Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into add-crypto-api

.github/PULL_REQUEST_TEMPLATE.md

-#### Required（必填, multiple choices, two at most）
-- **PR type（PR 类型） is ( ):**
-A. New features（新功能）---------------- D. Performance optimization（性能优化）
-B. Bug fixes（问题修复）------------------ E. Breaking changes（向后不兼容的改变）
-C. Function optimization（功能优化）------F.  Others（其它）
-
-- **PR changes（改动点）is ( ):**
-A. OPs（operators）---------------------- C. Docs（文档）
-B. APIs（接口）--------------------------- D. Others（其它）
-
-- **Use one sentence to describe what this PR does.（简述本次PR的目的和改动）**
-
------------------------
-#### Optional（选填, If None, please delete it）
-
-- **Describe what this PR does in detail. If this PR fixes an issue, please give the issue id.**
-  <!-- DESCRIBE THE BUG OR REQUIREMENT HERE. eg. #2020（格式为 #Issue编号）-->
-
-- **If you modified docs, please make sure that both Chinese and English docs were modified and provide a preview screenshot. （文档必填）**
-   <!-- ADD SCREENSHOT HERE IF APPLICABLE. -->
-
-- **Please write down other information you want to tell reviewers.**
+<!--  Demo: PR types: Bug fixes, Function optimization  -->
+<!--  One of [ New features | Bug fixes | Function optimization | Performance optimization | Breaking changes | Others ]  -->
+PR types:
+<!--  Demo: PR changes: OPs  -->
+<!--  One of [ OPs | APIs | Docs | Others ]  -->
+PR changes:
+<!--  Describe what this PR does  -->
+Describe:

paddle/fluid/framework/data_feed.cc

@@ -41,44 +41,44 @@ namespace paddle {
 namespace framework {
 
 void RecordCandidateList::ReSize(size_t length) {
-  _mutex.lock();
-  _capacity = length;
-  CHECK(_capacity > 0);  // NOLINT
-  _candidate_list.clear();
-  _candidate_list.resize(_capacity);
-  _full = false;
-  _cur_size = 0;
-  _total_size = 0;
-  _mutex.unlock();
+  mutex_.lock();
+  capacity_ = length;
+  CHECK(capacity_ > 0);  // NOLINT
+  candidate_list_.clear();
+  candidate_list_.resize(capacity_);
+  full_ = false;
+  cur_size_ = 0;
+  total_size_ = 0;
+  mutex_.unlock();
 }
 
 void RecordCandidateList::ReInit() {
-  _mutex.lock();
-  _full = false;
-  _cur_size = 0;
-  _total_size = 0;
-  _mutex.unlock();
+  mutex_.lock();
+  full_ = false;
+  cur_size_ = 0;
+  total_size_ = 0;
+  mutex_.unlock();
 }
 
 void RecordCandidateList::AddAndGet(const Record& record,
                                     RecordCandidate* result) {
-  _mutex.lock();
+  mutex_.lock();
   size_t index = 0;
-  ++_total_size;
+  ++total_size_;
   auto fleet_ptr = FleetWrapper::GetInstance();
-  if (!_full) {
-    _candidate_list[_cur_size++] = record;
-    _full = (_cur_size == _capacity);
+  if (!full_) {
+    candidate_list_[cur_size_++] = record;
+    full_ = (cur_size_ == capacity_);
   } else {
-    CHECK(_cur_size == _capacity);
-    index = fleet_ptr->LocalRandomEngine()() % _total_size;
-    if (index < _capacity) {
-      _candidate_list[index] = record;
+    CHECK(cur_size_ == capacity_);
+    index = fleet_ptr->LocalRandomEngine()() % total_size_;
+    if (index < capacity_) {
+      candidate_list_[index] = record;
     }
   }
-  index = fleet_ptr->LocalRandomEngine()() % _cur_size;
-  *result = _candidate_list[index];
-  _mutex.unlock();
+  index = fleet_ptr->LocalRandomEngine()() % cur_size_;
+  *result = candidate_list_[index];
+  mutex_.unlock();
 }
 
 void DataFeed::AddFeedVar(Variable* var, const std::string& name) {
@@ -1452,7 +1452,11 @@ void PaddleBoxDataFeed::PutToFeedVec(const std::vector<PvInstance>& pv_vec) {
 int PaddleBoxDataFeed::GetCurrentPhase() {
 #ifdef PADDLE_WITH_BOX_PS
   auto box_ptr = paddle::framework::BoxWrapper::GetInstance();
-  return box_ptr->PassFlag();  // join: 1, update: 0
+  if (box_ptr->Mode() == 1) {  // For AucRunner
+    return 1;
+  } else {
+    return box_ptr->Phase();
+  }
 #else
   LOG(WARNING) << "It should be complied with BOX_PS...";
   return current_phase_;

paddle/fluid/framework/data_feed.h

@@ -27,6 +27,7 @@ limitations under the License. */
 #include <string>
 #include <thread>  // NOLINT
 #include <unordered_map>
+#include <unordered_set>
 #include <utility>
 #include <vector>
 
@@ -34,6 +35,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/blocking_queue.h"
 #include "paddle/fluid/framework/channel.h"
 #include "paddle/fluid/framework/data_feed.pb.h"
+#include "paddle/fluid/framework/fleet/fleet_wrapper.h"
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/reader.h"
 #include "paddle/fluid/framework/variable.h"
@@ -484,13 +486,25 @@ paddle::framework::Archive<AR>& operator>>(paddle::framework::Archive<AR>& ar,
 
 struct RecordCandidate {
   std::string ins_id_;
-  std::unordered_multimap<uint16_t, FeatureKey> feas;
+  std::unordered_multimap<uint16_t, FeatureKey> feas_;
+  size_t shadow_index_ = -1;  // Optimization for Reservoir Sample
+
+  RecordCandidate() {}
+  RecordCandidate(const Record& rec,
+                  const std::unordered_set<uint16_t>& slot_index_to_replace) {
+    for (const auto& fea : rec.uint64_feasigns_) {
+      if (slot_index_to_replace.find(fea.slot()) !=
+          slot_index_to_replace.end()) {
+        feas_.insert({fea.slot(), fea.sign()});
+      }
+    }
+  }
 
   RecordCandidate& operator=(const Record& rec) {
-    feas.clear();
+    feas_.clear();
     ins_id_ = rec.ins_id_;
     for (auto& fea : rec.uint64_feasigns_) {
-      feas.insert({fea.slot(), fea.sign()});
+      feas_.insert({fea.slot(), fea.sign()});
     }
     return *this;
   }
@@ -499,22 +513,67 @@ struct RecordCandidate {
 class RecordCandidateList {
  public:
   RecordCandidateList() = default;
-  RecordCandidateList(const RecordCandidateList&) = delete;
-  RecordCandidateList& operator=(const RecordCandidateList&) = delete;
+  RecordCandidateList(const RecordCandidateList&) {}
 
+  size_t Size() { return cur_size_; }
   void ReSize(size_t length);
 
   void ReInit();
+  void ReInitPass() {
+    for (size_t i = 0; i < cur_size_; ++i) {
+      if (candidate_list_[i].shadow_index_ != i) {
+        candidate_list_[i].ins_id_ =
+            candidate_list_[candidate_list_[i].shadow_index_].ins_id_;
+        candidate_list_[i].feas_.swap(
+            candidate_list_[candidate_list_[i].shadow_index_].feas_);
+        candidate_list_[i].shadow_index_ = i;
+      }
+    }
+    candidate_list_.resize(cur_size_);
+  }
 
   void AddAndGet(const Record& record, RecordCandidate* result);
+  void AddAndGet(const Record& record, size_t& index_result) {  // NOLINT
+    // std::unique_lock<std::mutex> lock(mutex_);
+    size_t index = 0;
+    ++total_size_;
+    auto fleet_ptr = FleetWrapper::GetInstance();
+    if (!full_) {
+      candidate_list_.emplace_back(record, slot_index_to_replace_);
+      candidate_list_.back().shadow_index_ = cur_size_;
+      ++cur_size_;
+      full_ = (cur_size_ == capacity_);
+    } else {
+      index = fleet_ptr->LocalRandomEngine()() % total_size_;
+      if (index < capacity_) {
+        candidate_list_.emplace_back(record, slot_index_to_replace_);
+        candidate_list_[index].shadow_index_ = candidate_list_.size() - 1;
+      }
+    }
+    index = fleet_ptr->LocalRandomEngine()() % cur_size_;
+    index_result = candidate_list_[index].shadow_index_;
+  }
+  const RecordCandidate& Get(size_t index) const {
+    PADDLE_ENFORCE_LT(
+        index, candidate_list_.size(),
+        platform::errors::OutOfRange("Your index [%lu] exceeds the number of "
+                                     "elements in candidate_list[%lu].",
+                                     index, candidate_list_.size()));
+    return candidate_list_[index];
+  }
+  void SetSlotIndexToReplace(
+      const std::unordered_set<uint16_t>& slot_index_to_replace) {
+    slot_index_to_replace_ = slot_index_to_replace;
+  }
 
  private:
-  size_t _capacity = 0;
-  std::mutex _mutex;
-  bool _full = false;
-  size_t _cur_size = 0;
-  size_t _total_size = 0;
-  std::vector<RecordCandidate> _candidate_list;
+  size_t capacity_ = 0;
+  std::mutex mutex_;
+  bool full_ = false;
+  size_t cur_size_ = 0;
+  size_t total_size_ = 0;
+  std::vector<RecordCandidate> candidate_list_;
+  std::unordered_set<uint16_t> slot_index_to_replace_;
 };
 
 template <class AR>

paddle/fluid/framework/data_set.cc

@@ -1141,13 +1141,15 @@ void MultiSlotDataset::MergeByInsId() {
   VLOG(3) << "MultiSlotDataset::MergeByInsId end";
 }
 
-void MultiSlotDataset::GetRandomData(const std::set<uint16_t>& slots_to_replace,
-                                     std::vector<Record>* result) {
+void MultiSlotDataset::GetRandomData(
+    const std::unordered_set<uint16_t>& slots_to_replace,
+    std::vector<Record>* result) {
   int debug_erase_cnt = 0;
   int debug_push_cnt = 0;
   auto multi_slot_desc = data_feed_desc_.multi_slot_desc();
   slots_shuffle_rclist_.ReInit();
-  for (const auto& rec : slots_shuffle_original_data_) {
+  const auto& slots_shuffle_original_data = GetSlotsOriginalData();
+  for (const auto& rec : slots_shuffle_original_data) {
     RecordCandidate rand_rec;
     Record new_rec = rec;
     slots_shuffle_rclist_.AddAndGet(rec, &rand_rec);
@@ -1161,7 +1163,7 @@ void MultiSlotDataset::GetRandomData(const std::set<uint16_t>& slots_to_replace,
       }
     }
     for (auto slot : slots_to_replace) {
-      auto range = rand_rec.feas.equal_range(slot);
+      auto range = rand_rec.feas_.equal_range(slot);
       for (auto it = range.first; it != range.second; ++it) {
         new_rec.uint64_feasigns_.push_back({it->second, it->first});
         debug_push_cnt += 1;
@@ -1173,9 +1175,9 @@ void MultiSlotDataset::GetRandomData(const std::set<uint16_t>& slots_to_replace,
           << " repush feasign num: " << debug_push_cnt;
 }
 
-// slots shuffle to input_channel_ with needed-shuffle slots
-void MultiSlotDataset::SlotsShuffle(
-    const std::set<std::string>& slots_to_replace) {
+void MultiSlotDataset::PreprocessChannel(
+    const std::set<std::string>& slots_to_replace,
+    std::unordered_set<uint16_t>& index_slots) {  // NOLINT
   int out_channel_size = 0;
   if (cur_channel_ == 0) {
     for (size_t i = 0; i < multi_output_channel_.size(); ++i) {
@@ -1189,20 +1191,14 @@ void MultiSlotDataset::SlotsShuffle(
   VLOG(2) << "DatasetImpl<T>::SlotsShuffle() begin with input channel size: "
           << input_channel_->Size()
           << " output channel size: " << out_channel_size;
-  if (!slots_shuffle_fea_eval_) {
-    VLOG(3) << "DatasetImpl<T>::SlotsShuffle() end,"
-               "fea eval mode off, need to set on for slots shuffle";
-    return;
-  }
+
   if ((!input_channel_ || input_channel_->Size() == 0) &&
       slots_shuffle_original_data_.size() == 0 && out_channel_size == 0) {
     VLOG(3) << "DatasetImpl<T>::SlotsShuffle() end, no data to slots shuffle";
     return;
   }
-  platform::Timer timeline;
-  timeline.Start();
+
   auto multi_slot_desc = data_feed_desc_.multi_slot_desc();
-  std::set<uint16_t> index_slots;
   for (int i = 0; i < multi_slot_desc.slots_size(); ++i) {
     std::string cur_slot = multi_slot_desc.slots(i).name();
     if (slots_to_replace.find(cur_slot) != slots_to_replace.end()) {
@@ -1287,6 +1283,19 @@ void MultiSlotDataset::SlotsShuffle(
   }
   CHECK(input_channel_->Size() == 0)
       << "input channel should be empty before slots shuffle";
+}
+
+// slots shuffle to input_channel_ with needed-shuffle slots
+void MultiSlotDataset::SlotsShuffle(
+    const std::set<std::string>& slots_to_replace) {
+  PADDLE_ENFORCE_EQ(slots_shuffle_fea_eval_, true,
+                    platform::errors::PreconditionNotMet(
+                        "fea eval mode off, need to set on for slots shuffle"));
+  platform::Timer timeline;
+  timeline.Start();
+  std::unordered_set<uint16_t> index_slots;
+  PreprocessChannel(slots_to_replace, index_slots);
+
   std::vector<Record> random_data;
   random_data.clear();
   // get slots shuffled random_data

paddle/fluid/framework/data_set.h

@@ -67,6 +67,7 @@ class Dataset {
   virtual void SetParseContent(bool parse_content) = 0;
   virtual void SetParseLogKey(bool parse_logkey) = 0;
   virtual void SetEnablePvMerge(bool enable_pv_merge) = 0;
+  virtual bool EnablePvMerge() = 0;
   virtual void SetMergeBySid(bool is_merge) = 0;
   // set merge by ins id
   virtual void SetMergeByInsId(int merge_size) = 0;
@@ -108,10 +109,7 @@ class Dataset {
   virtual void LocalShuffle() = 0;
   // global shuffle data
   virtual void GlobalShuffle(int thread_num = -1) = 0;
-  // for slots shuffle
   virtual void SlotsShuffle(const std::set<std::string>& slots_to_replace) = 0;
-  virtual void GetRandomData(const std::set<uint16_t>& slots_to_replace,
-                             std::vector<Record>* result) = 0;
   // create readers
   virtual void CreateReaders() = 0;
   // destroy readers
@@ -183,6 +181,9 @@ class DatasetImpl : public Dataset {
   virtual int GetThreadNum() { return thread_num_; }
   virtual int GetTrainerNum() { return trainer_num_; }
   virtual Channel<T> GetInputChannel() { return input_channel_; }
+  virtual void SetInputChannel(const Channel<T>& input_channel) {
+    input_channel_ = input_channel;
+  }
   virtual int64_t GetFleetSendBatchSize() { return fleet_send_batch_size_; }
   virtual std::pair<std::string, std::string> GetHdfsConfig() {
     return std::make_pair(fs_name_, fs_ugi_);
@@ -192,6 +193,7 @@ class DatasetImpl : public Dataset {
     return data_feed_desc_;
   }
   virtual int GetChannelNum() { return channel_num_; }
+  virtual bool EnablePvMerge() { return enable_pv_merge_; }
   virtual std::vector<paddle::framework::DataFeed*> GetReaders();
   virtual void CreateChannel();
   virtual void RegisterClientToClientMsgHandler();
@@ -202,8 +204,9 @@ class DatasetImpl : public Dataset {
   virtual void LocalShuffle();
   virtual void GlobalShuffle(int thread_num = -1);
   virtual void SlotsShuffle(const std::set<std::string>& slots_to_replace) {}
-  virtual void GetRandomData(const std::set<uint16_t>& slots_to_replace,
-                             std::vector<Record>* result) {}
+  virtual const std::vector<T>& GetSlotsOriginalData() {
+    return slots_shuffle_original_data_;
+  }
   virtual void CreateReaders();
   virtual void DestroyReaders();
   virtual int64_t GetMemoryDataSize();
@@ -293,9 +296,13 @@ class MultiSlotDataset : public DatasetImpl<Record> {
     }
     std::vector<std::unordered_set<uint64_t>>().swap(local_tables_);
   }
+  virtual void PreprocessChannel(
+      const std::set<std::string>& slots_to_replace,
+      std::unordered_set<uint16_t>& index_slot);  // NOLINT
   virtual void SlotsShuffle(const std::set<std::string>& slots_to_replace);
-  virtual void GetRandomData(const std::set<uint16_t>& slots_to_replace,
-                             std::vector<Record>* result);
+  virtual void GetRandomData(
+      const std::unordered_set<uint16_t>& slots_to_replace,
+      std::vector<Record>* result);
   virtual ~MultiSlotDataset() {}
 };
 

paddle/fluid/framework/fleet/box_wrapper.cc

@@ -28,6 +28,8 @@ std::shared_ptr<BoxWrapper> BoxWrapper::s_instance_ = nullptr;
 cudaStream_t BoxWrapper::stream_list_[8];
 std::shared_ptr<boxps::BoxPSBase> BoxWrapper::boxps_ptr_ = nullptr;
 AfsManager* BoxWrapper::afs_manager = nullptr;
+int BoxWrapper::embedx_dim_ = 8;
+int BoxWrapper::expand_embed_dim_ = 0;
 
 void BasicAucCalculator::compute() {
   double* table[2] = {&_table[0][0], &_table[1][0]};
@@ -57,6 +59,94 @@ void BasicAucCalculator::compute() {
   _size = fp + tp;
 }
 
+void BoxWrapper::CheckEmbedSizeIsValid(int embedx_dim, int expand_embed_dim) {
+  PADDLE_ENFORCE_EQ(
+      embedx_dim_, embedx_dim,
+      platform::errors::InvalidArgument("SetInstance(): invalid embedx_dim. "
+                                        "When embedx_dim = %d, but got %d.",
+                                        embedx_dim_, embedx_dim));
+  PADDLE_ENFORCE_EQ(expand_embed_dim_, expand_embed_dim,
+                    platform::errors::InvalidArgument(
+                        "SetInstance(): invalid expand_embed_dim. When "
+                        "expand_embed_dim = %d, but got %d.",
+                        expand_embed_dim_, expand_embed_dim));
+}
+
+void BoxWrapper::PullSparse(const paddle::platform::Place& place,
+                            const std::vector<const uint64_t*>& keys,
+                            const std::vector<float*>& values,
+                            const std::vector<int64_t>& slot_lengths,
+                            const int hidden_size, const int expand_embed_dim) {
+#define EMBEDX_CASE(i, ...)                                                  \
+  case i: {                                                                  \
+    constexpr size_t EmbedxDim = i;                                          \
+    switch (expand_embed_dim) {                                              \
+      __VA_ARGS__                                                            \
+      default:                                                               \
+        PADDLE_THROW(platform::errors::InvalidArgument(                      \
+            "Unsupport this expand embedding size [%d]", expand_embed_dim)); \
+    }                                                                        \
+  } break
+
+#define PULLSPARSE_CASE(i, ...)                                             \
+  case i: {                                                                 \
+    constexpr size_t ExpandDim = i;                                         \
+    PullSparseCase<EmbedxDim, ExpandDim>(place, keys, values, slot_lengths, \
+                                         hidden_size, expand_embed_dim);    \
+  } break
+
+  CheckEmbedSizeIsValid(hidden_size - 3, expand_embed_dim);
+  switch (hidden_size - 3) {
+    EMBEDX_CASE(8, PULLSPARSE_CASE(0); PULLSPARSE_CASE(8);
+                PULLSPARSE_CASE(64););
+    EMBEDX_CASE(16, PULLSPARSE_CASE(0););
+    default:
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "Unsupport this embedding size [%d]", hidden_size - 3));
+  }
+#undef PULLSPARSE_CASE
+#undef EMBEDX_CASE
+}
+
+void BoxWrapper::PushSparseGrad(const paddle::platform::Place& place,
+                                const std::vector<const uint64_t*>& keys,
+                                const std::vector<const float*>& grad_values,
+                                const std::vector<int64_t>& slot_lengths,
+                                const int hidden_size,
+                                const int expand_embed_dim,
+                                const int batch_size) {
+#define EMBEDX_CASE(i, ...)                                                  \
+  case i: {                                                                  \
+    constexpr size_t EmbedxDim = i;                                          \
+    switch (expand_embed_dim) {                                              \
+      __VA_ARGS__                                                            \
+      default:                                                               \
+        PADDLE_THROW(platform::errors::InvalidArgument(                      \
+            "Unsupport this expand embedding size [%d]", expand_embed_dim)); \
+    }                                                                        \
+  } break
+
+#define PUSHSPARSE_CASE(i, ...)                                             \
+  case i: {                                                                 \
+    constexpr size_t ExpandDim = i;                                         \
+    PushSparseGradCase<EmbedxDim, ExpandDim>(place, keys, grad_values,      \
+                                             slot_lengths, hidden_size,     \
+                                             expand_embed_dim, batch_size); \
+  } break
+
+  CheckEmbedSizeIsValid(hidden_size - 3, expand_embed_dim);
+  switch (hidden_size - 3) {
+    EMBEDX_CASE(8, PUSHSPARSE_CASE(0); PUSHSPARSE_CASE(8);
+                PUSHSPARSE_CASE(64););
+    EMBEDX_CASE(16, PUSHSPARSE_CASE(0););
+    default:
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "Unsupport this embedding size [%d]", hidden_size - 3));
+  }
+#undef PUSHSPARSE_CASE
+#undef EMBEDX_CASE
+}
+
 void BasicAucCalculator::calculate_bucket_error() {
   double last_ctr = -1;
   double impression_sum = 0;
@@ -128,133 +218,112 @@ void BoxWrapper::EndPass(bool need_save_delta) const {
       ret, 0, platform::errors::PreconditionNotMet("EndPass failed in BoxPS."));
 }
 
-void BoxWrapper::PullSparse(const paddle::platform::Place& place,
-                            const std::vector<const uint64_t*>& keys,
-                            const std::vector<float*>& values,
-                            const std::vector<int64_t>& slot_lengths,
-                            const int hidden_size) {
-  VLOG(3) << "Begin PullSparse";
-  platform::Timer all_timer;
-  platform::Timer pull_boxps_timer;
-  all_timer.Start();
-
-  int64_t total_length =
-      std::accumulate(slot_lengths.begin(), slot_lengths.end(), 0UL);
-  auto buf =
-      memory::AllocShared(place, total_length * sizeof(boxps::FeatureValueGpu));
-  boxps::FeatureValueGpu* total_values_gpu =
-      reinterpret_cast<boxps::FeatureValueGpu*>(buf->ptr());
-
-  if (platform::is_cpu_place(place)) {
-    PADDLE_THROW(platform::errors::Unimplemented(
-        "Warning:: CPUPlace is not supported in PaddleBox now."));
-  } else if (platform::is_gpu_place(place)) {
-#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
-    VLOG(3) << "Begin copy keys, key_num[" << total_length << "]";
-    int device_id = BOOST_GET_CONST(platform::CUDAPlace, place).GetDeviceId();
-    LoDTensor& total_keys_tensor = keys_tensor[device_id];
-    uint64_t* total_keys = reinterpret_cast<uint64_t*>(
-        total_keys_tensor.mutable_data<int64_t>({total_length, 1}, place));
-
-    // construct slot_level lod info
-    auto slot_lengths_lod = slot_lengths;
-    for (size_t i = 1; i < slot_lengths_lod.size(); i++) {
-      slot_lengths_lod[i] += slot_lengths_lod[i - 1];
-    }
-    auto buf_key = memory::AllocShared(place, keys.size() * sizeof(uint64_t*));
-    auto buf_length =
-        memory::AllocShared(place, slot_lengths.size() * sizeof(int64_t));
-    uint64_t** gpu_keys = reinterpret_cast<uint64_t**>(buf_key->ptr());
-    int64_t* gpu_len = reinterpret_cast<int64_t*>(buf_length->ptr());
-    cudaMemcpy(gpu_keys, keys.data(), keys.size() * sizeof(uint64_t*),
-               cudaMemcpyHostToDevice);
-    cudaMemcpy(gpu_len, slot_lengths_lod.data(),
-               slot_lengths.size() * sizeof(int64_t), cudaMemcpyHostToDevice);
-
-    this->CopyKeys(place, gpu_keys, total_keys, gpu_len,
-                   static_cast<int>(slot_lengths.size()),
-                   static_cast<int>(total_length));
-    VLOG(3) << "Begin call PullSparseGPU in BoxPS";
-    pull_boxps_timer.Start();
-    int ret =
-        boxps_ptr_->PullSparseGPU(total_keys, total_values_gpu,
-                                  static_cast<int>(total_length), device_id);
-    PADDLE_ENFORCE_EQ(ret, 0, platform::errors::PreconditionNotMet(
-                                  "PullSparseGPU failed in BoxPS."));
-    pull_boxps_timer.Pause();
-
-    VLOG(3) << "Begin Copy result to tensor, total_length[" << total_length
-            << "]";
-    this->CopyForPull(place, gpu_keys, values, total_values_gpu, gpu_len,
-                      static_cast<int>(slot_lengths.size()), hidden_size,
-                      total_length);
-#else
-    PADDLE_THROW(platform::errors::PreconditionNotMet(
-        "Please compile WITH_GPU option, because NCCL doesn't support "
-        "windows."));
-#endif
-  } else {
-    PADDLE_THROW(platform::errors::PreconditionNotMet(
-        "PaddleBox: PullSparse Only Support CPUPlace or CUDAPlace Now."));
+void BoxWrapper::GetRandomReplace(const std::vector<Record>& pass_data) {
+  VLOG(0) << "Begin GetRandomReplace";
+  size_t ins_num = pass_data.size();
+  replace_idx_.resize(ins_num);
+  for (auto& cand_list : random_ins_pool_list) {
+    cand_list.ReInitPass();
+  }
+  std::vector<std::thread> threads;
+  for (int tid = 0; tid < auc_runner_thread_num_; ++tid) {
+    threads.push_back(std::thread([this, &pass_data, tid, ins_num]() {
+      int start = tid * ins_num / auc_runner_thread_num_;
+      int end = (tid + 1) * ins_num / auc_runner_thread_num_;
+      VLOG(3) << "GetRandomReplace begin for thread[" << tid
+              << "], and process [" << start << ", " << end
+              << "), total ins: " << ins_num;
+      auto& random_pool = random_ins_pool_list[tid];
+      for (int i = start; i < end; ++i) {
+        const auto& ins = pass_data[i];
+        random_pool.AddAndGet(ins, replace_idx_[i]);
+      }
+    }));
+  }
+  for (int tid = 0; tid < auc_runner_thread_num_; ++tid) {
+    threads[tid].join();
   }
-  all_timer.Pause();
-  VLOG(1) << "PullSparse total costs: " << all_timer.ElapsedSec()
-          << " s, of which BoxPS costs: " << pull_boxps_timer.ElapsedSec()
-          << " s";
-  VLOG(3) << "End PullSparse";
+  pass_done_semi_->Put(1);
+  VLOG(0) << "End GetRandomReplace";
 }
 
-void BoxWrapper::PushSparseGrad(const paddle::platform::Place& place,
-                                const std::vector<const uint64_t*>& keys,
-                                const std::vector<const float*>& grad_values,
-                                const std::vector<int64_t>& slot_lengths,
-                                const int hidden_size, const int batch_size) {
-  VLOG(3) << "Begin PushSparseGrad";
-  platform::Timer all_timer;
-  platform::Timer push_boxps_timer;
-  all_timer.Start();
-  int64_t total_length =
-      std::accumulate(slot_lengths.begin(), slot_lengths.end(), 0UL);
-  auto buf = memory::AllocShared(
-      place, total_length * sizeof(boxps::FeaturePushValueGpu));
-  boxps::FeaturePushValueGpu* total_grad_values_gpu =
-      reinterpret_cast<boxps::FeaturePushValueGpu*>(buf->ptr());
-  if (platform::is_cpu_place(place)) {
-    PADDLE_THROW(platform::errors::Unimplemented(
-        "Warning:: CPUPlace is not supported in PaddleBox now."));
-  } else if (platform::is_gpu_place(place)) {
-#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
-    int device_id = BOOST_GET_CONST(platform::CUDAPlace, place).GetDeviceId();
-    LoDTensor& cached_total_keys_tensor = keys_tensor[device_id];
-    uint64_t* total_keys =
-        reinterpret_cast<uint64_t*>(cached_total_keys_tensor.data<int64_t>());
-    VLOG(3) << "Begin copy grad tensor to boxps struct";
-    this->CopyForPush(place, grad_values, total_grad_values_gpu, slot_lengths,
-                      hidden_size, total_length, batch_size);
+void BoxWrapper::GetRandomData(
+    const std::vector<Record>& pass_data,
+    const std::unordered_set<uint16_t>& slots_to_replace,
+    std::vector<Record>* result) {
+  VLOG(0) << "Begin GetRandomData";
+  std::vector<std::thread> threads;
+  for (int tid = 0; tid < auc_runner_thread_num_; ++tid) {
+    threads.push_back(std::thread([this, &pass_data, tid, &slots_to_replace,
+                                   result]() {
+      int debug_erase_cnt = 0;
+      int debug_push_cnt = 0;
+      size_t ins_num = pass_data.size();
+      int start = tid * ins_num / auc_runner_thread_num_;
+      int end = (tid + 1) * ins_num / auc_runner_thread_num_;
+      VLOG(3) << "GetRandomData begin for thread[" << tid << "], and process ["
+              << start << ", " << end << "), total ins: " << ins_num;
+      const auto& random_pool = random_ins_pool_list[tid];
+      for (int i = start; i < end; ++i) {
+        const auto& ins = pass_data[i];
+        const RecordCandidate& rand_rec = random_pool.Get(replace_idx_[i]);
+        Record new_rec = ins;
+        for (auto it = new_rec.uint64_feasigns_.begin();
+             it != new_rec.uint64_feasigns_.end();) {
+          if (slots_to_replace.find(it->slot()) != slots_to_replace.end()) {
+            it = new_rec.uint64_feasigns_.erase(it);
+            debug_erase_cnt += 1;
+          } else {
+            ++it;
+          }
+        }
+        for (auto slot : slots_to_replace) {
+          auto range = rand_rec.feas_.equal_range(slot);
+          for (auto it = range.first; it != range.second; ++it) {
+            new_rec.uint64_feasigns_.push_back({it->second, it->first});
+            debug_push_cnt += 1;
+          }
+        }
+        (*result)[i] = std::move(new_rec);
+      }
+      VLOG(3) << "thread[" << tid << "]: erase feasign num: " << debug_erase_cnt
+              << " repush feasign num: " << debug_push_cnt;
+    }));
+  }
+  for (int tid = 0; tid < auc_runner_thread_num_; ++tid) {
+    threads[tid].join();
+  }
+  VLOG(0) << "End GetRandomData";
+}
 
-    VLOG(3) << "Begin call PushSparseGPU in BoxPS";
-    push_boxps_timer.Start();
-    int ret = boxps_ptr_->PushSparseGPU(
-        total_keys, total_grad_values_gpu, static_cast<int>(total_length),
-        BOOST_GET_CONST(platform::CUDAPlace, place).GetDeviceId());
-    PADDLE_ENFORCE_EQ(ret, 0, platform::errors::PreconditionNotMet(
-                                  "PushSparseGPU failed in BoxPS."));
-    push_boxps_timer.Pause();
-#else
-    PADDLE_THROW(platform::errors::PreconditionNotMet(
-        "Please compile WITH_GPU option, because NCCL doesn't support "
-        "windows."));
-#endif
-  } else {
-    PADDLE_THROW(platform::errors::PreconditionNotMet(
-        "PaddleBox: PushSparseGrad Only Support CPUPlace or CUDAPlace Now."));
+void BoxWrapper::AddReplaceFeasign(boxps::PSAgentBase* p_agent,
+                                   int feed_pass_thread_num) {
+  VLOG(0) << "Enter AddReplaceFeasign Function";
+  int semi;
+  pass_done_semi_->Get(semi);
+  VLOG(0) << "Last Pass had updated random pool done. Begin AddReplaceFeasign";
+  std::vector<std::thread> threads;
+  for (int tid = 0; tid < feed_pass_thread_num; ++tid) {
+    threads.push_back(std::thread([this, tid, p_agent, feed_pass_thread_num]() {
+      VLOG(3) << "AddReplaceFeasign begin for thread[" << tid << "]";
+      for (size_t pool_id = tid; pool_id < random_ins_pool_list.size();
+           pool_id += feed_pass_thread_num) {
+        auto& random_pool = random_ins_pool_list[pool_id];
+        for (size_t i = 0; i < random_pool.Size(); ++i) {
+          auto& ins_candidate = random_pool.Get(i);
+          for (const auto& pair : ins_candidate.feas_) {
+            p_agent->AddKey(pair.second.uint64_feasign_, tid);
+          }
+        }
+      }
+    }));
   }
-  all_timer.Pause();
-  VLOG(1) << "PushSparseGrad total cost: " << all_timer.ElapsedSec()
-          << " s, of which BoxPS cost: " << push_boxps_timer.ElapsedSec()
-          << " s";
-  VLOG(3) << "End PushSparseGrad";
+  for (int tid = 0; tid < feed_pass_thread_num; ++tid) {
+    threads[tid].join();
+  }
+  VLOG(0) << "End AddReplaceFeasign";
 }
+
 }  // end namespace framework
 }  // end namespace paddle
 #endif

paddle/fluid/framework/fleet/box_wrapper.cu

@@ -27,9 +27,12 @@ namespace framework {
   for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
        i += blockDim.x * gridDim.x)
 
-__global__ void PullCopy(float** dest, const boxps::FeatureValueGpu* src,
-                         const int64_t* len, int hidden, int slot_num,
-                         int total_len, uint64_t** keys) {
+template <size_t EMBEDX_DIM, size_t EXPAND_EMBED_DIM>
+__global__ void PullCopy(
+    float** dest,
+    const boxps::FeatureValueGpu<EMBEDX_DIM, EXPAND_EMBED_DIM>* src,
+    const int64_t* len, int hidden, int expand_dim, int slot_num, int total_len,
+    uint64_t** keys) {
   CUDA_KERNEL_LOOP(i, total_len) {
     int low = 0;
     int high = slot_num - 1;
@@ -52,15 +55,28 @@ __global__ void PullCopy(float** dest, const boxps::FeatureValueGpu* src,
       *(dest[x] + y * hidden + 2) = (src + i)->embed_w;
     }
     if ((src + i)->embedding_size == 0 || *(keys[x] + y) == 0) {
-      for (int j = 0; j < 8; j++) {
+      for (int j = 0; j < hidden - 3; j++) {
         *(dest[x] + y * hidden + 3 + j) = 0;
       }
     } else {
-      for (int j = 0; j < 8; j++) {
+      for (int j = 0; j < hidden - 3; j++) {
         *(dest[x] + y * hidden + 3 + j) = (src + i)->embedx[1 + j];
       }
     }
-  }
+    // process embed_expand
+    if (expand_dim > 0) {
+      int z = x + slot_num;
+      if ((src + i)->embed_expand_size[0] == 0 || *(keys[x] + y) == 0) {
+        for (int j = 0; j < expand_dim; j++) {
+          *(dest[z] + y * expand_dim + j) = 0;
+        }
+      } else {
+        for (int j = 0; j < expand_dim; j++) {
+          *(dest[z] + y * expand_dim + j) = (src + i)->embed_expand[1 + j];
+        }
+      }
+    }
+  }  // end kernel loop
 }
 
 __global__ void CopyKeysKernel(uint64_t** src_keys, uint64_t* dest_total_keys,
@@ -82,9 +98,11 @@ __global__ void CopyKeysKernel(uint64_t** src_keys, uint64_t* dest_total_keys,
   }
 }
 
-__global__ void PushCopy(boxps::FeaturePushValueGpu* dest, float** src,
-                         int64_t* len, int hidden, int slot_num, int total_len,
-                         int bs, int* slot_vector) {
+template <size_t EMBEDX_DIM, size_t EXPAND_EMBED_DIM>
+__global__ void PushCopy(
+    boxps::FeaturePushValueGpu<EMBEDX_DIM, EXPAND_EMBED_DIM>* dest, float** src,
+    int64_t* len, int hidden, int expand_dim, int slot_num, int total_len,
+    int bs, int* slot_vector) {
   CUDA_KERNEL_LOOP(i, total_len) {
     int low = 0;
     int high = slot_num - 1;
@@ -101,18 +119,25 @@ __global__ void PushCopy(boxps::FeaturePushValueGpu* dest, float** src,
     (dest + i)->show = *(src[x] + y * hidden);
     (dest + i)->clk = *(src[x] + y * hidden + 1);
     (dest + i)->embed_g = *(src[x] + y * hidden + 2) * -1. * bs;
-    for (int j = 0; j < 8; j++) {
+    for (int j = 0; j < hidden - 3; j++) {
       (dest + i)->embedx_g[j] = *(src[x] + y * hidden + 3 + j) * -1. * bs;
     }
+    if (expand_dim > 0) {
+      int z = x + slot_num;
+      for (int j = 0; j < expand_dim; j++) {
+        (dest + i)->embed_expand_g[j] =
+            *(src[z] + y * expand_dim + j) * -1. * bs;
+      }
+    }
   }
 }
 
 void BoxWrapper::CopyForPull(const paddle::platform::Place& place,
                              uint64_t** gpu_keys,
                              const std::vector<float*>& values,
-                             const boxps::FeatureValueGpu* total_values_gpu,
-                             const int64_t* gpu_len, const int slot_num,
-                             const int hidden_size,
+                             void* total_values_gpu, const int64_t* gpu_len,
+                             const int slot_num, const int hidden_size,
+                             const int expand_embed_dim,
                              const int64_t total_length) {
   auto stream = dynamic_cast<platform::CUDADeviceContext*>(
                     platform::DeviceContextPool::Instance().Get(
@@ -122,11 +147,40 @@ void BoxWrapper::CopyForPull(const paddle::platform::Place& place,
   float** gpu_values = reinterpret_cast<float**>(buf_value->ptr());
   cudaMemcpy(gpu_values, values.data(), values.size() * sizeof(float*),
              cudaMemcpyHostToDevice);
+#define EMBEDX_CASE(i, ...)                                                  \
+  case i: {                                                                  \
+    constexpr size_t EmbedxDim = i;                                          \
+    switch (expand_embed_dim) {                                              \
+      __VA_ARGS__                                                            \
+      default:                                                               \
+        PADDLE_THROW(platform::errors::InvalidArgument(                      \
+            "Unsupport this expand embedding size [%d]", expand_embed_dim)); \
+    }                                                                        \
+  } break
+
+#define EXPAND_EMBED_PULL_CASE(i, ...)                                       \
+  case i: {                                                                  \
+    constexpr size_t ExpandDim = i;                                          \
+    PullCopy<EmbedxDim,                                                      \
+             ExpandDim><<<(total_length + 512 - 1) / 512, 512, 0, stream>>>( \
+        gpu_values,                                                          \
+        reinterpret_cast<boxps::FeatureValueGpu<EmbedxDim, ExpandDim>*>(     \
+            total_values_gpu),                                               \
+        gpu_len, hidden_size, expand_embed_dim, slot_num, total_length,      \
+        gpu_keys);                                                           \
+  } break
 
-  PullCopy<<<(total_length + 512 - 1) / 512, 512, 0, stream>>>(
-      gpu_values, total_values_gpu, gpu_len, hidden_size, slot_num,
-      total_length, gpu_keys);
+  switch (hidden_size - 3) {
+    EMBEDX_CASE(8, EXPAND_EMBED_PULL_CASE(0); EXPAND_EMBED_PULL_CASE(8);
+                EXPAND_EMBED_PULL_CASE(64););
+    EMBEDX_CASE(16, EXPAND_EMBED_PULL_CASE(0););
+    default:
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "Unsupport this embedding size [%d]", hidden_size - 3));
+  }
   cudaStreamSynchronize(stream);
+#undef EXPAND_EMBED_PULL_CASE
+#undef EMBEDX_CASE
 }
 
 void BoxWrapper::CopyKeys(const paddle::platform::Place& place,
@@ -143,10 +197,10 @@ void BoxWrapper::CopyKeys(const paddle::platform::Place& place,
 
 void BoxWrapper::CopyForPush(const paddle::platform::Place& place,
                              const std::vector<const float*>& grad_values,
-                             boxps::FeaturePushValueGpu* total_grad_values_gpu,
+                             void* total_grad_values_gpu,
                              const std::vector<int64_t>& slot_lengths,
-                             const int hidden_size, const int64_t total_length,
-                             const int batch_size) {
+                             const int hidden_size, const int expand_embed_dim,
+                             const int64_t total_length, const int batch_size) {
   auto stream = dynamic_cast<platform::CUDADeviceContext*>(
                     platform::DeviceContextPool::Instance().Get(
                         BOOST_GET_CONST(platform::CUDAPlace, place)))
@@ -173,11 +227,42 @@ void BoxWrapper::CopyForPush(const paddle::platform::Place& place,
   cudaMemcpy(d_slot_vector, slot_vector_.data(),
              slot_lengths_lod.size() * sizeof(int), cudaMemcpyHostToDevice);
 
-  PushCopy<<<(total_length + 512 - 1) / 512, 512, 0, stream>>>(
-      total_grad_values_gpu, gpu_values, gpu_len, hidden_size,
-      slot_lengths.size(), total_length, batch_size, d_slot_vector);
+#define EMBEDX_CASE(i, ...)                                                  \
+  case i: {                                                                  \
+    constexpr size_t EmbedxDim = i;                                          \
+    switch (expand_embed_dim) {                                              \
+      __VA_ARGS__                                                            \
+      default:                                                               \
+        PADDLE_THROW(platform::errors::InvalidArgument(                      \
+            "Unsupport this expand embedding size [%d]", expand_embed_dim)); \
+    }                                                                        \
+  } break
+
+#define EXPAND_EMBED_PUSH_CASE(i, ...)                                       \
+  case i: {                                                                  \
+    constexpr size_t ExpandDim = i;                                          \
+    PushCopy<EmbedxDim,                                                      \
+             ExpandDim><<<(total_length + 512 - 1) / 512, 512, 0, stream>>>( \
+        reinterpret_cast<boxps::FeaturePushValueGpu<EmbedxDim, ExpandDim>*>( \
+            total_grad_values_gpu),                                          \
+        gpu_values, gpu_len, hidden_size, expand_embed_dim,                  \
+        slot_lengths.size(), total_length, batch_size, d_slot_vector);       \
+  } break
+
+  switch (hidden_size - 3) {
+    EMBEDX_CASE(8, EXPAND_EMBED_PUSH_CASE(0); EXPAND_EMBED_PUSH_CASE(8);
+                EXPAND_EMBED_PUSH_CASE(64););
+    EMBEDX_CASE(16, EXPAND_EMBED_PUSH_CASE(0););
+    default:
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "Unsupport this embedding size [%d]", hidden_size - 3));
+  }
+
   cudaStreamSynchronize(stream);
+#undef EXPAND_EMBED_PUSH_CASE
+#undef EMBEDX_CASE
 }
+
 }  // end namespace framework
 }  // end namespace paddle
 #endif

paddle/fluid/framework/fleet/box_wrapper.h

@@ -31,10 +31,12 @@ limitations under the License. */
 #include <map>
 #include <memory>
 #include <mutex>  // NOLINT
+#include <set>
 #include <string>
 #include <unordered_set>
 #include <utility>
 #include <vector>
+#include "paddle/fluid/framework/data_feed.h"
 #include "paddle/fluid/framework/data_set.h"
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/scope.h"
@@ -339,30 +341,54 @@ class BoxWrapper {
   void BeginPass() const;
   void EndPass(bool need_save_delta) const;
   void SetTestMode(bool is_test) const;
+
+  template <size_t EMBEDX_DIM, size_t EXPAND_EMBED_DIM = 0>
+  void PullSparseCase(const paddle::platform::Place& place,
+                      const std::vector<const uint64_t*>& keys,
+                      const std::vector<float*>& values,
+                      const std::vector<int64_t>& slot_lengths,
+                      const int hidden_size, const int expand_embed_dim);
+
   void PullSparse(const paddle::platform::Place& place,
                   const std::vector<const uint64_t*>& keys,
                   const std::vector<float*>& values,
                   const std::vector<int64_t>& slot_lengths,
-                  const int hidden_size);
+                  const int hidden_size, const int expand_embed_dim);
+
+  template <size_t EMBEDX_DIM, size_t EXPAND_EMBED_DIM = 0>
+  void PushSparseGradCase(const paddle::platform::Place& place,
+                          const std::vector<const uint64_t*>& keys,
+                          const std::vector<const float*>& grad_values,
+                          const std::vector<int64_t>& slot_lengths,
+                          const int hidden_size, const int expand_embed_dim,
+                          const int batch_size);
+
   void PushSparseGrad(const paddle::platform::Place& place,
                       const std::vector<const uint64_t*>& keys,
                       const std::vector<const float*>& grad_values,
                       const std::vector<int64_t>& slot_lengths,
-                      const int hidden_size, const int batch_size);
+                      const int hidden_size, const int expand_embed_dim,
+                      const int batch_size);
+
   void CopyForPull(const paddle::platform::Place& place, uint64_t** gpu_keys,
-                   const std::vector<float*>& values,
-                   const boxps::FeatureValueGpu* total_values_gpu,
+                   const std::vector<float*>& values, void* total_values_gpu,
                    const int64_t* gpu_len, const int slot_num,
-                   const int hidden_size, const int64_t total_length);
+                   const int hidden_size, const int expand_embed_dim,
+                   const int64_t total_length);
+
   void CopyForPush(const paddle::platform::Place& place,
                    const std::vector<const float*>& grad_values,
-                   boxps::FeaturePushValueGpu* total_grad_values_gpu,
+                   void* total_grad_values_gpu,
                    const std::vector<int64_t>& slot_lengths,
-                   const int hidden_size, const int64_t total_length,
-                   const int batch_size);
+                   const int hidden_size, const int expand_embed_dim,
+                   const int64_t total_length, const int batch_size);
+
   void CopyKeys(const paddle::platform::Place& place, uint64_t** origin_keys,
                 uint64_t* total_keys, const int64_t* gpu_len, int slot_num,
                 int total_len);
+
+  void CheckEmbedSizeIsValid(int embedx_dim, int expand_embed_dim);
+
   boxps::PSAgentBase* GetAgent() { return p_agent_; }
   void InitializeGPUAndLoadModel(
       const char* conf_file, const std::vector<int>& slot_vector,
@@ -440,6 +466,15 @@ class BoxWrapper {
   }
 
   static std::shared_ptr<BoxWrapper> GetInstance() {
+    PADDLE_ENFORCE_EQ(
+        s_instance_ == nullptr, false,
+        platform::errors::PreconditionNotMet(
+            "GetInstance failed in BoxPs, you should use SetInstance firstly"));
+    return s_instance_;
+  }
+
+  static std::shared_ptr<BoxWrapper> SetInstance(int embedx_dim = 8,
+                                                 int expand_embed_dim = 0) {
     if (nullptr == s_instance_) {
       // If main thread is guaranteed to init this, this lock can be removed
       static std::mutex mutex;
@@ -447,8 +482,13 @@ class BoxWrapper {
       if (nullptr == s_instance_) {
         VLOG(3) << "s_instance_ is null";
         s_instance_.reset(new paddle::framework::BoxWrapper());
-        s_instance_->boxps_ptr_.reset(boxps::BoxPSBase::GetIns());
+        s_instance_->boxps_ptr_.reset(
+            boxps::BoxPSBase::GetIns(embedx_dim, expand_embed_dim));
+        embedx_dim_ = embedx_dim;
+        expand_embed_dim_ = expand_embed_dim;
       }
+    } else {
+      LOG(WARNING) << "You have already used SetInstance() before";
     }
     return s_instance_;
   }
@@ -469,16 +509,16 @@ class BoxWrapper {
    public:
     MetricMsg() {}
     MetricMsg(const std::string& label_varname, const std::string& pred_varname,
-              int is_join, int bucket_size = 1000000)
+              int metric_phase, int bucket_size = 1000000)
         : label_varname_(label_varname),
           pred_varname_(pred_varname),
-          is_join_(is_join) {
+          metric_phase_(metric_phase) {
       calculator = new BasicAucCalculator();
       calculator->init(bucket_size);
     }
     virtual ~MetricMsg() {}
 
-    int IsJoin() const { return is_join_; }
+    int MetricPhase() const { return metric_phase_; }
     BasicAucCalculator* GetCalculator() { return calculator; }
     virtual void add_data(const Scope* exe_scope) {
       std::vector<int64_t> label_data;
@@ -514,20 +554,20 @@ class BoxWrapper {
    protected:
     std::string label_varname_;
     std::string pred_varname_;
-    int is_join_;
+    int metric_phase_;
     BasicAucCalculator* calculator;
   };
 
   class MultiTaskMetricMsg : public MetricMsg {
    public:
     MultiTaskMetricMsg(const std::string& label_varname,
-                       const std::string& pred_varname_list, int is_join,
+                       const std::string& pred_varname_list, int metric_phase,
                        const std::string& cmatch_rank_group,
                        const std::string& cmatch_rank_varname,
                        int bucket_size = 1000000) {
       label_varname_ = label_varname;
       cmatch_rank_varname_ = cmatch_rank_varname;
-      is_join_ = is_join;
+      metric_phase_ = metric_phase;
       calculator = new BasicAucCalculator();
       calculator->init(bucket_size);
       for (auto& cmatch_rank : string::split_string(cmatch_rank_group)) {
@@ -594,14 +634,14 @@ class BoxWrapper {
   class CmatchRankMetricMsg : public MetricMsg {
    public:
     CmatchRankMetricMsg(const std::string& label_varname,
-                        const std::string& pred_varname, int is_join,
+                        const std::string& pred_varname, int metric_phase,
                         const std::string& cmatch_rank_group,
                         const std::string& cmatch_rank_varname,
                         int bucket_size = 1000000) {
       label_varname_ = label_varname;
       pred_varname_ = pred_varname;
       cmatch_rank_varname_ = cmatch_rank_varname;
-      is_join_ = is_join;
+      metric_phase_ = metric_phase;
       calculator = new BasicAucCalculator();
       calculator->init(bucket_size);
       for (auto& cmatch_rank : string::split_string(cmatch_rank_group)) {
@@ -653,12 +693,12 @@ class BoxWrapper {
   class MaskMetricMsg : public MetricMsg {
    public:
     MaskMetricMsg(const std::string& label_varname,
-                  const std::string& pred_varname, int is_join,
+                  const std::string& pred_varname, int metric_phase,
                   const std::string& mask_varname, int bucket_size = 1000000) {
       label_varname_ = label_varname;
       pred_varname_ = pred_varname;
       mask_varname_ = mask_varname;
-      is_join_ = is_join;
+      metric_phase_ = metric_phase;
       calculator = new BasicAucCalculator();
       calculator->init(bucket_size);
     }
@@ -682,36 +722,59 @@ class BoxWrapper {
    protected:
     std::string mask_varname_;
   };
-  const std::vector<std::string>& GetMetricNameList() const {
-    return metric_name_list_;
+  const std::vector<std::string> GetMetricNameList(
+      int metric_phase = -1) const {
+    VLOG(0) << "Want to Get metric phase: " << metric_phase;
+    if (metric_phase == -1) {
+      return metric_name_list_;
+    } else {
+      std::vector<std::string> ret;
+      for (const auto& name : metric_name_list_) {
+        const auto iter = metric_lists_.find(name);
+        PADDLE_ENFORCE_NE(
+            iter, metric_lists_.end(),
+            platform::errors::InvalidArgument(
+                "The metric name you provided is not registered."));
+
+        if (iter->second->MetricPhase() == metric_phase) {
+          VLOG(0) << name << "'s phase is " << iter->second->MetricPhase()
+                  << ", we want";
+          ret.push_back(name);
+        } else {
+          VLOG(0) << name << "'s phase is " << iter->second->MetricPhase()
+                  << ", not we want";
+        }
+      }
+      return ret;
+    }
   }
-  int PassFlag() const { return pass_flag_; }
-  void FlipPassFlag() { pass_flag_ = 1 - pass_flag_; }
+  int Phase() const { return phase_; }
+  void FlipPhase() { phase_ = (phase_ + 1) % phase_num_; }
   std::map<std::string, MetricMsg*>& GetMetricList() { return metric_lists_; }
 
   void InitMetric(const std::string& method, const std::string& name,
                   const std::string& label_varname,
                   const std::string& pred_varname,
                   const std::string& cmatch_rank_varname,
-                  const std::string& mask_varname, bool is_join,
+                  const std::string& mask_varname, int metric_phase,
                   const std::string& cmatch_rank_group,
                   int bucket_size = 1000000) {
     if (method == "AucCalculator") {
       metric_lists_.emplace(name, new MetricMsg(label_varname, pred_varname,
-                                                is_join ? 1 : 0, bucket_size));
+                                                metric_phase, bucket_size));
     } else if (method == "MultiTaskAucCalculator") {
       metric_lists_.emplace(
           name, new MultiTaskMetricMsg(label_varname, pred_varname,
-                                       is_join ? 1 : 0, cmatch_rank_group,
+                                       metric_phase, cmatch_rank_group,
                                        cmatch_rank_varname, bucket_size));
     } else if (method == "CmatchRankAucCalculator") {
       metric_lists_.emplace(
           name, new CmatchRankMetricMsg(label_varname, pred_varname,
-                                        is_join ? 1 : 0, cmatch_rank_group,
+                                        metric_phase, cmatch_rank_group,
                                         cmatch_rank_varname, bucket_size));
     } else if (method == "MaskAucCalculator") {
       metric_lists_.emplace(
-          name, new MaskMetricMsg(label_varname, pred_varname, is_join ? 1 : 0,
+          name, new MaskMetricMsg(label_varname, pred_varname, metric_phase,
                                   mask_varname, bucket_size));
     } else {
       PADDLE_THROW(platform::errors::Unimplemented(
@@ -751,9 +814,13 @@ class BoxWrapper {
   const int feedpass_thread_num_ = 30;  // magic number
   static std::shared_ptr<BoxWrapper> s_instance_;
   std::unordered_set<std::string> slot_name_omited_in_feedpass_;
+  // EMBEDX_DIM and EXPAND_EMBED_DIM
+  static int embedx_dim_;
+  static int expand_embed_dim_;
 
   // Metric Related
-  int pass_flag_ = 1;  // join: 1, update: 0
+  int phase_ = 1;
+  int phase_num_ = 2;
   std::map<std::string, MetricMsg*> metric_lists_;
   std::vector<std::string> metric_name_list_;
   std::vector<int> slot_vector_;
@@ -762,6 +829,57 @@ class BoxWrapper {
 
  public:
   static AfsManager* afs_manager;
+
+  // Auc Runner
+ public:
+  void InitializeAucRunner(std::vector<std::vector<std::string>> slot_eval,
+                           int thread_num, int pool_size,
+                           std::vector<std::string> slot_list) {
+    mode_ = 1;
+    phase_num_ = static_cast<int>(slot_eval.size());
+    phase_ = phase_num_ - 1;
+    auc_runner_thread_num_ = thread_num;
+    pass_done_semi_ = paddle::framework::MakeChannel<int>();
+    pass_done_semi_->Put(1);  // Note: At most 1 pipeline in AucRunner
+    random_ins_pool_list.resize(thread_num);
+
+    std::unordered_set<std::string> slot_set;
+    for (size_t i = 0; i < slot_eval.size(); ++i) {
+      for (const auto& slot : slot_eval[i]) {
+        slot_set.insert(slot);
+      }
+    }
+    for (size_t i = 0; i < slot_list.size(); ++i) {
+      if (slot_set.find(slot_list[i]) != slot_set.end()) {
+        slot_index_to_replace_.insert(static_cast<int16_t>(i));
+      }
+    }
+    for (int i = 0; i < auc_runner_thread_num_; ++i) {
+      random_ins_pool_list[i].SetSlotIndexToReplace(slot_index_to_replace_);
+    }
+    VLOG(0) << "AucRunner configuration: thread number[" << thread_num
+            << "], pool size[" << pool_size << "], runner_group[" << phase_num_
+            << "]";
+    VLOG(0) << "Slots that need to be evaluated:";
+    for (auto e : slot_index_to_replace_) {
+      VLOG(0) << e << ": " << slot_list[e];
+    }
+  }
+  void GetRandomReplace(const std::vector<Record>& pass_data);
+  void AddReplaceFeasign(boxps::PSAgentBase* p_agent, int feed_pass_thread_num);
+  void GetRandomData(const std::vector<Record>& pass_data,
+                     const std::unordered_set<uint16_t>& slots_to_replace,
+                     std::vector<Record>* result);
+  int Mode() const { return mode_; }
+
+ private:
+  int mode_ = 0;  // 0 means train/test 1 means auc_runner
+  int auc_runner_thread_num_ = 1;
+  bool init_done_ = false;
+  paddle::framework::Channel<int> pass_done_semi_;
+  std::unordered_set<uint16_t> slot_index_to_replace_;
+  std::vector<RecordCandidateList> random_ins_pool_list;
+  std::vector<size_t> replace_idx_;
 };
 #endif
 
@@ -810,7 +928,38 @@ class BoxHelper {
     VLOG(3) << "After PreLoadIntoMemory()";
   }
   void WaitFeedPassDone() { feed_data_thread_->join(); }
+  void SlotsShuffle(const std::set<std::string>& slots_to_replace) {
+#ifdef PADDLE_WITH_BOX_PS
+    auto box_ptr = BoxWrapper::GetInstance();
+    PADDLE_ENFORCE_EQ(box_ptr->Mode(), 1,
+                      platform::errors::PreconditionNotMet(
+                          "Should call InitForAucRunner first."));
+    box_ptr->FlipPhase();
 
+    std::unordered_set<uint16_t> index_slots;
+    dynamic_cast<MultiSlotDataset*>(dataset_)->PreprocessChannel(
+        slots_to_replace, index_slots);
+    const std::vector<Record>& pass_data =
+        dynamic_cast<MultiSlotDataset*>(dataset_)->GetSlotsOriginalData();
+    if (!get_random_replace_done_) {
+      box_ptr->GetRandomReplace(pass_data);
+      get_random_replace_done_ = true;
+    }
+    std::vector<Record> random_data;
+    random_data.resize(pass_data.size());
+    box_ptr->GetRandomData(pass_data, index_slots, &random_data);
+
+    auto new_input_channel = paddle::framework::MakeChannel<Record>();
+    new_input_channel->Open();
+    new_input_channel->Write(std::move(random_data));
+    new_input_channel->Close();
+    dynamic_cast<MultiSlotDataset*>(dataset_)->SetInputChannel(
+        new_input_channel);
+    if (dataset_->EnablePvMerge()) {
+      dataset_->PreprocessInstance();
+    }
+#endif
+  }
 #ifdef PADDLE_WITH_BOX_PS
   // notify boxps to feed this pass feasigns from SSD to memory
   static void FeedPassThread(const std::deque<Record>& t, int begin_index,
@@ -881,6 +1030,10 @@ class BoxHelper {
     for (size_t i = 0; i < tnum; ++i) {
       threads[i].join();
     }
+
+    if (box_ptr->Mode() == 1) {
+      box_ptr->AddReplaceFeasign(p_agent, tnum);
+    }
     VLOG(3) << "Begin call EndFeedPass in BoxPS";
     box_ptr->EndFeedPass(p_agent);
 #endif
@@ -892,7 +1045,10 @@ class BoxHelper {
   int year_;
   int month_;
   int day_;
+  bool get_random_replace_done_ = false;
 };
 
 }  // end namespace framework
 }  // end namespace paddle
+
+#include "paddle/fluid/framework/fleet/box_wrapper_impl.h"

paddle/fluid/framework/fleet/box_wrapper_impl.h

+/* Copyright (c) 2020 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
+#ifdef PADDLE_WITH_BOX_PS
+#include <vector>
+namespace paddle {
+namespace framework {
+
+template <size_t EMBEDX_DIM, size_t EXPAND_EMBED_DIM>
+void BoxWrapper::PullSparseCase(const paddle::platform::Place& place,
+                                const std::vector<const uint64_t*>& keys,
+                                const std::vector<float*>& values,
+                                const std::vector<int64_t>& slot_lengths,
+                                const int hidden_size,
+                                const int expand_embed_dim) {
+  VLOG(3) << "Begin PullSparse";
+  platform::Timer all_timer;
+  platform::Timer pull_boxps_timer;
+  all_timer.Start();
+
+  int64_t total_length =
+      std::accumulate(slot_lengths.begin(), slot_lengths.end(), 0UL);
+  auto buf = memory::AllocShared(
+      place, total_length *
+                 sizeof(boxps::FeatureValueGpu<EMBEDX_DIM, EXPAND_EMBED_DIM>));
+  boxps::FeatureValueGpu<EMBEDX_DIM, EXPAND_EMBED_DIM>* total_values_gpu =
+      reinterpret_cast<boxps::FeatureValueGpu<EMBEDX_DIM, EXPAND_EMBED_DIM>*>(
+          buf->ptr());
+
+  if (platform::is_cpu_place(place)) {
+    PADDLE_THROW(platform::errors::Unimplemented(
+        "Warning:: CPUPlace is not supported in PaddleBox now."));
+  } else if (platform::is_gpu_place(place)) {
+#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
+    VLOG(3) << "Begin copy keys, key_num[" << total_length << "]";
+    int device_id = BOOST_GET_CONST(platform::CUDAPlace, place).GetDeviceId();
+    LoDTensor& total_keys_tensor = keys_tensor[device_id];
+    uint64_t* total_keys = reinterpret_cast<uint64_t*>(
+        total_keys_tensor.mutable_data<int64_t>({total_length, 1}, place));
+
+    // construct slot_level lod info
+    auto slot_lengths_lod = slot_lengths;
+    for (size_t i = 1; i < slot_lengths_lod.size(); i++) {
+      slot_lengths_lod[i] += slot_lengths_lod[i - 1];
+    }
+    auto buf_key = memory::AllocShared(place, keys.size() * sizeof(uint64_t*));
+    auto buf_length =
+        memory::AllocShared(place, slot_lengths.size() * sizeof(int64_t));
+    uint64_t** gpu_keys = reinterpret_cast<uint64_t**>(buf_key->ptr());
+    int64_t* gpu_len = reinterpret_cast<int64_t*>(buf_length->ptr());
+    cudaMemcpy(gpu_keys, keys.data(), keys.size() * sizeof(uint64_t*),
+               cudaMemcpyHostToDevice);
+    cudaMemcpy(gpu_len, slot_lengths_lod.data(),
+               slot_lengths.size() * sizeof(int64_t), cudaMemcpyHostToDevice);
+
+    this->CopyKeys(place, gpu_keys, total_keys, gpu_len,
+                   static_cast<int>(slot_lengths.size()),
+                   static_cast<int>(total_length));
+    VLOG(3) << "Begin call PullSparseGPU in BoxPS";
+    pull_boxps_timer.Start();
+    int ret = boxps_ptr_->PullSparseGPU(
+        total_keys, reinterpret_cast<void*>(total_values_gpu),
+        static_cast<int>(total_length), device_id);
+    PADDLE_ENFORCE_EQ(ret, 0, platform::errors::PreconditionNotMet(
+                                  "PullSparseGPU failed in BoxPS."));
+    pull_boxps_timer.Pause();
+
+    VLOG(3) << "Begin Copy result to tensor, total_length[" << total_length
+            << "]";
+    this->CopyForPull(place, gpu_keys, values,
+                      reinterpret_cast<void*>(total_values_gpu), gpu_len,
+                      static_cast<int>(slot_lengths.size()), hidden_size,
+                      expand_embed_dim, total_length);
+#else
+    PADDLE_THROW(platform::errors::PreconditionNotMet(
+        "Please compile WITH_GPU option, because NCCL doesn't support "
+        "windows."));
+#endif
+  } else {
+    PADDLE_THROW(platform::errors::PreconditionNotMet(
+        "PaddleBox: PullSparse Only Support CPUPlace or CUDAPlace Now."));
+  }
+  all_timer.Pause();
+  VLOG(1) << "PullSparse total costs: " << all_timer.ElapsedSec()
+          << " s, of which BoxPS costs: " << pull_boxps_timer.ElapsedSec()
+          << " s";
+  VLOG(3) << "End PullSparse";
+}
+
+template <size_t EMBEDX_DIM, size_t EXPAND_EMBED_DIM>
+void BoxWrapper::PushSparseGradCase(
+    const paddle::platform::Place& place,
+    const std::vector<const uint64_t*>& keys,
+    const std::vector<const float*>& grad_values,
+    const std::vector<int64_t>& slot_lengths, const int hidden_size,
+    const int expand_embed_dim, const int batch_size) {
+  VLOG(3) << "Begin PushSparseGrad";
+  platform::Timer all_timer;
+  platform::Timer push_boxps_timer;
+  all_timer.Start();
+  int64_t total_length =
+      std::accumulate(slot_lengths.begin(), slot_lengths.end(), 0UL);
+  auto buf = memory::AllocShared(
+      place,
+      total_length *
+          sizeof(boxps::FeaturePushValueGpu<EMBEDX_DIM, EXPAND_EMBED_DIM>));
+  boxps::FeaturePushValueGpu<EMBEDX_DIM, EXPAND_EMBED_DIM>*
+      total_grad_values_gpu = reinterpret_cast<
+          boxps::FeaturePushValueGpu<EMBEDX_DIM, EXPAND_EMBED_DIM>*>(
+          buf->ptr());
+  if (platform::is_cpu_place(place)) {
+    PADDLE_THROW(platform::errors::Unimplemented(
+        "Warning:: CPUPlace is not supported in PaddleBox now."));
+  } else if (platform::is_gpu_place(place)) {
+#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
+    int device_id = BOOST_GET_CONST(platform::CUDAPlace, place).GetDeviceId();
+    LoDTensor& cached_total_keys_tensor = keys_tensor[device_id];
+    uint64_t* total_keys =
+        reinterpret_cast<uint64_t*>(cached_total_keys_tensor.data<int64_t>());
+    VLOG(3) << "Begin copy grad tensor to boxps struct";
+    this->CopyForPush(place, grad_values, total_grad_values_gpu, slot_lengths,
+                      hidden_size, expand_embed_dim, total_length, batch_size);
+
+    VLOG(3) << "Begin call PushSparseGPU in BoxPS";
+    push_boxps_timer.Start();
+    int ret = boxps_ptr_->PushSparseGPU(
+        total_keys, reinterpret_cast<void*>(total_grad_values_gpu),
+        static_cast<int>(total_length),
+        BOOST_GET_CONST(platform::CUDAPlace, place).GetDeviceId());
+    PADDLE_ENFORCE_EQ(ret, 0, platform::errors::PreconditionNotMet(
+                                  "PushSparseGPU failed in BoxPS."));
+    push_boxps_timer.Pause();
+#else
+    PADDLE_THROW(platform::errors::PreconditionNotMet(
+        "Please compile WITH_GPU option, because NCCL doesn't support "
+        "windows."));
+#endif
+  } else {
+    PADDLE_THROW(platform::errors::PreconditionNotMet(
+        "PaddleBox: PushSparseGrad Only Support CPUPlace or CUDAPlace Now."));
+  }
+  all_timer.Pause();
+  VLOG(1) << "PushSparseGrad total cost: " << all_timer.ElapsedSec()
+          << " s, of which BoxPS cost: " << push_boxps_timer.ElapsedSec()
+          << " s";
+  VLOG(3) << "End PushSparseGrad";
+}
+
+}  // namespace framework
+}  // namespace paddle
+#endif

paddle/fluid/framework/parallel_executor.cc

@@ -31,6 +31,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/ir/memory_optimize_pass/memory_optimization_var_info.h"
 #include "paddle/fluid/framework/ir/memory_optimize_pass/reference_count_pass_helper.h"
 #include "paddle/fluid/framework/ir/multi_devices_graph_pass/set_reader_device_info_utils.h"
+#include "paddle/fluid/platform/event.h"
 #include "paddle/fluid/platform/profiler.h"
 
 DECLARE_double(eager_delete_tensor_gb);
@@ -820,6 +821,8 @@ void ParallelExecutor::BCastParamsToDevices(
 FetchResultType ParallelExecutor::Run(
     const std::vector<std::string> &fetch_tensors, bool return_merged) {
   VLOG(3) << "enter ParallelExecutor Run";
+  platform::RecordEvent parallel_executor_event(
+      "ParallelExecutor::Run", paddle::platform::EventRole::kSpecial);
 #ifdef WITH_GPERFTOOLS
   if (gProfileStarted) {
     ProfilerFlush();

paddle/fluid/framework/section_worker.cc

@@ -211,7 +211,7 @@ void SectionWorker::TrainFiles() {
     auto& metric_list = box_ptr->GetMetricList();
     for (auto iter = metric_list.begin(); iter != metric_list.end(); iter++) {
       auto* metric_msg = iter->second;
-      if (metric_msg->IsJoin() != box_ptr->PassFlag()) {
+      if (box_ptr->Phase() != metric_msg->MetricPhase()) {
         continue;
       }
       metric_msg->add_data(exe_scope);
@@ -367,7 +367,7 @@ void SectionWorker::TrainFilesWithProfiler() {
     auto& metric_list = box_ptr->GetMetricList();
     for (auto iter = metric_list.begin(); iter != metric_list.end(); iter++) {
       auto* metric_msg = iter->second;
-      if (metric_msg->IsJoin() != box_ptr->PassFlag()) {
+      if (box_ptr->Phase() != metric_msg->MetricPhase()) {
         continue;
       }
       metric_msg->add_data(exe_scope);

paddle/fluid/operators/controlflow/op_variant.h

@@ -43,7 +43,8 @@ class OpVariant {
   const AttrType &Attr(const std::string &name) const {
     auto &attrs = Attrs();
     auto it = attrs.find(name);
-    PADDLE_ENFORCE(it != attrs.end(), "Cannot find attribute %s", name);
+    PADDLE_ENFORCE_NE(it, attrs.end(), platform::errors::NotFound(
+                                           "Cannot find attribute %s.", name));
     return BOOST_GET_CONST(AttrType, it->second);
   }
 

paddle/fluid/operators/dequantize_log_op.cc

@@ -31,9 +31,9 @@ struct DequantizeFunctor<platform::CPUDeviceContext, T> {
     int ind = in->numel();
     for (size_t i = 0; i < (unsigned)ind; i++) {
       if (input_data[i] < 0) {
-        output_data[i] = -std::pow(2.0, dict_data[input_data[i] + 128]);
+        output_data[i] = -dict_data[input_data[i] + 128];
       } else {
-        output_data[i] = std::pow(2.0, dict_data[input_data[i]]);
+        output_data[i] = dict_data[input_data[i]];
       }
     }
   }

paddle/fluid/operators/dequantize_log_op.cu

@@ -26,9 +26,9 @@ __global__ void KeDequantize(const T* in, const float* dict, int num,
   const int idx = threadIdx.x + blockIdx.x * blockDim.x;
   if (idx < num) {
     if (in[idx] < 0) {
-      out[idx] = -std::pow(static_cast<float>(2.0), dict[in[idx] + 128]);
+      out[idx] = -dict[in[idx] + 128];
     } else {
-      out[idx] = std::pow(static_cast<float>(2.0), dict[in[idx]]);
+      out[idx] = dict[in[idx]];
     }
   }
 }

paddle/fluid/operators/elementwise/elementwise_op.h

@@ -104,7 +104,7 @@ class ElementwiseOp : public framework::OperatorWithKernel {
       int axis = ctx.Attr<int>("axis");
       int rankdiff = ctx.Input<Tensor>("X")->dims().size() -
                      ctx.Input<Tensor>("Y")->dims().size();
-      return (axis == -1) || (axis == rankdiff);
+      return (rankdiff == 0) || (axis == -1) || (axis == rankdiff);
     };
 
     if (platform::CanMKLDNNBeUsed(ctx) &&
@@ -243,9 +243,7 @@ class ElementwiseOpGrad : public framework::OperatorWithKernel {
 #ifdef PADDLE_WITH_MKLDNN
     // If broadcasting is needed, use native implementation
     auto CanMKLDNNElementwiseAddGradBeUsed = [&]() {
-      auto dx = ctx.Output<Tensor>(framework::GradVarName("X"));
-      auto dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
-      return (dx != nullptr && dy != nullptr && dx->dims() == dy->dims());
+      return (ctx.Input<Tensor>("X")->dims() == ctx.Input<Tensor>("Y")->dims());
     };
 
     if (platform::CanMKLDNNBeUsed(ctx) &&

paddle/fluid/operators/elementwise/mkldnn/elementwise_add_mkldnn_op.cc

@@ -85,6 +85,7 @@ class EltwiseAddMKLDNNGradKernel : public ElemwiseGradKernel<T> {
       in->set_format(out->format());
     };
 
+    // TODO(jczaja): Double check if vcopy works for blocked data
     auto blas = math::GetBlas<paddle::platform::CPUDeviceContext, T>(ctx);
     if (dx) {
       blas.VCOPY(dout->numel(), dout->data<T>(),

paddle/fluid/operators/hierarchical_sigmoid_op.cc

@@ -257,7 +257,7 @@ class HierarchicalSigmoidGradOpGradVarTypeInference
 };
 
 DECLARE_NO_NEED_BUFFER_VARS_INFERER(
-    HierarchicalSigmoidGradOpNoNeedBufferVarInference, "Bias");
+    HierarchicalSigmoidGradOpNoNeedBufferVarInferer, "Bias");
 
 }  // namespace operators
 }  // namespace paddle
@@ -270,7 +270,7 @@ REGISTER_OPERATOR(
     ops::HierarchicalSigmoidGradMaker<paddle::imperative::OpBase>);
 REGISTER_OPERATOR(hierarchical_sigmoid_grad, ops::HierarchicalSigmoidGradOp,
                   ops::HierarchicalSigmoidGradOpGradVarTypeInference,
-                  ops::HierarchicalSigmoidGradOpNoNeedBufferVarInference);
+                  ops::HierarchicalSigmoidGradOpNoNeedBufferVarInferer);
 REGISTER_OP_CPU_KERNEL(
     hierarchical_sigmoid,
     ops::HierarchicalSigmoidOpKernel<paddle::platform::CPUDeviceContext, float>,

paddle/fluid/operators/index_select_op.cc

@@ -138,7 +138,7 @@ class IndexSelectGradMaker : public framework::SingleGradOpMaker<T> {
   }
 };
 
-DECLARE_NO_NEED_BUFFER_VARS_INFERER(IndexSelectGradNoNeedBufferVarsInference,
+DECLARE_NO_NEED_BUFFER_VARS_INFERER(IndexSelectGradNoNeedBufferVarsInferer,
                                     "X");
 }  // namespace operators
 }  // namespace paddle
@@ -148,7 +148,7 @@ REGISTER_OPERATOR(index_select, ops::IndexSelectOp, ops::IndexSelectOpMaker,
                   ops::IndexSelectGradMaker<paddle::framework::OpDesc>,
                   ops::IndexSelectGradMaker<paddle::imperative::OpBase>);
 REGISTER_OPERATOR(index_select_grad, ops::IndexSelectGradOp,
-                  ops::IndexSelectGradNoNeedBufferVarsInference);
+                  ops::IndexSelectGradNoNeedBufferVarsInferer);
 REGISTER_OP_CPU_KERNEL(
     index_select,
     ops::IndexSelectKernel<paddle::platform::CPUDeviceContext, float>,

paddle/fluid/operators/instance_norm_op.cc

@@ -603,7 +603,7 @@ class InstanceNormDoubleGradKernel<platform::CPUDeviceContext, T>
   }
 };
 
-DECLARE_INPLACE_OP_INFERER(InstanceNormDoubleGradOpInplaceInference,
+DECLARE_INPLACE_OP_INFERER(InstanceNormDoubleGradOpInplaceInferer,
                            {"DY", "DDY"});
 
 }  // namespace operators
@@ -618,7 +618,7 @@ REGISTER_OPERATOR(instance_norm_grad, ops::InstanceNormGradOp,
                   ops::InstanceNormDoubleGradMaker<paddle::framework::OpDesc>,
                   ops::InstanceNormDoubleGradMaker<paddle::imperative::OpBase>);
 REGISTER_OPERATOR(instance_norm_grad_grad, ops::InstanceNormDoubleGradOp,
-                  ops::InstanceNormDoubleGradOpInplaceInference);
+                  ops::InstanceNormDoubleGradOpInplaceInferer);
 
 REGISTER_OP_CPU_KERNEL(
     instance_norm,

paddle/fluid/operators/interpolate_op.cc

@@ -585,7 +585,7 @@ class InterpolateGradMaker : public framework::SingleGradOpMaker<T> {
   }
 };
 
-DECLARE_NO_NEED_BUFFER_VARS_INFERER(InterpolateGradNoNeedBufferVarsInference,
+DECLARE_NO_NEED_BUFFER_VARS_INFERER(InterpolateGradNoNeedBufferVarsInferer,
                                     "X");
 
 }  // namespace operators
@@ -596,22 +596,22 @@ REGISTER_OPERATOR(bilinear_interp, ops::InterpolateOp, ops::InterpolateOpMaker,
                   ops::InterpolateGradMaker<paddle::framework::OpDesc>,
                   ops::InterpolateGradMaker<paddle::imperative::OpBase>);
 REGISTER_OPERATOR(bilinear_interp_grad, ops::InterpolateOpGrad,
-                  ops::InterpolateGradNoNeedBufferVarsInference);
+                  ops::InterpolateGradNoNeedBufferVarsInferer);
 REGISTER_OPERATOR(nearest_interp, ops::InterpolateOp, ops::InterpolateOpMaker,
                   ops::InterpolateGradMaker<paddle::framework::OpDesc>,
                   ops::InterpolateGradMaker<paddle::imperative::OpBase>);
 REGISTER_OPERATOR(nearest_interp_grad, ops::InterpolateOpGrad,
-                  ops::InterpolateGradNoNeedBufferVarsInference);
+                  ops::InterpolateGradNoNeedBufferVarsInferer);
 REGISTER_OPERATOR(trilinear_interp, ops::InterpolateOp, ops::InterpolateOpMaker,
                   ops::InterpolateGradMaker<paddle::framework::OpDesc>,
                   ops::InterpolateGradMaker<paddle::imperative::OpBase>);
 REGISTER_OPERATOR(trilinear_interp_grad, ops::InterpolateOpGrad,
-                  ops::InterpolateGradNoNeedBufferVarsInference);
+                  ops::InterpolateGradNoNeedBufferVarsInferer);
 REGISTER_OPERATOR(bicubic_interp, ops::InterpolateOp, ops::InterpolateOpMaker,
                   ops::InterpolateGradMaker<paddle::framework::OpDesc>,
                   ops::InterpolateGradMaker<paddle::imperative::OpBase>);
 REGISTER_OPERATOR(bicubic_interp_grad, ops::InterpolateOpGrad,
-                  ops::InterpolateGradNoNeedBufferVarsInference);
+                  ops::InterpolateGradNoNeedBufferVarsInferer);
 REGISTER_OP_CPU_KERNEL(bilinear_interp, ops::InterpolateKernel<float>,
                        ops::InterpolateKernel<double>,
                        ops::InterpolateKernel<uint8_t>);
@@ -631,7 +631,7 @@ REGISTER_OPERATOR(linear_interp, ops::InterpolateOp, ops::InterpolateOpMaker,
                   ops::InterpolateGradMaker<paddle::framework::OpDesc>,
                   ops::InterpolateGradMaker<paddle::imperative::OpBase>);
 REGISTER_OPERATOR(linear_interp_grad, ops::InterpolateOpGrad,
-                  ops::InterpolateGradNoNeedBufferVarsInference);
+                  ops::InterpolateGradNoNeedBufferVarsInferer);
 REGISTER_OP_CPU_KERNEL(linear_interp, ops::InterpolateKernel<float>,
                        ops::InterpolateKernel<double>,
                        ops::InterpolateKernel<uint8_t>);

paddle/fluid/operators/kldiv_loss_op.cc

@@ -166,7 +166,7 @@ class KLDivLossOpGradMaker : public framework::SingleGradOpMaker<T> {
   }
 };
 
-DECLARE_NO_NEED_BUFFER_VARS_INFERER(KLDivLossGradNoNeedBufferVarInference, "X");
+DECLARE_NO_NEED_BUFFER_VARS_INFERER(KLDivLossGradNoNeedBufferVarInferer, "X");
 
 }  // namespace operators
 }  // namespace paddle
@@ -176,7 +176,7 @@ REGISTER_OPERATOR(kldiv_loss, ops::KLDivLossOp, ops::KLDivLossOpMaker,
                   ops::KLDivLossOpGradMaker<paddle::framework::OpDesc>,
                   ops::KLDivLossOpGradMaker<paddle::imperative::OpBase>);
 REGISTER_OPERATOR(kldiv_loss_grad, ops::KLDivLossOpGrad,
-                  ops::KLDivLossGradNoNeedBufferVarInference);
+                  ops::KLDivLossGradNoNeedBufferVarInferer);
 REGISTER_OP_CPU_KERNEL(
     kldiv_loss, ops::KLDivLossKernel<paddle::platform::CPUDeviceContext, float>,
     ops::KLDivLossKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/layer_norm_op.cc

@@ -220,7 +220,7 @@ class LayerNormGradOpMaker : public framework::SingleGradOpMaker<T> {
   }
 };
 
-DECLARE_NO_NEED_BUFFER_VARS_INFERER(LayerNormGradNoNeedBufferVarInference,
+DECLARE_NO_NEED_BUFFER_VARS_INFERER(LayerNormGradNoNeedBufferVarInferer,
                                     "Bias");
 
 }  // namespace operators
@@ -231,7 +231,7 @@ REGISTER_OPERATOR(layer_norm, ops::LayerNormOp, ops::LayerNormOpMaker,
                   ops::LayerNormGradOpMaker<paddle::framework::OpDesc>,
                   ops::LayerNormGradOpMaker<paddle::imperative::OpBase>);
 REGISTER_OPERATOR(layer_norm_grad, ops::LayerNormGradOp,
-                  ops::LayerNormGradNoNeedBufferVarInference);
+                  ops::LayerNormGradNoNeedBufferVarInferer);
 REGISTER_OP_CPU_KERNEL(
     layer_norm, ops::LayerNormKernel<paddle::platform::CPUDeviceContext, float>,
     ops::LayerNormKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/linear_chain_crf_op.cc

@@ -345,7 +345,7 @@ class LinearChainCRFGradMaker : public framework::SingleGradOpMaker<T> {
   }
 };
 
-DECLARE_NO_NEED_BUFFER_VARS_INFERER(LinearChainCRFGradNoNeedBufferVarsInference,
+DECLARE_NO_NEED_BUFFER_VARS_INFERER(LinearChainCRFGradNoNeedBufferVarsInferer,
                                     "Transition", "Emission");
 
 }  // namespace operators
@@ -357,7 +357,7 @@ REGISTER_OPERATOR(linear_chain_crf, ops::LinearChainCRFOp,
                   ops::LinearChainCRFGradMaker<paddle::framework::OpDesc>,
                   ops::LinearChainCRFGradMaker<paddle::imperative::OpBase>);
 REGISTER_OPERATOR(linear_chain_crf_grad, ops::LinearChainCRFGradOp,
-                  ops::LinearChainCRFGradNoNeedBufferVarsInference);
+                  ops::LinearChainCRFGradNoNeedBufferVarsInferer);
 REGISTER_OP_CPU_KERNEL(
     linear_chain_crf,
     ops::LinearChainCRFOpKernel<paddle::platform::CPUDeviceContext, float>,

paddle/fluid/operators/lod_reset_op.cc

@@ -223,7 +223,7 @@ DECLARE_INPLACE_OP_INFERER(LoDResetGradInplaceInferer,
                            {framework::GradVarName("Out"),
                             framework::GradVarName("X")});
 
-DECLARE_NO_NEED_BUFFER_VARS_INFERER(LoDResetGradNoNeedBufferVarInference, "X");
+DECLARE_NO_NEED_BUFFER_VARS_INFERER(LoDResetGradNoNeedBufferVarInferer, "X");
 
 }  // namespace operators
 }  // namespace paddle
@@ -234,7 +234,7 @@ REGISTER_OPERATOR(lod_reset, ops::LoDResetOp, ops::LoDResetOpMaker,
                   ops::LoDResetGradMaker<paddle::imperative::OpBase>,
                   ops::LoDResetOpVarTypeInference, ops::LoDResetInplaceInferer);
 REGISTER_OPERATOR(lod_reset_grad, ops::LoDResetGradOp,
-                  ops::LoDResetGradNoNeedBufferVarInference,
+                  ops::LoDResetGradNoNeedBufferVarInferer,
                   ops::LoDResetGradInplaceInferer);
 
 REGISTER_OP_CPU_KERNEL(

paddle/fluid/operators/lookup_table_op.cc

@@ -130,7 +130,7 @@ or not. And the output only shares the LoD information with input Ids.
   }
 };
 
-DECLARE_NO_NEED_BUFFER_VARS_INFERER(LookupTableGradOpNoBuffer, "W");
+DECLARE_NO_NEED_BUFFER_VARS_INFERER(LookupTableGradOpNoBufferVarsInferer, "W");
 
 template <typename T>
 class LookupTableGradOpMaker : public framework::SingleGradOpMaker<T> {
@@ -198,7 +198,7 @@ REGISTER_OPERATOR(lookup_table, ops::LookupTableOp, ops::LookupTableOpMaker,
                   ops::LookupTableGradOpMaker<paddle::imperative::OpBase>);
 
 REGISTER_OPERATOR(lookup_table_grad, ops::LookupTableOpGrad,
-                  ops::LookupTableGradOpNoBuffer,
+                  ops::LookupTableGradOpNoBufferVarsInferer,
                   ops::LookupTableOpGradVarTypeInference);
 
 REGISTER_OP_CPU_KERNEL(lookup_table, ops::LookupTableKernel<float>,

paddle/fluid/operators/lookup_table_v2_op.cc

@@ -118,7 +118,8 @@ or not. And the output only shares the LoD information with input Ids.
   }
 };
 
-DECLARE_NO_NEED_BUFFER_VARS_INFERER(LookupTableV2GradOpNoBuffer, "W");
+DECLARE_NO_NEED_BUFFER_VARS_INFERER(LookupTableV2GradOpNoBufferVarsInferer,
+                                    "W");
 
 template <typename T>
 class LookupTableV2GradOpMaker : public framework::SingleGradOpMaker<T> {
@@ -187,7 +188,7 @@ REGISTER_OPERATOR(lookup_table_v2, ops::LookupTableV2Op,
                   ops::LookupTableV2GradOpMaker<paddle::imperative::OpBase>);
 
 REGISTER_OPERATOR(lookup_table_v2_grad, ops::LookupTableV2OpGrad,
-                  ops::LookupTableV2GradOpNoBuffer,
+                  ops::LookupTableV2GradOpNoBufferVarsInferer,
                   ops::LookupTableV2OpGradVarTypeInference);
 
 REGISTER_OP_CPU_KERNEL(lookup_table_v2, ops::LookupTableV2Kernel<float>,

paddle/fluid/operators/mean_op.cc

@@ -83,7 +83,7 @@ class MeanGradMaker : public framework::SingleGradOpMaker<T> {
   }
 };
 
-DECLARE_NO_NEED_BUFFER_VARS_INFERER(MeanGradNoNeedBufferVarsInference, "X");
+DECLARE_NO_NEED_BUFFER_VARS_INFERER(MeanGradNoNeedBufferVarsInferer, "X");
 
 }  // namespace operators
 }  // namespace paddle
@@ -93,7 +93,7 @@ REGISTER_OPERATOR(mean, ops::MeanOp, ops::MeanOpMaker, ops::MeanOpInferVarType,
                   ops::MeanGradMaker<paddle::framework::OpDesc>,
                   ops::MeanGradMaker<paddle::imperative::OpBase>);
 REGISTER_OPERATOR(mean_grad, ops::MeanGradOp,
-                  ops::MeanGradNoNeedBufferVarsInference);
+                  ops::MeanGradNoNeedBufferVarsInferer);
 REGISTER_OP_CPU_KERNEL(
     mean, ops::MeanKernel<paddle::platform::CPUDeviceContext, float>,
     ops::MeanKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/mkldnn/activation_mkldnn_op.cc

@@ -62,8 +62,9 @@ class MKLDNNActivationGradKernel
 template <typename T>
 void eltwise_forward(const framework::ExecutionContext &ctx,
                      mkldnn::algorithm algorithm) {
-  PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
-                 "It must use CPUPlace.");
+  PADDLE_ENFORCE_EQ(platform::is_cpu_place(ctx.GetPlace()), true,
+                    paddle::platform::errors::PreconditionNotMet(
+                        "Operator DNNL eletwise_forward must use CPUPlace"));
   auto &dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
 
   const auto *x = ctx.Input<Tensor>("X");

paddle/fluid/operators/mkldnn/batch_norm_mkldnn_op.cc

@@ -144,7 +144,11 @@ class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
 
     auto src_tz = paddle::framework::vectorize<int64_t>(x->dims());
     auto scale_tz = paddle::framework::vectorize<int64_t>(scale->dims());
-    PADDLE_ENFORCE(scale_tz.size() == 1, "Dims of scale tensor is NOT 1");
+    PADDLE_ENFORCE_EQ(
+        scale_tz.size(), 1,
+        platform::errors::InvalidArgument(
+            "Dims of scale tensor must be 1, but received scale's size is %d",
+            scale_tz.size()));
     const unsigned int C = scale_tz[0];
 
     // MKLDNN requires a single piece of memory for scale and shift/bias data
@@ -248,7 +252,11 @@ class BatchNormMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
 
     auto src_tz = paddle::framework::vectorize<int64_t>(x->dims());
     auto scale_tz = paddle::framework::vectorize<int64_t>(scale->dims());
-    PADDLE_ENFORCE(scale_tz.size() == 1, "Dims of scale tensor is NOT 1");
+    PADDLE_ENFORCE_EQ(
+        scale_tz.size(), 1,
+        platform::errors::InvalidArgument(
+            "Dims of scale tensor must be 1, but received scale's size is %d",
+            scale_tz.size()));
 
     const unsigned int C = scale_tz[0];
 

paddle/fluid/operators/mkldnn/concat_mkldnn_op.cc

@@ -134,6 +134,15 @@ class ConcatMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     EnforceLayouts(multi_input);
     Tensor* output = ctx.Output<Tensor>("Out");
     int concat_axis = ctx.Attr<int>("axis");
+    const int rank = multi_input[0]->dims().size();
+    PADDLE_ENFORCE_EQ(
+        concat_axis >= -rank && concat_axis < rank, true,
+        platform::errors::InvalidArgument(
+            "The axis is expected to be in range of [%d, %d), but got %d",
+            -rank, rank, concat_axis));
+    if (concat_axis < 0) {
+      concat_axis = concat_axis + rank;
+    }
     auto& dev_ctx =
         ctx.template device_context<paddle::platform::MKLDNNDeviceContext>();
     auto place = GetCpuPlace(ctx);

paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc

@@ -94,8 +94,9 @@ template <typename T, typename K>
 class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
  public:
   void Compute(const paddle::framework::ExecutionContext& ctx) const override {
-    PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
-                   platform::errors::InvalidArgument("It must use CPUPlace."));
+    PADDLE_ENFORCE_EQ(platform::is_cpu_place(ctx.GetPlace()), true,
+                      paddle::platform::errors::PreconditionNotMet(
+                          "Operator DNNL Conv must use CPUPlace"));
     bool is_INT8 =
         std::is_same<T, int8_t>::value || std::is_same<T, uint8_t>::value;
     if (!is_INT8) {
@@ -784,9 +785,9 @@ template <typename T>
 class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
  public:
   void Compute(const paddle::framework::ExecutionContext& ctx) const override {
-    PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
-                   platform::errors::InvalidArgument("It must use CPUPlace."));
-
+    PADDLE_ENFORCE_EQ(platform::is_cpu_place(ctx.GetPlace()), true,
+                      paddle::platform::errors::PreconditionNotMet(
+                          "Operator DNNL ConvGrad must use CPUPlace"));
     auto& dev_ctx =
         ctx.template device_context<platform::MKLDNNDeviceContext>();
     const auto& mkldnn_engine = dev_ctx.GetEngine();

paddle/fluid/operators/mkldnn/conv_transpose_mkldnn_op.cc

@@ -29,9 +29,9 @@ template <typename T>
 class ConvTransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
  public:
   void Compute(const paddle::framework::ExecutionContext& ctx) const override {
-    PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
-                   platform::errors::InvalidArgument("It must use CPUPlace."));
-
+    PADDLE_ENFORCE_EQ(platform::is_cpu_place(ctx.GetPlace()), true,
+                      paddle::platform::errors::PreconditionNotMet(
+                          "Operator DNNL ConvTranspose must use CPUPlace"));
     const bool is_test = ctx.Attr<bool>("is_test");
     PADDLE_ENFORCE_EQ(is_test, true,
                       platform::errors::InvalidArgument(

paddle/fluid/operators/mkldnn/lrn_mkldnn_op.cc

@@ -27,10 +27,12 @@ class LRNMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
  public:
   void Compute(const paddle::framework::ExecutionContext& ctx) const override {
     const bool is_float_type = std::is_same<T, float>::value;
-    PADDLE_ENFORCE(is_float_type, "MKLDNN LRN must use float data.");
-    PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
-                   "MKLDNN LRN must use CPUPlace.");
-
+    PADDLE_ENFORCE_EQ(
+        is_float_type, true,
+        platform::errors::PreconditionNotMet("DNNL LRN must use float data."));
+    PADDLE_ENFORCE_EQ(platform::is_cpu_place(ctx.GetPlace()), true,
+                      paddle::platform::errors::PreconditionNotMet(
+                          "Operator DNNL LRN must use CPUPlace"));
     auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
 
     auto x = ctx.Input<Tensor>("X");
@@ -93,12 +95,16 @@ class LRNMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
  public:
   void Compute(const paddle::framework::ExecutionContext& ctx) const override {
     const bool is_float_type = std::is_same<T, float>::value;
-    PADDLE_ENFORCE(is_float_type, "MKLDNN LRN must use float data.");
-    PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
-                   "MKLDNN LRN must use CPUPlace.");
-    PADDLE_ENFORCE(
-        !ctx.Attr<bool>("is_test"),
-        "is_test attribute should be set to False in training phase.");
+    PADDLE_ENFORCE_EQ(is_float_type, true,
+                      platform::errors::PreconditionNotMet(
+                          "DNNL LRN GradOpKernl must use float data."));
+    PADDLE_ENFORCE_EQ(platform::is_cpu_place(ctx.GetPlace()), true,
+                      paddle::platform::errors::PreconditionNotMet(
+                          "Operator DNNL LRNGrad must use CPUPlace"));
+    PADDLE_ENFORCE_EQ(
+        ctx.Attr<bool>("is_test"), false,
+        platform::errors::PreconditionNotMet(
+            "is_test attribute should be set to False in training phase."));
 
     auto x = ctx.Input<Tensor>("X");
     auto mid = ctx.Input<Tensor>("MidOut");

paddle/fluid/operators/mkldnn/mkldnn_activation_op.h

@@ -30,12 +30,8 @@ class MKLDNNActivationKernel
     : public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    PADDLE_ENFORCE(context.Input<framework::Tensor>("X") != nullptr,
-                   "Cannot get input tensor X, variable name = %s",
-                   context.InputName("X"));
-    PADDLE_ENFORCE(context.Output<framework::Tensor>("Out") != nullptr,
-                   "Cannot find output tensor Out, variable name = %s",
-                   context.OutputName("Out"));
+    OP_INOUT_CHECK(context.HasInput("X"), "Input", "X", "Activation");
+    OP_INOUT_CHECK(context.HasInput("Out"), "Output", "Out", "Activation");
     Functor functor;
 
     auto attrs = functor.GetAttrs();

paddle/fluid/operators/mkldnn/mul_mkldnn_op.cc

@@ -333,9 +333,9 @@ template <typename XT, typename YT>
 class MulMKLDNNKernel : public framework::OpKernel<XT> {
  public:
   void Compute(const ExecutionContext &ctx) const override {
-    PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()),
-                   "It must use CPUPlace.");
-
+    PADDLE_ENFORCE_EQ(platform::is_cpu_place(ctx.GetPlace()), true,
+                      paddle::platform::errors::PreconditionNotMet(
+                          "Operator DNNL Mul must use CPUPlace"));
     auto &dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
     const auto &mkldnn_engine = dev_ctx.GetEngine();
 

paddle/fluid/operators/mkldnn/pool_mkldnn_op.cc

@@ -33,61 +33,19 @@ template <typename T>
 class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
  public:
   void Compute(const paddle::framework::ExecutionContext& ctx) const override {
-    PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
-                   "It must use CPUPlace.");
+    PADDLE_ENFORCE_EQ(platform::is_cpu_place(ctx.GetPlace()), true,
+                      paddle::platform::errors::PreconditionNotMet(
+                          "Operator DNNL Pool must use CPUPlace"));
     auto& dev_ctx =
         ctx.template device_context<platform::MKLDNNDeviceContext>();
+    const auto& mkldnn_engine = dev_ctx.GetEngine();
 
     const Tensor* input = ctx.Input<Tensor>("X");
     Tensor* output = ctx.Output<Tensor>("Out");
 
-    PADDLE_ENFORCE_EQ(input->layout(), DataLayout::kMKLDNN,
-                      "Wrong layout set for Input tensor");
-    PADDLE_ENFORCE_NE(input->format(), MKLDNNMemoryFormat::undef,
-                      "Wrong format set for Input tensor");
-
-    std::string pooling_type = ctx.Attr<std::string>("pooling_type");
-
-    std::vector<int> ksize_temp = ctx.Attr<std::vector<int>>("ksize");
-    std::vector<int64_t> ksize(begin(ksize_temp), end(ksize_temp));
-
-    std::vector<int> strides_temp = ctx.Attr<std::vector<int>>("strides");
-    std::vector<int64_t> strides(begin(strides_temp), end(strides_temp));
-
-    std::vector<int> paddings_temp = ctx.Attr<std::vector<int>>("paddings");
-    std::vector<int64_t> paddings(begin(paddings_temp), end(paddings_temp));
-
-    bool global_pooling = ctx.Attr<bool>("global_pooling");
-    std::string padding_algorithm = ctx.Attr<std::string>("padding_algorithm");
-
-    // Only 2D pooling is supported now
-    PADDLE_ENFORCE_EQ(ksize.size(), 2, "ksize must be 2D, i.e. 2D pooling");
-    PADDLE_ENFORCE_EQ(pooling_type == "max" || pooling_type == "avg", true,
-                      "pooling_type must be 'max' or 'avg'");
-    PADDLE_ENFORCE_EQ(input->dims().size(), 4,
-                      "Input dim must be with 4, i.e. NCHW");
-
-    auto input_dims = input->dims();
-    framework::DDim data_dims =
-        framework::slice_ddim(input_dims, 2, input_dims.size());
-
-    if (global_pooling) {
-      UpdateKsize(&ksize, data_dims);
-    }
-
-    UpdatePadding(&paddings, global_pooling, 0, padding_algorithm, data_dims,
-                  strides, ksize);
-
-    auto src_tz = paddle::framework::vectorize<int64_t>(input->dims());
-    auto dst_tz = paddle::framework::vectorize<int64_t>(output->dims());
-
-    auto is_test = ctx.Attr<bool>("is_test");
-
-    platform::PoolingMKLDNNHandler<T> handler(
-        src_tz, dst_tz, ksize, strides, paddings, pooling_type,
-        ctx.Attr<bool>("ceil_mode"), input->format(),
-        paddle::framework::ToMKLDNNDataType(input->type()), is_test, dev_ctx,
-        ctx.GetPlace(), ctx.OutputName("Out"), ctx.Attr<bool>("exclusive"));
+    platform::PoolingMKLDNNHandler<T> handler(ctx, dev_ctx, mkldnn_engine,
+                                              ctx.GetPlace(), input, output,
+                                              ctx.OutputName("Out"));
 
     auto src_memory = handler.AcquireSrcMemory(input);
     auto dst_memory = handler.AcquireDstMemory(output);
@@ -95,7 +53,8 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     auto pool_p = handler.AcquireForwardPrimitive();
 
     mkldnn::stream astream(dev_ctx.GetEngine());
-    if ((is_test == false) && (pooling_type == "max")) {
+    if ((ctx.Attr<bool>("is_test") == false) &&
+        (ctx.Attr<std::string>("pooling_type") == "max")) {
       // Training
       auto workspace_memory = handler.AcquireWorkspaceMemory();
       pool_p->execute(astream, {{MKLDNN_ARG_SRC, *src_memory},
@@ -117,9 +76,9 @@ template <typename T>
 class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
  public:
   void Compute(const paddle::framework::ExecutionContext& ctx) const override {
-    PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
-                   "It must use CPUPlace.");
-
+    PADDLE_ENFORCE_EQ(platform::is_cpu_place(ctx.GetPlace()), true,
+                      paddle::platform::errors::PreconditionNotMet(
+                          "Operator DNNL PoolGrad must use CPUPlace"));
     const Tensor* in_x = ctx.Input<Tensor>("X");
     const Tensor* out_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
     Tensor* in_x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));

paddle/fluid/operators/mkldnn/softmax_mkldnn_op.cc

@@ -129,9 +129,9 @@ template <typename T>
 class SoftmaxMKLDNNGradKernel : public paddle::framework::OpKernel<T> {
  public:
   void Compute(const paddle::framework::ExecutionContext& ctx) const override {
-    PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
-                   "It must use CPUPlace.");
-
+    PADDLE_ENFORCE_EQ(platform::is_cpu_place(ctx.GetPlace()), true,
+                      paddle::platform::errors::PreconditionNotMet(
+                          "Operator DNNL SoftmaxGrad must use CPUPlace"));
     auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
     const Tensor* output = ctx.Input<Tensor>("Out");
     auto* dout = ctx.template Input<Tensor>(framework::GradVarName("Out"));

paddle/fluid/operators/mkldnn/sum_mkldnn_op.cc

@@ -49,8 +49,9 @@ template <typename T>
 class SumMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
  public:
   void Compute(const paddle::framework::ExecutionContext& ctx) const override {
-    PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
-                   "It must use CPUPlace.");
+    PADDLE_ENFORCE_EQ(platform::is_cpu_place(ctx.GetPlace()), true,
+                      paddle::platform::errors::PreconditionNotMet(
+                          "Operator DNNL Sum must use CPUPlace"));
     auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
     const auto& mkldnn_engine = dev_ctx.GetEngine();
     auto in_vars = ctx.MultiInputVar("X");

paddle/fluid/operators/mkldnn/transpose_mkldnn_op.cc

@@ -28,8 +28,9 @@ template <typename T>
 class TransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
  public:
   void Compute(const paddle::framework::ExecutionContext& ctx) const override {
-    PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
-                   "It must use CPUPlace.");
+    PADDLE_ENFORCE_EQ(platform::is_cpu_place(ctx.GetPlace()), true,
+                      paddle::platform::errors::PreconditionNotMet(
+                          "Operator DNNL Transpose must use CPUPlace"));
     auto& dev_ctx =
         ctx.template device_context<paddle::platform::MKLDNNDeviceContext>();
     const auto& mkldnn_engine = dev_ctx.GetEngine();
@@ -73,8 +74,9 @@ template <typename T>
 class TransposeMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
  public:
   void Compute(const paddle::framework::ExecutionContext& ctx) const override {
-    PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
-                   "It must use CPUPlace.");
+    PADDLE_ENFORCE_EQ(platform::is_cpu_place(ctx.GetPlace()), true,
+                      paddle::platform::errors::PreconditionNotMet(
+                          "Operator DNNL TransposeGrad must use CPUPlace"));
     auto* out_grad =
         ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
     auto* x_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));

paddle/fluid/operators/nccl/nccl_gpu_common.cc

@@ -51,7 +51,7 @@ void Communicator::InitAll(const std::vector<int>& gpus) {
   for (size_t i = 0; i < gpus.size(); ++i) {
     (*comm_id_map)[gpus[i]] = i;
   }
-  PADDLE_ENFORCE(
+  PADDLE_ENFORCE_CUDA_SUCCESS(
       dynload::ncclCommInitAll(global_comms->data(), gpus.size(), gpus.data()));
   inited = true;
 }

paddle/fluid/operators/nce_op.cc

@@ -307,7 +307,7 @@ class NCEOpGradVarTypeInference : public framework::VarTypeInference {
   }
 };
 
-DECLARE_NO_NEED_BUFFER_VARS_INFERER(NCEGradOpNoNeedBufferVarInference, "Bias");
+DECLARE_NO_NEED_BUFFER_VARS_INFERER(NCEGradOpNoNeedBufferVarInferer, "Bias");
 
 }  // namespace operators
 }  // namespace paddle
@@ -317,7 +317,7 @@ REGISTER_OPERATOR(nce, ops::NCEOp, ops::NCEOpMaker,
                   ops::NCEGradOpMaker<paddle::framework::OpDesc>,
                   ops::NCEGradOpMaker<paddle::imperative::OpBase>);
 REGISTER_OPERATOR(nce_grad, ops::NCEOpGrad, ops::NCEOpGradVarTypeInference,
-                  ops::NCEGradOpNoNeedBufferVarInference);
+                  ops::NCEGradOpNoNeedBufferVarInferer);
 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/pad2d_op.cc

@@ -656,7 +656,7 @@ class Pad2dOpGradMaker : public framework::SingleGradOpMaker<T> {
 };
 
 // TODO(zjl): Paddings can also be skipped!
-DECLARE_NO_NEED_BUFFER_VARS_INFERER(Pad2dOpGradNoNeedBufferVarsInference, "X");
+DECLARE_NO_NEED_BUFFER_VARS_INFERER(Pad2dOpGradNoNeedBufferVarsInferer, "X");
 
 }  // namespace operators
 }  // namespace paddle
@@ -667,7 +667,7 @@ REGISTER_OPERATOR(pad2d, ops::Pad2dOp, ops::Pad2dOpMaker,
                   ops::Pad2dOpGradMaker<paddle::framework::OpDesc>,
                   ops::Pad2dOpGradMaker<paddle::imperative::OpBase>);
 REGISTER_OPERATOR(pad2d_grad, ops::Pad2dOpGrad,
-                  ops::Pad2dOpGradNoNeedBufferVarsInference);
+                  ops::Pad2dOpGradNoNeedBufferVarsInferer);
 REGISTER_OP_CPU_KERNEL(pad2d, ops::Pad2dCPUKernel<float>,
                        ops::Pad2dCPUKernel<double>, ops::Pad2dCPUKernel<int>,
                        ops::Pad2dCPUKernel<int64_t>);

paddle/fluid/operators/pool_with_index_op.cc

@@ -316,7 +316,7 @@ class MaxPoolWithIndexGradOpMaker : public framework::SingleGradOpMaker<T> {
 };
 
 DECLARE_NO_NEED_BUFFER_VARS_INFERER(
-    MaxPoolWithIndexOpGradNoNeedBufferVarsInference, "X");
+    MaxPoolWithIndexOpGradNoNeedBufferVarsInferer, "X");
 
 }  // namespace operators
 }  // namespace paddle
@@ -328,7 +328,7 @@ REGISTER_OPERATOR(max_pool2d_with_index, ops::MaxPoolWithIndexOp,
                   ops::MaxPoolWithIndexGradOpMaker<paddle::framework::OpDesc>,
                   ops::MaxPoolWithIndexGradOpMaker<paddle::imperative::OpBase>);
 REGISTER_OPERATOR(max_pool2d_with_index_grad, ops::MaxPoolWithIndexOpGrad,
-                  ops::MaxPoolWithIndexOpGradNoNeedBufferVarsInference);
+                  ops::MaxPoolWithIndexOpGradNoNeedBufferVarsInferer);
 
 REGISTER_OP_CPU_KERNEL(
     max_pool2d_with_index,
@@ -347,7 +347,7 @@ REGISTER_OPERATOR(max_pool3d_with_index, ops::MaxPoolWithIndexOp,
                   ops::MaxPoolWithIndexGradOpMaker<paddle::framework::OpDesc>,
                   ops::MaxPoolWithIndexGradOpMaker<paddle::imperative::OpBase>);
 REGISTER_OPERATOR(max_pool3d_with_index_grad, ops::MaxPoolWithIndexOpGrad,
-                  ops::MaxPoolWithIndexOpGradNoNeedBufferVarsInference);
+                  ops::MaxPoolWithIndexOpGradNoNeedBufferVarsInferer);
 
 REGISTER_OP_CPU_KERNEL(
     max_pool3d_with_index,

paddle/fluid/operators/pull_box_extended_sparse_op.cc

+//   Copyright (c) 2020 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/pull_box_extended_sparse_op.h"
+
+namespace paddle {
+namespace operators {
+
+class PullBoxExtendedSparseOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE_GE(
+        ctx->Inputs("Ids").size(), 1UL,
+        platform::errors::InvalidArgument(
+            "Inputs(Ids) of PullBoxExtendedSparseOp should not be empty."));
+    PADDLE_ENFORCE_GE(
+        ctx->Outputs("Out").size(), 1UL,
+        platform::errors::InvalidArgument(
+            "Outputs(Out) of PullBoxExtendedSparseOp should not be empty."));
+    PADDLE_ENFORCE_GE(ctx->Outputs("OutExtend").size(), 1UL,
+                      platform::errors::InvalidArgument(
+                          "Outputs(OutExtend) of PullBoxExtendedSparseOp "
+                          "should not be empty."));
+    auto emb_size = static_cast<int64_t>(ctx->Attrs().Get<int>("emb_size"));
+    auto emb_extended_size =
+        static_cast<int64_t>(ctx->Attrs().Get<int>("emb_extended_size"));
+    auto all_ids_dim = ctx->GetInputsDim("Ids");
+    const size_t n_ids = all_ids_dim.size();
+    std::vector<framework::DDim> outs_dims;
+    std::vector<framework::DDim> outs_extended_dims;
+    outs_dims.resize(n_ids);
+    outs_extended_dims.resize(n_ids);
+    for (size_t i = 0; i < n_ids; ++i) {
+      const auto ids_dims = all_ids_dim[i];
+      int ids_rank = ids_dims.size();
+      PADDLE_ENFORCE_EQ(ids_dims[ids_rank - 1], 1,
+                        platform::errors::InvalidArgument(
+                            "Shape error in %lu id, the last dimension of the "
+                            "'Ids' tensor must be 1.",
+                            i));
+      auto out_dim = framework::vectorize(
+          framework::slice_ddim(ids_dims, 0, ids_rank - 1));
+      out_dim.push_back(emb_size);
+      outs_dims[i] = framework::make_ddim(out_dim);
+
+      auto out_extended_dim = framework::vectorize(
+          framework::slice_ddim(ids_dims, 0, ids_rank - 1));
+      out_extended_dim.push_back(emb_extended_size);
+      outs_extended_dims[i] = framework::make_ddim(out_extended_dim);
+    }
+    ctx->SetOutputsDim("Out", outs_dims);
+    ctx->SetOutputsDim("OutExtend", outs_extended_dims);
+    for (size_t i = 0; i < n_ids; ++i) {
+      ctx->ShareLoD("Ids", "Out", i, i);
+      ctx->ShareLoD("Ids", "OutExtend", i, i);
+    }
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(framework::proto::VarType::FP32,
+                                   ctx.device_context());
+  }
+};
+
+class PullBoxExtendedSparseOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("Ids",
+             "Input tensors with type int32 or int64 "
+             "contains the ids to be looked up in BoxPS. "
+             "The last dimension size must be 1.")
+        .AsDuplicable();
+    AddOutput("Out", "The lookup results tensors.").AsDuplicable();
+    AddOutput("OutExtend", "The lookup extended results tensors.")
+        .AsDuplicable();
+    AddAttr<int>("emb_size", "(int, the embedding hidden size").SetDefault(1);
+    AddAttr<int>("emb_extended_size",
+                 "(int, the extended_embedding hidden size")
+        .SetDefault(128);
+    AddComment(R"DOC(
+Pull Box Extended Sparse Operator.
+
+This operator is used to perform lookups on the BoxPS,
+then concatenated into a dense tensor.
+
+The input Ids can carry the LoD (Level of Details) information,
+or not. And the output only shares the LoD information with input Ids.
+
+)DOC");
+  }
+};
+
+template <typename T>
+class PushBoxExtendedSparseOpMaker : public framework::SingleGradOpMaker<T> {
+ public:
+  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
+
+ protected:
+  void Apply(GradOpPtr<T> op) const override {
+    op->SetType("push_box_extended_sparse");
+    op->SetInput("Ids", this->Input("Ids"));
+    op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
+    op->SetInput(framework::GradVarName("OutExtend"),
+                 this->OutputGrad("OutExtend"));
+    op->SetOutput(framework::GradVarName("Out"), this->OutputGrad("Out"));
+    op->SetAttrMap(this->Attrs());
+  }
+};
+
+class PushBoxExtendedSparseOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {}
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(OperatorWithKernel::IndicateVarDataType(
+                                       ctx, framework::GradVarName("Out")),
+                                   ctx.device_context());
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(
+    pull_box_extended_sparse, ops::PullBoxExtendedSparseOp,
+    ops::PullBoxExtendedSparseOpMaker,
+    ops::PushBoxExtendedSparseOpMaker<paddle::framework::OpDesc>,
+    ops::PushBoxExtendedSparseOpMaker<paddle::imperative::OpBase>);
+
+REGISTER_OPERATOR(push_box_extended_sparse, ops::PushBoxExtendedSparseOp);
+
+REGISTER_OP_CPU_KERNEL(pull_box_extended_sparse,
+                       ops::PullBoxExtendedSparseCPUKernel<float>,
+                       ops::PullBoxExtendedSparseCPUKernel<double>);
+
+REGISTER_OP_CPU_KERNEL(push_box_extended_sparse,
+                       ops::PushBoxExtendedSparseCPUKernel<float>,
+                       ops::PushBoxExtendedSparseCPUKernel<double>);

paddle/fluid/operators/pull_box_extended_sparse_op.cu

+//   Copyright (c) 2020 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/pull_box_extended_sparse_op.h"
+#include "paddle/fluid/platform/cuda_primitives.h"
+#include "paddle/fluid/platform/gpu_info.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class PullBoxExtendedSparseCUDAKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    PullBoxExtendedSparseFunctor<T>(ctx);
+  }
+};
+
+template <typename T>
+class PushBoxExtendedSparseCUDAKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    PushBoxExtendedSparseFunctor<T>(ctx);
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(pull_box_extended_sparse,
+                        ops::PullBoxExtendedSparseCUDAKernel<float>,
+                        ops::PullBoxExtendedSparseCUDAKernel<double>);
+REGISTER_OP_CUDA_KERNEL(push_box_extended_sparse,
+                        ops::PushBoxExtendedSparseCUDAKernel<float>,
+                        ops::PushBoxExtendedSparseCUDAKernel<double>);

paddle/fluid/operators/pull_box_extended_sparse_op.h

+//   Copyright (c) 2020 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 <memory>
+#include <vector>
+#include "paddle/fluid/framework/fleet/box_wrapper.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/tensor.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+static void PullBoxExtendedSparseFunctor(
+    const framework::ExecutionContext &ctx) {
+  auto inputs = ctx.MultiInput<framework::Tensor>("Ids");
+  auto outputs = ctx.MultiOutput<framework::Tensor>("Out");
+  auto outputs_extend = ctx.MultiOutput<framework::Tensor>("OutExtend");
+  const auto slot_size = inputs.size();
+  std::vector<const uint64_t *> all_keys(slot_size);
+  // BoxPS only supports float now
+  std::vector<float *> all_values(slot_size * 2);
+  std::vector<int64_t> slot_lengths(slot_size);
+  for (size_t i = 0; i < slot_size; i++) {
+    const auto *slot = inputs[i];
+    const uint64_t *single_slot_keys =
+        reinterpret_cast<const uint64_t *>(slot->data<int64_t>());
+    all_keys[i] = single_slot_keys;
+    slot_lengths[i] = slot->numel();
+    auto *output = outputs[i]->mutable_data<T>(ctx.GetPlace());
+    auto *output_extend = outputs_extend[i]->mutable_data<T>(ctx.GetPlace());
+    all_values[i] = reinterpret_cast<float *>(output);
+    all_values[i + slot_size] = reinterpret_cast<float *>(output_extend);
+  }
+#ifdef PADDLE_WITH_BOX_PS
+  auto emb_size = ctx.Attr<int>("emb_size");
+  auto emb_extended_size = ctx.Attr<int>("emb_extended_size");
+  auto box_ptr = paddle::framework::BoxWrapper::GetInstance();
+  box_ptr->PullSparse(ctx.GetPlace(), all_keys, all_values, slot_lengths,
+                      emb_size, emb_extended_size);
+#endif
+}
+
+template <typename T>
+static void PushBoxExtendedSparseFunctor(
+    const framework::ExecutionContext &ctx) {
+  auto inputs = ctx.MultiInput<framework::LoDTensor>("Ids");
+  auto d_output =
+      ctx.MultiInput<framework::Tensor>(framework::GradVarName("Out"));
+  auto d_output_extend =
+      ctx.MultiInput<framework::Tensor>(framework::GradVarName("OutExtend"));
+  const auto slot_size = inputs.size();
+  std::vector<const uint64_t *> all_keys(slot_size);
+  std::vector<const float *> all_grad_values(slot_size * 2);
+  std::vector<int64_t> slot_lengths(slot_size);
+  int batch_size = -1;
+  for (size_t i = 0; i < slot_size; i++) {
+    const auto *slot = inputs[i];
+    const uint64_t *single_slot_keys =
+        reinterpret_cast<const uint64_t *>(slot->data<int64_t>());
+    all_keys[i] = single_slot_keys;
+    slot_lengths[i] = slot->numel();
+    int cur_batch_size =
+        slot->lod().size() ? slot->lod()[0].size() - 1 : slot->dims()[0];
+    if (batch_size == -1) {
+      batch_size = cur_batch_size;
+    } else {
+      PADDLE_ENFORCE_EQ(batch_size, cur_batch_size,
+                        platform::errors::PreconditionNotMet(
+                            "The batch size of all input slots should be same,"
+                            "please cheack"));
+    }
+    const float *grad_value = d_output[i]->data<float>();
+    const float *grad_value_extend = d_output_extend[i]->data<float>();
+    all_grad_values[i] = reinterpret_cast<const float *>(grad_value);
+    all_grad_values[i + slot_size] =
+        reinterpret_cast<const float *>(grad_value_extend);
+  }
+#ifdef PADDLE_WITH_BOX_PS
+  auto emb_size = ctx.Attr<int>("emb_size");
+  auto emb_extended_size = ctx.Attr<int>("emb_extended_size");
+  auto box_ptr = paddle::framework::BoxWrapper::GetInstance();
+  box_ptr->PushSparseGrad(ctx.GetPlace(), all_keys, all_grad_values,
+                          slot_lengths, emb_size, emb_extended_size,
+                          batch_size);
+#endif
+}
+
+using LoDTensor = framework::LoDTensor;
+template <typename T>
+class PullBoxExtendedSparseCPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    PullBoxExtendedSparseFunctor<T>(ctx);
+  }
+};
+
+template <typename T>
+class PushBoxExtendedSparseCPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    PushBoxExtendedSparseFunctor<T>(ctx);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/pull_box_sparse_op.h

@@ -44,7 +44,7 @@ static void PullBoxSparseFunctor(const framework::ExecutionContext &ctx) {
   auto hidden_size = ctx.Attr<int>("size");
   auto box_ptr = paddle::framework::BoxWrapper::GetInstance();
   box_ptr->PullSparse(ctx.GetPlace(), all_keys, all_values, slot_lengths,
-                      hidden_size);
+                      hidden_size, 0);
 #endif
 }
 
@@ -81,7 +81,7 @@ static void PushBoxSparseFunctor(const framework::ExecutionContext &ctx) {
   auto hidden_size = ctx.Attr<int>("size");
   auto box_ptr = paddle::framework::BoxWrapper::GetInstance();
   box_ptr->PushSparseGrad(ctx.GetPlace(), all_keys, all_grad_values,
-                          slot_lengths, hidden_size, batch_size);
+                          slot_lengths, hidden_size, 0, batch_size);
 #endif
 }
 

paddle/fluid/operators/push_dense_op.cc

@@ -56,7 +56,7 @@ The input gradients is all dense gradient tensors in a table.
   }
 };
 
-DECLARE_NO_NEED_BUFFER_VARS_INFERER(PushDenseNoNeedBufferVarsInference, "Ids");
+DECLARE_NO_NEED_BUFFER_VARS_INFERER(PushDenseNoNeedBufferVarsInferer, "Ids");
 
 }  // namespace operators
 }  // namespace paddle
@@ -66,5 +66,5 @@ REGISTER_OPERATOR(
     push_dense, ops::PushDenseOp, ops::PushDenseOpMaker,
     paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
     paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>,
-    ops::PushDenseNoNeedBufferVarsInference);
+    ops::PushDenseNoNeedBufferVarsInferer);
 REGISTER_OP_CPU_KERNEL(push_dense, ops::PushDenseCPUKernel<float>)

paddle/fluid/operators/reader/blocking_queue.h

@@ -34,9 +34,11 @@ class BlockingQueue {
  public:
   explicit BlockingQueue(size_t capacity, bool speed_test_mode = false)
       : capacity_(capacity), speed_test_mode_(speed_test_mode) {
-    PADDLE_ENFORCE_GT(
-        capacity_, static_cast<size_t>(0),
-        "The capacity of a reader::BlockingQueue must be greater than 0.");
+    PADDLE_ENFORCE_GT(capacity_, static_cast<size_t>(0),
+                      platform::errors::InvalidArgument(
+                          "The capacity of a reader::BlockingQueue must be "
+                          "greater than 0, but received capacity is %d.",
+                          capacity_));
   }
 
   bool Send(const T& elem) {
@@ -49,7 +51,10 @@ class BlockingQueue {
           << "WARNING: Sending an element to a closed reader::BlokcingQueue.";
       return false;
     }
-    PADDLE_ENFORCE_LT(queue_.size(), capacity_);
+    PADDLE_ENFORCE_LT(
+        queue_.size(), capacity_,
+        platform::errors::PermissionDenied(
+            "The queue size cannot exceed the set queue capacity."));
     queue_.push_back(elem);
     receive_cv_.notify_one();
     return true;
@@ -65,7 +70,10 @@ class BlockingQueue {
           << "WARNING: Sending an element to a closed reader::BlokcingQueue.";
       return false;
     }
-    PADDLE_ENFORCE_LT(queue_.size(), capacity_);
+    PADDLE_ENFORCE_LT(
+        queue_.size(), capacity_,
+        platform::errors::PermissionDenied(
+            "The queue size cannot exceed the set queue capacity."));
     queue_.emplace_back(std::move(elem));
     receive_cv_.notify_one();
     return true;
@@ -77,7 +85,9 @@ class BlockingQueue {
                      [&] { return !queue_.empty() || closed_ || killed_; });
     EnforceNotKilled();
     if (!queue_.empty()) {
-      PADDLE_ENFORCE_NOT_NULL(elem);
+      PADDLE_ENFORCE_NOT_NULL(
+          elem, platform::errors::InvalidArgument(
+                    "The holder to receive queue data is null pointer."));
       *elem = queue_.front();
       if (LIKELY(!speed_test_mode_)) {
         queue_.pop_front();
@@ -85,7 +95,10 @@ class BlockingQueue {
       send_cv_.notify_one();
       return true;
     } else {
-      PADDLE_ENFORCE(closed_);
+      PADDLE_ENFORCE_EQ(closed_, true,
+                        platform::errors::PermissionDenied(
+                            "Blocking queue status error, if queue is empty "
+                            "when pop data, it should be closed."));
       VLOG(3) << "queue is closed! return nothing.";
       return false;
     }
@@ -136,9 +149,9 @@ class BlockingQueue {
 
  private:
   inline void EnforceNotKilled() {
-    PADDLE_ENFORCE_NE(
-        killed_, true,
-        "Blocking queue is killed because the data reader raises an exception");
+    PADDLE_ENFORCE_NE(killed_, true, platform::errors::Fatal(
+                                         "Blocking queue is killed because the "
+                                         "data reader raises an exception."));
   }
 
  private:

paddle/fluid/operators/reader/buffered_reader.cc

@@ -62,7 +62,6 @@ BufferedReader::BufferedReader(
 }
 
 void BufferedReader::ReadTillBufferFullAsync() {
-  PADDLE_ENFORCE_EQ(position_.size(), 0U);
   for (size_t i = 0; i < buffer_size_; ++i) {
     ReadAsync(i);
   }
@@ -87,8 +86,10 @@ void BufferedReader::ReadAsync(size_t i) {
       if (gpu.empty()) {
         gpu.resize(cpu.size());
       } else {
-        PADDLE_ENFORCE_EQ(gpu.size(), cpu.size(),
-                          "Input tensor number not matched");
+        PADDLE_ENFORCE_EQ(
+            gpu.size(), cpu.size(),
+            platform::errors::InvalidArgument(
+                "Input tensor number on GPU and CPU devices are not matched."));
       }
 
       std::vector<void *> gpu_ptrs;

paddle/fluid/operators/reader/create_ctr_reader_op.cc

@@ -36,8 +36,9 @@ class CreateCTRReaderOp : public framework::OperatorBase {
     auto* queue_holder_var = scope.FindVar(queue_name);
     PADDLE_ENFORCE_NOT_NULL(
         queue_holder_var,
-        "No LoDTensorBlockingQueueHolder variable with name %s found",
-        queue_name);
+        platform::errors::PreconditionNotMet(
+            "No LoDTensorBlockingQueueHolder variable with name %s found",
+            queue_name));
     auto* queue_holder =
         queue_holder_var->template GetMutable<LoDTensorBlockingQueueHolder>();
 

paddle/fluid/operators/reader/create_custom_reader_op.cc

@@ -96,11 +96,14 @@ class CreateCustomReaderOpMaker : public DecoratedReaderMakerBase {
 class CustomReaderInferShape : public framework::InferShapeBase {
  public:
   void operator()(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(!ctx->IsRuntime(),
-                   "'CustomReaderInferShape' should only be invoked during "
-                   "compile time.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                   "The output decorated reader should not be null.");
+    PADDLE_ENFORCE_NE(
+        ctx->IsRuntime(), true,
+        platform::errors::PreconditionNotMet(
+            "'CustomReaderInferShape' should only be invoked during "
+            "compile time."));
+    PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
+                      platform::errors::NotFound(
+                          "The output decorated reader should not be null."));
     const auto* sub_block =
         ctx->Attrs().Get<framework::BlockDesc*>("sub_block");
     const auto sink_var_names =
@@ -109,7 +112,9 @@ class CustomReaderInferShape : public framework::InferShapeBase {
     std::vector<int32_t> res_lod_levels;
     for (const std::string& var_name : sink_var_names) {
       auto* sink_var = sub_block->FindVar(var_name);
-      PADDLE_ENFORCE_NOT_NULL(sink_var);
+      PADDLE_ENFORCE_NOT_NULL(
+          sink_var, platform::errors::NotFound(
+                        "The sink variable is not found in CustomReader."));
       res_dims.emplace_back(sink_var->GetShape());
       res_lod_levels.push_back(sink_var->GetLoDLevel());
     }
@@ -124,7 +129,9 @@ class CustomReaderInferVarType : public framework::VarTypeInference {
  public:
   void operator()(framework::InferVarTypeContext* ctx) const override {
     auto& out_var_name = ctx->Output("Out")[0];
-    PADDLE_ENFORCE(ctx->HasVar(out_var_name));
+    PADDLE_ENFORCE_EQ(ctx->HasVar(out_var_name), true,
+                      platform::errors::NotFound(
+                          "The output reader variable should not be null."));
     ctx->SetType(out_var_name, framework::proto::VarType::READER);
 
     auto sink_var_names = BOOST_GET_CONST(std::vector<std::string>,
@@ -134,7 +141,9 @@ class CustomReaderInferVarType : public framework::VarTypeInference {
     std::vector<framework::proto::VarType::Type> res_data_types;
     for (const std::string& var_name : sink_var_names) {
       framework::VarDesc* var = sub_block->FindVar(var_name);
-      PADDLE_ENFORCE_NOT_NULL(var);
+      PADDLE_ENFORCE_NOT_NULL(
+          var, platform::errors::NotFound(
+                   "The sink variable is not found in CustomReader."));
       res_data_types.emplace_back(var->GetDataType());
     }
     ctx->SetDataTypes(out_var_name, res_data_types);
@@ -149,11 +158,13 @@ void CustomReader::ReadNextImpl(std::vector<framework::LoDTensor>* out) {
     // There is not next data.
     return;
   }
-  PADDLE_ENFORCE(source_var_names_.size() == underlying_outs.size(),
-                 "The size of source_var_names(%d) and the size of "
-                 "underlying_outs(%d) are not consistent. Each feeding element "
-                 "must have its own source variable.",
-                 source_var_names_.size(), underlying_outs.size());
+  PADDLE_ENFORCE_EQ(
+      source_var_names_.size(), underlying_outs.size(),
+      platform::errors::InvalidArgument(
+          "The size of source_var_names(%d) and the size of "
+          "underlying_outs(%d) are not consistent. Each feeding element "
+          "must have its own source variable.",
+          source_var_names_.size(), underlying_outs.size()));
   // The scope for CustomReader's sub-block should be independent and shouldn't
   // be any other computation scope's child. Otherwise, data preprocessing and
   // compution cannot be concurrent.

paddle/fluid/operators/reader/lod_tensor_blocking_queue.h

@@ -201,9 +201,10 @@ class OrderedMultiDeviceLoDTensorBlockingQueue {
 class LoDTensorBlockingQueueHolder {
  public:
   void InitOnce(size_t capacity, bool speed_test_mode = false) {
-    PADDLE_ENFORCE(
-        queue_ == nullptr,
-        "LoDTensorBlockingQueueHolder::InitOnce() can only be called once");
+    PADDLE_ENFORCE_EQ(
+        queue_, nullptr,
+        platform::errors::AlreadyExists("LoDTensorBlockingQueueHolder::"
+                                        "InitOnce() can only be called once"));
     queue_.reset(new LoDTensorBlockingQueue(capacity, speed_test_mode));
   }
 

paddle/fluid/operators/reader/py_reader.cc

@@ -25,7 +25,9 @@ PyReader::PyReader(
     const std::vector<framework::proto::VarType::Type>& var_types,
     const std::vector<bool>& need_check_feed)
     : framework::FileReader(dims, var_types, need_check_feed) {
-  PADDLE_ENFORCE(queue != nullptr, "LoDTensorBlockingQueue must not be null");
+  PADDLE_ENFORCE_NOT_NULL(queue,
+                          platform::errors::PreconditionNotMet(
+                              "LoDTensorBlockingQueue must not be null."));
   queue_ = queue;
 }
 

paddle/fluid/operators/reader/read_op.cc

@@ -78,7 +78,10 @@ class ReadInferVarType : public framework::StaticGraphVarTypeInference {
       std::string reader_name = Input(ctx, "Reader")[0];
       auto& out_names = Output(ctx, "Out");
       auto dtypes = GetDataTypes(ctx, reader_name);
-      PADDLE_ENFORCE_EQ(dtypes.size(), out_names.size());
+      PADDLE_ENFORCE_EQ(dtypes.size(), out_names.size(),
+                        platform::errors::InvalidArgument(
+                            "The number of input reader's dtypes do not match "
+                            "the output variable number."));
       for (size_t i = 0; i < dtypes.size(); ++i) {
         SetType(ctx, out_names[i], framework::proto::VarType::LOD_TENSOR);
         SetDataType(ctx, out_names[i], dtypes[i]);

paddle/fluid/operators/reader/reader_op_registry.cc

@@ -62,12 +62,14 @@ void FileReaderMakerBase::Make() {
 }
 
 void FileReaderInferShape::operator()(framework::InferShapeContext* ctx) const {
-  PADDLE_ENFORCE(
-      !ctx->IsRuntime(),
-      "'FileReaderInferShape' should only be invoked during compile time.");
-
-  PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                 "The output file reader should not be null.");
+  PADDLE_ENFORCE_NE(
+      ctx->IsRuntime(), true,
+      platform::errors::PreconditionNotMet("'FileReaderInferShape' should only "
+                                           "be invoked during compile time."));
+
+  PADDLE_ENFORCE_EQ(
+      ctx->HasOutput("Out"), true,
+      platform::errors::NotFound("The output file reader should not be null."));
   bool use_data_config = ctx->Attrs().Get<bool>("use_data_config");
   if (use_data_config) {
     const auto shape_concat =
@@ -77,21 +79,26 @@ void FileReaderInferShape::operator()(framework::InferShapeContext* ctx) const {
     ctx->SetReaderDims("Out", shapes);
 
     const auto lod_levels = ctx->Attrs().Get<std::vector<int>>("lod_levels");
-    PADDLE_ENFORCE_EQ(lod_levels.size(), shapes.size(),
-                      "The number of 'lod_levels'(%d) doesn't match the number "
-                      "of 'shapes'(%d).",
-                      lod_levels.size(), shapes.size());
+    PADDLE_ENFORCE_EQ(
+        lod_levels.size(), shapes.size(),
+        platform::errors::InvalidArgument(
+            "The number of 'lod_levels'(%d) doesn't match the number "
+            "of 'shapes'(%d).",
+            lod_levels.size(), shapes.size()));
     const auto dtypes = ctx->Attrs().Get<std::vector<int>>("dtypes");
     PADDLE_ENFORCE_EQ(
         dtypes.size(), shapes.size(),
-        "The number of 'dtypes'(%d) doesn't match the number of 'shapes'(%d).",
-        dtypes.size(), shapes.size());
+        platform::errors::InvalidArgument("The number of 'dtypes'(%d) doesn't "
+                                          "match the number of 'shapes'(%d).",
+                                          dtypes.size(), shapes.size()));
     const auto need_check_feed =
         ctx->Attrs().Get<std::vector<int>>("need_check_feed");
-    PADDLE_ENFORCE_EQ(need_check_feed.size(), shapes.size(),
-                      "The number of 'need_check_feed'(%d) doesn't match the "
-                      "number of 'shapes'(%d).",
-                      need_check_feed.size(), shapes.size());
+    PADDLE_ENFORCE_EQ(
+        need_check_feed.size(), shapes.size(),
+        platform::errors::InvalidArgument(
+            "The number of 'need_check_feed'(%d) doesn't match the "
+            "number of 'shapes'(%d).",
+            need_check_feed.size(), shapes.size()));
     framework::VarDesc* reader =
         BOOST_GET(framework::VarDesc*, ctx->GetOutputVarPtrs("Out")[0]);
     reader->SetLoDLevels(lod_levels);
@@ -105,14 +112,18 @@ void FileReaderInferVarType::operator()(
 
 void DecoratedReaderInferShape::operator()(
     framework::InferShapeContext* ctx) const {
-  PADDLE_ENFORCE(!ctx->IsRuntime(),
-                 "'DecoratedReaderInferShape' should only be invoked during "
-                 "compile time.");
-
-  PADDLE_ENFORCE(ctx->HasInput("UnderlyingReader"),
-                 "Input(UnderlyingReader) should not be null.");
-  PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                 "The output decorated reader should not be null.");
+  PADDLE_ENFORCE_NE(
+      ctx->IsRuntime(), true,
+      platform::errors::PreconditionNotMet(
+          "'DecoratedReaderInferShape' should only be invoked during "
+          "compile time."));
+
+  PADDLE_ENFORCE_EQ(ctx->HasInput("UnderlyingReader"), true,
+                    platform::errors::NotFound(
+                        "Input(UnderlyingReader) should not be null."));
+  PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
+                    platform::errors::NotFound(
+                        "The output decorated reader should not be null."));
   ctx->SetReaderDims("Out", ctx->GetReaderDims("UnderlyingReader"));
 
   framework::VarDesc* in_reader = BOOST_GET(

paddle/fluid/operators/reshape_op.cc

@@ -545,12 +545,12 @@ class Reshape2DoubleGradOp : public framework::OperatorWithKernel {
   }
 };
 
-DECLARE_INPLACE_OP_INFERER(ReshapeOpInplaceInToOut, {"X", "Out"});
-DECLARE_INPLACE_OP_INFERER(ReshapeGradInplaceInToOut,
+DECLARE_INPLACE_OP_INFERER(ReshapeOpInplaceInferer, {"X", "Out"});
+DECLARE_INPLACE_OP_INFERER(ReshapeGradInplaceInferer,
                            {framework::GradVarName("Out"),
                             framework::GradVarName("X")});
-DECLARE_INPLACE_OP_INFERER(ReshapeDoubleGradInplaceInToOut, {"DDX", "DDOut"});
-DECLARE_NO_NEED_BUFFER_VARS_INFERER(ReshapeDoubleGradOpNoNeedBufferVarInference,
+DECLARE_INPLACE_OP_INFERER(ReshapeDoubleGradInplaceInferer, {"DDX", "DDOut"});
+DECLARE_NO_NEED_BUFFER_VARS_INFERER(ReshapeDoubleGradOpNoNeedBufferVarInferer,
                                     "DOut");
 
 }  // namespace operators
@@ -562,9 +562,9 @@ REGISTER_OPERATOR(
     reshape, ops::ReshapeOp, ops::ReshapeOpMaker,
     paddle::framework::DefaultGradOpMaker<paddle::framework::OpDesc, true>,
     paddle::framework::DefaultGradOpMaker<paddle::imperative::OpBase, true>,
-    ops::ReshapeOpInplaceInToOut);
+    ops::ReshapeOpInplaceInferer);
 REGISTER_OPERATOR(reshape_grad, ops::ReshapeGradOp,
-                  ops::ReshapeGradInplaceInToOut);
+                  ops::ReshapeGradInplaceInferer);
 
 REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape, float, ops::ReshapeKernel, double,
                                ops::ReshapeKernel, int, ops::ReshapeKernel,
@@ -576,14 +576,14 @@ REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape_grad, float, ops::ReshapeGradKernel,
 REGISTER_OPERATOR(reshape2, ops::Reshape2Op, ops::Reshape2OpMaker,
                   ops::Reshape2GradMaker<paddle::framework::OpDesc>,
                   ops::Reshape2GradMaker<paddle::imperative::OpBase>,
-                  ops::ReshapeOpInplaceInToOut);
+                  ops::ReshapeOpInplaceInferer);
 REGISTER_OPERATOR(reshape2_grad, ops::Reshape2GradOp,
                   ops::Reshape2DoubleGradMaker<paddle::framework::OpDesc>,
                   ops::Reshape2DoubleGradMaker<paddle::imperative::OpBase>,
-                  ops::ReshapeGradInplaceInToOut);
+                  ops::ReshapeGradInplaceInferer);
 REGISTER_OPERATOR(reshape2_grad_grad, ops::Reshape2DoubleGradOp,
-                  ops::ReshapeDoubleGradInplaceInToOut,
-                  ops::ReshapeDoubleGradOpNoNeedBufferVarInference);
+                  ops::ReshapeDoubleGradInplaceInferer,
+                  ops::ReshapeDoubleGradOpNoNeedBufferVarInferer);
 
 REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape2, float, ops::ReshapeKernel, double,
                                ops::ReshapeKernel, int8_t, ops::ReshapeKernel,

paddle/fluid/operators/scale_op.cc

@@ -104,7 +104,7 @@ class ScaleGradMaker : public framework::SingleGradOpMaker<T> {
   }
 };
 
-DECLARE_INPLACE_OP_INFERER(ScaleOpInplace, {"X", "Out"});
+DECLARE_INPLACE_OP_INFERER(ScaleOpInplaceInferer, {"X", "Out"});
 }  // namespace operators
 }  // namespace paddle
 
@@ -113,7 +113,7 @@ namespace ops = paddle::operators;
 REGISTER_OPERATOR(scale, ops::ScaleOp, ops::ScaleOpMaker,
                   ops::ScaleGradMaker<paddle::framework::OpDesc>,
                   ops::ScaleGradMaker<paddle::imperative::OpBase>,
-                  ops::ScaleOpVarTypeInference, ops::ScaleOpInplace);
+                  ops::ScaleOpVarTypeInference, ops::ScaleOpInplaceInferer);
 REGISTER_OP_CPU_KERNEL(
     scale, ops::ScaleKernel<paddle::platform::CPUDeviceContext, float>,
     ops::ScaleKernel<paddle::platform::CPUDeviceContext, double>,

paddle/fluid/operators/shape_op.h

@@ -20,15 +20,23 @@ namespace paddle {
 namespace operators {
 
 using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+using SelectedRows = framework::SelectedRows;
 
 template <typename T>
 class ShapeKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* in_t = ctx.Input<Tensor>("Input");
+    auto* in_var = ctx.InputVar("Input");
+    framework::DDim in_dims;
+    if (in_var->IsType<SelectedRows>()) {
+      in_dims = in_var->Get<SelectedRows>().value().dims();
+    } else {
+      in_dims = in_var->Get<LoDTensor>().dims();
+    }
     auto* out_t = ctx.Output<Tensor>("Out");
+    out_t->Resize({in_dims.size()});
     auto out_data = out_t->mutable_data<int32_t>(platform::CPUPlace());
-    auto in_dims = in_t->dims();
     for (int i = 0; i < in_dims.size(); ++i) {
       out_data[i] = in_dims[i];
     }

paddle/fluid/operators/softmax_with_cross_entropy_op.cc

@@ -287,10 +287,10 @@ class SoftmaxGradMaker : public framework::SingleGradOpMaker<T> {
   }
 };
 
-DECLARE_INPLACE_OP_INFERER(SoftmaxWithCrossEntropyInplaceInference,
+DECLARE_INPLACE_OP_INFERER(SoftmaxWithCrossEntropyInplaceInferer,
                            {"Logits", "Softmax"});
 
-DECLARE_INPLACE_OP_INFERER(SoftmaxWithCrossEntropyGradInplaceInference,
+DECLARE_INPLACE_OP_INFERER(SoftmaxWithCrossEntropyGradInplaceInferer,
                            {"Softmax", framework::GradVarName("Logits")});
 
 }  // namespace operators
@@ -302,10 +302,10 @@ REGISTER_OPERATOR(softmax_with_cross_entropy, ops::SoftmaxWithCrossEntropyOp,
                   ops::SoftmaxWithCrossEntropyOpMaker,
                   ops::SoftmaxGradMaker<paddle::framework::OpDesc>,
                   ops::SoftmaxGradMaker<paddle::imperative::OpBase>,
-                  ops::SoftmaxWithCrossEntropyInplaceInference);
+                  ops::SoftmaxWithCrossEntropyInplaceInferer);
 REGISTER_OPERATOR(softmax_with_cross_entropy_grad,
                   ops::SoftmaxWithCrossEntropyOpGrad,
-                  ops::SoftmaxWithCrossEntropyGradInplaceInference);
+                  ops::SoftmaxWithCrossEntropyGradInplaceInferer);
 REGISTER_OP_CPU_KERNEL(softmax_with_cross_entropy,
                        ops::SoftmaxWithCrossEntropyKernel<float>,
                        ops::SoftmaxWithCrossEntropyKernel<double>);

paddle/fluid/operators/sum_op.cc

@@ -299,7 +299,7 @@ class SumGradOpBaseMaker : public imperative::GradOpBaseMakerBase {
   }
 };
 
-DECLARE_INPLACE_OP_INFERER(SumInplace, {"X", "Out"});
+DECLARE_INPLACE_OP_INFERER(SumInplaceInferer, {"X", "Out"});
 
 }  // namespace operators
 }  // namespace paddle
@@ -308,7 +308,7 @@ namespace ops = paddle::operators;
 
 REGISTER_OPERATOR(sum, ops::SumOp, ops::SumOpMaker, ops::SumGradDescMaker,
                   ops::SumGradOpBaseMaker, ops::SumOpVarTypeInference,
-                  ops::SumInplace);
+                  ops::SumInplaceInferer);
 
 REGISTER_OP_CPU_KERNEL(
     sum, ops::SumKernel<paddle::platform::CPUDeviceContext, float>,

paddle/fluid/platform/device_tracer.cc

@@ -40,6 +40,9 @@ namespace {
 thread_local std::deque<int> block_id_stack;
 // Tracking the nested event stacks.
 thread_local std::deque<Event *> annotation_stack;
+// stack to strore event sunch as pe and so on
+static std::deque<Event *> main_thread_annotation_stack{};
+static std::deque<std::string> main_thread_annotation_stack_name{};
 
 std::map<uint32_t, int32_t> system_thread_id_map;
 
@@ -638,15 +641,49 @@ DeviceTracer *GetDeviceTracer() {
   return tracer;
 }
 
-void SetCurAnnotation(Event *event) {
-  if (!annotation_stack.empty()) {
+std::string SetCurAnnotation(Event *event) {
+  std::string ret;
+  if (!annotation_stack.empty() && event->role() != EventRole::kSpecial) {
     event->set_parent(annotation_stack.back());
     event->set_name(annotation_stack.back()->name() + "/" + event->name());
   }
+
   annotation_stack.push_back(event);
+
+  if (!main_thread_annotation_stack_name.empty() && !annotation_stack.empty() &&
+      main_thread_annotation_stack.back()->thread_id() !=
+          annotation_stack.back()->thread_id()) {
+    ret = main_thread_annotation_stack_name.back() + "/" + event->name();
+  } else {
+    ret = event->name();
+  }
+  if (event->role() == EventRole::kSpecial) {
+    std::string name = event->name();
+    if (!main_thread_annotation_stack_name.empty()) {
+      name = main_thread_annotation_stack_name.back() + "/" + event->name();
+    }
+    main_thread_annotation_stack_name.push_back(name);
+    main_thread_annotation_stack.push_back(event);
+  }
+
+  return ret;
 }
 
-void ClearCurAnnotation() { annotation_stack.pop_back(); }
+void ClearCurAnnotation() {
+  if (!main_thread_annotation_stack_name.empty() && !annotation_stack.empty() &&
+      main_thread_annotation_stack.back()->thread_id() !=
+          annotation_stack.back()->thread_id()) {
+    annotation_stack.back()->set_name(main_thread_annotation_stack_name.back() +
+                                      "/" + annotation_stack.back()->name());
+  }
+  if (!main_thread_annotation_stack.empty() &&
+      main_thread_annotation_stack.back()->name() ==
+          annotation_stack.back()->name()) {
+    main_thread_annotation_stack_name.pop_back();
+    main_thread_annotation_stack.pop_back();
+  }
+  annotation_stack.pop_back();
+}
 
 Event *CurAnnotation() {
   if (annotation_stack.empty()) return nullptr;

paddle/fluid/platform/device_tracer.h

@@ -137,7 +137,7 @@ class DeviceTracer {
 DeviceTracer* GetDeviceTracer();
 
 // Set a name for the cuda kernel operation being launched by the thread.
-void SetCurAnnotation(Event* event);
+std::string SetCurAnnotation(Event* event);
 // Clear the name after the operation is done.
 void ClearCurAnnotation();
 // Current name of the operation being run in the thread.

paddle/fluid/platform/event.h

@@ -29,6 +29,7 @@ enum class EventRole {
   kOrdinary,  // only record op time with op type key
   kInnerOp,   // record op detail time with op type key
   kUniqueOp,  // record op detail time with op unique name key
+  kSpecial,   // record event such as PE which is outer of thread local
 };
 
 class Event {

paddle/fluid/platform/mkldnn_reuse.h

@@ -21,6 +21,7 @@ limitations under the License. */
 #include "boost/optional.hpp"
 #include "paddle/fluid/framework/data_layout_transform.h"
 #include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/operators/pool_op.h"
 #include "paddle/fluid/platform/mkldnn_helper.h"
 #include "paddle/fluid/platform/place.h"
 
@@ -592,41 +593,100 @@ template <typename T>
 class PoolingMKLDNNHandler : public MKLDNNHandlerT<T, mkldnn::pooling_forward,
                                                    mkldnn::pooling_backward> {
  public:
-  PoolingMKLDNNHandler(
-      const std::vector<int64_t>& src_dims,
-      const std::vector<int64_t>& dst_dims, const std::vector<int64_t>& ksize,
-      const std::vector<int64_t>& strides, const std::vector<int64_t>& paddings,
-      const std::string& pooling_type, bool ceil_mode,
-      const MKLDNNMemoryFormat fmt, mkldnn::memory::data_type dt, bool is_test,
-      const platform::MKLDNNDeviceContext& dev_ctx, platform::Place cpu_place,
-      const std::string& unique_name, bool exclude_padding)
+  PoolingMKLDNNHandler(const paddle::framework::ExecutionContext& ctx,
+                       const MKLDNNDeviceContext& dev_ctx,
+                       const mkldnn::engine mkldnn_engine,
+                       platform::Place cpu_place, const Tensor* input,
+                       Tensor* output, const std::string& unique_name)
       : platform::MKLDNNHandlerT<T, mkldnn::pooling_forward,
                                  mkldnn::pooling_backward>(
             dev_ctx, dev_ctx.GetEngine(), cpu_place,
-            platform::CreateKey(src_dims, dt, unique_name)) {
-    auto src_md = mkldnn::memory::desc(src_dims, dt, fmt);
-    /* create memory descriptor for pooling without specified format
-     * ('any') which lets a primitive (pooling in this case) choose
-     * the memory format preferred for best performance
-     */
-    auto dst_md =
-        platform::MKLDNNMemDesc(dst_dims, dt, MKLDNNMemoryFormat::any);
+            platform::CreateKey(framework::vectorize(input->dims()),
+                                framework::ToMKLDNNDataType(input->type()),
+                                unique_name)) {
+    if (!this->isCached()) {
+      PADDLE_ENFORCE_EQ(input->layout(), DataLayout::kMKLDNN,
+                        platform::errors::InvalidArgument(
+                            "Wrong layout set for Input tensor"));
+      PADDLE_ENFORCE_NE(input->format(), MKLDNNMemoryFormat::undef,
+                        platform::errors::InvalidArgument(
+                            "Wrong format set for Input tensor"));
+
+      const std::string pooling_type = ctx.Attr<std::string>("pooling_type");
+
+      std::vector<int> ksize_temp = ctx.Attr<std::vector<int>>("ksize");
+      std::vector<int64_t> ksize(begin(ksize_temp), end(ksize_temp));
+
+      std::vector<int> strides_temp = ctx.Attr<std::vector<int>>("strides");
+      std::vector<int64_t> strides(begin(strides_temp), end(strides_temp));
+
+      std::vector<int> paddings_temp = ctx.Attr<std::vector<int>>("paddings");
+      std::vector<int64_t> paddings(begin(paddings_temp), end(paddings_temp));
+
+      const bool global_pooling = ctx.Attr<bool>("global_pooling");
+      const std::string padding_algorithm =
+          ctx.Attr<std::string>("padding_algorithm");
+
+      // Only 2D pooling is supported now
+      PADDLE_ENFORCE_EQ(ksize.size(), 2,
+                        platform::errors::InvalidArgument(
+                            "ksize must be 2D, i.e. 2D pooling"));
+      PADDLE_ENFORCE_EQ(pooling_type == "max" || pooling_type == "avg", true,
+                        platform::errors::InvalidArgument(
+                            "pooling_type must be 'max' or 'avg'"));
+      PADDLE_ENFORCE_EQ(input->dims().size(), 4,
+                        platform::errors::InvalidArgument(
+                            "Input dim must be with 4, i.e. NCHW"));
+
+      const auto input_dims = input->dims();
+      framework::DDim data_dims =
+          framework::slice_ddim(input_dims, 2, input_dims.size());
+
+      if (global_pooling) {
+        operators::UpdateKsize(&ksize, data_dims);
+      }
 
-    auto mkldnn_paddings = ToMkldnnPadding(paddings);
+      operators::UpdatePadding(&paddings, global_pooling, 0, padding_algorithm,
+                               data_dims, strides, ksize);
+
+      const auto src_tz = paddle::framework::vectorize(input->dims());
+      const auto dst_tz = paddle::framework::vectorize(output->dims());
+
+      const auto is_test = ctx.Attr<bool>("is_test");
+
+      const auto dt = framework::ToMKLDNNDataType(input->type());
+      const auto fmt = input->format();
+
+      const auto exclude_padding = ctx.Attr<bool>("exclusive");
+
+      const auto src_md = mkldnn::memory::desc(src_tz, dt, fmt);
+      /* create memory descriptor for pooling without specified format
+       * ('any') which lets a primitive (pooling in this case) choose
+       * the memory format preferred for best performance
+       */
+
+      const auto dst_md =
+          platform::MKLDNNMemDesc(dst_tz, dt, MKLDNNMemoryFormat::any);
 
-    if (ceil_mode) {
-      CorrectOutputSize(src_dims, dst_dims, ksize, paddings, strides,
-                        mkldnn_paddings[1]);
+      auto mkldnn_paddings = ToMkldnnPadding(paddings);
+
+      const bool ceil_mode = ctx.Attr<bool>("ceil_mode");
+
+      if (ceil_mode) {
+        CorrectOutputSize(src_tz, dst_tz, ksize, paddings, strides,
+                          mkldnn_paddings[1]);
+      }
+      this->AcquireForwardPrimitiveDescriptor(
+          is_test ? mkldnn::prop_kind::forward_inference
+                  : mkldnn::prop_kind::forward_training,
+          pooling_type == "max"
+              ? mkldnn::algorithm::pooling_max
+              : (exclude_padding
+                     ? mkldnn::algorithm::pooling_avg_exclude_padding
+                     : mkldnn::algorithm::pooling_avg_include_padding),
+          src_md, dst_md, strides, ksize, mkldnn_paddings[0],
+          mkldnn_paddings[1]);
     }
-    this->AcquireForwardPrimitiveDescriptor(
-        is_test ? mkldnn::prop_kind::forward_inference
-                : mkldnn::prop_kind::forward_training,
-        pooling_type == "max"
-            ? mkldnn::algorithm::pooling_max
-            : (exclude_padding
-                   ? mkldnn::algorithm::pooling_avg_exclude_padding
-                   : mkldnn::algorithm::pooling_avg_include_padding),
-        src_md, dst_md, strides, ksize, mkldnn_paddings[0], mkldnn_paddings[1]);
   }
 
   PoolingMKLDNNHandler(
@@ -1190,8 +1250,11 @@ static std::shared_ptr<mkldnn::memory> SetDstMemory(
     const std::shared_ptr<ConvMKLDNNHandler>& handler,
     std::vector<mkldnn::primitive>* pipeline) {
   const T* residual_param_data = residual_param->data<T>();
-  PADDLE_ENFORCE(residual_param_data != nullptr,
-                 "Provide data if you want MKLDNN conv+elementwise_add fusion");
+  PADDLE_ENFORCE_NOT_NULL(
+      residual_param_data,
+      platform::errors::PreconditionNotMet("Residual parameter is required for "
+                                           "the DNNL conv+elementwise_add "
+                                           "fusion, but now it is missing"));
   std::shared_ptr<mkldnn::memory> user_residual_memory_p =
       handler->AcquireResidualDataMemory(user_residual_md,
                                          to_void_cast<T>(residual_param_data));

paddle/fluid/platform/profiler.cc

@@ -73,8 +73,7 @@ RecordEvent::RecordEvent(const std::string &name, const EventRole role) {
   // lock is not needed, the code below is thread-safe
   Event *e = PushEvent(name, role);
   // Maybe need the same push/pop behavior.
-  SetCurAnnotation(e);
-  name_ = e->name();
+  name_ = SetCurAnnotation(e);
 }
 
 RecordEvent::~RecordEvent() {
@@ -86,7 +85,7 @@ RecordEvent::~RecordEvent() {
                           BlockDepth(), g_thread_id);
   }
   ClearCurAnnotation();
-  PopEvent(name_);
+  PopEvent(name_, role_);
 }
 
 void MemEvenRecorder::PushMemRecord(const void *ptr, const Place &place,
@@ -187,8 +186,8 @@ Event *PushEvent(const std::string &name, const EventRole role) {
   return GetEventList().Record(EventType::kPushRange, name, g_thread_id, role);
 }
 
-void PopEvent(const std::string &name) {
-  GetEventList().Record(EventType::kPopRange, name, g_thread_id);
+void PopEvent(const std::string &name, const EventRole role) {
+  GetEventList().Record(EventType::kPopRange, name, g_thread_id, role);
 }
 void EnableProfiler(ProfilerState state) {
   PADDLE_ENFORCE_NE(state, ProfilerState::kDisabled,

paddle/fluid/platform/profiler.h

@@ -197,7 +197,7 @@ void PushMemEvent(uint64_t start_ns, uint64_t end_ns, size_t bytes,
 void PopMemEvent(uint64_t start_ns, uint64_t end_ns, size_t bytes,
                  const Place& place, const std::string& annotation);
 Event* PushEvent(const std::string& name, const EventRole role);
-void PopEvent(const std::string& name);
+void PopEvent(const std::string& name, const EventRole role);
 // Return the event list of all threads. Assumed the returned value calls
 // event_lists, event_lists[i][j] represents the j-th Event of i-th thread.
 std::vector<std::vector<Event>> GetAllEvents();

paddle/fluid/platform/profiler_helper.h

@@ -22,12 +22,12 @@ limitations under the License. */
 #include <memory>
 #include <mutex>  // NOLINT
 #include <random>
+#include <set>
 #include <stack>
 #include <string>
 #include <unordered_map>
 #include <utility>
 #include <vector>
-
 #ifdef PADDLE_WITH_CUDA
 #include <cuda.h>
 #endif  // PADDLE_WITH_CUDA
@@ -283,7 +283,8 @@ std::function<bool(const EventItem &, const EventItem &)> SetSortedFunc(
 void SetEvent(bool merge_thread, const Event &analyze_event,
               size_t *max_name_width, std::list<Event> *pushed_events,
               std::vector<EventItem> *event_items,
-              std::unordered_map<std::string, int> *event_idx) {
+              std::unordered_map<std::string, int> *event_idx,
+              const std::set<std::string> &main_thread_event_name) {
   if (analyze_event.type() == EventType::kPushRange) {
     pushed_events->push_back(analyze_event);
   } else if (analyze_event.type() == EventType::kPopRange) {
@@ -313,8 +314,35 @@ void SetEvent(bool merge_thread, const Event &analyze_event,
       if (merge_thread) {
         event_name = rit->name();
       } else {
-        event_name =
-            "thread" + std::to_string(rit->thread_id()) + "::" + rit->name();
+        if (!main_thread_event_name.empty()) {
+          auto origin_name = rit->name();
+          int index = 1;
+          int split_pos = 0;
+          while ((split_pos = FindNthReversePos(origin_name, '/', index)) !=
+                 -1) {
+            auto prefix_str = origin_name.substr(0, split_pos);
+            if (main_thread_event_name.count(prefix_str)) {
+              break;
+            }
+            index++;
+          }
+          if (split_pos == -1 && !main_thread_event_name.count(rit->name())) {
+            event_name = "thread" + std::to_string(rit->thread_id()) + "::" +
+                         rit->name();
+          } else {
+            if (!main_thread_event_name.count(rit->name())) {
+              event_name =
+                  origin_name.substr(0, split_pos + 1) + "thread" +
+                  std::to_string(rit->thread_id()) + "::" +
+                  origin_name.substr(split_pos + 1, origin_name.length() - 1);
+            } else {
+              event_name = rit->name();
+            }
+          }
+        } else {
+          event_name =
+              "thread" + std::to_string(rit->thread_id()) + "::" + rit->name();
+        }
       }
       auto print_name_size = event_name.size();
       int found_pos = 0;
@@ -608,6 +636,16 @@ void AnalyzeEvent(
     std::function<bool(const EventItem &, const EventItem &)> sorted_func,
     EventSortingKey sorted_by, size_t *max_name_width, OverHead *overhead,
     bool merge_thread) {
+  // In oreder to deal with special event in main thread
+  std::set<std::string> main_thread_event_name;
+  for (size_t i = 0; i < (*analyze_events).size(); i++) {
+    for (size_t j = 0; j < (*analyze_events)[i].size(); j++) {
+      Event event = (*analyze_events)[i][j];
+      if (event.role() == EventRole::kSpecial) {
+        main_thread_event_name.insert(event.name());
+      }
+    }
+  }
   for (size_t i = 0; i < (*analyze_events).size(); i++) {
     double total = 0.;  // the total time in one thread
     std::list<Event> pushed_events;
@@ -618,8 +656,10 @@ void AnalyzeEvent(
 
     for (size_t j = 0; j < (*analyze_events)[i].size(); j++) {
       Event analyze_event = (*analyze_events)[i][j];
-      SetEvent(merge_thread, analyze_event, max_name_width, &pushed_events,
-               &event_items, &event_idx);
+      if (!(analyze_event.role() == EventRole::kSpecial && !merge_thread)) {
+        SetEvent(merge_thread, analyze_event, max_name_width, &pushed_events,
+                 &event_items, &event_idx, main_thread_event_name);
+      }
     }
 
     auto table_size = event_items.size();

paddle/fluid/platform/profiler_test.cc

@@ -59,7 +59,7 @@ TEST(RecordEvent, RecordEvent) {
       PushEvent(name, EventRole::kOrdinary);
       int counter = 1;
       while (counter != i * 1000) counter++;
-      PopEvent(name);
+      PopEvent(name, EventRole::kOrdinary);
     }
   }
 
@@ -109,7 +109,7 @@ TEST(RecordEvent, RecordEvent) {
 
   // Bad Usage:
   PushEvent("event_without_pop", EventRole::kOrdinary);
-  PopEvent("event_without_push");
+  PopEvent("event_without_push", EventRole::kOrdinary);
   std::vector<std::vector<Event>> events = paddle::platform::GetAllEvents();
 
   int cuda_startup_count = 0;

paddle/fluid/pybind/box_helper_py.cc

@@ -54,6 +54,8 @@ void BindBoxHelper(py::module* m) {
       .def("preload_into_memory", &framework::BoxHelper::PreLoadIntoMemory,
            py::call_guard<py::gil_scoped_release>())
       .def("load_into_memory", &framework::BoxHelper::LoadIntoMemory,
+           py::call_guard<py::gil_scoped_release>())
+      .def("slots_shuffle", &framework::BoxHelper::SlotsShuffle,
            py::call_guard<py::gil_scoped_release>());
 }  // end BoxHelper
 
@@ -61,9 +63,9 @@ void BindBoxHelper(py::module* m) {
 void BindBoxWrapper(py::module* m) {
   py::class_<framework::BoxWrapper, std::shared_ptr<framework::BoxWrapper>>(
       *m, "BoxWrapper")
-      .def(py::init([]() {
+      .def(py::init([](int embedx_dim, int expand_embed_dim) {
         // return std::make_shared<paddle::framework::BoxHelper>(dataset);
-        return framework::BoxWrapper::GetInstance();
+        return framework::BoxWrapper::SetInstance(embedx_dim, expand_embed_dim);
       }))
       .def("save_base", &framework::BoxWrapper::SaveBase,
            py::call_guard<py::gil_scoped_release>())
@@ -76,13 +78,15 @@ void BindBoxWrapper(py::module* m) {
       .def("initialize_gpu_and_load_model",
            &framework::BoxWrapper::InitializeGPUAndLoadModel,
            py::call_guard<py::gil_scoped_release>())
+      .def("initialize_auc_runner", &framework::BoxWrapper::InitializeAucRunner,
+           py::call_guard<py::gil_scoped_release>())
       .def("init_metric", &framework::BoxWrapper::InitMetric,
            py::call_guard<py::gil_scoped_release>())
       .def("get_metric_msg", &framework::BoxWrapper::GetMetricMsg,
            py::call_guard<py::gil_scoped_release>())
       .def("get_metric_name_list", &framework::BoxWrapper::GetMetricNameList,
            py::call_guard<py::gil_scoped_release>())
-      .def("flip_pass_flag", &framework::BoxWrapper::FlipPassFlag,
+      .def("flip_phase", &framework::BoxWrapper::FlipPhase,
            py::call_guard<py::gil_scoped_release>())
       .def("init_afs_api", &framework::BoxWrapper::InitAfsAPI,
            py::call_guard<py::gil_scoped_release>())

paddle/fluid/pybind/data_set_py.cc

@@ -291,6 +291,8 @@ void BindDataset(py::module *m) {
            py::call_guard<py::gil_scoped_release>())
       .def("set_fleet_send_sleep_seconds",
            &framework::Dataset::SetFleetSendSleepSeconds,
+           py::call_guard<py::gil_scoped_release>())
+      .def("enable_pv_merge", &framework::Dataset::EnablePvMerge,
            py::call_guard<py::gil_scoped_release>());
 
   py::class_<IterableDatasetWrapper>(*m, "IterableDatasetWrapper")

paddle/scripts/conda_build.py

@@ -116,7 +116,7 @@ python setup.py install
     """
         self.cuda100 = r"""
     - cudatoolkit>=10.0, <10.1
-    - cudnn>=7.3, <7.4
+    - cudnn>=7.6, <7.7
     """
         self.cuda_info = [(self.cuda90, "cuda9.0", ".post97"),
                           (self.cuda100, "cuda10.0", ".post107")]

paddle/scripts/paddle_build.sh

@@ -59,9 +59,9 @@ function init() {
 }
 
 function cmake_base() {
-    # build script will not fail if *.deb does not exist
+    # Build script will not fail if *.deb does not exist
     rm *.deb 2>/dev/null || true
-    # delete previous built whl packages
+    # Delete previous built whl packages
     rm -rf python/dist 2>/dev/null || true
 
     # Support build for all python versions, currently
@@ -199,9 +199,7 @@ function cmake_base() {
         -DWITH_DISTRIBUTE=${distibuted_flag}
         -DWITH_MKL=${WITH_MKL:-ON}
         -DWITH_AVX=${WITH_AVX:-OFF}
-        -DWITH_GOLANG=${WITH_GOLANG:-OFF}
         -DCUDA_ARCH_NAME=${CUDA_ARCH_NAME:-All}
-        -DCUDA_ARCH_BIN=${CUDA_ARCH_BIN}
         -DWITH_PYTHON=${WITH_PYTHON:-ON}
         -DCUDNN_ROOT=/usr/
         -DWITH_TESTING=${WITH_TESTING:-ON}
@@ -231,9 +229,7 @@ EOF
         -DWITH_MKL=${WITH_MKL:-ON} \
         -DWITH_AVX=${WITH_AVX:-OFF} \
         -DNOAVX_CORE_FILE=${NOAVX_CORE_FILE:-""} \
-        -DWITH_GOLANG=${WITH_GOLANG:-OFF} \
         -DCUDA_ARCH_NAME=${CUDA_ARCH_NAME:-All} \
-        -DCUDA_ARCH_BIN=${CUDA_ARCH_BIN} \
         -DWITH_PYTHON=${WITH_PYTHON:-ON} \
         -DCUDNN_ROOT=/usr/ \
         -DWITH_TESTING=${WITH_TESTING:-ON} \
@@ -1080,7 +1076,7 @@ EOF
     if [[ "$1" != "" ]]; then
       parallel_number=$1
     fi
-    cmake .. -DWITH_DISTRIBUTE=OFF -DON_INFER=ON -DCUDA_ARCH_NAME=${CUDA_ARCH_NAME:-Auto} -DCUDA_ARCH_BIN=${CUDA_ARCH_BIN}
+    cmake .. -DWITH_DISTRIBUTE=OFF -DON_INFER=ON -DCUDA_ARCH_NAME=${CUDA_ARCH_NAME:-Auto}
 
     make -j ${parallel_number} fluid_lib_dist
     make -j ${parallel_number} inference_lib_dist

python/paddle/fluid/contrib/layers/nn.py

@@ -34,7 +34,8 @@ __all__ = [
     'fused_elemwise_activation', 'sequence_topk_avg_pooling', 'var_conv_2d',
     'match_matrix_tensor', 'tree_conv', 'fused_embedding_seq_pool',
     'multiclass_nms2', 'search_pyramid_hash', 'shuffle_batch', 'partial_concat',
-    'partial_sum', 'tdm_child', 'rank_attention', 'tdm_sampler', 'batch_fc'
+    'partial_sum', 'tdm_child', 'rank_attention', 'tdm_sampler', 'batch_fc',
+    '_pull_box_extended_sparse'
 ]
 
 
@@ -1361,3 +1362,50 @@ def batch_fc(input, param_size, param_attr, bias_size, bias_attr, act=None):
                 "Bias": b},
         outputs={"Out": pre_act})
     return helper.append_activation(pre_act)
+
+
+def _pull_box_extended_sparse(input, size, extend_size=64, dtype='float32'):
+    """
+    **Pull Box Extended Sparse Layer**
+    This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in
+    BoxPS lookup table. The result of this lookup is the embedding of each ID in the
+    :attr:`input`.
+    Args:
+        input(Variable|list of Variable): Input is a Tensor<int64> Variable, which
+            contains the IDs information.
+        size(int): The embedding size parameter, which indicates the size of
+            each embedding vector respectively.
+        extend_size(int): The embedding size parameter in extended dim, 
+            which indicates the size of each embedding vector respectively.
+        dtype(str): The dtype refers to the data type of output tensor. Only supports
+      float32 now.
+    Returns:
+        Variable|list of Variable: The tensor variable storing the embeddings of the \
+                  supplied inputs.
+    Examples:
+        .. code-block:: python
+          import paddle.fluid as fluid
+          data = fluid.layers.data(name='sequence', shape=[1], dtype='int64', lod_level=1)
+          emb, emb_ex = fluid.contrib.layers._pull_box_extended_sparse(input=data, size=8, extend_size=128)
+    """
+    helper = LayerHelper('pull_box_extended_sparse', **locals())
+    helper.input_dtype()
+    inputs = helper.multiple_input()
+    outs = [
+        helper.create_variable_for_type_inference(dtype)
+        for i in range(len(inputs))
+    ]
+    outs_extend = [
+        helper.create_variable_for_type_inference(dtype)
+        for i in range(len(inputs))
+    ]
+    helper.append_op(
+        type='pull_box_extended_sparse',
+        inputs={'Ids': inputs},
+        outputs={'Out': outs,
+                 'OutExtend': outs_extend},
+        attrs={'emb_size': size,
+               'emb_extended_size': extend_size})
+    if len(outs) == 1:
+        return outs[0], outs_extend[0]
+    return outs, outs_extend

python/paddle/fluid/contrib/slim/quantization/quantization_pass.py

@@ -43,7 +43,7 @@ _fake_quant_dequant_op_list = [
 _out_scale_op_list = [
     "conv2d", "depthwise_conv2d", "mul", "matmul", "relu", "leaky_relu",
     "relu6", "sigmoid", "tanh", "prelu", "swish", "softmax", "batch_norm",
-    "elementwise_add", "pool2d", "reshape2", "transpose2"
+    "elementwise_add", "pool2d", "reshape2", "transpose2", "concat"
 ]
 
 # list op real input and output names, to avoid processing input such as AxisTensor.
@@ -1156,14 +1156,13 @@ class OutScaleForTrainingPass(object):
         assert isinstance(graph,
                           IrGraph), 'graph must be the instance of IrGraph.'
         self._is_test = graph.is_test()
-        ops = graph.all_op_nodes()
-        for op_node in ops:
-            name = op_node.name()
-            if name in self._teller_set:
-                if len(op_node.output_arg_names()) != 1:
-                    continue
-                in_node = graph._find_node_by_name(
-                    op_node.outputs, op_node.output_arg_names()[0])
+        target_ops = []
+        for op in graph.all_op_nodes():
+            if op.name() in self._teller_set:
+                target_ops.append(op)
+        for op in target_ops:
+            for output_var_name in _get_op_output_var_names(op):
+                in_node = graph._find_node_by_name(op.outputs, output_var_name)
                 out_node = graph.create_var_node_from_desc(in_node.var())
                 scale_node = graph.create_persistable_node(
                     name=self._scale_name(in_node.name()),
@@ -1263,13 +1262,13 @@ class OutScaleForInferencePass(object):
         """
         assert isinstance(graph,
                           IrGraph), 'graph must be the instance of IrGraph.'
-        ops = graph.all_op_nodes()
-        for op_node in ops:
-            name = op_node.name()
-            if name in self._teller_set:
-                if len(op_node.output_arg_names()) != 1:
-                    continue
-                scale_name = self._scale_name(op_node.output_arg_names()[0])
+        op_nodes = graph.all_op_nodes()
+        for op_node in op_nodes:
+            if op_node.name() in self._teller_set:
+                output_var_name = _get_op_output_var_names(op_node)
+                assert len(output_var_name) == 1, "Only support collecting " \
+                    "output for op that only has an activation output for now."
+                scale_name = self._scale_name(output_var_name[0])
                 scale_v = np.array(
                     self._scope.find_var(scale_name).get_tensor())[0]
                 op_node.op()._set_attr("out_threshold", float(scale_v))

python/paddle/fluid/dataset.py

@@ -1079,3 +1079,24 @@ class BoxPSDataset(InMemoryDataset):
 
     def _dynamic_adjust_after_train(self):
         pass
+
+    def slots_shuffle(self, slots):
+        """
+        Slots Shuffle 
+        Slots Shuffle is a shuffle method in slots level, which is usually used 
+        in sparse feature with large scale of instances. To compare the metric, i.e.
+        auc while doing slots shuffle on one or several slots with baseline to 
+        evaluate the importance level of slots(features).
+        
+        Args:
+            slots(list[string]): the set of slots(string) to do slots shuffle.
+
+        Examples:
+            import paddle.fluid as fluid
+            dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
+            dataset.set_merge_by_lineid()
+            #suppose there is a slot 0
+            dataset.slots_shuffle(['0'])
+        """
+        slots_set = set(slots)
+        self.boxps.slots_shuffle(slots_set)

python/paddle/fluid/dygraph/dygraph_to_static/call_transformer.py

@@ -32,12 +32,23 @@ class CallTransformer(gast.NodeTransformer):
         self.wrapper_root = wrapper_root
         self.root = wrapper_root.node
 
-    def _is_builtin_call(self, node):
+    def _no_need_convert_call(self, node):
+        """
+        Determines whether a function needs to be transformed by `convert_call`.
+        It doesn't need to be transformed when a function satisfies the following conditions:
+          1. It's a api of paddle
+          2. It's a python builtin function not include `len`
+        """
         assert isinstance(node, gast.Call)
+        if is_paddle_api(node):
+            return True
+
         func_str = ast_to_source_code(node.func).strip()
         try:
-            from paddle.fluid.dygraph.dygraph_to_static.convert_call_func import is_builtin
-            return eval("is_builtin({})".format(func_str))
+            from paddle.fluid.dygraph.dygraph_to_static.convert_call_func import is_builtin_len, is_builtin
+            is_builtin = eval("is_builtin({})".format(func_str))
+            is_builtin_len = eval("is_builtin_len({})".format(func_str))
+            return is_builtin and not is_builtin_len
         except Exception:
             return False
 
@@ -46,10 +57,8 @@ class CallTransformer(gast.NodeTransformer):
 
     def visit_Call(self, node):
         self.generic_visit(node)
-        if is_paddle_api(node):
-            return node
 
-        if self._is_builtin_call(node):
+        if self._no_need_convert_call(node):
             return node
 
         func_str = ast_to_source_code(node.func).strip()

python/paddle/fluid/dygraph/dygraph_to_static/convert_builtins_func.py

+# Copyright (c) 2020 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 framework
+from paddle.fluid import core
+from paddle.fluid.layers import nn
+from paddle.fluid.layers import control_flow
+
+
+def convert_len(var):
+    """
+    return variable(length) from shape ops based on var.type
+
+    Note: In addition to some ast transformations, some block-related
+          operations are added in `len` transformation, such as appending
+          `shape_op` in var.block.
+    """
+    if isinstance(var, framework.Variable):
+        if var.type in [
+                core.VarDesc.VarType.LOD_TENSOR,
+                core.VarDesc.VarType.SELECTED_ROWS
+        ]:
+            # Note: Length of var may be known ahead of time in dygraph,
+            # but it probably represents batch size which can be variant.
+            # so we return a variable dynamically inferred from var.shape.
+            return nn.shape(var)[0]
+        elif var.type == core.VarDesc.VarType.LOD_TENSOR_ARRAY:
+            return control_flow.array_length(var)
+        else:
+            raise TypeError(
+                'len(var) only supports LoDTensor/LoDTensorArray/SelectedRows, but received %s.'
+                % type(var))
+    else:
+        return len(var)

python/paddle/fluid/dygraph/dygraph_to_static/convert_call_func.py

@@ -29,6 +29,7 @@ import six
 
 from paddle.fluid.dygraph.dygraph_to_static import ProgramTranslator
 from paddle.fluid.dygraph.layers import Layer
+from paddle.fluid.dygraph.dygraph_to_static.convert_builtins_func import convert_len
 
 DECORATOR_NAMES = ['declarative', 'dygraph_to_static_func']
 program_translator = ProgramTranslator()
@@ -49,6 +50,12 @@ def is_builtin(func):
         return False
 
 
+def is_builtin_len(func):
+    if isinstance(func, types.BuiltinFunctionType) and func.__name__ == 'len':
+        return True
+    return False
+
+
 def is_paddle_func(func):
     m = inspect.getmodule(func)
     return m is not None and m.__name__.startswith("paddle")
@@ -91,10 +98,10 @@ def convert_call(func):
     func_self = None
     converted_call = None
 
-    if is_builtin(func):
-        return func
+    if is_builtin_len(func):
+        return convert_len
 
-    if is_paddle_func(func):
+    if is_builtin(func) or is_paddle_func(func):
         return func
 
     if inspect.isfunction(func):

python/paddle/fluid/dygraph/dygraph_to_static/loop_transformer.py

@@ -166,13 +166,19 @@ class NameVisitor(gast.NodeVisitor):
 
         in_loop_vars = self.in_loop_vars[node]
         in_loop_name_strs = self._var_nodes_to_names(in_loop_vars)
+
         before_loop_body_vars = self.before_loop_body_vars[node]
+        before_loop_body_vars = self._remove_target_vars_of_for(
+            before_loop_body_vars, node)
         before_loop_name_strs = self._var_nodes_to_names(before_loop_body_vars)
+
         after_loop_vars = self.current_seen_vars - before_loop_body_vars - in_loop_vars
+        after_loop_vars = self._remove_target_vars_of_for(after_loop_vars, node)
         after_loop_name_strs = self._var_nodes_to_names(after_loop_vars,
                                                         read_context)
         condition_vars = self.condition_vars[node]
         condition_names = self._var_nodes_to_names(condition_vars)
+
         write_vars = self.write_in_loop[node]
         write_names = self._var_nodes_to_names(write_vars)
 
@@ -203,6 +209,7 @@ class NameVisitor(gast.NodeVisitor):
                 # vars out
                 loop_var_names.add(name)
                 create_var_names.add(name)
+
         return loop_var_names, create_var_names
 
     def visit_Name(self, node):
@@ -221,8 +228,8 @@ class NameVisitor(gast.NodeVisitor):
             self.in_loop_vars[loop_node].add(node)
             if type(node.ctx) in write_context:
                 self.write_in_loop[loop_node].add(node)
-            if self.in_condition:
-                self.condition_vars[loop_node].add(node)
+        if self.in_condition:
+            self.condition_vars[loop_node].add(node)
         self.generic_visit(node)
 
     def visit_FunctionDef(self, node):
@@ -309,11 +316,60 @@ class NameVisitor(gast.NodeVisitor):
         return False
 
     def _is_call_func_name_node(self, node):
-        parent_node = self.node_to_wrapper_map[node].parent.node
+        parent_node = self._get_parent_node(node)
         if isinstance(parent_node, gast.Call) and parent_node.func == node:
             return True
         return False
 
+    def _get_parent_node(self, node):
+        wrapper_node = self.node_to_wrapper_map.get(node)
+        if wrapper_node:
+            parent_node = wrapper_node.parent.node
+            return parent_node
+        return None
+
+    def _remove_target_vars_of_for(self, before_or_after_loop_vars, loop_node):
+        """
+        Remove target vars of gast.For from before_loop_vars or after_loop_vars.
+        :param before_or_after_loop_vars: before_loop_vars or after_loop_vars of loop_node.
+        :param loop_node: Current loop node.
+        """
+
+        removed_vars = set()
+        for name_node in before_or_after_loop_vars:
+            if not isinstance(name_node, gast.Name):
+                continue
+
+            parent_node = self._get_parent_node(name_node)
+
+            # NOTE: gast.For.target can be gast.Tuple.
+            #  For example: `for i, j in enumerate(x)` has two target vars: i and j
+            if isinstance(parent_node, gast.Tuple):
+                parent_node = self._get_parent_node(parent_node)
+
+            if isinstance(parent_node,
+                          gast.For) and parent_node is not loop_node:
+                target_node = parent_node.target
+
+                if isinstance(target_node, gast.Tuple):
+                    target_vars = target_node.elts
+                else:
+                    target_vars = [target_node]
+
+                if name_node in target_vars:
+                    removed_vars.add(name_node)
+
+        removed_vars_name_strs = {var.id for var in removed_vars}
+
+        for var in before_or_after_loop_vars:
+            if not isinstance(var, gast.Name):
+                continue
+            if var.id in removed_vars_name_strs and var not in self.condition_vars[
+                    loop_node]:
+                removed_vars.add(var)
+
+        return before_or_after_loop_vars - removed_vars
+
 
 class LoopTransformer(gast.NodeTransformer):
     """

python/paddle/fluid/dygraph/nn.py

@@ -771,14 +771,19 @@ class Pool2D(layers.Layer):
         ceil_mode (bool, optional): Whether to use the ceil function to calculate output height and width.
             False is the default. If it is set to False, the floor function will be used. Default: False.
         exclusive (bool, optional): Whether to exclude padding points in average pooling mode. Default: True.
+        data_format (string): The data format of the input and output data. An optional string from: `"NCHW"`, `"NHWC"`.
+            The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
+            ``[batch_size, input_channels, input_height, input_width]``. When it is `"NHWC"`, the data is 
+            stored in the order of: ``[batch_size, input_height, input_width, input_channels]``
 
     Returns:
         None
 
     Raises:
-        ValueError: If 'pool_type' is not "max" nor "avg"
-        ValueError: If 'global_pooling' is False and 'pool_size' is -1
-        ValueError: If 'use_cudnn' is not a bool value.
+        ValueError: If ``pool_type`` is not "max" nor "avg".
+        ValueError: If ``global_pooling`` is False and ``pool_size`` is -1.
+        ValueError: If ``use_cudnn`` is not a bool value.
+        ValueError: If ``data_format`` is not "NCHW" nor "NHWC".
 
     Examples:
 
@@ -806,7 +811,10 @@ class Pool2D(layers.Layer):
                  global_pooling=False,
                  use_cudnn=True,
                  ceil_mode=False,
-                 exclusive=True):
+                 exclusive=True,
+                 data_format="NCHW"):
+        data_format = data_format.upper()  # supprt NHWC, nhwc, etc.
+        pool_type = pool_type.lower()  # supprt max, Max, etc.
         if pool_type not in ["max", "avg"]:
             raise ValueError(
                 "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.",
@@ -820,6 +828,11 @@ class Pool2D(layers.Layer):
         if not isinstance(use_cudnn, bool):
             raise ValueError("use_cudnn should be True or False")
 
+        if data_format not in ["NCHW", "NHWC"]:
+            raise ValueError(
+                "Attr(data_format) should be 'NCHW' or 'NHWC'. Received "
+                "Attr(data_format): %s." % str(data_format))
+
         super(Pool2D, self).__init__()
 
         self._pool_type = pool_type
@@ -831,6 +844,7 @@ class Pool2D(layers.Layer):
         self._use_cudnn = use_cudnn
         self._ceil_mode = ceil_mode
         self._exclusive = exclusive
+        self._data_format = data_format
         self._l_type = 'pool2d'
 
     def forward(self, input):
@@ -839,7 +853,8 @@ class Pool2D(layers.Layer):
                      'global_pooling', self._global_pooling, 'strides',
                      self._pool_stride, 'paddings', self._pool_padding,
                      'use_cudnn', self._use_cudnn, 'ceil_mode', self._ceil_mode,
-                     'use_mkldnn', False, 'exclusive', self._exclusive)
+                     'use_mkldnn', False, 'exclusive', self._exclusive,
+                     'data_format', self._data_format)
             return core.ops.pool2d(input, *attrs)
 
         check_variable_and_dtype(
@@ -856,6 +871,7 @@ class Pool2D(layers.Layer):
             "ceil_mode": self._ceil_mode,
             "use_mkldnn": False,
             "exclusive": self._exclusive,
+            "data_format": self._data_format,
         }
         inputs = {"X": [input]}
 

python/paddle/fluid/layers/loss.py

@@ -1536,9 +1536,11 @@ def teacher_student_sigmoid_loss(input,
           cost = fluid.layers.teacher_student_sigmoid_loss(input=similarity, label=label)
 
     """
-    check_variable_and_dtype(input, "input", ['float32', 'float64'],
+    check_variable_and_dtype(input, "input",
+                             ['float32', 'float64', 'int32', 'int64'],
                              'teacher_student_sigmoid_loss')
-    check_variable_and_dtype(label, "label", ['float32', 'float64'],
+    check_variable_and_dtype(label, "label",
+                             ['float32', 'float64', 'int32', 'int64'],
                              'teacher_student_sigmoid_loss')
 
     helper = LayerHelper('teacher_student_sigmoid_loss', **locals())

python/paddle/fluid/layers/nn.py

@@ -1902,7 +1902,7 @@ def pool2d(input,
                              None by default.
         exclusive (bool): Whether to exclude padding points in average pooling
                           mode, default is `true`.
-        data_format (string): The data format of the input and output data. An optional string from: `"NCHW"`, `"NDHW"`.
+        data_format (string): The data format of the input and output data. An optional string from: `"NCHW"`, `"NHWC"`.
                 The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
                 `[batch_size, input_channels, input_height, input_width]`.
 
@@ -11045,8 +11045,26 @@ def shape(input):
 
     Get the shape of the input.
 
+    .. code-block:: text
+
+        Case1:
+            Given N-D Tensor:
+                input = [ [1, 2, 3, 4], [5, 6, 7, 8] ]
+
+            Then:
+                input.shape = [2, 4]
+
+        Case2:
+            Given SelectedRows:
+                input.rows = [0, 4, 19]
+                input.height = 20
+                input.value = [ [1, 2], [3, 4], [5, 6] ]  # inner tensor
+            Then:
+                input.shape = [3, 2]
+
     Args:
-        input (Variable): The input N-D Tensor. Datatype can be float32, float64, int32, int64.
+        input (Variable): The input can be N-D Tensor or SelectedRows with data type float32, float64, int32, int64.
+                          If input variable is type of SelectedRows, returns the shape of it's inner tensor.
 
     Returns:
         Variable (Tensor): The shape of the input variable.
@@ -11057,7 +11075,7 @@ def shape(input):
             import paddle.fluid as fluid
             import numpy as np
 
-            inputs = fluid.layers.data(name="x", shape=[3, 100, 100], dtype="float32")
+            inputs = fluid.data(name="x", shape=[3, 100, 100], dtype="float32")
             output = fluid.layers.shape(inputs)
 
             exe = fluid.Executor(fluid.CPUPlace())

python/paddle/fluid/tests/unittests/dygraph_to_static/bert_utils.py

@@ -49,10 +49,13 @@ def mask(batch_tokens, total_token_num, vocab_size, CLS=1, SEP=2, MASK=3):
     max_len = max([len(sent) for sent in batch_tokens])
     mask_label = []
     mask_pos = []
-    np.random.seed(SEED)
-    prob_mask = np.random.rand(total_token_num)
+    # NOTE: numpy random is not thread-safe, for async DataLoader,
+    # using np.random.seed() directly is risky, using RandomState
+    # class is a better way
+    self_random = np.random.RandomState(SEED)
+    prob_mask = self_random.rand(total_token_num)
     # Note: the first token is [CLS], so [low=1]
-    replace_ids = np.random.randint(1, high=vocab_size, size=total_token_num)
+    replace_ids = self_random.randint(1, high=vocab_size, size=total_token_num)
     pre_sent_len = 0
     prob_index = 0
     for sent_index, sent in enumerate(batch_tokens):
@@ -85,7 +88,9 @@ def mask(batch_tokens, total_token_num, vocab_size, CLS=1, SEP=2, MASK=3):
 
         # ensure at least mask one word in a sentence
         while not mask_flag:
-            token_index = int(np.random.randint(1, high=len(sent) - 1, size=1))
+            token_index = int(
+                self_random.randint(
+                    1, high=len(sent) - 1, size=1))
             if sent[token_index] != SEP and sent[token_index] != CLS:
                 mask_label.append(sent[token_index])
                 sent[token_index] = MASK
@@ -244,13 +249,16 @@ class DataReader(object):
 
     def build_fake_data(self):
         for _ in range(1000000):
-            random.seed(SEED)
-            sent0_len = random.randint(50, 100)
-            sent1_len = random.randint(50, 100)
+            # NOTE: python random has bug in python2,
+            # we should avoid using random module,
+            # please using numpy.random
+            self_random = np.random.RandomState(SEED)
+            sent0_len = self_random.randint(50, 100)
+            sent1_len = self_random.randint(50, 100)
 
             token_ids = [1] \
-                        + [random.randint(0, 10000) for i in range(sent0_len-1)] \
-                        + [random.randint(0, 10000) for i in range(sent1_len-1)] \
+                        + [self_random.randint(0, 10000) for i in range(sent0_len-1)] \
+                        + [self_random.randint(0, 10000) for i in range(sent1_len-1)] \
                         + [2]
 
             sent_ids = [0 for i in range(sent0_len)

python/paddle/fluid/tests/unittests/dygraph_to_static/test_len.py

+# Copyright (c) 2020 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
+import paddle.fluid as fluid
+from paddle.fluid.dygraph import declarative
+from paddle.fluid.dygraph.dygraph_to_static import convert_call
+
+SEED = 2020
+np.random.seed(SEED)
+
+
+def len_with_tensor(x):
+    x = fluid.dygraph.to_variable(x)
+    x_len = len(x)
+    return x_len
+
+
+def len_with_lod_tensor_array(x):
+    x = fluid.dygraph.to_variable(x)
+
+    i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
+    arr = fluid.layers.array_write(x, i=i)
+    arr_len = len(arr)
+
+    return arr_len
+
+
+class TestLen(unittest.TestCase):
+    def setUp(self):
+        self.place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
+        ) else fluid.CPUPlace()
+        self.x_data = np.random.random([10, 16]).astype('float32')
+        self.init_func()
+
+    def init_func(self):
+        self.func = len_with_tensor
+
+    def _run(self, to_static):
+        with fluid.dygraph.guard(self.place):
+            if to_static:
+                out = declarative(self.func)(self.x_data)
+            else:
+                out = self.func(self.x_data)
+
+            if isinstance(out, fluid.core.VarBase):
+                out = out.numpy()
+            return out
+
+    def test_len(self):
+        dygraph_res = self._run(to_static=False)
+        static_res = self._run(to_static=True)
+        self.assertTrue(np.allclose(dygraph_res, static_res))
+
+
+class TestLenWithTensorArray(TestLen):
+    def init_func(self):
+        self.func = len_with_lod_tensor_array
+
+
+# Note: Variable(SelectedRows) is not exposed directly in dygraph.
+# The unittest is used to test coverage by fake transformed code.
+def len_with_selected_rows(place):
+    block = fluid.default_main_program().global_block()
+    # create selected_rows variable
+    var = block.create_var(
+        name="X",
+        dtype="float32",
+        persistable=True,
+        type=fluid.core.VarDesc.VarType.SELECTED_ROWS)
+    # y is Variable(SelectedRows)
+    y = fluid.layers.merge_selected_rows(var)
+    y_len = convert_call(len)(y)
+
+    # z is inner tensor with shape [4, 2]
+    z = fluid.layers.get_tensor_from_selected_rows(y)
+    z_len = convert_call(len)(z)
+
+    # set data for selected_rows
+    x_rows = [0, 2, 2, 4, 19]
+    row_numel = 2
+    np_array = np.ones((len(x_rows), row_numel)).astype("float32")
+
+    x_var = fluid.global_scope().var("X").get_selected_rows()
+    x_var.set_rows(x_rows)
+    x_var.set_height(20)
+    x_tensor = x_var.get_tensor()
+    x_tensor.set(np_array, place)
+
+    exe = fluid.Executor(place=place)
+    result = exe.run(fluid.default_main_program(), fetch_list=[y_len, z_len])
+    return result
+
+
+class TestLenWithSelectedRows(unittest.TestCase):
+    def setUp(self):
+        self.place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
+        ) else fluid.CPUPlace()
+
+    def test_len(self):
+        selected_rows_var_len, var_tensor_len = len_with_selected_rows(
+            self.place)
+        self.assertEqual(selected_rows_var_len, var_tensor_len)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/dygraph_to_static/test_loop.py

@@ -132,6 +132,19 @@ def var_create_in_for_loop(max_len):
     return ret
 
 
+def nested_for_loop_dyfunc():
+    two = fluid.layers.fill_constant(shape=[1], value=2, dtype="int32")
+    three = fluid.layers.fill_constant(shape=[1], value=3, dtype="int32")
+    for j in range(two):
+        for i in range(10):
+            a = 2
+
+    for i in range(three):
+        b = fluid.layers.zeros(shape=[1], dtype='float32')
+
+    return b
+
+
 class TestNameVisitor(unittest.TestCase):
     def setUp(self):
         self.loop_funcs = [
@@ -142,6 +155,8 @@ class TestNameVisitor(unittest.TestCase):
         ]
         self.create_var_names = [set(), set(["ret"]), set()]
 
+        self.nested_for_loop_func = nested_for_loop_dyfunc
+
     def test_loop_vars(self):
         for i in range(len(self.loop_funcs)):
             func = self.loop_funcs[i]
@@ -155,6 +170,28 @@ class TestNameVisitor(unittest.TestCase):
                     self.assertEqual(loop_var_names, self.loop_var_names[i])
                     self.assertEqual(create_var_names, self.create_var_names[i])
 
+    def test_nested_loop_vars(self):
+        func = self.nested_for_loop_func
+        test_func = inspect.getsource(func)
+        gast_root = gast.parse(test_func)
+        name_visitor = NameVisitor(gast_root)
+
+        self.loop_var_names = [
+            set(["j", "two"]),
+            set(["i", "three", "b"]),
+            set(["i"]),
+        ]
+        self.create_var_names = [set(), set(["b"]), set()]
+        i = 0
+        for node in gast.walk(gast_root):
+            if isinstance(node, (gast.While, gast.For)):
+                loop_var_names, create_var_names = name_visitor.get_loop_var_names(
+                    node)
+                # print(loop_var_names)
+                self.assertEqual(loop_var_names, self.loop_var_names[i])
+                self.assertEqual(create_var_names, self.create_var_names[i])
+                i += 1
+
 
 class TestTransformWhileLoop(unittest.TestCase):
     def setUp(self):

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

@@ -172,6 +172,7 @@ class TestBoxPSPreload(unittest.TestCase):
             exe.run(fluid.default_startup_program())
             datasets[0].load_into_memory()
             datasets[0].begin_pass()
+            datasets[0].slots_shuffle([])
             datasets[1].preload_into_memory()
             exe.train_from_dataset(
                 program=fluid.default_main_program(),

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

@@ -125,6 +125,7 @@ class TestDataset(unittest.TestCase):
         dataset.set_trainer_num(4)
         dataset.set_hdfs_config("my_fs_name", "my_fs_ugi")
         dataset.set_download_cmd("./read_from_afs my_fs_name my_fs_ugi")
+        dataset.enable_pv_merge()
 
         thread_num = dataset.get_thread_num()
         self.assertEqual(thread_num, 12)
@@ -231,7 +232,7 @@ class TestDataset(unittest.TestCase):
         dataset.set_pipe_command("cat")
         dataset.set_use_var(slots_vars)
         dataset.load_into_memory()
-        dataset.set_fea_eval(10000, True)
+        dataset.set_fea_eval(1, True)
         dataset.slots_shuffle(["slot1"])
         dataset.local_shuffle()
         dataset.set_generate_unique_feasigns(True, 15)

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

@@ -26,9 +26,9 @@ def dequantize_log(x, dict_data):
     output_data_f = output_data.flatten()
     for i in range(x_f.size):
         if x_f[i] < 0:
-            output_data_f[i] = -np.power(2, dict_data[x_f[i] + 128])
+            output_data_f[i] = -dict_data[x_f[i] + 128]
         else:
-            output_data_f[i] = np.power(2, dict_data[x_f[i]])
+            output_data_f[i] = dict_data[x_f[i]]
     return output_data_f.reshape(x.shape)
 
 

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

@@ -17,7 +17,6 @@ import paddle.fluid.core as core
 import os
 import unittest
 import paddle.fluid.layers as layers
-from paddle.fluid.layers.nn import _pull_box_sparse
 
 
 class TestDataFeed(unittest.TestCase):
@@ -57,9 +56,9 @@ class TestDataFeed(unittest.TestCase):
             lod_level=0,
             append_batch_size=False)
 
-        emb_x, emb_y = _pull_box_sparse([x, y], size=2)
-        emb_xp = _pull_box_sparse(x, size=2)
-        concat = layers.concat([emb_x, emb_y], axis=1)
+        emb_x, emb_y = fluid.contrib.layers._pull_box_extended_sparse(
+            [x, y], size=2, extend_size=128)
+        concat = layers.concat([emb_x[0], emb_x[1], emb_y[0], emb_y[1]], axis=1)
         fc = layers.fc(input=concat,
                        name="fc",
                        size=1,

python/paddle/fluid/tests/unittests/test_pool2d_op.py

@@ -1295,6 +1295,78 @@ class TestDygraphPool2DAPIError(unittest.TestCase):
                 name='x1', shape=[3, 32, 32, 5], dtype="int32")
             self.assertRaises(TypeError, pool2d, data2)
 
+    def test_data_format_error(self):
+        with program_guard(Program(), Program()):
+            # the data_format must be 'NCHW' or 'NHWC'
+            data1 = np.random.random((3, 32, 32, 5)).astype('float32')
+            self.assertRaises(
+                ValueError,
+                fluid.dygraph.Pool2D,
+                pool_size=2,
+                pool_type='max',
+                pool_stride=1,
+                global_pooling=False,
+                data_format='NWHC')
+
+
+class TestDygraphPool2DAPI(unittest.TestCase):
+    def test_nhwc(self):
+        with fluid.dygraph.guard():
+            data = np.random.random((3, 32, 32, 5)).astype('float32')
+            x = fluid.dygraph.to_variable(data)
+            pool2d = fluid.dygraph.Pool2D(
+                pool_size=2,
+                pool_type='max',
+                pool_stride=1,
+                pool_padding=[0, 0],
+                global_pooling=False,
+                data_format='NHWC')
+            out1 = pool2d(x)
+            out2 = pool2D_forward_naive(
+                data, [2, 2], [1, 1],
+                paddings=[0, 0],
+                pool_type='max',
+                data_format='NHWC')
+            self.assertTrue(np.allclose(out1.numpy(), out2))
+
+    def test_lower_case(self):
+        with fluid.dygraph.guard():
+            data = np.random.random((3, 32, 32, 5)).astype('float32')
+            x = fluid.dygraph.to_variable(data)
+            pool2d = fluid.dygraph.Pool2D(
+                pool_size=2,
+                pool_type='max',
+                pool_stride=1,
+                pool_padding=[0, 0],
+                global_pooling=False,
+                data_format='nhwc')
+            out1 = pool2d(x)
+            out2 = pool2D_forward_naive(
+                data, [2, 2], [1, 1],
+                paddings=[0, 0],
+                pool_type='max',
+                data_format='NHWC')
+            self.assertTrue(np.allclose(out1.numpy(), out2))
+
+    def test_upper_case(self):
+        with fluid.dygraph.guard():
+            data = np.random.random((3, 32, 32, 5)).astype('float32')
+            x = fluid.dygraph.to_variable(data)
+            pool2d = fluid.dygraph.Pool2D(
+                pool_size=2,
+                pool_type='MAX',
+                pool_stride=1,
+                pool_padding=[0, 0],
+                global_pooling=False,
+                data_format='nhwc')
+            out1 = pool2d(x)
+            out2 = pool2D_forward_naive(
+                data, [2, 2], [1, 1],
+                paddings=[0, 0],
+                pool_type='max',
+                data_format='NHWC')
+            self.assertTrue(np.allclose(out1.numpy(), out2))
+
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -17,6 +17,8 @@ from __future__ import print_function
 import unittest
 import numpy as np
 from op_test import OpTest
+from paddle.fluid import core
+from paddle.fluid.op import Operator
 
 
 class TestShapeOp(OpTest):
@@ -45,5 +47,41 @@ class case2(TestShapeOp):
         self.shape = [1, 2, 3]
 
 
+class TestShapeWithSelectedRows(unittest.TestCase):
+    def get_places(self):
+        places = [core.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            places.append(core.CUDAPlace(0))
+        return places
+
+    def check_with_place(self, place):
+        scope = core.Scope()
+        x_rows = [0, 1, 5, 4, 19]
+        height = 20
+        row_numel = 2
+
+        np_array = np.ones((len(x_rows), row_numel)).astype("float32")
+
+        # initialize input variable X
+        x = scope.var('X').get_selected_rows()
+        x.set_rows(x_rows)
+        x.set_height(height)
+        x_tensor = x.get_tensor()
+        x_tensor.set(np_array, place)
+
+        # initialize input variable Out
+        out_shape = scope.var("Out").get_tensor()
+        op = Operator("shape", Input="X", Out="Out")
+
+        op.run(scope, place)
+
+        out_shape = np.array(out_shape).tolist()
+        self.assertListEqual([5, 2], out_shape)
+
+    def test_check_output(self):
+        for place in self.get_places():
+            self.check_with_place(place)
+
+
 if __name__ == '__main__':
     unittest.main()

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

@@ -50,7 +50,7 @@ class TestVarBase(unittest.TestCase):
     def test_tensor_to_variable(self):
         with fluid.dygraph.guard():
             t = fluid.Tensor()
-            t.set(np.ndarray([5, 30]), fluid.CPUPlace())
+            t.set(np.random.random((1024, 1024)), fluid.CPUPlace())
             var = fluid.dygraph.to_variable(t)
             self.assertTrue(np.array_equal(t, var.numpy()))
 

python/paddle/fluid/transpiler/collective.py

@@ -314,7 +314,8 @@ class LocalSGD(Collective):
                     name=self.snapshot_name(param.name),
                     shape=param.shape,
                     persistable=True,
-                    stop_gradient=True)
+                    stop_gradient=True,
+                    dtype=param.dtype)
 
                 block._insert_op(
                     idx + 1,

tools/check_api_approvals.sh

@@ -283,6 +283,16 @@ if [ "${ADDED_OP_USE_DEFAULT_GRAD_MAKER}" != "" ]; then
   check_approval 1 32832641 6836917
 fi
 
+# Get the list of PR authors with unresolved unit test issues
+pip install PyGithub
+# For getting PR related data
+wget https://paddle-ci.gz.bcebos.com/blk/block.txt
+HASUTFIXED=`python ${PADDLE_ROOT}/tools/check_ut.py | grep "has unit-test to be fixed" || true`
+if [ "${HASUTFIXED}" != "" ]; then
+  echo_line="${HASUTFIXED} You must have one RD (chalsliu (Recommend) or kolinwei) approval.\n"
+  check_approval 1 45041955 22165420
+fi
+
 if [ -n "${echo_list}" ];then
   echo "****************"
   echo -e "${echo_list[@]}"

tools/check_ut.py

+#!/bin/env python
+# Copyright (c) 2020 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.
+""" Get pull requests. """
+
+import os
+import time
+import os.path
+from github import Github
+
+
+class PRChecker(object):
+    """ PR Checker. """
+
+    def __init__(self):
+        self.github = Github(os.getenv('GITHUB_API_TOKEN'), timeout=60)
+        self.repo = None
+
+    def check(self):
+        """ check pr """
+        filename = 'block.txt'
+        pr_id = os.getenv('GIT_PR_ID')
+        if not pr_id:
+            print('No PR ID')
+            exit(0)
+        print(pr_id)
+        if not os.path.isfile(filename):
+            print('No author to check')
+            exit(0)
+        self.repo = self.github.get_repo('PaddlePaddle/Paddle')
+        pr = self.repo.get_pull(int(pr_id))
+        user = pr.user.login
+        with open(filename) as f:
+            for l in f:
+                if l.rstrip('\r\n') == user:
+                    print('{} has UT to be fixed, so CI failed.'.format(user))
+                    exit(1)
+        exit(0)
+
+
+if __name__ == '__main__':
+    pr_checker = PRChecker()
+    pr_checker.check()

tools/count_invalid_enforce.sh

@@ -45,7 +45,7 @@ function walk_dir(){
             if [ $level -le 1 ]; then
                 enforce_scan $1"/"$file total_check_cnt valid_check_cnt
                 dir_name=$1
-                echo "${dir_name#../}"/"$file - total: ${total_check_cnt}, valid: ${valid_check_cnt}, invalid: $(($total_check_cnt-$valid_check_cnt))"
+                echo "${dir_name#../}/"$file" | ${total_check_cnt} | ${valid_check_cnt} | $(($total_check_cnt-$valid_check_cnt))"
                 ALL_PADDLE_CHECK_CNT=$(($ALL_PADDLE_CHECK_CNT+$total_check_cnt))
                 VALID_PADDLE_CHECK_CNT=$(($VALID_PADDLE_CHECK_CNT+$valid_check_cnt))
                 walk_dir $1"/"$file $level

tools/file_invalid_enforce.sh

@@ -29,6 +29,15 @@
 
 ROOT_DIR=../paddle/fluid/operators
 
+white_list_str = "\
+    layer_norm_op.cc \
+    box_clip_op.cc \
+    box_clip_op.h \
+    random_crop_op.h \
+    elementwise_op_function.cu.h \
+    fused_elemwise_activation_op.cc \
+    auc_op.cu"
+
 function enforce_scan(){
     paddle_check=`grep -r -zoE "(PADDLE_ENFORCE[A-Z_]{0,9}|PADDLE_THROW)\(.[^,\);]*.[^;]*\);\s" $1 || true`
     total_check_cnt=`echo "$paddle_check" | grep -cE "(PADDLE_ENFORCE|PADDLE_THROW)" || true`
@@ -45,14 +54,16 @@ function walk_dir(){
     for file in `ls $1`
     do
         if [ -f $1"/"$file ];then
-            enforce_scan $1"/"$file file_total_check_cnt file_valid_check_cnt
-            file_invalid_check_cnt=$(($total_check_cnt-$valid_check_cnt))
-            if [ $file_invalid_check_cnt -gt 0 ];then
-                echo "- $file | ${file_total_check_cnt} | ${file_valid_check_cnt} | ${file_invalid_check_cnt}"
+            in_white_list=$(echo $white_list_str | grep "${file}")
+            if [[ "$in_white_list" == "" ]];then
+                enforce_scan $1"/"$file file_total_check_cnt file_valid_check_cnt
+                file_invalid_check_cnt=$(($total_check_cnt-$valid_check_cnt))
+                if [ $file_invalid_check_cnt -gt 0 ];then
+                    echo "- $file | ${file_total_check_cnt} | ${file_valid_check_cnt} | ${file_invalid_check_cnt}"
+                fi
             fi
         fi
         if [ -d $1"/"$file ];then
-            
             dir_array[$i]=$1"/"$file
             ((i++))
         fi

tools/manylinux1/Dockerfile.CI35-GCC4.8

-Dockerfile.cuda9_cudnn7_gcc48_py35_centos6
\ No newline at end of file

tools/manylinux1/Dockerfile.cuda9_cudnn7_gcc8_py35_centos6 → tools/manylinux1/Dockerfile.cuda10_cudnn7_gcc8_py35_centos6

@@ -3,7 +3,7 @@
 # which requires some headers and symbols not present on CentOS-5 (e.g.,
 # signalfd.h, pipe2, O_NONBLOCK, SOCK_NONBLOCK, etc.). See
 # https://github.com/sandstorm-io/capnproto/issues/350.
-FROM nvidia/cuda:9.0-cudnn7-devel-centos6
+FROM nvidia/cuda:10.1-cudnn7-devel-centos6
 MAINTAINER Numenta, based on the ManyLinux project
 
 ENV LC_ALL en_US.UTF-8

tools/manylinux1/Dockerfile.cuda10_cudnn7_gcc8_ubuntu16

+# A image for building paddle binaries
+# Use cuda devel base image for both cpu and gpu environment
+# When you modify it, please be aware of cudnn-runtime version
+FROM nvidia/cuda:10.1-cudnn7-devel-ubuntu16.04
+MAINTAINER PaddlePaddle Authors <paddle-dev@baidu.com>
+
+ARG UBUNTU_MIRROR
+RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com/ubuntu#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi'
+
+# ENV variables
+ARG WITH_GPU
+ARG WITH_AVX
+
+ENV WOBOQ OFF
+ENV WITH_GPU=${WITH_GPU:-ON}
+ENV WITH_AVX=${WITH_AVX:-ON}
+
+ENV HOME /root
+# Add bash enhancements
+COPY ./paddle/scripts/docker/root/ /root/
+
+# gcc8.2
+RUN wget -q https://paddle-docker-tar.bj.bcebos.com/home/users/tianshuo/bce-python-sdk-0.8.27/gcc-8.2.0.tar.xz && \
+  tar -xvf gcc-8.2.0.tar.xz && \
+  cd gcc-8.2.0 && \
+  sed -i 's#ftp://gcc.gnu.org/pub/gcc/infrastructure/#https://paddle-ci.gz.bcebos.com/#g' ./contrib/download_prerequisites && \
+  unset LIBRARY_PATH CPATH C_INCLUDE_PATH PKG_CONFIG_PATH CPLUS_INCLUDE_PATH INCLUDE && \
+  ./contrib/download_prerequisites && \
+  cd .. && mkdir temp_gcc82 && cd temp_gcc82 && \
+  ../gcc-8.2.0/configure --prefix=/usr/local/gcc-8.2 --enable-threads=posix --disable-checking --disable-multilib && \
+  make -j8 && make install
+
+ENV PATH=/usr/local/gcc-8.2/bin:$PATH
+RUN rm -rf /temp_gcc82 && rm -rf /gcc-8.2.0.tar.xz && rm -rf /gcc-8.2.0
+
+# Prepare packages for Python
+RUN apt-get update && \
+    apt-get install -y make build-essential libssl-dev zlib1g-dev libbz2-dev \
+    libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev \
+    xz-utils tk-dev libffi-dev liblzma-dev
+
+# Downgrade gcc&&g++
+RUN apt-get update
+WORKDIR /usr/bin
+RUN apt install -y gcc-4.8 g++-4.8
+RUN cp gcc gcc.bak
+RUN cp g++ g++.bak
+RUN rm gcc
+RUN rm g++
+RUN ln -s gcc-4.8 gcc
+RUN ln -s g++-4.8 g++
+
+# Install Python3.6
+RUN mkdir -p /root/python_build/ && wget -q https://www.sqlite.org/2018/sqlite-autoconf-3250300.tar.gz && \
+    tar -zxf sqlite-autoconf-3250300.tar.gz && cd sqlite-autoconf-3250300 && \
+    ./configure -prefix=/usr/local && make -j8 && make install && cd ../ && rm sqlite-autoconf-3250300.tar.gz && \
+    wget -q https://www.python.org/ftp/python/3.6.0/Python-3.6.0.tgz && \
+    tar -xzf Python-3.6.0.tgz && cd Python-3.6.0 && \
+    CFLAGS="-Wformat" ./configure --prefix=/usr/local/ --enable-shared > /dev/null && \
+    make -j8 > /dev/null && make altinstall > /dev/null
+
+# Install Python3.7
+RUN wget -q https://www.python.org/ftp/python/3.7.0/Python-3.7.0.tgz && \
+    tar -xzf Python-3.7.0.tgz && cd Python-3.7.0 && \
+    CFLAGS="-Wformat" ./configure --prefix=/usr/local/ --enable-shared > /dev/null && \
+    make -j8 > /dev/null && make altinstall > /dev/null
+RUN rm -r /root/python_build
+
+RUN apt-get update && \
+    apt-get install -y --allow-downgrades --allow-change-held-packages \
+    patchelf python3 python3-dev python3-pip \
+    git python-pip python-dev python-opencv openssh-server bison \
+    wget unzip unrar tar xz-utils bzip2 gzip coreutils ntp \
+    curl sed grep graphviz libjpeg-dev zlib1g-dev  \
+    python-matplotlib gcc-4.8 g++-4.8 \
+    automake locales clang-format swig  \
+    liblapack-dev liblapacke-dev \
+    clang-3.8 llvm-3.8 libclang-3.8-dev \
+    net-tools libtool && \
+    apt-get clean -y
+
+# install cmake
+WORKDIR /home
+RUN wget -q https://cmake.org/files/v3.16/cmake-3.16.0-Linux-x86_64.tar.gz
+RUN tar -zxvf cmake-3.16.0-Linux-x86_64.tar.gz
+RUN rm cmake-3.16.0-Linux-x86_64.tar.gz
+ENV PATH=/home/cmake-3.16.0-Linux-x86_64/bin:$PATH
+
+# Install Python2.7.15 to replace original python
+WORKDIR /home
+ENV version=2.7.15
+RUN wget https://www.python.org/ftp/python/$version/Python-$version.tgz
+RUN tar -xvf Python-$version.tgz
+WORKDIR /home/Python-$version
+RUN ./configure --enable-unicode=ucs4 --enable-shared CFLAGS=-fPIC --prefix=/usr/local/python2.7.15
+RUN make && make install
+
+RUN echo "export PATH=/usr/local/python2.7.15/include:${PATH}" >> ~/.bashrc
+RUN echo "export PATH=/usr/local/python2.7.15/bin:${PATH}" >> ~/.bashrc
+RUN echo "export LD_LIBRARY_PATH=/usr/local/python2.7.15/lib:${LD_LIBRARY_PATH}" >> ~/.bashrc
+RUN echo "export CPLUS_INCLUDE_PATH=/usr/local/python2.7.15/include/python2.7:$CPLUS_INCLUDE_PATH" >> ~/.bashrc
+ENV PATH=/usr/local/python2.7.15/include:${PATH}
+ENV PATH=/usr/local/python2.7.15/bin:${PATH}
+ENV LD_LIBRARY_PATH=/usr/local/python2.7.15/lib:${LD_LIBRARY_PATH}
+ENV CPLUS_INCLUDE_PATH=/usr/local/python2.7.15/include/python2.7:$CPLUS_INCLUDE_PATH
+RUN mv /usr/bin/python /usr/bin/python.bak
+RUN ln -s /usr/local/python2.7.15/bin/python2.7 /usr/local/bin/python
+RUN ln -s /usr/local/python2.7.15/bin/python2.7 /usr/bin/python
+WORKDIR /home
+RUN wget https://files.pythonhosted.org/packages/b0/d1/8acb42f391cba52e35b131e442e80deffbb8d0676b93261d761b1f0ef8fb/setuptools-40.6.2.zip
+RUN apt-get -y install unzip
+RUN unzip setuptools-40.6.2.zip
+WORKDIR /home/setuptools-40.6.2
+RUN python setup.py build
+RUN python setup.py install
+WORKDIR /home
+RUN wget https://files.pythonhosted.org/packages/69/81/52b68d0a4de760a2f1979b0931ba7889202f302072cc7a0d614211bc7579/pip-18.0.tar.gz
+RUN tar -zxvf pip-18.0.tar.gz
+WORKDIR pip-18.0
+RUN python setup.py install
+
+WORKDIR /home
+RUN rm Python-$version.tgz setuptools-40.6.2.zip pip-18.0.tar.gz && \
+    rm -r Python-$version setuptools-40.6.2 pip-18.0
+
+# Install Go and glide
+RUN wget -qO- https://paddle-ci.cdn.bcebos.com/go1.8.1.linux-amd64.tar.gz | \
+    tar -xz -C /usr/local && \
+    mkdir /root/gopath && \
+    mkdir /root/gopath/bin && \
+    mkdir /root/gopath/src
+ENV GOROOT=/usr/local/go GOPATH=/root/gopath
+# should not be in the same line with GOROOT definition, otherwise docker build could not find GOROOT.
+ENV PATH=${PATH}:${GOROOT}/bin:${GOPATH}/bin
+# install glide
+RUN curl -s -q https://glide.sh/get | sh
+
+# Install TensorRT
+# following TensorRT.tar.gz is not the default official one, we do two miny changes:
+# 1. Remove the unnecessary files to make the library small. TensorRT.tar.gz only contains include and lib now,
+#    and its size is only one-third of the official one.
+# 2. Manually add ~IPluginFactory() in IPluginFactory class of NvInfer.h, otherwise, it couldn't work in paddle.
+#    See https://github.com/PaddlePaddle/Paddle/issues/10129 for details.
+
+RUN wget -q https://paddlepaddledeps.cdn.bcebos.com/TensorRT-6.0.1.5.Ubuntu-16.04.x86_64-gnu.cuda-10.1.cudnn7.6.tar.gz --no-check-certificate && \
+    tar -zxf TensorRT-6.0.1.5.Ubuntu-16.04.x86_64-gnu.cuda-10.1.cudnn7.6.tar.gz -C /usr/local && \
+    cp -rf /usr/local/TensorRT-6.0.1.5/include/* /usr/include/ && cp -rf /usr/local/TensorRT-6.0.1.5/lib/* /usr/lib/ 
+
+# git credential to skip password typing
+RUN git config --global credential.helper store
+
+# Fix locales to en_US.UTF-8
+RUN localedef -i en_US -f UTF-8 en_US.UTF-8
+
+# FIXME: due to temporary ipykernel dependency issue, specify ipykernel jupyter
+# version util jupyter fixes this issue.
+
+# specify sphinx version as 1.5.6 and remove -U option for [pip install -U
+# sphinx-rtd-theme] since -U option will cause sphinx being updated to newest
+# version(1.7.1 for now), which causes building documentation failed.
+RUN pip3 --no-cache-dir install -U wheel py-cpuinfo==5.0.0 && \
+    pip3 --no-cache-dir install -U docopt PyYAML sphinx==1.5.6 && \
+    pip3 --no-cache-dir install sphinx-rtd-theme==0.1.9 recommonmark && \
+    pip3.6 --no-cache-dir install -U wheel py-cpuinfo==5.0.0 && \
+    pip3.6 --no-cache-dir install -U docopt PyYAML sphinx==1.5.6 && \
+    pip3.6 --no-cache-dir install sphinx-rtd-theme==0.1.9 recommonmark && \
+    pip3.7 --no-cache-dir install -U wheel py-cpuinfo==5.0.0 && \
+    pip3.7 --no-cache-dir install -U docopt PyYAML sphinx==1.5.6 && \
+    pip3.7 --no-cache-dir install sphinx-rtd-theme==0.1.9 recommonmark && \
+    pip --no-cache-dir install -U wheel py-cpuinfo==5.0.0 && \
+    pip --no-cache-dir install -U docopt PyYAML sphinx==1.5.6 && \
+    pip --no-cache-dir install sphinx-rtd-theme==0.1.9 recommonmark
+
+RUN pip3 --no-cache-dir install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
+    pip3 --no-cache-dir install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
+    pip3 --no-cache-dir install opencv-python && \
+    pip3.6 --no-cache-dir install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
+    pip3.6 --no-cache-dir install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
+    pip3.6 --no-cache-dir install opencv-python && \
+    pip3.7 --no-cache-dir install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
+    pip3.7 --no-cache-dir install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
+    pip3.7 --no-cache-dir install opencv-python && \
+    pip --no-cache-dir install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
+    pip --no-cache-dir install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
+    pip --no-cache-dir install opencv-python
+
+#For docstring checker
+RUN pip3 --no-cache-dir install pylint pytest astroid isort
+RUN pip3.6 --no-cache-dir install pylint pytest astroid isort
+RUN pip3.7 --no-cache-dir install pylint pytest astroid isort
+RUN pip --no-cache-dir install pylint pytest astroid isort LinkChecker
+
+RUN pip3 --no-cache-dir install coverage                
+RUN pip3.6 --no-cache-dir install coverage             
+RUN pip3.7 --no-cache-dir install coverage            
+RUN pip --no-cache-dir install coverage
+
+COPY ./python/requirements.txt /root/
+RUN pip3 --no-cache-dir install -r /root/requirements.txt
+RUN pip3.6 --no-cache-dir install -r /root/requirements.txt
+RUN pip3.7 --no-cache-dir install -r /root/requirements.txt
+RUN pip --no-cache-dir install -r /root/requirements.txt
+
+# To fix https://github.com/PaddlePaddle/Paddle/issues/1954, we use
+# the solution in https://urllib3.readthedocs.io/en/latest/user-guide.html#ssl-py2
+RUN apt-get install -y libssl-dev libffi-dev && apt-get clean -y
+RUN pip3 --no-cache-dir install certifi urllib3[secure]
+RUN pip3.6 --no-cache-dir install certifi urllib3[secure]
+RUN pip3.7 --no-cache-dir install certifi urllib3[secure]
+RUN pip --no-cache-dir install certifi urllib3[secure]
+
+
+# Install woboq_codebrowser to /woboq
+RUN git clone https://github.com/woboq/woboq_codebrowser /woboq && \
+    (cd /woboq \
+     cmake -DLLVM_CONFIG_EXECUTABLE=/usr/bin/llvm-config-3.8 \
+           -DCMAKE_BUILD_TYPE=Release . \
+     make)
+
+# ar mishandles 4GB files
+# https://sourceware.org/bugzilla/show_bug.cgi?id=14625
+# remove them when apt-get support 2.27 and higher version
+RUN wget -q https://paddle-ci.gz.bcebos.com/binutils_2.27.orig.tar.gz && \
+    tar -xzf binutils_2.27.orig.tar.gz && \
+    cd binutils-2.27 && \
+    ./configure && make -j && make install && cd .. && rm -rf binutils-2.27 binutils_2.27.orig.tar.gz
+
+RUN wget --no-check-certificate https://pslib.bj.bcebos.com/openmpi-1.4.5.tar.gz && tar -xzf openmpi-1.4.5.tar.gz && \
+    cd openmpi-1.4.5 && ./configure --prefix=/usr/local && make all -j8 && make install -j8 && \
+    export LD_LIBRARY_PATH=/usr/local/lib/:$LD_LIBRARY_PATH && export PATH=/usr/local/bin:$PATH && cd .. && \
+    rm -rf openmpi-1.4.5.tar.gz && pip --no-cache-dir install mpi4py && ln -fs /bin/bash /bin/sh && \
+    apt-get install libprotobuf-dev -y
+RUN pip --no-cache-dir install -U netifaces==0.10.9
+
+# ccache 3.7.9
+RUN wget https://paddle-ci.gz.bcebos.com/ccache-3.7.9.tar.gz && \
+    tar xf ccache-3.7.9.tar.gz && mkdir /usr/local/ccache-3.7.9 && cd ccache-3.7.9 && \
+    ./configure -prefix=/usr/local/ccache-3.7.9 && \
+    make -j8 && make install && \
+    ln -s /usr/local/ccache-3.7.9/bin/ccache /usr/local/bin/ccache
+
+# Configure OpenSSH server. c.f. https://docs.docker.com/engine/examples/running_ssh_service
+RUN mkdir /var/run/sshd
+RUN echo 'root:root' | chpasswd
+RUN sed -ri 's/^PermitRootLogin\s+.*/PermitRootLogin yes/' /etc/ssh/sshd_config
+RUN sed -ri 's/UsePAM yes/#UsePAM yes/g' /etc/ssh/sshd_config
+CMD source ~/.bashrc
+EXPOSE 22