "paddle/fluid/framework/tensor.h"
 #include "paddle/fluid/platform/device_context.h"
 
@@ -92,9 +94,27 @@ inline int clz(const T& value) {
 
 inline size_t FindLastSet(size_t x) { return sizeof(size_t) * 8 - clz(x); }
 #endif  // !_WIN32
+// set a code interface to create multiple code
+class Code {
+ public:
+  virtual ~Code() {}
+  virtual size_t calc_index(int bit) const = 0;
+  virtual bool calc_bit(int bit) const = 0;
+  virtual int get_length() const = 0;
+};
+// set a CodeTable interface to create multiple code table
+class CodeTable {
+ public:
+  virtual std::unique_ptr<Code> get_code(int64_t code) const = 0;
+  virtual size_t size() const = 0;
+  virtual int get_max_code_length() const = 0;
+  virtual ~CodeTable() {}
+};
 
-struct SimpleCode {
-  SimpleCode(size_t code, size_t num_classes) : c_(code + num_classes) {}
+class SimpleCode : public Code {
+ public:
+  SimpleCode(size_t code, size_t num_classes, const int64_t* ids)
+      : c_(static_cast<size_t>(ids[code]) + num_classes) {}
   /**
    * Here the id of root shoud be 1 rather than 0, thus the encoding of class c
    * is `c + num_classes` and all siblings can get the same weight indice using
@@ -104,41 +124,121 @@ struct SimpleCode {
    * Binary classification path is the suffixes of encoding, thus leave out the
    * left most bit in calc_bit.
    */
-  inline size_t calc_index(int bit) const { return (c_ >> (bit + 1)) - 1; }
-  inline bool calc_bit(int bit) const { return c_ & (1 << bit); }
-  inline int get_length() const { return FindLastSet(c_) - 1; }
+  size_t calc_index(int bit) const { return (c_ >> (bit + 1)) - 1; }
+  bool calc_bit(int bit) const { return c_ & (1 << bit); }
+  int get_length() const { return FindLastSet(c_) - 1; }
 
  private:
   size_t c_;
 };
 
-struct SimpleCodeTable {
-  explicit SimpleCodeTable(size_t num_classes) : num_classes_(num_classes) {}
-  SimpleCode operator()(size_t code) const {
-    return SimpleCode(code, num_classes_);
+template <typename T>
+class CustomCode : public Code {
+ public:
+  CustomCode(const framework::Tensor& ptable, const framework::Tensor& pcode,
+             const int64_t* ids, int index)
+      : ids_(ids), index_(index) {
+    ptable_ = ptable.Slice(index, index + 1);
+    pcode_ = pcode.Slice(index, index + 1);
+  }
+  /**
+   * Here the id of root shoud be 1 rather than 0, thus the encoding of class c
+   * is `c + num_classes` and all siblings can get the same weight indice using
+   * prefixes.
+   * Weight index is the prefixes of encoding, thus leave out the right most
+   * bit in calc_index.
+   * Binary classification path is the suffixes of encoding, thus leave out the
+   * left most bit in calc_bit.
+   */
+  size_t calc_index(int bit) const { return ptable_.data<T>()[bit]; }
+  bool calc_bit(int bit) const { return pcode_.data<T>()[bit]; }
+  int get_length() const {
+    int length = 0;
+
+    for (int i = 0; i < static_cast<int>(ptable_.dims()[1]); i++) {
+      if (ptable_.data<T>()[i] >= 0) {
+        length++;
+      } else {
+        return length;
+      }
+    }
+    return length;
+  }
+
+ private:
+  framework::Tensor ptable_;
+  framework::Tensor pcode_;
+  const int64_t* ids_;
+  const int index_;
+};
+
+class SimpleCodeTable : public CodeTable {
+ public:
+  SimpleCodeTable(size_t num_classes, const int64_t* ids)
+      : num_classes_(num_classes), ids_(ids) {}
+  std::unique_ptr<Code> get_code(int64_t code) const {
+    std::unique_ptr<Code> coder(new SimpleCode(code, num_classes_, ids_));
+    return coder;
   }
   size_t size() const { return num_classes_; }
   int get_max_code_length() const { return FindLastSet(num_classes_ - 1); }
 
  private:
   size_t num_classes_;
+  const int64_t* ids_;
+};
+
+template <typename T>
+class CustomCodeTable : public CodeTable {
+ public:
+  CustomCodeTable(const framework::Tensor& ptable,
+                  const framework::Tensor& pcode, const int64_t* ids)
+      : ptable_(ptable), pcode_(pcode), ids_(ids) {}
+
+  std::unique_ptr<Code> get_code(int64_t code) const {
+    std::unique_ptr<Code> coder(new CustomCode<T>(ptable_, pcode_, ids_, code));
+    return coder;
+  }
+
+  size_t size() const { return static_cast<size_t>(ptable_.dims()[1]); }
+  int get_max_code_length() const {
+    return static_cast<size_t>(ptable_.dims()[1]);
+  }
+
+ private:
+  const framework::Tensor& ptable_;
+  const framework::Tensor& pcode_;
+  const int64_t* ids_;
 };
 
 template <typename T>
 class MatrixBitCodeFunctor {
  public:
-  explicit MatrixBitCodeFunctor(size_t num_classes, const int64_t* ids)
-      : num_classes_(num_classes), ids_(ids) {}
+  MatrixBitCodeFunctor(size_t num_classes, const int64_t* ids)
+      : num_classes_(num_classes),
+        ids_(ids),
+        code_table_(new SimpleCodeTable(num_classes, ids)) {}
+
+  MatrixBitCodeFunctor(const framework::Tensor& ptable,
+                       const framework::Tensor& pcode, const int64_t* ids)
+      : num_classes_(static_cast<size_t>(ptable.dims()[1])),
+        ids_(ids),
+        code_table_(new CustomCodeTable<int64_t>(ptable, pcode, ids)) {}
   /* For j < code_length
        tmat(i, j) += vec(0, index(i, j))
   */
-  void Add(framework::Tensor* tmat, const framework::Tensor& vec);
+  void Add(const framework::Tensor& vec, framework::Tensor* tmat);
 
   /* For j < code_length
        vec(0, index(i, j)) += tmat(i, j)
   */
   void AddGrad(const framework::Tensor& tmat, framework::Tensor* vec);
 
+  /* For selected rows For j < code_length
+       vec(0, index(i, j)) += tmat(i, j)
+  */
+  void AddGrad(const framework::Tensor& tmat, framework::SelectedRows* vec);
+
   /* For j < code_length
     sum(i, 0) = \sum_j bit(i, j) * tmat(i, j)
   */
@@ -159,6 +259,12 @@ class MatrixBitCodeFunctor {
   */
   void MulGradWeight(const framework::Tensor& tmat, framework::Tensor* weight,
                      const framework::Tensor& input);
+  /* For SelectedRows Weight, For index(i, j) >= 0:
+      weight.row(index(i, j)) += tmat(i, j) * input.row(i)
+  */
+  void MulGradWeight(const framework::Tensor& tmat,
+                     framework::SelectedRows* weight,
+                     const framework::Tensor& input);
   /* For j < code_length
     input.row(i) += tmat(i, j) * weight.row(index(i, j))
   */
@@ -167,6 +273,7 @@ class MatrixBitCodeFunctor {
 
   size_t num_classes_;
   const int64_t* ids_;
+  std::unique_ptr<CodeTable> code_table_;
 };
 }  // namespace math
 }  // namespace operators

paddle/fluid/operators/math/sampler.cc

@@ -60,75 +60,30 @@ float LogUniformSampler::Probability(int64_t value) const {
   return (log((value + 2.0) / (value + 1.0))) / log_range_;
 }
 
-CustomSampler::CustomSampler(int64_t range, const float* probabilities,
+CustomSampler::CustomSampler(int64_t range, const float *probabilities,
+                             const int *alias, const float *alias_probabilities,
                              unsigned int seed)
     : Sampler(range, seed) {
-  random_engine_ = std::make_shared<std::mt19937_64>(seed_);
+  random_engine_ = std::make_shared<std::mt19937>(seed_);
   real_dist_ = std::make_shared<std::uniform_real_distribution<>>(0, 1);
   int_dist_ = std::make_shared<std::uniform_int_distribution<>>(0, range);
-  alias_probs_ = std::make_shared<std::vector<float>>(range + 1);
-  alias_ = std::make_shared<std::vector<int64_t>>(range + 1);
-  probs_ = std::make_shared<std::vector<float>>(range + 1);
-
-  std::queue<std::pair<int64_t, float>> bigs;
-  std::queue<std::pair<int64_t, float>> littles;
-  for (int64_t i = 0; i <= range; ++i) {
-    (*probs_)[i] = probabilities[i];
-    float normal_prob = probabilities[i] * (range + 1);
-    if (normal_prob - 1.0 > 1e-4) {
-      bigs.emplace(i, normal_prob);
-    } else if (1.0 - normal_prob > 1e-4) {
-      littles.emplace(i, normal_prob);
-    } else {
-      (*alias_probs_)[i] = normal_prob;
-      (*alias_)[i] = -1;
-    }
-  }
-
-  while ((!littles.empty()) && (!bigs.empty())) {
-    auto big = bigs.front();
-    auto little = littles.front();
-    bigs.pop();
-    littles.pop();
-    (*alias_probs_)[little.first] = little.second;
-    (*alias_)[little.first] = big.first;
-    auto big_left = big.second - (1 - little.second);
-    if (big_left - 1.0 > 1e-4) {
-      bigs.emplace(big.first, big_left);
-    } else if (1.0 - big_left > 1e-4) {
-      littles.emplace(big.first, big_left);
-    } else {
-      (*alias_probs_)[big.first] = big_left;
-      (*alias_)[big.first] = -1;
-    }
-  }
 
-  if (!littles.empty()) {  // littles.second is close to 1.0
-    auto little = littles.front();
-    (*alias_probs_)[little.first] = 1.0;
-    (*alias_)[little.first] = -1;
-  }
-
-  if (!bigs.empty()) {  // bigs.second is close to 1.0
-    auto big = bigs.front();
-    (*alias_probs_)[big.first] = 1.0;
-    (*alias_)[big.first] = -1;
-  }
+  alias_probs_ = alias_probabilities;
+  probs_ = probabilities;
+  alias_ = alias;
 }
 
 int64_t CustomSampler::Sample() const {
   auto index = (*int_dist_)(*random_engine_);
   auto p = (*real_dist_)(*random_engine_);
-  if (p > (*alias_probs_)[index]) {
-    return (*alias_)[index];
+  if (p > alias_probs_[index]) {
+    return alias_[index];
   } else {
     return index;
   }
 }
 
-float CustomSampler::Probability(int64_t value) const {
-  return (*probs_)[value];
-}
+float CustomSampler::Probability(int64_t value) const { return probs_[value]; }
 
 }  // namespace math
 }  // namespace operators

paddle/fluid/operators/math/sampler.h

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+
 #include <cstdint>
 #include <memory>
 #include <random>
@@ -38,9 +39,12 @@ class Sampler {
       seed_ = seed;
     }
   }
+
   virtual ~Sampler();
+
   // Sample a single value
   virtual int64_t Sample() const = 0;
+
   // The probability that a single call to Sample() returns the given value.
   virtual float Probability(int64_t value) const = 0;
 
@@ -99,6 +103,7 @@ class LogUniformSampler : public Sampler {
 class CustomSampler : public Sampler {
  public:
   explicit CustomSampler(int64_t range, const float* probabilities,
+                         const int* alias, const float* alias_probabilities,
                          unsigned int seed = 0UL);
 
   ~CustomSampler() override {}
@@ -108,10 +113,10 @@ class CustomSampler : public Sampler {
   float Probability(int64_t value) const override;
 
  private:
-  std::shared_ptr<std::vector<float>> alias_probs_;
-  std::shared_ptr<std::vector<int64_t>> alias_;
-  std::shared_ptr<std::vector<float>> probs_;
-  std::shared_ptr<std::mt19937_64> random_engine_;
+  const float* alias_probs_;
+  const int* alias_;
+  const float* probs_;
+  std::shared_ptr<std::mt19937> random_engine_;
   std::shared_ptr<std::uniform_real_distribution<>> real_dist_;
   std::shared_ptr<std::uniform_int_distribution<>> int_dist_;
 };

paddle/fluid/operators/math/sequence_pooling.cu

@@ -16,13 +16,12 @@ limitations under the License. */
 #include "paddle/fluid/operators/math/math_function.h"
 #include "paddle/fluid/operators/math/sequence_pooling.h"
 #include "paddle/fluid/platform/cuda_primitives.h"
+#include "paddle/fluid/platform/macros.h"
 
 namespace paddle {
 namespace operators {
 namespace math {
 
-#define FLT_MAX __FLT_MAX__
-
 template <typename T>
 struct MaxPoolFunctor {
   HOSTDEVICE void operator()(const T* input, const size_t start,

paddle/fluid/operators/nce_op.cc

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/nce_op.h"
 
+#include <string>
 #include <vector>
 
 namespace paddle {
@@ -25,7 +26,7 @@ class NCEOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
-  void InferShape(framework::InferShapeContext* ctx) const override {
+  void InferShape(framework::InferShapeContext *ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("Input"));
     PADDLE_ENFORCE(ctx->HasInput("Label"));
     PADDLE_ENFORCE(ctx->HasInput("Weight"));
@@ -67,7 +68,7 @@ class NCEOp : public framework::OperatorWithKernel {
 
  protected:
   framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext& ctx) const override {
+      const framework::ExecutionContext &ctx) const override {
     return framework::OpKernelType(
         framework::ToDataType(ctx.Input<Tensor>("Input")->type()),
         platform::CPUPlace());
@@ -101,11 +102,24 @@ class NCEOpMaker : public framework::OpProtoAndCheckerMaker {
         .AsDispensable();
 
     AddInput(
-        "CustomDistribution",
+        "CustomDistProbs",
         "(Tensor) It is used in 'CostumDist' sampler. "
         "It is a tensor with shape [num_total_classes]."
         "The i-th element is the probsbility of the i-th class being sampled.")
         .AsDispensable();
+    AddInput(
+        "CustomDistAlias",
+        "(Tensor) It is used in 'CostumDist' sampler. "
+        "It is a tensor with shape [num_total_classes]."
+        "The i-th element is the probsbility of the i-th class being sampled.")
+        .AsDispensable();
+    AddInput(
+        "CustomDistAliasProbs",
+        "(Tensor) It is used in 'CostumDist' sampler. "
+        "It is a tensor with shape [num_total_classes]."
+        "The i-th element is the probsbility of the i-th class being sampled.")
+        .AsDispensable();
+
     AddOutput("Cost",
               "(Tensor) A tensor of shape [batch_size, 1]. Cost of samples.");
     AddOutput("SampleLogits",
@@ -124,21 +138,22 @@ class NCEOpMaker : public framework::OpProtoAndCheckerMaker {
               "kernel to compute grads."
               "")
         .AsIntermediate();
+
     AddAttr<int>("num_total_classes",
                  "Total number of classes in all samples.");
     AddAttr<int>("num_neg_samples",
                  "The number of negative classes. The default value is 10.")
         .SetDefault(10);
-
     AddAttr<int>("sampler",
                  "(int) Which sampler to be used to sample negative class."
                  "0: Uniform; 1: LogUniform; 2: CostumDist.")
         .SetDefault(0);
-
     AddAttr<int>("seed",
                  "(int) The seed used in sampler. If it is 0, "
                  "the sampler will generate a seed randomly.")
         .SetDefault(0);
+    AddAttr<bool>("is_sparse", "(boolean, default false) Sparse update.")
+        .SetDefault(false);
 
     AddAttr<std::vector<int>>("custom_neg_classes",
                               "This attribute only be used in unitest. Classes "
@@ -156,11 +171,19 @@ By default this operator uses a uniform distribution for sampling.
   }
 };
 
+class NCEOpGradDescMaker : public framework::DefaultGradOpDescMaker<true> {
+  using ::paddle::framework::DefaultGradOpDescMaker<
+      true>::DefaultGradOpDescMaker;
+
+ protected:
+  virtual std::string GradOpType() const { return "nce_grad"; }
+};
+
 class NCEOpGrad : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
-  void InferShape(framework::InferShapeContext* ctx) const override {
+  void InferShape(framework::InferShapeContext *ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("Input"));
     PADDLE_ENFORCE(ctx->HasInput("Weight"));
     PADDLE_ENFORCE(ctx->HasInput("Cost"));
@@ -190,20 +213,45 @@ class NCEOpGrad : public framework::OperatorWithKernel {
 
  protected:
   framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext& ctx) const override {
+      const framework::ExecutionContext &ctx) const override {
     return framework::OpKernelType(
         framework::ToDataType(ctx.Input<Tensor>("Input")->type()),
         platform::CPUPlace());
   }
 };
 
+class NCEOpGradVarTypeInference : public framework::VarTypeInference {
+ public:
+  void operator()(const framework::OpDesc &op_desc,
+                  framework::BlockDesc *block) const override {
+    auto weight_grad = op_desc.Output(framework::GradVarName("Weight")).front();
+    auto bias_grad = op_desc.Output(framework::GradVarName("Bias")).front();
+
+    auto attr = op_desc.GetAttr("is_sparse");
+    bool is_sparse = boost::get<bool>(attr);
+    if (is_sparse) {
+      VLOG(30) << "nce_op_grad op " << weight_grad << " and " << bias_grad
+               << " is set to SelectedRows";
+      block->Var(weight_grad)
+          ->SetType(framework::proto::VarType::SELECTED_ROWS);
+      block->Var(bias_grad)->SetType(framework::proto::VarType::SELECTED_ROWS);
+    } else {
+      VLOG(30) << "nce_op_grad op " << weight_grad << " and " << bias_grad
+               << " is set to LoDTensor";
+      block->Var(weight_grad)->SetType(framework::proto::VarType::LOD_TENSOR);
+      block->Var(bias_grad)->SetType(framework::proto::VarType::LOD_TENSOR);
+    }
+    block->Var(weight_grad)->SetDataType(block->Var("Input")->GetDataType());
+    block->Var(bias_grad)->SetDataType(block->Var("Input")->GetDataType());
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OPERATOR(nce, ops::NCEOp, ops::NCEOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
-REGISTER_OPERATOR(nce_grad, ops::NCEOpGrad);
+REGISTER_OPERATOR(nce, ops::NCEOp, ops::NCEOpGradDescMaker, ops::NCEOpMaker);
+REGISTER_OPERATOR(nce_grad, ops::NCEOpGrad, ops::NCEOpGradVarTypeInference);
 REGISTER_OP_CPU_KERNEL(nce, ops::NCEKernel<paddle::platform::CPUPlace, float>,
                        ops::NCEKernel<paddle::platform::CPUPlace, double>);
 REGISTER_OP_CPU_KERNEL(nce_grad,

paddle/fluid/operators/nce_op.h

@@ -16,26 +16,32 @@ limitations under the License. */
 
 #include <math.h>
 #include <random>
+#include <set>
 #include <vector>
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/selected_rows.h"
 #include "paddle/fluid/operators/math/sampler.h"
 #include "unsupported/Eigen/CXX11/Tensor"
+
 namespace paddle {
 namespace operators {
 
 using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+using SelectedRows = framework::SelectedRows;
 using Sampler = math::Sampler;
+using DDim = framework::DDim;
 
 template <typename T, int MajorType = Eigen::RowMajor,
           typename IndexType = Eigen::DenseIndex>
 using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
 
 template <typename DeviceContext, typename T>
-void PrepareSamples(const framework::ExecutionContext& context,
-                    Sampler* sampler) {
+void PrepareSamples(const framework::ExecutionContext &context,
+                    Sampler *sampler) {
   auto label = context.Input<Tensor>("Label");
-  const int64_t* label_data = label->data<int64_t>();
+  const int64_t *label_data = label->data<int64_t>();
   auto label_dims = label->dims();
   //  int num_total_classes = context.Attr<int>("num_total_classes");
   // for unitest
@@ -44,7 +50,7 @@ void PrepareSamples(const framework::ExecutionContext& context,
 
   auto sample_labels = context.Output<Tensor>("SampleLabels");
   auto sample_labels_dims = sample_labels->dims();
-  int64_t* sample_labels_data =
+  int64_t *sample_labels_data =
       sample_labels->mutable_data<int64_t>(context.GetPlace());
 
   int num_label = label_dims.size() == 2 ? label_dims[1] : 1;
@@ -70,13 +76,13 @@ void PrepareSamples(const framework::ExecutionContext& context,
 template <typename DeviceContext, typename T>
 class NCEKernel : public framework::OpKernel<T> {
  public:
-  void Compute(const framework::ExecutionContext& context) const override {
+  void Compute(const framework::ExecutionContext &context) const override {
     int sampler_type = context.Attr<int>("sampler");
     int seed = context.Attr<int>("seed");
     int num_total_classes = context.Attr<int>("num_total_classes");
     int num_neg_samples = context.Attr<int>("num_neg_samples");
 
-    Sampler* sampler;
+    Sampler *sampler;
     switch (sampler_type) {
       case 0: {
         sampler = new math::UniformSampler(num_total_classes - 1, seed);
@@ -87,11 +93,19 @@ class NCEKernel : public framework::OpKernel<T> {
         break;
       }
       case 2: {
-        auto custom_dist = context.Input<Tensor>("CustomDistribution");
-        const float* custom_dist_data = custom_dist->data<float>();
-        PADDLE_ENFORCE_EQ(custom_dist->numel(), num_total_classes);
-        sampler = new math::CustomSampler(num_total_classes - 1,
-                                          custom_dist_data, seed);
+        auto dist_probs = context.Input<Tensor>("CustomDistProbs");
+        auto dist_alias = context.Input<Tensor>("CustomDistAlias");
+        auto dist_alias_probs = context.Input<Tensor>("CustomDistAliasProbs");
+
+        PADDLE_ENFORCE_EQ(dist_probs->numel(), num_total_classes);
+        PADDLE_ENFORCE_EQ(dist_alias->numel(), num_total_classes);
+        PADDLE_ENFORCE_EQ(dist_alias_probs->numel(), num_total_classes);
+
+        const float *probs_data = dist_probs->data<float>();
+        const int *alias_data = dist_alias->data<int>();
+        const float *alias_probs_data = dist_alias_probs->data<float>();
+        sampler = new math::CustomSampler(num_total_classes - 1, probs_data,
+                                          alias_data, alias_probs_data, seed);
         break;
       }
       default: { PADDLE_THROW("Unsupported SamplerType."); }
@@ -99,17 +113,17 @@ class NCEKernel : public framework::OpKernel<T> {
 
     PrepareSamples<DeviceContext, T>(context, sampler);
     auto sample_labels = context.Output<Tensor>("SampleLabels");
-    const int64_t* sample_labels_data = sample_labels->data<int64_t>();
+    const int64_t *sample_labels_data = sample_labels->data<int64_t>();
     auto sample_out = context.Output<Tensor>("SampleLogits");
-    T* sample_out_data = sample_out->mutable_data<T>(context.GetPlace());
+    T *sample_out_data = sample_out->mutable_data<T>(context.GetPlace());
     auto label = context.Input<Tensor>("Label");
     auto sample_weight = context.Input<Tensor>("SampleWeight");
-    const T* sample_weight_data = nullptr;
+    const T *sample_weight_data = nullptr;
     if (sample_weight != nullptr) {
       sample_weight_data = sample_weight->data<T>();
     }
     auto out = context.Output<Tensor>("Cost");
-    T* out_data = out->mutable_data<T>(context.GetPlace());
+    T *out_data = out->mutable_data<T>(context.GetPlace());
     int64_t num_true_class = 1;
     if (label != nullptr) {
       num_true_class = label->dims()[1];
@@ -119,7 +133,7 @@ class NCEKernel : public framework::OpKernel<T> {
     // forward bias
     auto bias = context.Input<Tensor>("Bias");
     if (bias != nullptr) {
-      const T* bias_data = bias->data<T>();
+      const T *bias_data = bias->data<T>();
       for (int64_t i = 0; i < sample_labels->numel(); ++i) {
         sample_out_data[i] = bias_data[sample_labels_data[i]];
       }
@@ -158,16 +172,16 @@ class NCEKernel : public framework::OpKernel<T> {
 template <typename DeviceContext, typename T>
 class NCEGradKernel : public framework::OpKernel<T> {
  public:
-  void Compute(const framework::ExecutionContext& context) const override {
+  void Compute(const framework::ExecutionContext &context) const override {
     auto d_out = context.Input<Tensor>(framework::GradVarName("Cost"));
-    const T* d_out_data = d_out->data<T>();
+    const T *d_out_data = d_out->data<T>();
     auto label = context.Input<Tensor>("Label");
     auto sample_out = context.Input<Tensor>("SampleLogits");
-    const T* sample_out_data = sample_out->data<T>();
+    const T *sample_out_data = sample_out->data<T>();
     auto sample_labels = context.Input<Tensor>("SampleLabels");
-    const int64_t* sample_labels_data = sample_labels->data<int64_t>();
+    const int64_t *sample_labels_data = sample_labels->data<int64_t>();
     auto sample_weight = context.Input<Tensor>("SampleWeight");
-    const T* sample_weight_data = nullptr;
+    const T *sample_weight_data = nullptr;
     if (sample_weight != nullptr) {
       sample_weight_data = sample_weight->data<T>();
     }
@@ -180,7 +194,7 @@ class NCEGradKernel : public framework::OpKernel<T> {
 
     int sampler_type = context.Attr<int>("sampler");
     int seed = context.Attr<int>("seed");
-    Sampler* sampler;
+    Sampler *sampler;
     switch (sampler_type) {
       case 0: {
         sampler = new math::UniformSampler(num_total_classes - 1, seed);
@@ -191,11 +205,19 @@ class NCEGradKernel : public framework::OpKernel<T> {
         break;
       }
       case 2: {
-        auto custom_dist = context.Input<Tensor>("CustomDistribution");
-        const float* custom_dist_data = custom_dist->data<float>();
-        PADDLE_ENFORCE_EQ(custom_dist->numel(), num_total_classes);
-        sampler = new math::CustomSampler(num_total_classes - 1,
-                                          custom_dist_data, seed);
+        auto dist_probs = context.Input<Tensor>("CustomDistProbs");
+        auto dist_alias = context.Input<Tensor>("CustomDistAlias");
+        auto dist_alias_probs = context.Input<Tensor>("CustomDistAliasProbs");
+
+        PADDLE_ENFORCE_EQ(dist_probs->numel(), num_total_classes);
+        PADDLE_ENFORCE_EQ(dist_alias->numel(), num_total_classes);
+        PADDLE_ENFORCE_EQ(dist_alias_probs->numel(), num_total_classes);
+
+        const float *probs_data = dist_probs->data<float>();
+        const int *alias_data = dist_alias->data<int>();
+        const float *alias_probs_data = dist_alias_probs->data<float>();
+        sampler = new math::CustomSampler(num_total_classes - 1, probs_data,
+                                          alias_data, alias_probs_data, seed);
         break;
       }
       default: { PADDLE_THROW("Unsupported SamplerType."); }
@@ -203,7 +225,7 @@ class NCEGradKernel : public framework::OpKernel<T> {
 
     //    T b = 1. / num_total_classes * num_neg_samples;
     Tensor sample_grad;  // tmp tensor
-    T* sample_grad_data =
+    T *sample_grad_data =
         sample_grad.mutable_data<T>(sample_labels->dims(), context.GetPlace());
     // backward cost
     for (int64_t i = 0; i < sample_labels->numel(); ++i) {
@@ -217,32 +239,105 @@ class NCEGradKernel : public framework::OpKernel<T> {
                                 : w * (o * (1 - o) / (o + b));
       sample_grad_data[i] *= d_out_data[sample_idx];
     }
-    // get d_bias
-    auto d_bias = context.Output<Tensor>(framework::GradVarName("Bias"));
-    if (d_bias != nullptr) {
-      T* d_bias_data = d_bias->mutable_data<T>(context.GetPlace());
-      std::fill(d_bias_data, d_bias_data + d_bias->numel(), 0.0);
+
+    bool is_sparse = context.Attr<bool>("is_sparse");
+
+    if (!is_sparse) {
+      // get d_bias
+      auto d_bias = context.Output<Tensor>(framework::GradVarName("Bias"));
+      if (d_bias != nullptr) {
+        T *d_bias_data = d_bias->mutable_data<T>(context.GetPlace());
+        std::fill(d_bias_data, d_bias_data + d_bias->numel(), 0.0);
+        for (int64_t i = 0; i < sample_labels->numel(); ++i) {
+          d_bias_data[sample_labels_data[i]] += sample_grad_data[i];
+        }
+      }
+      // get d_w
+      auto d_w = context.Output<Tensor>(framework::GradVarName("Weight"));
+      if (d_w != nullptr) {
+        auto d_w_data = d_w->mutable_data<T>(context.GetPlace());
+        std::fill(d_w_data, d_w_data + d_w->numel(), 0.0);
+        auto d_w_matrix = EigenMatrix<T>::From(*d_w);
+        auto x_matrix = EigenMatrix<T>::From(*(context.Input<Tensor>("Input")));
+        for (int64_t i = 0; i < sample_labels->numel(); ++i) {
+          d_w_matrix.chip(sample_labels_data[i], 0) +=
+              x_matrix.chip(static_cast<int>(i / sample_labels->dims()[1]), 0) *
+              sample_grad_data[i];
+        }
+      }
+    } else {
+      std::vector<int64_t> labels;
       for (int64_t i = 0; i < sample_labels->numel(); ++i) {
-        d_bias_data[sample_labels_data[i]] += sample_grad_data[i];
+        labels.push_back(sample_labels_data[i]);
       }
-    }
-    // get d_w
-    auto d_w = context.Output<Tensor>(framework::GradVarName("Weight"));
-    if (d_w != nullptr) {
-      auto d_w_data = d_w->mutable_data<T>(context.GetPlace());
-      std::fill(d_w_data, d_w_data + d_w->numel(), 0.0);
-      auto d_w_matrix = EigenMatrix<T>::From(*d_w);
+      std::set<T> st(labels.begin(), labels.end());
+      labels.assign(st.begin(), st.end());
+
+      auto *bias_var = context.InputVar("Bias");
+      DDim bias_dim;
+      if (bias_var->IsType<LoDTensor>()) {
+        bias_dim = context.Input<LoDTensor>("Bias")->dims();
+      } else if (bias_var->IsType<SelectedRows>()) {
+        auto *table_t = context.Input<SelectedRows>("Bias");
+        bias_dim = table_t->value().dims();
+      } else {
+        PADDLE_THROW(
+            "The parameter Bias of a NCE_OP "
+            "must be either LoDTensor or SelectedRows");
+      }
+
+      auto d_bias =
+          context.Output<SelectedRows>(framework::GradVarName("Bias"));
+      d_bias->set_rows(labels);
+      d_bias->set_height(bias_dim[0]);
+
+      d_bias->mutable_value()->Resize(
+          {static_cast<int64_t>(labels.size()), bias_dim[1]});
+      T *d_bias_data =
+          d_bias->mutable_value()->mutable_data<T>(context.GetPlace());
+      std::fill(d_bias_data, d_bias_data + labels.size(), 0.0);
+      for (int64_t i = 0; i < sample_labels->numel(); ++i) {
+        d_bias_data[d_bias->Index(sample_labels_data[i])] +=
+            sample_grad_data[i];
+      }
+
+      auto *table_var = context.InputVar("Weight");
+      DDim table_dim;
+      if (table_var->IsType<LoDTensor>()) {
+        table_dim = context.Input<LoDTensor>("Weight")->dims();
+      } else if (table_var->IsType<SelectedRows>()) {
+        auto *table_t = context.Input<SelectedRows>("Weight");
+        table_dim = table_t->value().dims();
+      } else {
+        PADDLE_THROW(
+            "The parameter Weight of a NCE_OP "
+            "must be either LoDTensor or SelectedRows");
+      }
+
+      auto d_w = context.Output<SelectedRows>(framework::GradVarName("Weight"));
+
+      d_w->set_rows(labels);
+      d_w->set_height(table_dim[0]);
+
+      auto *d_table_value = d_w->mutable_value();
+      d_table_value->Resize(
+          {static_cast<int64_t>(labels.size()), table_dim[1]});
+      auto d_w_data = d_table_value->mutable_data<T>(context.GetPlace());
+      std::fill(d_w_data, d_w_data + d_table_value->numel(), 0.0);
+
+      auto d_w_matrix = EigenMatrix<T>::From(*d_table_value);
       auto x_matrix = EigenMatrix<T>::From(*(context.Input<Tensor>("Input")));
       for (int64_t i = 0; i < sample_labels->numel(); ++i) {
-        d_w_matrix.chip(sample_labels_data[i], 0) +=
+        d_w_matrix.chip(d_w->Index(sample_labels_data[i]), 0) +=
             x_matrix.chip(static_cast<int>(i / sample_labels->dims()[1]), 0) *
             sample_grad_data[i];
       }
     }
+
     // get d_x
     auto d_x = context.Output<Tensor>(framework::GradVarName("Input"));
     if (d_x != nullptr) {
-      auto* d_x_data = d_x->mutable_data<T>(context.GetPlace());
+      auto *d_x_data = d_x->mutable_data<T>(context.GetPlace());
       std::fill(d_x_data, d_x_data + d_x->numel(), 0.0);
       auto d_x_matrix = EigenMatrix<T>::From(*d_x);
       auto w_matrix = EigenMatrix<T>::From(*(context.Input<Tensor>("Weight")));
@@ -251,6 +346,7 @@ class NCEGradKernel : public framework::OpKernel<T> {
             w_matrix.chip(sample_labels_data[i], 0) * sample_grad_data[i];
       }
     }
+
     delete sampler;
   }
 };

paddle/fluid/operators/reader/CMakeLists.txt

@@ -28,6 +28,12 @@ reader_library(create_multi_pass_reader_op SRCS create_multi_pass_reader_op.cc)
 reader_library(create_custom_reader_op SRCS create_custom_reader_op.cc)
 reader_library(create_py_reader_op SRCS create_py_reader_op.cc)
 
+if (NOT WIN32 AND NOT ON_INFER)
+    cc_library(ctr_reader SRCS ctr_reader.cc DEPS gzstream reader zlib)
+    cc_test(ctr_reader_test SRCS ctr_reader_test.cc DEPS ctr_reader)
+    reader_library(create_ctr_reader_op SRCS create_ctr_reader_op.cc DEPS ctr_reader)
+endif ()
+
 cc_test(reader_blocking_queue_test SRCS reader_blocking_queue_test.cc)
 # Export local libraries to parent
 # set(READER_LIBRARY ${LOCAL_READER_LIBS} PARENT_SCOPE)

paddle/fluid/operators/reader/create_ctr_reader_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/reader/ctr_reader.h"
+
+#include "paddle/fluid/operators/reader/lod_tensor_blocking_queue.h"
+#include "paddle/fluid/operators/reader/reader_op_registry.h"
+
+namespace paddle {
+namespace operators {
+namespace reader {
+
+class CreateCTRReaderOp : public framework::OperatorBase {
+ public:
+  using framework::OperatorBase::OperatorBase;
+
+ private:
+  void RunImpl(const framework::Scope& scope,
+               const platform::Place& dev_place) const override {
+    auto* out = scope.FindVar(Output("Out"))
+                    ->template GetMutable<framework::ReaderHolder>();
+    if (out->Get() != nullptr) return;
+
+    const std::string& queue_name = Input("blocking_queue");
+    auto* queue_holder_var = scope.FindVar(queue_name);
+    PADDLE_ENFORCE_NOT_NULL(
+        queue_holder_var,
+        "No LoDTensorBlockingQueueHolder variable with name %s found",
+        queue_name);
+    auto* queue_holder =
+        queue_holder_var->template GetMutable<LoDTensorBlockingQueueHolder>();
+
+    int thread_num = Attr<int>("thread_num");
+    std::vector<std::string> slots = Attr<std::vector<std::string>>("slots");
+    int batch_size = Attr<int>("batch_size");
+    std::vector<std::string> file_list =
+        Attr<std::vector<std::string>>("file_list");
+    out->Reset(std::make_shared<CTRReader>(queue_holder->GetQueue(), batch_size,
+                                           thread_num, slots, file_list));
+  }
+};
+
+class CreateCTRReaderOpMaker : public FileReaderMakerBase {
+ protected:
+  void Apply() override {
+    AddInput("blocking_queue",
+             "Name of the `LoDTensorBlockingQueueHolder` variable");
+    AddAttr<int>("thread_num", "the thread num to read data");
+    AddAttr<int>("batch_size", "the batch size of read data");
+    AddAttr<std::vector<std::string>>("file_list",
+                                      "The list of files that need to read");
+    AddAttr<std::vector<std::string>>(
+        "slots", "the slots that should be extract from file");
+
+    AddComment(R"DOC(
+			Create CTRReader to support read ctr data with cpp.
+      )DOC");
+  }
+};
+
+}  // namespace reader
+}  // namespace operators
+}  // namespace paddle
+
+namespace reader = ::paddle::operators::reader;
+
+REGISTER_FILE_READER_OPERATOR(create_ctr_reader, reader::CreateCTRReaderOp,
+                              reader::CreateCTRReaderOpMaker);

paddle/fluid/operators/reader/ctr_reader.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/reader/ctr_reader.h"
+
+#include <gzstream.h>
+
+#include <cstdlib>
+#include <fstream>
+#include <iostream>
+#include <sstream>
+#include <string>
+#include <unordered_map>
+
+#include <algorithm>
+#include <random>
+
+namespace paddle {
+namespace operators {
+namespace reader {
+
+static inline void string_split(const std::string& s, const char delimiter,
+                                std::vector<std::string>* output) {
+  size_t start = 0;
+  size_t end = s.find_first_of(delimiter);
+
+  while (end <= std::string::npos) {
+    output->emplace_back(s.substr(start, end - start));
+    if (end == std::string::npos) {
+      break;
+    }
+    start = end + 1;
+    end = s.find_first_of(delimiter, start);
+  }
+}
+
+static inline void parse_line(
+    const std::string& line,
+    const std::unordered_map<std::string, size_t>& slot_to_index,
+    int64_t* label,
+    std::unordered_map<std::string, std::vector<int64_t>>* slot_to_data) {
+  std::vector<std::string> ret;
+  string_split(line, ' ', &ret);
+  *label = std::stoi(ret[2]) > 0;
+
+  for (size_t i = 3; i < ret.size(); ++i) {
+    const std::string& item = ret[i];
+    std::vector<std::string> feasign_and_slot;
+    string_split(item, ':', &feasign_and_slot);
+    if (feasign_and_slot.size() == 2 &&
+        slot_to_index.find(feasign_and_slot[1]) != slot_to_index.end()) {
+      int64_t feasign = std::strtoll(feasign_and_slot[0].c_str(), NULL, 10);
+      (*slot_to_data)[feasign_and_slot[1]].push_back(feasign);
+    }
+  }
+
+  // NOTE:: if the slot has no value, then fill [0] as it's data.
+  for (auto& item : slot_to_index) {
+    if (slot_to_data->find(item.first) == slot_to_data->end()) {
+      (*slot_to_data)[item.first].push_back(0);
+    }
+  }
+}
+
+class Reader {
+ public:
+  virtual ~Reader() {}
+  virtual bool HasNext() = 0;
+  virtual void NextLine(std::string* line) = 0;
+};
+
+class GzipReader : public Reader {
+ public:
+  explicit GzipReader(const std::string& file_name)
+      : gzstream_(file_name.c_str()) {}
+
+  ~GzipReader() {}
+
+  bool HasNext() override { return gzstream_.peek() != EOF; }
+
+  void NextLine(std::string* line) override { std::getline(gzstream_, *line); }
+
+ private:
+  igzstream gzstream_;
+};
+
+class MultiGzipReader : public Reader {
+ public:
+  explicit MultiGzipReader(const std::vector<std::string>& file_list) {
+    for (auto& file : file_list) {
+      readers_.emplace_back(std::make_shared<GzipReader>(file));
+    }
+  }
+
+  bool HasNext() override {
+    if (current_reader_index_ >= readers_.size()) {
+      return false;
+    }
+    if (!readers_[current_reader_index_]->HasNext()) {
+      current_reader_index_++;
+      return HasNext();
+    }
+    return true;
+  }
+
+  void NextLine(std::string* line) override {
+    readers_[current_reader_index_]->NextLine(line);
+  }
+
+ private:
+  std::vector<std::shared_ptr<GzipReader>> readers_;
+  size_t current_reader_index_ = 0;
+};
+
+void MonitorThread(std::vector<ReaderThreadStatus>* thread_status,
+                   std::shared_ptr<LoDTensorBlockingQueue> queue) {
+  VLOG(30) << "monitor thread in";
+  bool reader_thread_is_running = true;
+  while (reader_thread_is_running) {
+    VLOG(30) << "reader_thread_is_running";
+    reader_thread_is_running = false;
+    for (size_t i = 0; i < (*thread_status).size(); ++i) {
+      if ((*thread_status)[i] == Running) {
+        VLOG(30) << "reader is running!";
+        reader_thread_is_running = true;
+      }
+    }
+    std::this_thread::sleep_for(std::chrono::milliseconds(1000));
+  }
+  VLOG(30) << "all reader thread is stopped, push empty data into queue";
+  queue->Push({});
+  VLOG(30) << "monitor thread exited";
+}
+
+void ReadThread(const std::vector<std::string>& file_list,
+                const std::vector<std::string>& slots, int batch_size,
+                int thread_id, std::vector<ReaderThreadStatus>* thread_status,
+                std::shared_ptr<LoDTensorBlockingQueue> queue) {
+  VLOG(30) << "[" << thread_id << "]"
+           << " reader thread start! thread_id = " << thread_id;
+  for (auto& file : file_list) {
+    VLOG(30) << "[" << thread_id << "]"
+             << " file " << file;
+  }
+  (*thread_status)[thread_id] = Running;
+  VLOG(30) << "set status to running";
+
+  std::unordered_map<std::string, size_t> slot_to_index;
+  for (size_t i = 0; i < slots.size(); ++i) {
+    slot_to_index[slots[i]] = i;
+  }
+
+  std::string line;
+
+  std::vector<std::unordered_map<std::string, std::vector<int64_t>>> batch_data;
+  std::vector<int64_t> batch_label;
+
+  MultiGzipReader reader(file_list);
+
+  VLOG(30) << "reader inited";
+
+  while (reader.HasNext()) {
+    batch_data.clear();
+    batch_data.reserve(batch_size);
+
+    batch_label.clear();
+    batch_label.reserve(batch_size);
+
+    // read batch_size data
+    for (int i = 0; i < batch_size; ++i) {
+      if (reader.HasNext()) {
+        reader.NextLine(&line);
+        std::unordered_map<std::string, std::vector<int64_t>> slot_to_data;
+        int64_t label;
+        parse_line(line, slot_to_index, &label, &slot_to_data);
+        batch_data.push_back(slot_to_data);
+        batch_label.push_back(label);
+      } else {
+        break;
+      }
+    }
+
+    std::vector<framework::LoDTensor> lod_datas;
+
+    // first insert tensor for each slots
+    for (auto& slot : slots) {
+      std::vector<size_t> lod_data{0};
+      std::vector<int64_t> batch_feasign;
+
+      for (size_t i = 0; i < batch_data.size(); ++i) {
+        auto& feasign = batch_data[i][slot];
+        lod_data.push_back(lod_data.back() + feasign.size());
+        batch_feasign.insert(batch_feasign.end(), feasign.begin(),
+                             feasign.end());
+      }
+
+      framework::LoDTensor lod_tensor;
+      framework::LoD lod{lod_data};
+      lod_tensor.set_lod(lod);
+      int64_t* tensor_data = lod_tensor.mutable_data<int64_t>(
+          framework::make_ddim({1, static_cast<int64_t>(batch_feasign.size())}),
+          platform::CPUPlace());
+      memcpy(tensor_data, batch_feasign.data(),
+             batch_feasign.size() * sizeof(int64_t));
+      lod_datas.push_back(lod_tensor);
+    }
+
+    // insert label tensor
+    framework::LoDTensor label_tensor;
+    auto* label_tensor_data = label_tensor.mutable_data<int64_t>(
+        framework::make_ddim({1, static_cast<int64_t>(batch_label.size())}),
+        platform::CPUPlace());
+    memcpy(label_tensor_data, batch_label.data(),
+           batch_label.size() * sizeof(int64_t));
+    lod_datas.push_back(label_tensor);
+
+    queue->Push(lod_datas);
+    VLOG(40) << "push one data, queue_size=" << queue->Size();
+  }
+
+  (*thread_status)[thread_id] = Stopped;
+  VLOG(30) << "set status to stopped, thread " << thread_id << " exited";
+}
+
+}  // namespace reader
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/reader/ctr_reader.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 <sys/time.h>
+
+#include <chrono>  // NOLINT
+#include <cstdlib>
+#include <fstream>
+#include <iostream>
+#include <sstream>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "paddle/fluid/framework/reader.h"
+#include "paddle/fluid/framework/threadpool.h"
+#include "paddle/fluid/operators/reader/lod_tensor_blocking_queue.h"
+
+namespace paddle {
+namespace operators {
+namespace reader {
+
+enum ReaderThreadStatus { Running, Stopped };
+
+void ReadThread(const std::vector<std::string>& file_list,
+                const std::vector<std::string>& slots, int batch_size,
+                int thread_id, std::vector<ReaderThreadStatus>* thread_status,
+                std::shared_ptr<LoDTensorBlockingQueue> queue);
+
+// monitor all running thread, if they are all stopped,
+// then push an empty data into LoDTensorBlockingQueue
+void MonitorThread(std::vector<ReaderThreadStatus>* thread_status,
+                   std::shared_ptr<LoDTensorBlockingQueue> queue);
+
+class CTRReader : public framework::FileReader {
+ public:
+  explicit CTRReader(const std::shared_ptr<LoDTensorBlockingQueue>& queue,
+                     int batch_size, int thread_num,
+                     const std::vector<std::string>& slots,
+                     const std::vector<std::string>& file_list)
+      : batch_size_(batch_size), slots_(slots), file_list_(file_list) {
+    PADDLE_ENFORCE_GT(thread_num, 0, "thread num should be larger then 0!");
+    PADDLE_ENFORCE(queue != nullptr, "LoDTensorBlockingQueue must not be null");
+    PADDLE_ENFORCE_GT(file_list.size(), 0, "file list should not be empty");
+    thread_num_ =
+        file_list_.size() > thread_num ? thread_num : file_list_.size();
+    queue_ = queue;
+    SplitFiles();
+    for (size_t i = 0; i < thread_num_; ++i) {
+      read_thread_status_.push_back(Stopped);
+    }
+  }
+
+  ~CTRReader() {}
+
+  void ReadNext(std::vector<framework::LoDTensor>* out) override {
+    bool success;
+    *out = queue_->Pop(&success);
+    if (!success) out->clear();
+  }
+
+  void Shutdown() override {
+    VLOG(3) << "Shutdown reader";
+    if (status_ == ReaderStatus::kStopped) {
+      return;
+    }
+    // shutdown should stop all the reader thread
+    for (auto& read_thread : read_threads_) {
+      read_thread->join();
+    }
+    monitor_thread_->join();
+
+    read_threads_.clear();
+    monitor_thread_.reset(nullptr);
+    queue_->Close();
+    status_ = ReaderStatus::kStopped;
+  }
+
+  void Start() override {
+    VLOG(3) << "Start reader";
+    PADDLE_ENFORCE_EQ(read_threads_.size(), 0, "read thread should be empty!");
+    queue_->ReOpen();
+    VLOG(3) << "reopen success";
+    VLOG(3) << "thread_num " << thread_num_;
+    for (int thread_id = 0; thread_id < thread_num_; thread_id++) {
+      read_threads_.emplace_back(new std::thread(
+          std::bind(&ReadThread, file_groups_[thread_id], slots_, batch_size_,
+                    thread_id, &read_thread_status_, queue_)));
+    }
+    monitor_thread_.reset(new std::thread(
+        std::bind(&MonitorThread, &read_thread_status_, queue_)));
+    status_ = ReaderStatus::kRunning;
+  }
+
+ private:
+  void SplitFiles() {
+    file_groups_.resize(thread_num_);
+    for (size_t i = 0; i < file_list_.size(); ++i) {
+      auto& file_name = file_list_[i];
+      std::ifstream f(file_name.c_str());
+      PADDLE_ENFORCE(f.good(), "file %s not exist!", file_name);
+      file_groups_[i % thread_num_].push_back(file_name);
+    }
+  }
+
+ private:
+  size_t thread_num_;
+  const int batch_size_;
+  const std::vector<std::string> slots_;
+  const std::vector<std::string> file_list_;
+  std::shared_ptr<LoDTensorBlockingQueue> queue_;
+  std::vector<std::unique_ptr<std::thread>> read_threads_;
+  std::unique_ptr<std::thread> monitor_thread_;
+  std::vector<ReaderThreadStatus> read_thread_status_;
+  std::vector<std::vector<std::string>> file_groups_;
+};
+
+}  // namespace reader
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/reader/ctr_reader_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/reader/ctr_reader.h"
+
+#include <gzstream.h>
+#include <time.h>
+
+#include <math.h>
+#include <stdio.h>
+#include <cstring>
+#include <fstream>
+#include <tuple>
+
+#include "gtest/gtest.h"
+
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/operators/reader/blocking_queue.h"
+
+using paddle::operators::reader::LoDTensorBlockingQueue;
+using paddle::operators::reader::LoDTensorBlockingQueueHolder;
+using paddle::operators::reader::CTRReader;
+using paddle::framework::LoDTensor;
+using paddle::framework::LoD;
+using paddle::framework::DDim;
+using paddle::platform::CPUPlace;
+using paddle::framework::make_ddim;
+
+static void generatedata(const std::vector<std::string>& data,
+                         const std::string& file_name) {
+  std::ifstream in(file_name.c_str());
+  if (in.good()) {
+    VLOG(3) << "file " << file_name << " exist, delete it first!";
+    remove(file_name.c_str());
+  } else {
+    in.close();
+  }
+
+  ogzstream out(file_name.c_str());
+  PADDLE_ENFORCE(out.good(), "open file %s failed!", file_name);
+  for (auto& c : data) {
+    out << c;
+  }
+  out.close();
+  PADDLE_ENFORCE(out.good(), "save file %s failed!", file_name);
+}
+
+static inline void check_all_data(
+    const std::vector<std::string>& ctr_data,
+    const std::vector<std::string>& slots, const std::vector<DDim>& label_dims,
+    const std::vector<int64_t>& label_value,
+    const std::vector<std::tuple<LoD, std::vector<int64_t>>>& data_slot_6002,
+    const std::vector<std::tuple<LoD, std::vector<int64_t>>>& data_slot_6003,
+    size_t batch_num, size_t batch_size,
+    std::shared_ptr<LoDTensorBlockingQueue> queue, CTRReader* reader) {
+  std::vector<LoDTensor> out;
+  for (size_t i = 0; i < batch_num; ++i) {
+    reader->ReadNext(&out);
+    ASSERT_EQ(out.size(), slots.size() + 1);
+    auto& label_tensor = out.back();
+    ASSERT_EQ(label_tensor.dims(), label_dims[i]);
+    for (size_t j = 0; j < batch_size && i * batch_num + j < ctr_data.size();
+         ++j) {
+      auto& label = label_tensor.data<int64_t>()[j];
+      ASSERT_TRUE(label == 0 || label == 1);
+      ASSERT_EQ(label, label_value[i * batch_size + j]);
+    }
+    auto& tensor_6002 = out[0];
+    ASSERT_EQ(std::get<0>(data_slot_6002[i]), tensor_6002.lod());
+    ASSERT_EQ(std::memcmp(std::get<1>(data_slot_6002[i]).data(),
+                          tensor_6002.data<int64_t>(),
+                          tensor_6002.dims()[1] * sizeof(int64_t)),
+              0);
+  }
+  reader->ReadNext(&out);
+  ASSERT_EQ(out.size(), 0);
+  ASSERT_EQ(queue->Size(), 0);
+}
+
+TEST(CTR_READER, read_data) {
+  const std::vector<std::string> ctr_data = {
+      "aaaa 1 0 0:6002 1:6003 2:6004 3:6005 4:6006 -1\n",
+      "bbbb 1 0 5:6003 6:6003 7:6003 8:6004 9:6004 -1\n",
+      "cccc 1 1 10:6002 11:6002 12:6002 13:6002 14:6002 -2\n",
+      "dddd 1 0 15:6003 16:6003 17:6003 18:6003 19:6004 -3\n",
+      "1111 1 1 20:6001 21:6001 22:6001 23:6001 24:6001 12\n",
+      "2222 1 1 25:6004 26:6004 27:6004 28:6005 29:6005 aa\n",
+      "3333 1 0 30:6002 31:6003 32:6004 33:6004 34:6005 er\n",
+      "eeee 1 1 35:6003 36:6003 37:6005 38:6005 39:6005 dd\n",
+      "ffff 1 1 40:6002 41:6003 42:6004 43:6004 44:6005 66\n",
+      "gggg 1 1 46:6006 45:6006 47:6003 48:6003 49:6003 ba\n",
+  };
+  std::string gz_file_name = "test_ctr_reader_data.gz";
+  generatedata(ctr_data, gz_file_name);
+
+  std::vector<int64_t> label_value = {0, 0, 1, 0, 1, 1, 0, 1, 1, 1};
+
+  std::tuple<LoD, std::vector<int64_t>> a1({{0, 1, 2, 7}},
+                                           {0, 0, 10, 11, 12, 13, 14});
+  std::tuple<LoD, std::vector<int64_t>> a2({{0, 1, 2, 3}}, {0, 0, 0});
+  std::tuple<LoD, std::vector<int64_t>> a3({{0, 1, 2, 3}}, {30, 0, 40});
+  std::tuple<LoD, std::vector<int64_t>> a4({{0, 1}}, {0});
+  std::vector<std::tuple<LoD, std::vector<int64_t>>> data_slot_6002{a1, a2, a3,
+                                                                    a4};
+
+  std::tuple<LoD, std::vector<int64_t>> b1({{0, 1, 4, 5}}, {1, 5, 6, 7, 0});
+  std::tuple<LoD, std::vector<int64_t>> b2({{0, 4, 5, 6}},
+                                           {15, 16, 17, 18, 0, 0});
+  std::tuple<LoD, std::vector<int64_t>> b3({{0, 1, 3, 4}}, {31, 35, 36, 41});
+  std::tuple<LoD, std::vector<int64_t>> b4({{0, 3}}, {47, 48, 49});
+  std::vector<std::tuple<LoD, std::vector<int64_t>>> data_slot_6003{b1, b2, b3,
+                                                                    b4};
+
+  std::vector<DDim> label_dims = {{1, 3}, {1, 3}, {1, 3}, {1, 1}};
+
+  LoDTensorBlockingQueueHolder queue_holder;
+  int capacity = 64;
+  queue_holder.InitOnce(capacity, {}, false);
+
+  std::shared_ptr<LoDTensorBlockingQueue> queue = queue_holder.GetQueue();
+
+  int batch_size = 3;
+  int thread_num = 1;
+  std::vector<std::string> slots = {"6002", "6003"};
+  std::vector<std::string> file_list;
+  for (int i = 0; i < thread_num; ++i) {
+    file_list.push_back(gz_file_name);
+  }
+
+  CTRReader reader(queue, batch_size, thread_num, slots, file_list);
+
+  reader.Start();
+  size_t batch_num =
+      std::ceil(static_cast<float>(ctr_data.size()) / batch_size) * thread_num;
+  check_all_data(ctr_data, slots, label_dims, label_value, data_slot_6002,
+                 data_slot_6003, batch_num, batch_size, queue, &reader);
+
+  reader.Shutdown();
+
+  reader.Start();
+  check_all_data(ctr_data, slots, label_dims, label_value, data_slot_6002,
+                 data_slot_6003, batch_num, batch_size, queue, &reader);
+  reader.Shutdown();
+}

paddle/fluid/platform/float16.h

@@ -1039,6 +1039,11 @@ HOSTDEVICE inline float16 exp(const float16& a) {
   return float16(::expf(static_cast<float>(a)));
 }
 
+template <>
+HOSTDEVICE inline float16 erf(const float16& a) {
+  return float16(::erff(static_cast<float>(a)));
+}
+
 template <>
 HOSTDEVICE inline float16 log(const float16& a) {
   return float16(::logf(static_cast<float>(a)));

paddle/fluid/platform/gpu_info.cc

@@ -20,12 +20,12 @@ limitations under the License. */
 #include "paddle/fluid/platform/enforce.h"
 
 #ifndef _WIN32
-const float fraction_of_gpu_memory_to_use = 0.92f;
+constexpr static float fraction_of_gpu_memory_to_use = 0.92f;
 #else
 // fraction_of_gpu_memory_to_use cannot be too high on windows,
 // since the win32 graphic sub-system can occupy some GPU memory
 // which may lead to insufficient memory left for paddle
-const float fraction_of_gpu_memory_to_use = 0.5f;
+constexpr static float fraction_of_gpu_memory_to_use = 0.5f;
 #endif
 
 DEFINE_double(fraction_of_gpu_memory_to_use, fraction_of_gpu_memory_to_use,

paddle/fluid/platform/mkldnn_helper.h

@@ -14,6 +14,7 @@ limitations under the License. */
 #pragma once
 
 #include <mkldnn.h>
+#include <algorithm>
 #include <string>
 #include <vector>
 #include "paddle/fluid/framework/operator.h"
@@ -292,5 +293,21 @@ inline mkldnn::memory::format data_format_to_memory_format(
   }
 }
 
+inline mkldnn::memory::format StringToMKLDNNFormat(std::string* format) {
+  std::transform(format->begin(), format->end(), format->begin(), ::tolower);
+
+  if (!format->compare("nchw")) {
+    return mkldnn::memory::format::nchw;
+  } else if (!format->compare("nchw16c")) {
+    return mkldnn::memory::format::nChw16c;
+  } else if (!format->compare("nchw8c")) {
+    return mkldnn::memory::format::nChw8c;
+  } else if (!format->compare("nhwc")) {
+    return mkldnn::memory::format::nhwc;
+  } else {
+    return mkldnn::memory::format::any;
+  }
+}
+
 }  // namespace platform
 }  // namespace paddle

paddle/fluid/pybind/protobuf.cc

@@ -29,8 +29,16 @@ limitations under the License. */
 namespace pybind11 {
 namespace detail {
 
+#if !defined(PYBIND11_HIDDEN)
+#ifdef _WIN32
+#define PYBIND11_HIDDEN __declspec(dllexport)
+#else
+#define PYBIND11_HIDDEN __attribute__((visibility("hidden")))
+#endif
+#endif
+
 // Can be replaced by a generic lambda in C++14
-struct __attribute__((visibility("hidden"))) paddle_variant_caster_visitor
+struct PYBIND11_HIDDEN paddle_variant_caster_visitor
     : public boost::static_visitor<handle> {
   return_value_policy policy;
   handle parent;

paddle/fluid/pybind/pybind.cc

@@ -860,6 +860,12 @@ All parameter, weight, gradient are variables in Paddle.
             self.remove_unnecessary_lock_ = b;
           },
           R"DOC(The type is BOOL. If set True, some locks in GPU ops would be released and ParallelExecutor would run faster. Default False.)DOC")
+      .def_property(
+          "num_trainers",
+          [](const BuildStrategy &self) { return self.num_trainers_; },
+          [](BuildStrategy &self, int num_trainers) {
+            self.num_trainers_ = num_trainers;
+          })
       .def_property(
           "fuse_elewise_add_act_ops",
           [](const BuildStrategy &self) {

paddle/legacy/cuda/src/hl_cuda_device.cc

@@ -137,10 +137,10 @@ inline pid_t gettid() {
 #define __NR_gettid 224
 #endif
   pid_t tid = syscall(__NR_gettid);
-#endif
 #else   // _WIN32
   pid_t tid = _getpid();
 #endif  // _WIN32
+#endif
   CHECK_NE((int)tid, -1);
   return tid;
 }

paddle/scripts/paddle_build.sh

@@ -469,18 +469,21 @@ function assert_api_spec_approvals() {
         BRANCH="develop"
     fi
 
-    API_CHANGE=`git diff --name-only upstream/$BRANCH | grep "paddle/fluid/API.spec" || true`
-    echo "checking API.spec change, PR: ${GIT_PR_ID}, changes: ${API_CHANGE}"
-    if [ ${API_CHANGE} ] && [ "${GIT_PR_ID}" != "" ]; then
-        # NOTE: per_page=10000 should be ok for all cases, a PR review > 10000 is not human readable.
-        APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \
-        python ${PADDLE_ROOT}/tools/check_pr_approval.py 2 7845005 2887803 728699 13348433`
-        echo "current pr ${GIT_PR_ID} got approvals: ${APPROVALS}"
-        if [ "${APPROVALS}" == "FALSE" ]; then
-            echo "You must have at least 2 approvals for the api change!"
-        exit 1
-        fi
-    fi
+    API_FILES=("paddle/fluid/API.spec" "paddle/fluid/framework/operator.h")
+    for API_FILE in ${API_FILES[*]}; do
+      API_CHANGE=`git diff --name-only upstream/$BRANCH | grep "${API_FILE}" || true`
+      echo "checking ${API_FILE} change, PR: ${GIT_PR_ID}, changes: ${API_CHANGE}"
+      if [ ${API_CHANGE} ] && [ "${GIT_PR_ID}" != "" ]; then
+          # NOTE: per_page=10000 should be ok for all cases, a PR review > 10000 is not human readable.
+          APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \
+          python ${PADDLE_ROOT}/tools/check_pr_approval.py 2 7845005 2887803 728699 13348433`
+          echo "current pr ${GIT_PR_ID} got approvals: ${APPROVALS}"
+          if [ "${APPROVALS}" == "FALSE" ]; then
+              echo "You must have at least 2 approvals for the api change! ${API_FILE}"
+          exit 1
+          fi
+      fi
+    done
 }
 
 

python/paddle/fluid/contrib/reader/ctr_reader.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
+
+from paddle.fluid import core
+from paddle.fluid.executor import global_scope
+from paddle.fluid.framework import default_main_program, \
+    default_startup_program, Variable
+from paddle.fluid.unique_name import generate as unique_name
+
+
+def monkey_patch_reader_methods(reader):
+    def __get_reader__():
+        scope = global_scope()
+        var = scope.find_var(reader.name)
+        return var.get_reader()
+
+    def reset():
+        return __get_reader__().reset()
+
+    reader.reset = reset
+    reader.stop_gradient = True
+    reader.persistable = True
+    return reader
+
+
+def _copy_reader_var_(block, var):
+    new_var = block.create_var(name=var.name, type=core.VarDesc.VarType.READER)
+    new_var.desc.set_shapes(var.desc.shapes())
+    new_var.desc.set_dtypes(var.desc.dtypes())
+    new_var.persistable = True
+    return new_var
+
+
+def ctr_reader(feed_data,
+               capacity,
+               thread_num,
+               batch_size,
+               file_list,
+               slots,
+               name=None):
+    """
+    Create a CTR reader for data feeding in Python
+
+    This layer returns a Reader Variable.
+    The Reader provides :code:`decorate_paddle_reader()` and
+    :code:`decorate_tensor_provider()` to set a Python generator as the data
+    source in Python side. When :code:`Executor::Run()` is invoked in C++
+    side, the data from the generator would be read automatically. Unlike
+    :code:`DataFeeder.feed()`, the data reading process and
+    :code:`Executor::Run()` process can run in parallel using
+    :code:`py_reader`. The :code:`start()` method of the Reader should be
+    called when each pass begins, while the :code:`reset()` method should be
+    called when the pass ends and :code:`fluid.core.EOFException` raises.
+    Note that :code:`Program.clone()` method cannot clone :code:`py_reader`.
+
+    Args:
+       capacity(int): The buffer capacity maintained by :code:`py_reader`.
+       thread_num(list|tuple): List of tuples which declaring data shapes.
+       batch_size(list|tuple): List of strs which declaring data type.
+       file_list(list|tuple): List of ints which declaring data lod_level.
+       slots(bool): Whether use double buffer or not.
+       name(basestring): The prefix Python queue name and Reader name. None will
+            be generated automatically.
+
+    Returns:
+       Variable: A Reader from which we can get feeding data.
+
+    Examples:
+
+        1. The basic usage of :code:`py_reader` is as follows:
+    """
+    if name is None:
+        queue_name = unique_name('lod_tensor_blocking_queue')
+        reader_name = unique_name('create_ctr_reader')
+    else:
+        queue_name = "_".join([name, "queue"])
+        reader_name = "_".join([name, "reader"])
+
+    var = global_scope().var(queue_name)
+    feed_queue = core.init_lod_tensor_blocking_queue(var, capacity, shapes)
+
+    startup_blk = default_startup_program().current_block()
+    reader_var = startup_blk.create_var(name=reader_name)
+    startup_blk.append_op(
+        type='create_ctr_reader',
+        inputs={'blocking_queue': [queue_name]},
+        outputs={'Out': [reader_var]},
+        attrs={
+            'thread_num': thread_num,
+            'batch_size': batch_size,
+            'file_list': file_list,
+            'slots': slots,
+        })
+
+    reader_var.persistable = True
+
+    main_prog_reader_var = _copy_reader_var_(
+        default_main_program().current_block(), reader_var)
+
+    reader = monkey_patch_reader_methods(main_prog_reader_var)
+
+    # monkey patch py_reader special methods
+    reader.queue = feed_queue
+    reader.exited = False
+
+    main_blk = default_main_program().current_block()
+    main_blk.append_op(
+        type='read', inputs={'Reader': [reader]}, outputs={'Out': feed_data})
+
+    return reader

python/paddle/fluid/io.py

@@ -637,8 +637,8 @@ def save_inference_model(dirname,
     if isinstance(target_vars, Variable):
         target_vars = [target_vars]
     elif export_for_deployment:
-        if not (bool(target_vars) and all(
-                isinstance(var, Variable) for var in target_vars)):
+        if not (bool(target_vars) and
+                all(isinstance(var, Variable) for var in target_vars)):
             raise ValueError("'target_vars' should be a list of Variable.")
 
     if main_program is None:
@@ -667,10 +667,15 @@ def save_inference_model(dirname,
     if export_for_deployment:
         main_program = main_program.clone()
         global_block = main_program.global_block()
+        need_to_remove_op_index = []
         for i, op in enumerate(global_block.ops):
             op.desc.set_is_target(False)
             if op.type == "feed" or op.type == "fetch":
-                global_block._remove_op(i)
+                need_to_remove_op_index.append(i)
+
+        for index in need_to_remove_op_index[::-1]:
+            global_block._remove_op(index)
+
         main_program.desc.flush()
 
         main_program = main_program._prune(targets=target_vars)

python/paddle/fluid/layers/nn.py

@@ -4394,7 +4394,8 @@ def nce(input,
         name=None,
         sampler="uniform",
         custom_dist=None,
-        seed=0):
+        seed=0,
+        is_sparse=False):
     """
     ${comment}
 
@@ -4420,11 +4421,12 @@ def nce(input,
         sampler (str): The sampler used to sample class from negtive classes.
                        It can be 'uniform', 'log_uniform' or 'custom_dist'.
                        default: 'uniform'.
-        custom_dist (Variable): A tensor with shape [num_total_classes].
+        custom_dist (float[]): A float[] with size=num_total_classes.
                        It is used when sampler is set to 'custom_dist'.
                        custom_dist[i] is the probsbility of i-th class to be sampled.
                        default: None.
         seed (int): The seed used in sampler. default: 0.
+        is_sparse(bool): The flag indicating whether to use sparse update, the weight@GRAD and bias@GRAD will be changed to SelectedRows.
 
     Returns:
         Variable: The output nce loss.
@@ -4476,12 +4478,7 @@ def nce(input,
         shape=[num_total_classes, dim],
         is_bias=False,
         dtype=input.dtype)
-    inputs = {
-        'Input': input,
-        'Label': label,
-        'Weight': w,
-        'SampleWeight': sample_weight if sample_weight is not None else []
-    }
+    inputs = {}
     if helper.bias_attr:
         b = helper.create_parameter(
             attr=helper.bias_attr,
@@ -4493,18 +4490,10 @@ def nce(input,
     sample_logits = helper.create_variable_for_type_inference(dtype=input.dtype)
     sample_labels = helper.create_variable_for_type_inference(dtype=label.dtype)
 
-    if num_neg_samples is None:
-        num_neg_samples = 10
-    else:
-        num_neg_samples = int(num_neg_samples)
-
-    inputs = {
-        'Input': input,
-        'Label': label,
-        'Weight': w,
-        'Bias': b,
-        'SampleWeight': sample_weight if sample_weight is not None else []
-    }
+    inputs['Input'] = input
+    inputs['Label'] = label
+    inputs['Weight'] = w
+    inputs['SampleWeight'] = sample_weight if sample_weight is not None else []
 
     if sampler == "uniform":
         sampler = 0
@@ -4512,17 +4501,73 @@ def nce(input,
         sampler = 1
     elif sampler == "custom_dist":
         assert custom_dist is not None
-        assert isinstance(custom_dist, Variable)
-        inputs['CustomDistribution'] = custom_dist
+        # assert isinstance(custom_dist, Variable)
+
+        custom_dist_len = len(custom_dist)
+        alias_probs_ = [0] * custom_dist_len
+        alias_ = [0] * custom_dist_len
+        bigs = []
+        littles = []
+        for i in range(custom_dist_len):
+            normal_prob = custom_dist[i] * custom_dist_len
+            if normal_prob - 1.0 > 1e-4:
+                bigs.append((i, normal_prob))
+            elif 1.0 - normal_prob > 1e-4:
+                littles.append((i, normal_prob))
+            else:
+                alias_probs_[i] = normal_prob
+                alias_[i] = -1
+
+        while len(bigs) and len(littles):
+            big = bigs.pop(0)
+            little = littles.pop(0)
+
+            big_idx = big[0]
+            big_prob = big[1]
+
+            alias_probs_[little[0]] = little[1]
+            alias_[little[0]] = big_idx
+            big_left = big[1] + little[1] - 1
+            if big_left - 1.0 > 1e-4:
+                bigs.append((big_idx, big_left))
+            elif 1.0 - big_left > 1e-4:
+                littles.append((big_idx, big_left))
+            else:
+                alias_probs_[big_idx] = big_left
+                alias_[big_idx] = -1
+
+        if len(bigs):
+            big = bigs.pop(0)
+            alias_probs_[big[0]] = 1.0
+            alias_[big[0]] = -1
+        if len(littles):
+            little = littles.pop(0)
+            alias_probs_[little[0]] = 1.0
+            alias_[little[0]] = -1
+
+        probs = assign(input=np.array(custom_dist).astype('float32'))
+        custom_alias = assign(input=np.array(alias_).astype('int32'))
+        custom_alias_probs = assign(
+            input=np.array(alias_probs_).astype('float32'))
+
+        inputs['CustomDistProbs'] = probs
+        inputs['CustomDistAlias'] = custom_alias
+        inputs['CustomDistAliasProbs'] = custom_alias_probs
         sampler = 2
     else:
         raise Exception("Unsupported sampler type.")
 
+    if num_neg_samples is None:
+        num_neg_samples = 10
+    else:
+        num_neg_samples = int(num_neg_samples)
+
     attrs = {
         'num_total_classes': int(num_total_classes),
         'num_neg_samples': num_neg_samples,
         'seed': seed,
-        'sampler': sampler
+        'sampler': sampler,
+        'is_sparse': is_sparse
     }
 
     helper.append_op(
@@ -4542,27 +4587,43 @@ def hsigmoid(input,
              num_classes,
              param_attr=None,
              bias_attr=None,
-             name=None):
+             name=None,
+             path_table=None,
+             path_code=None,
+             is_custom=False,
+             is_sparse=False):
     """
     The hierarchical sigmoid operator is used to accelerate the training
     process of language model. This operator organizes the classes into a
-    complete binary tree, each leaf node represents a class(a word) and each
+    complete binary tree, or you can use is_custom to pass your own tree to 
+    implement hierarchical. Each leaf node represents a class(a word) and each
     internal node acts as a binary classifier. For each word there's a unique
     path from root to it's leaf node, hsigmoid calculate the cost for each
     internal node on the path, and sum them to get a total cost. hsigmoid can
     achive a acceleration from :math:`O(N)` to :math:`O(logN)`, where :math:`N`
     represents the size of word dict.
 
-    Refer to `Hierarchical Probabilistic Neural Network Language Model
+    Using default tree you can Refer to `Hierarchical Probabilistic Neural Network Language Model
     <http://www.iro.umontreal.ca/~lisa/pointeurs/hierarchical-nnlm-aistats05.pdf>`_
 
+    And if you want to use the costumed tree by set 'is_custom' as true you may need to do following things first:
+        1. using your word dict to build a binary tree, each leaf node should be an word of your word dict
+        2. build a dict to store word_id -> word's leaf to root path, we call it path_table.
+        3. build a dict to store word_id -> code of word's leaf to root path, we call it path_code. Code
+         means label of each binary classification, using 1 indicate true, 0 indicate false.
+        4. now, each word should has its path and code along the path, you can pass a batch of path and code 
+        related to the same batch of inputs.
+
+
     Args:
         input (Variable): The input tensor variable with shape
             :math:`[N \\times D]`, where :math:`N` is the size of mini-batch,
             and :math:`D` is the feature size.
         label (Variable): The tensor variable contains labels of training data.
             It's a tensor with shape is :math:`[N \\times 1]`.
-        num_classes: (int), The number of classes, must not be less than 2.
+        num_classes: (int), The number of classes, must not be less than 2. with default tree this has to be set, 
+            it should never be None under is_custom=False, but while is_custom is true, it should be non leaf num 
+            which indicates the num of classes using by binary classify.
         param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
              of hsigmoid. If it is set to None or one attribute of ParamAttr, hsigmoid
              will create ParamAttr as param_attr. If the Initializer of the param_attr
@@ -4574,9 +4635,19 @@ def hsigmoid(input,
              is not set, the bias is initialized zero. Default: None.
         name (str|None): A name for this layer(optional). If set None, the layer
              will be named automatically. Default: None.
+        path_table: (Variable|None) this variable can store each batch of samples' path to root, 
+            it should be in leaf -> root order
+            path_table should have the same shape with path_code, and for each sample i path_table[i] indicates a np.array like 
+            structure and each element in this array is indexes in parent nodes' Weight Matrix. 
+        path_code:  (Variable|None) this variable can store each batch of samples' code, 
+            each code consist with every code of parent nodes. it should be in leaf -> root order
+        is_custom: (bool|False)using user defined binary tree instead of default complete binary tree, if costum is 
+             set you need to set path_table/path_code/num_classes, otherwise num_classes should be set
+        is_sparse: (bool|False)using sparse update instead of dense update, if set, the gradient 
+             of W and input will be sparse.
 
     Returns:
-        Out: (Tensor) The cost of hierarchical sigmoid operator. the shape is [N, 1]
+        Out: (LodTensor) The cost of hierarchical sigmoid operator. the shape is [N, 1]
 
     Examples:
 
@@ -4592,27 +4663,62 @@ def hsigmoid(input,
     out = helper.create_variable_for_type_inference(dtype)
     pre_out = helper.create_variable_for_type_inference(dtype)
     dim = input.shape[1]
-    if num_classes < 2:
-        raise ValueError("num_classes must not be less than 2.")
-    weights = helper.create_parameter(
-        attr=helper.param_attr,
-        shape=[num_classes - 1, dim],
-        is_bias=False,
-        dtype=input.dtype)
-    inputs = {"X": input, "W": weights, "Label": label}
-    if helper.bias_attr:
-        bias = helper.create_parameter(
-            attr=helper.bias_attr,
-            shape=[1, num_classes - 1],
-            is_bias=True,
+    if ((num_classes is None) or (num_classes < 2)) and (not is_custom):
+        raise ValueError(
+            "num_classes must not be less than 2 with default tree")
+
+    if (is_custom) and (path_code is None):
+        raise ValueError("path_code should not be None with costum tree")
+    elif (is_custom) and (path_table is None):
+        raise ValueError("path_table should not be None with costum tree")
+    elif (is_custom) and (num_classes is None):
+        raise ValueError("num_classes should not be None with costum tree")
+    else:
+        pass
+
+    weights = None
+
+    if not is_custom:
+        weights = helper.create_parameter(
+            attr=helper.param_attr,
+            shape=[num_classes - 1, dim],
+            is_bias=False,
             dtype=input.dtype)
-        inputs['Bias'] = bias
+    else:
+        weights = helper.create_parameter(
+            attr=helper.param_attr,
+            shape=[num_classes, dim],
+            is_bias=False,
+            dtype=input.dtype)
+    inputs = {
+        "X": input,
+        "W": weights,
+        "PTable": path_table,
+        "PathCode": path_code,
+        "Label": label
+    }
+    if helper.bias_attr:
+        if not is_custom:
+            bias = helper.create_parameter(
+                attr=helper.bias_attr,
+                shape=[num_classes - 1, 1],
+                is_bias=True,
+                dtype=input.dtype)
+            inputs['Bias'] = bias
+        else:
+            bias = helper.create_parameter(
+                attr=helper.bias_attr,
+                shape=[num_classes, 1],
+                is_bias=True,
+                dtype=input.dtype)
+            inputs['Bias'] = bias
     helper.append_op(
         type="hierarchical_sigmoid",
         inputs=inputs,
         outputs={"Out": out,
                  "PreOut": pre_out},
-        attrs={"num_classes": num_classes})
+        attrs={"num_classes": num_classes,
+               "is_sparse": is_sparse})
     return out
 
 
@@ -5870,9 +5976,10 @@ def image_resize(input,
         raise ValueError(
             "The 'resample' of image_resize can only be 'BILINEAR' or 'NEAREST' currently."
         )
+    resample_type = resample_methods[resample]
     if out_shape is None and scale is None:
         raise ValueError("One of out_shape and scale must not be None.")
-    helper = LayerHelper('interpolate', **locals())
+    helper = LayerHelper('{}_interp'.format(resample_type), **locals())
     dtype = helper.input_dtype()
 
     def _is_list_or_turple_(data):
@@ -5906,18 +6013,16 @@ def image_resize(input,
 
     out = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
-        type='interpolate',
+        type='{}_interp'.format(resample_type),
         inputs=inputs,
         outputs={"Out": out},
-        attrs={
-            "out_h": out_h,
-            "out_w": out_w,
-            "interp_method": resample_methods[resample]
-        })
+        attrs={"out_h": out_h,
+               "out_w": out_w,
+               "interp_method": resample_type})
     return out
 
 
-@templatedoc(op_type="interpolate")
+@templatedoc(op_type="bilinear_interp")
 def resize_bilinear(input,
                     out_shape=None,
                     scale=None,
@@ -5973,7 +6078,7 @@ def resize_bilinear(input,
     return image_resize(input, out_shape, scale, name, 'BILINEAR', actual_shape)
 
 
-@templatedoc(op_type="interpolate")
+@templatedoc(op_type="nearest_interp")
 def resize_nearest(input,
                    out_shape=None,
                    scale=None,
@@ -6475,7 +6580,7 @@ def crop(x, shape=None, offsets=None, name=None):
     helper = LayerHelper('crop', **locals())
 
     if not (isinstance(shape, list) or isinstance(shape, tuple) or \
-                    isinstance(shape, Variable)):
+            isinstance(shape, Variable)):
         raise ValueError("The shape should be a list, tuple or Variable.")
 
     if offsets is None:
@@ -6597,7 +6702,7 @@ def affine_grid(theta, out_shape, name=None):
     helper = LayerHelper('affine_grid')
 
     if not (isinstance(out_shape, list) or isinstance(out_shape, tuple) or \
-        isinstance(out_shape, Variable)):
+            isinstance(out_shape, Variable)):
         raise ValueError("The out_shape should be a list, tuple or Variable.")
 
     if not isinstance(theta, Variable):
@@ -6838,6 +6943,13 @@ def elu(x, alpha=1.0, name=None):
 
     Returns:
         output(${out_type}): ${out_comment}
+
+    Examples:
+
+        .. code-block:: python
+
+            x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32")
+            y = fluid.layers.elu(x, alpha=0.2)
     """
     helper = LayerHelper('elu', **locals())
     out = helper.create_variable_for_type_inference(dtype=x.dtype)
@@ -6861,6 +6973,13 @@ def relu6(x, threshold=6.0, name=None):
 
     Returns:
         output(${out_type}): ${out_comment}
+
+    Examples:
+
+        .. code-block:: python
+
+            x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32")
+            y = fluid.layers.relu6(x, threshold=6.0)
     """
     helper = LayerHelper('relu6', **locals())
     out = helper.create_variable_for_type_inference(dtype=x.dtype)
@@ -6884,6 +7003,13 @@ def pow(x, factor=1.0, name=None):
 
     Returns:
         output(${out_type}): ${out_comment}
+
+    Examples:
+
+        .. code-block:: python
+
+            x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32")
+            y = fluid.layers.pow(x, factor=2.0)
     """
     helper = LayerHelper('pow', **locals())
     out = helper.create_variable_for_type_inference(dtype=x.dtype)
@@ -6908,6 +7034,13 @@ def stanh(x, scale_a=2.0 / 3.0, scale_b=1.7159, name=None):
 
     Returns:
         output(${out_type}): ${out_comment}
+
+    Examples:
+
+        .. code-block:: python
+
+            x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32")
+            y = fluid.layers.stanh(x, scale_a=0.67, scale_b=1.72)
     """
     helper = LayerHelper('stanh', **locals())
     out = helper.create_variable_for_type_inference(dtype=x.dtype)
@@ -6933,6 +7066,13 @@ def hard_sigmoid(x, slope=0.2, offset=0.5, name=None):
 
     Returns:
         output(${out_type}): ${out_comment}
+
+    Examples:
+
+        .. code-block:: python
+
+            x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32")
+            y = fluid.layers.hard_sigmoid(x, slope=0.3, offset=0.8)
     """
     helper = LayerHelper('hard_sigmoid', **locals())
     out = helper.create_variable_for_type_inference(dtype=x.dtype)
@@ -6957,6 +7097,13 @@ def swish(x, beta=1.0, name=None):
 
     Returns:
         output(${out_type}): ${out_comment}
+
+    Examples:
+
+        .. code-block:: python
+
+            x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32")
+            y = fluid.layers.swish(x, beta=2.0)
     """
     helper = LayerHelper('swish', **locals())
     out = helper.create_variable_for_type_inference(dtype=x.dtype)

python/paddle/fluid/parallel_executor.py

@@ -124,16 +124,11 @@ class ParallelExecutor(object):
                     os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
                 exec_strategy.num_threads = cpu_num * 2
 
-        # Set 1 thread num under nccl2 distribute 
-        #   env to make sure all gpus run ops in same order.
-        if num_trainers > 1:
-            assert (use_cuda)
-            # FIXME(gongwb): avoid this set.
-            exec_strategy.num_threads = 1
-
         if build_strategy is None:
             build_strategy = BuildStrategy()
 
+        build_strategy.num_trainers = num_trainers
+
         main = main_program
         main = main if main else framework.default_main_program()
         if scope == None:

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

@@ -63,7 +63,7 @@ function(py_test_modules TARGET_NAME)
     set(multiValueArgs MODULES DEPS ENVS)
     cmake_parse_arguments(py_test_modules "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
     add_test(NAME ${TARGET_NAME}
-             COMMAND env PYTHONPATH=${PADDLE_BINARY_DIR}/python ${py_test_modules_ENVS}
+             COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${PADDLE_BINARY_DIR}/python ${py_test_modules_ENVS}
              ${PYTHON_EXECUTABLE} ${PADDLE_SOURCE_DIR}/tools/test_runner.py ${py_test_modules_MODULES}
              WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
     if (py_test_modules_SERIAL)
@@ -81,12 +81,14 @@ list(REMOVE_ITEM TEST_OPS test_dist_se_resnext)
 list(REMOVE_ITEM TEST_OPS test_dist_transformer)
 list(REMOVE_ITEM TEST_OPS test_parallel_executor_transformer)
 list(REMOVE_ITEM TEST_OPS test_image_classification_resnet)
-list(REMOVE_ITEM TEST_OPS test_interpolate_op)
+list(REMOVE_ITEM TEST_OPS test_bilinear_interp_op)
+list(REMOVE_ITEM TEST_OPS test_nearest_interp_op)
 foreach(TEST_OP ${TEST_OPS})
     py_test_modules(${TEST_OP} MODULES ${TEST_OP})
 endforeach(TEST_OP)
 py_test_modules(test_warpctc_op MODULES test_warpctc_op ENVS FLAGS_warpctc_dir=${WARPCTC_LIB_DIR} SERIAL)
-py_test_modules(test_interpolate_op MODULES test_interpolate_op SERIAL)
+py_test_modules(test_bilinear_interp_op MODULES test_bilinear_interp_op SERIAL)
+py_test_modules(test_nearest_interp_op MODULES test_nearest_interp_op SERIAL)
 if(WITH_DISTRIBUTE)
     py_test_modules(test_dist_train MODULES test_dist_train SERIAL)
     set_tests_properties(test_listen_and_serv_op PROPERTIES TIMEOUT 20)

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

@@ -18,7 +18,7 @@ import unittest
 import numpy as np
 import paddle.fluid.core as core
 from op_test import OpTest
-from scipy.special import expit
+from scipy.special import expit, erf
 
 
 class TestActivation(OpTest):
@@ -295,6 +295,23 @@ class TestRelu(TestActivation):
         self.check_grad(['X'], 'Out', max_relative_error=0.007)
 
 
+class TestGelu(TestActivation):
+    def setUp(self):
+        self.op_type = "gelu"
+        self.init_dtype()
+
+        x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
+        out = 0.5 * x * (1.0 + erf(x / np.sqrt(2.0)))
+
+        self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
+        self.outputs = {'Out': out}
+
+    def test_check_grad(self):
+        if self.dtype == np.float16:
+            return
+        self.check_grad(['X'], 'Out', max_relative_error=0.007)
+
+
 class TestBRelu(TestActivation):
     def setUp(self):
         self.op_type = "brelu"
@@ -628,6 +645,7 @@ create_test_act_fp16_class(TestCos, grad_atol=0.85)
 create_test_act_fp16_class(TestSin)
 create_test_act_fp16_class(TestRound, grad_check=False)
 create_test_act_fp16_class(TestRelu)
+create_test_act_fp16_class(TestGelu)
 create_test_act_fp16_class(TestBRelu)
 create_test_act_fp16_class(TestRelu6)
 create_test_act_fp16_class(TestSoftRelu)

python/paddle/fluid/tests/unittests/test_interpolate_op.py → python/paddle/fluid/tests/unittests/test_bilinear_interp_op.py

@@ -20,36 +20,6 @@ from op_test import OpTest
 import paddle.fluid.core as core
 
 
-def nearest_neighbor_interp_np(X,
-                               out_h,
-                               out_w,
-                               out_size=None,
-                               actual_shape=None):
-    """nearest neighbor interpolation implement in shape [N, C, H, W]"""
-    if out_size is not None:
-        out_h = out_size[0]
-        out_w = out_size[1]
-    if actual_shape is not None:
-        out_h = actual_shape[0]
-        out_w = actual_shape[1]
-    n, c, in_h, in_w = X.shape
-
-    ratio_h = ratio_w = 0.0
-    if out_h > 1:
-        ratio_h = (in_h - 1.0) / (out_h - 1.0)
-    if out_w > 1:
-        ratio_w = (in_w - 1.0) / (out_w - 1.0)
-
-    out = np.zeros((n, c, out_h, out_w))
-    for i in range(out_h):
-        in_i = int(ratio_h * i + 0.5)
-        for j in range(out_w):
-            in_j = int(ratio_w * j + 0.5)
-            out[:, :, i, j] = X[:, :, in_i, in_j]
-
-    return out.astype(X.dtype)
-
-
 def bilinear_interp_np(input, out_h, out_w, out_size=None, actual_shape=None):
     """bilinear interpolation implement in shape [N, C, H, W]"""
     if out_size is not None:
@@ -87,22 +57,16 @@ def bilinear_interp_np(input, out_h, out_w, out_size=None, actual_shape=None):
     return out.astype(input.dtype)
 
 
-INTERPOLATE_FUNCS = {
-    'bilinear': bilinear_interp_np,
-    'nearest': nearest_neighbor_interp_np,
-}
-
-
-class TestInterpolateOp(OpTest):
+class TestBilinearInterpOp(OpTest):
     def setUp(self):
         self.out_size = None
         self.actual_shape = None
         self.init_test_case()
-        self.op_type = "interpolate"
+        self.op_type = "bilinear_interp"
         input_np = np.random.random(self.input_shape).astype("float32")
 
-        output_np = INTERPOLATE_FUNCS[self.interp_method](
-            input_np, self.out_h, self.out_w, self.out_size, self.actual_shape)
+        output_np = bilinear_interp_np(input_np, self.out_h, self.out_w,
+                                       self.out_size, self.actual_shape)
         self.inputs = {'X': input_np}
         if self.out_size is not None:
             self.inputs['OutSize'] = self.out_size
@@ -129,7 +93,7 @@ class TestInterpolateOp(OpTest):
         self.out_size = np.array([3, 3]).astype("int32")
 
 
-class TestBilinearInterpCase1(TestInterpolateOp):
+class TestBilinearInterpCase1(TestBilinearInterpOp):
     def init_test_case(self):
         self.interp_method = 'bilinear'
         self.input_shape = [4, 1, 7, 8]
@@ -137,7 +101,7 @@ class TestBilinearInterpCase1(TestInterpolateOp):
         self.out_w = 1
 
 
-class TestBilinearInterpCase2(TestInterpolateOp):
+class TestBilinearInterpCase2(TestBilinearInterpOp):
     def init_test_case(self):
         self.interp_method = 'bilinear'
         self.input_shape = [3, 3, 9, 6]
@@ -145,7 +109,7 @@ class TestBilinearInterpCase2(TestInterpolateOp):
         self.out_w = 12
 
 
-class TestBilinearInterpCase3(TestInterpolateOp):
+class TestBilinearInterpCase3(TestBilinearInterpOp):
     def init_test_case(self):
         self.interp_method = 'bilinear'
         self.input_shape = [1, 1, 128, 64]
@@ -153,7 +117,7 @@ class TestBilinearInterpCase3(TestInterpolateOp):
         self.out_w = 128
 
 
-class TestBilinearInterpCase4(TestInterpolateOp):
+class TestBilinearInterpCase4(TestBilinearInterpOp):
     def init_test_case(self):
         self.interp_method = 'bilinear'
         self.input_shape = [4, 1, 7, 8]
@@ -162,7 +126,7 @@ class TestBilinearInterpCase4(TestInterpolateOp):
         self.out_size = np.array([2, 2]).astype("int32")
 
 
-class TestBilinearInterpCase5(TestInterpolateOp):
+class TestBilinearInterpCase5(TestBilinearInterpOp):
     def init_test_case(self):
         self.interp_method = 'bilinear'
         self.input_shape = [3, 3, 9, 6]
@@ -171,7 +135,7 @@ class TestBilinearInterpCase5(TestInterpolateOp):
         self.out_size = np.array([11, 11]).astype("int32")
 
 
-class TestBilinearInterpCase6(TestInterpolateOp):
+class TestBilinearInterpCase6(TestBilinearInterpOp):
     def init_test_case(self):
         self.interp_method = 'bilinear'
         self.input_shape = [1, 1, 128, 64]
@@ -180,7 +144,7 @@ class TestBilinearInterpCase6(TestInterpolateOp):
         self.out_size = np.array([65, 129]).astype("int32")
 
 
-class TestBilinearInterpActualShape(TestInterpolateOp):
+class TestBilinearInterpActualShape(TestBilinearInterpOp):
     def init_test_case(self):
         self.interp_method = 'bilinear'
         self.input_shape = [3, 2, 32, 16]
@@ -189,25 +153,16 @@ class TestBilinearInterpActualShape(TestInterpolateOp):
         self.out_size = np.array([66, 40]).astype("int32")
 
 
-class TestBilinearInterpBigScale(TestInterpolateOp):
-    def init_test_case(self):
-        self.interp_method = 'bilinear'
-        self.input_shape = [4, 4, 64, 32]
-        self.out_h = 100
-        self.out_w = 50
-        self.out_size = np.array([101, 51]).astype('int32')
-
-
-class TestInterpolateOpUint8(OpTest):
+class TestBilinearInterpOpUint8(OpTest):
     def setUp(self):
         self.out_size = None
         self.actual_shape = None
         self.init_test_case()
-        self.op_type = "interpolate"
+        self.op_type = "bilinear_interp"
         input_np = np.random.randint(
             low=0, high=256, size=self.input_shape).astype("uint8")
-        output_np = INTERPOLATE_FUNCS[self.interp_method](
-            input_np, self.out_h, self.out_w, self.out_size, self.actual_shape)
+        output_np = bilinear_interp_np(input_np, self.out_h, self.out_w,
+                                       self.out_size, self.actual_shape)
         self.inputs = {'X': input_np}
         if self.out_size is not None:
             self.inputs['OutSize'] = self.out_size
@@ -228,7 +183,7 @@ class TestInterpolateOpUint8(OpTest):
         self.out_w = 9
 
 
-class TestBilinearInterpCase1Uint8(TestInterpolateOpUint8):
+class TestBilinearInterpCase1Uint8(TestBilinearInterpOpUint8):
     def init_test_case(self):
         self.interp_method = 'bilinear'
         self.input_shape = [2, 3, 128, 64]
@@ -236,7 +191,7 @@ class TestBilinearInterpCase1Uint8(TestInterpolateOpUint8):
         self.out_w = 50
 
 
-class TestBilinearInterpCase2Uint8(TestInterpolateOpUint8):
+class TestBilinearInterpCase2Uint8(TestBilinearInterpOpUint8):
     def init_test_case(self):
         self.interp_method = 'bilinear'
         self.input_shape = [4, 1, 7, 8]
@@ -245,91 +200,5 @@ class TestBilinearInterpCase2Uint8(TestInterpolateOpUint8):
         self.out_size = np.array([6, 15]).astype("int32")
 
 
-class TestNearestNeighborInterpCase1(TestInterpolateOp):
-    def init_test_case(self):
-        self.interp_method = 'nearest'
-        self.input_shape = [4, 1, 7, 8]
-        self.out_h = 1
-        self.out_w = 1
-
-
-class TestNearestNeighborInterpCase2(TestInterpolateOp):
-    def init_test_case(self):
-        self.interp_method = 'nearest'
-        self.input_shape = [3, 3, 9, 6]
-        self.out_h = 12
-        self.out_w = 12
-
-
-class TestNearestNeighborInterpCase3(TestInterpolateOp):
-    def init_test_case(self):
-        self.interp_method = 'nearest'
-        self.input_shape = [1, 1, 128, 64]
-        self.out_h = 64
-        self.out_w = 128
-
-
-class TestNearestNeighborInterpCase4(TestInterpolateOp):
-    def init_test_case(self):
-        self.interp_method = 'nearest'
-        self.input_shape = [4, 1, 7, 8]
-        self.out_h = 1
-        self.out_w = 1
-        self.out_size = np.array([2, 2]).astype("int32")
-
-
-class TestNearestNeighborInterpCase5(TestInterpolateOp):
-    def init_test_case(self):
-        self.interp_method = 'nearest'
-        self.input_shape = [3, 3, 9, 6]
-        self.out_h = 12
-        self.out_w = 12
-        self.out_size = np.array([11, 11]).astype("int32")
-
-
-class TestNearestNeighborInterpCase6(TestInterpolateOp):
-    def init_test_case(self):
-        self.interp_method = 'nearest'
-        self.input_shape = [1, 1, 128, 64]
-        self.out_h = 64
-        self.out_w = 128
-        self.out_size = np.array([65, 129]).astype("int32")
-
-
-class TestNearestNeighborInterpActualShape(TestInterpolateOp):
-    def init_test_case(self):
-        self.interp_method = 'nearest'
-        self.input_shape = [3, 2, 32, 16]
-        self.out_h = 64
-        self.out_w = 32
-        self.out_size = np.array([66, 40]).astype("int32")
-
-
-class TestNearestNeighborInterpBigScale(TestInterpolateOp):
-    def init_test_case(self):
-        self.interp_method = 'nearest'
-        self.input_shape = [4, 4, 64, 32]
-        self.out_h = 100
-        self.out_w = 50
-        self.out_size = np.array([101, 51]).astype('int32')
-
-
-class TestNearestNeighborInterpCase1Uint8(TestInterpolateOpUint8):
-    def init_test_case(self):
-        self.interp_method = 'nearest'
-        self.input_shape = [2, 3, 128, 64]
-        self.out_h = 120
-        self.out_w = 50
-
-
-class TestNearestNeighborInterpCase2Uint8(TestInterpolateOpUint8):
-    def init_test_case(self):
-        self.interp_method = 'nearest'
-        self.input_shape = [4, 1, 7, 8]
-        self.out_h = 5
-        self.out_w = 13
-        self.out_size = np.array([6, 15]).astype("int32")
-
-
 if __name__ == "__main__":
     unittest.main()

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

@@ -16,6 +16,8 @@ from __future__ import print_function
 
 import unittest
 import numpy as np
+import paddle.fluid.core as core
+import paddle.fluid as fluid
 import math
 from op_test import OpTest
 
@@ -40,6 +42,29 @@ class CodeTable(object):
         return self.c & (1 << bit)
 
 
+class CodeTableWithCustomTree(object):
+    def __init__(self, path_table, path_code, index):
+        self.ptable_ = path_table
+        self.pcode_ = path_code
+        self.index_ = index
+
+    def cal_index(self, bit):
+        return self.ptable_[self.index_][bit]
+
+    def get_length(self):
+        length = 0
+        for ele in self.ptable_[self.index_]:  # find the first -1 to stop trace
+
+            if ele >= 0:
+                length = length + 1
+            else:
+                return length
+        return length
+
+    def cal_bit(self, bit):
+        return self.pcode_[self.index_][bit]
+
+
 def hsigmoid(x, w, label, bias, num_classes):
     batch_size = x.shape[0]
     code_length = find_latest_set(num_classes - 1)
@@ -52,7 +77,7 @@ def hsigmoid(x, w, label, bias, num_classes):
         length = code_table.get_length()
         for j in range(length):
             idx = code_table.cal_index(j)
-            pre_output[i][j] += bias[0][idx]
+            pre_output[i][j] += bias[idx][0]
     for i in range(batch_size):
         code_table = CodeTable(num_classes, label[i])
         length = code_table.get_length()
@@ -77,17 +102,58 @@ def hsigmoid(x, w, label, bias, num_classes):
     return pre_output, out
 
 
+def hsigmoidWithCustomTree(x, w, path_table, path_code, label, bias,
+                           num_classes):
+    batch_size = x.shape[0]
+    code_length = len(path_table[0])
+    code_table = [0 for _ in range(code_length)]
+    # init pre_out with shape [N, code_length]
+    pre_output = np.zeros((batch_size, code_length))
+    pre_sum = np.zeros((batch_size, 1))
+    out = np.zeros((batch_size, 1)).astype("float32")
+    if isinstance(bias, np.ndarray):
+        for i in range(batch_size):
+            code_table = CodeTableWithCustomTree(path_table, path_code, i)
+            length = code_table.get_length()
+            for j in range(length):
+                idx = code_table.cal_index(j)
+                pre_output[i][j] += bias[idx][0]
+    for i in range(batch_size):
+        code_table = CodeTableWithCustomTree(path_table, path_code, i)
+        length = code_table.get_length()
+        for j in range(length):
+            idx = code_table.cal_index(j)
+            pre_output[i][j] += np.dot(w[idx], x[i])
+    # clip[-40.0, 40.0]
+    pre_output = np.clip(pre_output, -40.0, 40.0)
+    # out(i, 0) = \sum_j  bit(i, j) * preout(i, j)
+    for i in range(batch_size):
+        code_table = CodeTableWithCustomTree(path_table, path_code, i)
+        length = code_table.get_length()
+        sum = 0.0
+        for j in range(length):
+            if code_table.cal_bit(j):
+                sum += pre_output[i][j]
+        out[i] = -1.0 * sum
+    # soft relu
+    pre_output = np.log(1 + np.exp(pre_output))
+    pre_sum = pre_output.sum(1).reshape((batch_size, 1))
+    out += pre_sum
+    return pre_output, out
+
+
 class TestHSigmoidOp(OpTest):
     def setUp(self):
         self.op_type = "hierarchical_sigmoid"
         num_classes = 6
         feature_size = 8
         batch_size = 4
-        x = np.random.random((batch_size, feature_size)).astype("float32")
-        w = np.random.random((num_classes - 1, feature_size)).astype("float32")
+        x = np.random.random((batch_size, feature_size)).astype("float32") * 2
+        w = np.random.random(
+            (num_classes - 1, feature_size)).astype("float32") * 2
         label = np.random.randint(0, num_classes, (batch_size, 1))
-        bias = np.random.random((1, num_classes - 1)).astype("float32")
-        self.attrs = {'num_classes': num_classes}
+        bias = np.random.random((num_classes - 1, 1)).astype("float32")
+        self.attrs = {'num_classes': num_classes, 'is_sparse': False}
         self.inputs = {'X': x, 'W': w, 'Label': label, 'Bias': bias}
         pre_output, out = hsigmoid(x, w, label, bias, num_classes)
         self.outputs = {'PreOut': pre_output, 'Out': out}
@@ -99,5 +165,185 @@ class TestHSigmoidOp(OpTest):
         self.check_grad(['Bias', 'X', 'W'], ['Out'], no_grad_set=set('Label'))
 
 
+class TestHSigmoidOpSparse(OpTest):
+    def setUp(self):
+        self.op_type = "hierarchical_sigmoid"
+        num_classes = 6  #using 1,2,3,4,5,6 to build a huffman tree and select 1,2,5,6 as sample
+        feature_size = 8
+        batch_size = 4
+        x = np.random.random((batch_size, feature_size)).astype("float32")
+        w = np.random.random((num_classes - 1, feature_size)).astype("float32")
+        label = np.array([0, 1, 4, 5])
+        path_table = np.array(
+            [(0, 2, -1, -1, -1), (0, 1, 3, -1, -1), (0, 1, 4, -1, -1),
+             (0, 2, -1, -1,
+              -1)])  #np.array to store 1,2,5,6s' non-leaf path(root -> leaf)
+        path_code = np.array([(0, 0, -1, -1, -1), (1, 1, 1, -1, -1), (
+            1, 0, 0, -1, -1), (0, 1, -1, -1, -1)])  #np.array to store 
+        bias = np.random.random((num_classes - 1, 1)).astype("float32")
+        self.attrs = {'num_classes': num_classes, 'is_sparse': True}
+        self.inputs = {
+            'X': x,
+            'W': w,
+            'PTable': path_table,
+            'PathCode': path_code,
+            'Label': label,
+            'Bias': bias
+        }
+        pre_output, out = hsigmoidWithCustomTree(x, w, path_table, path_code,
+                                                 label, bias, num_classes)
+        self.outputs = {'PreOut': pre_output, 'Out': out}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestHSigmoidOpWithSparseGrad(unittest.TestCase):
+    def hs_net_conf(self, is_sparse):
+        input_word = fluid.layers.data(name="x", shape=[1], dtype='int64')
+        path_table = fluid.layers.data(
+            name='path_table', shape=[3], dtype='int64')
+        path_code = fluid.layers.data(
+            name='path_code', shape=[3], dtype='int64')
+        label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+
+        data_list = [input_word, path_table, path_code, label]
+
+        emb = fluid.layers.embedding(
+            input=input_word,
+            is_sparse=is_sparse,
+            size=[3, 3],
+            param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(
+                scale=1 / math.sqrt(3))))
+
+        cost = fluid.layers.hsigmoid(
+            input=emb,
+            label=label,
+            bias_attr=True,
+            num_classes=3,
+            path_table=path_table,
+            path_code=path_code,
+            is_custom=True,
+            is_sparse=is_sparse)
+
+        avg_cost = fluid.layers.reduce_mean(cost)
+
+        return avg_cost, data_list
+
+    def training_test(self, is_sparse):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            start_up = fluid.default_startup_program()
+            start_up.random_seed = 1  # Fix random seed
+            x = np.arange(6).reshape(6)
+            path_table = np.array([(1, 2, -1), (1, 2, -1)])
+            path_code = np.array([(1, 0, -1), (0, 0, -1)])
+            label = np.array([1, 4])
+
+            loss, data_list = self.hs_net_conf(is_sparse)
+            optimizer = fluid.optimizer.SGD(learning_rate=1e-3)
+            optimizer.minimize(loss)
+
+            main_program = fluid.default_main_program()
+            place = fluid.CPUPlace()
+            feeder = fluid.DataFeeder(feed_list=data_list, place=place)
+            exe = fluid.Executor(place)
+
+            exe.run(start_up)
+            result = list()
+            for i in range(10):
+                data = [([[x[i % 2]]], [list(path_table[i % 2])],
+                         [list(path_code[i % 2])], [label[i % 2]])]
+
+                loss_val = exe.run(main_program,
+                                   feed=feeder.feed(data),
+                                   fetch_list=[loss])
+                result.append(loss_val)
+        return result
+
+    def test_hs_grad_with_sparse(self):
+        dense_result = self.training_test(is_sparse=False)
+        sparse_result = self.training_test(is_sparse=True)
+        assert (dense_result == sparse_result)
+
+
+class TestHSigmoidOpWithCostumTree(OpTest):
+    def setUp(self):
+        self.op_type = "hierarchical_sigmoid"
+        num_classes = 6  #using 1,2,3,4,5,6 to build a huffman tree and select 1,2,5,6 as sample
+        feature_size = 8
+        batch_size = 4
+        x = np.random.random((batch_size, feature_size)).astype("float32") * 2
+        w = np.random.random(
+            (num_classes - 1, feature_size)).astype("float32") * 2
+        label = np.array([0, 1, 4, 5])
+        path_table = np.array(
+            [(0, 2, -1, -1, -1), (0, 1, 3, -1, -1), (0, 1, 4, -1, -1),
+             (0, 2, -1, -1,
+              -1)])  #np.array to store 1,2,5,6s' non-leaf path(root -> leaf)
+        path_code = np.array([(0, 0, -1, -1, -1), (1, 1, 1, -1, -1), (
+            1, 0, 0, -1, -1), (0, 1, -1, -1, -1)])  #np.array to store 
+        bias = np.random.random((num_classes - 1, 1)).astype("float32")
+        self.attrs = {'num_classes': num_classes, 'is_sparse': False}
+        self.inputs = {
+            'X': x,
+            'W': w,
+            'PTable': path_table,
+            'PathCode': path_code,
+            'Label': label,
+            'Bias': bias
+        }
+        pre_output, out = hsigmoidWithCustomTree(x, w, path_table, path_code,
+                                                 label, bias, num_classes)
+        self.outputs = {'PreOut': pre_output, 'Out': out}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['Bias', 'X', 'W'], ['Out'], no_grad_set=set('Label'))
+
+
+class TestHSigmoidOpWithCostumTreeWithoutBias(OpTest):
+    def setUp(self):
+        self.op_type = "hierarchical_sigmoid"
+        num_classes = 6  #using 1,2,3,4,5,6 to build a huffman tree and select 1,2,5,6 as sample
+        feature_size = 8
+        batch_size = 4
+        x = np.random.random((batch_size, feature_size)).astype("float32") * 2
+        w = np.random.random(
+            (num_classes - 1, feature_size)).astype("float32") * 2
+        label = np.array([0, 1, 4, 5])
+        path_table = np.array(
+            [(0, 2, -1, -1, -1), (0, 1, 3, -1, -1), (0, 1, 4, -1, -1),
+             (0, 2, -1, -1,
+              -1)])  #np.array to store 1,2,5,6s' non-leaf path(root -> leaf)
+        path_code = np.array([(0, 0, -1, -1, -1), (1, 1, 1, -1, -1), (
+            1, 0, 0, -1, -1), (0, 1, -1, -1, -1)])  #np.array to store 
+        # bias = np.random.random((num_classes - 1, 1)).astype("float32")
+        self.attrs = {'num_classes': num_classes, 'is_sparse': False}
+        self.inputs = {
+            'X': x,
+            'W': w,
+            'PTable': path_table,
+            'PathCode': path_code,
+            'Label': label,
+        }
+        pre_output, out = hsigmoidWithCustomTree(
+            x=x,
+            w=w,
+            path_table=path_table,
+            path_code=path_code,
+            label=label,
+            bias=None,
+            num_classes=num_classes)
+        self.outputs = {'PreOut': pre_output, 'Out': out}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X', 'W'], ['Out'], no_grad_set=set('Label'))
+
+
 if __name__ == '__main__':
     unittest.main()

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

@@ -185,6 +185,25 @@ class TestBook(unittest.TestCase):
                     input=x, label=y, num_classes=2))
         print(str(program))
 
+        # test hsigmod with custom tree structure
+        program2 = Program()
+        with program_guard(program2):
+            x2 = layers.data(name='x2', shape=[4, 8], dtype='float32')
+            y2 = layers.data(name='y2', shape=[4], dtype='int64')
+            path_table = layers.data(
+                name='path_table', shape=[4, 6], dtype='int64')
+            path_code = layers.data(
+                name='path_code', shape=[4, 6], dtype='int64')
+            self.assertIsNotNone(
+                layers.hsigmoid(
+                    input=x2,
+                    label=y2,
+                    num_classes=6,
+                    path_table=path_table,
+                    path_code=path_code,
+                    is_custom=True))
+            print(str(program2))
+
     def test_sequence_expand(self):
         program = Program()
         with program_guard(program):

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

@@ -14,8 +14,12 @@
 
 from __future__ import print_function
 
-import unittest
 import numpy as np
+import unittest
+
+import paddle.fluid as fluid
+import paddle.fluid.initializer as initializer
+
 from op_test import OpTest
 
 
@@ -59,7 +63,7 @@ def nce(input, weight, bias, sample_weight, labels, num_classes,
 
 class TestNCE(OpTest):
     def generate_data(self, dim, batch_size, num_classes, num_true_class,
-                      num_neg_samples):
+                      num_neg_samples, is_sparse):
         input = np.random.randn(batch_size, dim).astype(np.float32)
         weight = np.random.randn(num_classes, dim).astype(np.float32)
         bias = np.random.randn(num_classes).astype(np.float32)
@@ -70,7 +74,8 @@ class TestNCE(OpTest):
             'num_neg_samples': num_neg_samples,
             'custom_neg_classes': list(range(num_neg_samples)),
             'seed': 0,
-            'sampler': 0
+            'sampler': 0,
+            'is_sparse': is_sparse
         }
         self.inputs = {
             'Input': input,
@@ -81,7 +86,7 @@ class TestNCE(OpTest):
         }
 
     def set_data(self):
-        self.generate_data(5, 5, 4, 1, 2)
+        self.generate_data(5, 5, 4, 1, 2, False)
 
     def compute(self):
         out = nce(self.inputs['Input'], self.inputs['Weight'],
@@ -107,9 +112,110 @@ class TestNCE(OpTest):
             ["Input", "Weight", "Bias"], "Cost", max_relative_error=0.02)
 
 
-class TestNCECase1(TestNCE):
+class TestNCECase1Tensor(TestNCE):
     def set_data(self):
-        self.generate_data(10, 20, 10, 2, 5)
+        self.generate_data(10, 20, 10, 2, 5, False)
+
+
+class TestNCECase1SelectedRows(unittest.TestCase):
+    def setUp(self):
+        self.base_lr = 0.0001
+        self.batch_size = 8
+
+    @staticmethod
+    def get_place():
+        place = fluid.core.CPUPlace()
+        return place
+
+    @staticmethod
+    def get_train_data(batch_size):
+        batchs = []
+        for i in range(batch_size):
+            input = np.random.randn(batch_size, 10).astype(np.float32)
+            labels = np.random.randint(0, 20, (batch_size, 1))
+            batchs.append([input, labels])
+        return batchs
+
+    def get_optimizer(self):
+        # SGD optimizer
+        optimizer = fluid.optimizer.SGD(learning_rate=self.base_lr)
+        return optimizer
+
+    def train_network(self, num_total_classes, num_neg_samples, sampler,
+                      custom_dist, is_sparse):
+        input = fluid.layers.data(name="input", shape=[10], dtype="float32")
+        label = fluid.layers.data(name="label", shape=[1], dtype="int64")
+
+        w_param = fluid.default_main_program().global_block().create_parameter(
+            shape=[num_total_classes, 10],
+            dtype='float32',
+            name='nce_w',
+            initializer=initializer.ConstantInitializer())
+        b_param = fluid.default_main_program().global_block().create_parameter(
+            shape=[num_total_classes, 1],
+            dtype='float32',
+            name='nce_b',
+            initializer=initializer.ConstantInitializer())
+
+        cost = fluid.layers.nce(input=input,
+                                label=label,
+                                num_total_classes=num_total_classes,
+                                sampler=sampler,
+                                custom_dist=custom_dist,
+                                sample_weight=None,
+                                param_attr='nce_w',
+                                bias_attr='nce_b',
+                                seed=1,
+                                num_neg_samples=num_neg_samples,
+                                is_sparse=is_sparse)
+        avg_cost = fluid.layers.mean(cost)
+        # optimizer
+        optimizer = self.get_optimizer()
+        optimizer.minimize(avg_cost)
+
+        return [avg_cost, [input, label]]
+
+    def test_input_is_selected_rows(self):
+        place = self.get_place()
+        exe = fluid.Executor(place)
+
+        data = self.get_train_data(self.batch_size)
+        nid_freq_arr = np.random.dirichlet(np.ones(20) * 1000).astype('float32')
+
+        rets = []
+        # for dense
+        dense_scope = fluid.core.Scope()
+        dense_startup_program = fluid.framework.Program()
+        dense_train_program = fluid.framework.Program()
+        with fluid.scope_guard(dense_scope):
+            with fluid.program_guard(dense_train_program,
+                                     dense_startup_program):
+                cost, feeds = self.train_network(20, 5, "custom_dist",
+                                                 nid_freq_arr.tolist(), False)
+                feeder = fluid.DataFeeder(feed_list=feeds, place=place)
+                exe.run(dense_startup_program)
+                loss_val = exe.run(dense_train_program,
+                                   feed=feeder.feed(data),
+                                   fetch_list=[cost.name])
+                rets.append(np.mean(loss_val))
+
+        # for sparse
+        sparse_scope = fluid.core.Scope()
+        sparse_startup_program = fluid.framework.Program()
+        sparse_train_program = fluid.framework.Program()
+        with fluid.scope_guard(sparse_scope):
+            with fluid.program_guard(sparse_train_program,
+                                     sparse_startup_program):
+                cost, feeds = self.train_network(20, 5, "custom_dist",
+                                                 nid_freq_arr.tolist(), True)
+                feeder = fluid.DataFeeder(feed_list=feeds, place=place)
+                exe.run(sparse_startup_program)
+                loss_val = exe.run(sparse_train_program,
+                                   feed=feeder.feed(data),
+                                   fetch_list=[cost.name])
+                rets.append(np.mean(loss_val))
+
+        self.assertEqual(rets[0], rets[1])
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/test_nearest_interp_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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from op_test import OpTest
+import paddle.fluid.core as core
+
+
+def nearest_neighbor_interp_np(X,
+                               out_h,
+                               out_w,
+                               out_size=None,
+                               actual_shape=None):
+    """nearest neighbor interpolation implement in shape [N, C, H, W]"""
+    if out_size is not None:
+        out_h = out_size[0]
+        out_w = out_size[1]
+    if actual_shape is not None:
+        out_h = actual_shape[0]
+        out_w = actual_shape[1]
+    n, c, in_h, in_w = X.shape
+
+    ratio_h = ratio_w = 0.0
+    if out_h > 1:
+        ratio_h = (in_h - 1.0) / (out_h - 1.0)
+    if out_w > 1:
+        ratio_w = (in_w - 1.0) / (out_w - 1.0)
+
+    out = np.zeros((n, c, out_h, out_w))
+    for i in range(out_h):
+        in_i = int(ratio_h * i + 0.5)
+        for j in range(out_w):
+            in_j = int(ratio_w * j + 0.5)
+            out[:, :, i, j] = X[:, :, in_i, in_j]
+
+    return out.astype(X.dtype)
+
+
+class TestNearestInterpOp(OpTest):
+    def setUp(self):
+        self.out_size = None
+        self.actual_shape = None
+        self.init_test_case()
+        self.op_type = "nearest_interp"
+        input_np = np.random.random(self.input_shape).astype("float32")
+
+        output_np = nearest_neighbor_interp_np(input_np, self.out_h, self.out_w,
+                                               self.out_size, self.actual_shape)
+        self.inputs = {'X': input_np}
+        if self.out_size is not None:
+            self.inputs['OutSize'] = self.out_size
+        if self.actual_shape is not None:
+            self.inputs['OutSize'] = self.actual_shape
+        self.attrs = {
+            'out_h': self.out_h,
+            'out_w': self.out_w,
+            'interp_method': self.interp_method
+        }
+        self.outputs = {'Out': output_np}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out', in_place=True)
+
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [2, 3, 4, 4]
+        self.out_h = 2
+        self.out_w = 2
+        self.out_size = np.array([3, 3]).astype("int32")
+
+
+class TestNearestNeighborInterpCase1(TestNearestInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [4, 1, 7, 8]
+        self.out_h = 1
+        self.out_w = 1
+
+
+class TestNearestNeighborInterpCase2(TestNearestInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [3, 3, 9, 6]
+        self.out_h = 12
+        self.out_w = 12
+
+
+class TestNearestNeighborInterpCase3(TestNearestInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [1, 1, 128, 64]
+        self.out_h = 64
+        self.out_w = 128
+
+
+class TestNearestNeighborInterpCase4(TestNearestInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [4, 1, 7, 8]
+        self.out_h = 1
+        self.out_w = 1
+        self.out_size = np.array([2, 2]).astype("int32")
+
+
+class TestNearestNeighborInterpCase5(TestNearestInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [3, 3, 9, 6]
+        self.out_h = 12
+        self.out_w = 12
+        self.out_size = np.array([11, 11]).astype("int32")
+
+
+class TestNearestNeighborInterpCase6(TestNearestInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [1, 1, 128, 64]
+        self.out_h = 64
+        self.out_w = 128
+        self.out_size = np.array([65, 129]).astype("int32")
+
+
+class TestNearestNeighborInterpActualShape(TestNearestInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [3, 2, 32, 16]
+        self.out_h = 64
+        self.out_w = 32
+        self.out_size = np.array([66, 40]).astype("int32")
+
+
+class TestNearestInterpOpUint8(OpTest):
+    def setUp(self):
+        self.out_size = None
+        self.actual_shape = None
+        self.init_test_case()
+        self.op_type = "nearest_interp"
+        input_np = np.random.randint(
+            low=0, high=256, size=self.input_shape).astype("uint8")
+        output_np = nearest_neighbor_interp_np(input_np, self.out_h, self.out_w,
+                                               self.out_size, self.actual_shape)
+        self.inputs = {'X': input_np}
+        if self.out_size is not None:
+            self.inputs['OutSize'] = self.out_size
+        self.attrs = {
+            'out_h': self.out_h,
+            'out_w': self.out_w,
+            'interp_method': self.interp_method
+        }
+        self.outputs = {'Out': output_np}
+
+    def test_check_output(self):
+        self.check_output_with_place(place=core.CPUPlace(), atol=1)
+
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [1, 3, 9, 6]
+        self.out_h = 10
+        self.out_w = 9
+
+
+class TestNearestNeighborInterpCase1Uint8(TestNearestInterpOpUint8):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [2, 3, 128, 64]
+        self.out_h = 120
+        self.out_w = 50
+
+
+class TestNearestNeighborInterpCase2Uint8(TestNearestInterpOpUint8):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [4, 1, 7, 8]
+        self.out_h = 5
+        self.out_w = 13
+        self.out_size = np.array([6, 15]).astype("int32")
+
+
+if __name__ == "__main__":
+    unittest.main()