From b7df7f9eb153748c9d99365f25065ed6e882e4b1 Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Fri, 3 Nov 2017 11:39:26 +0800
Subject: [PATCH 01/43] remove usused ProtoDataProvider related codes

---
 paddle/gserver/CMakeLists.txt                 |   1 -
 paddle/gserver/dataproviders/DataProvider.cpp |   4 +-
 .../dataproviders/ProtoDataProvider.cpp       | 932 ------------------
 .../gserver/dataproviders/ProtoDataProvider.h | 179 ----
 paddle/gserver/tests/CMakeLists.txt           |  11 -
 paddle/gserver/tests/proto_files.txt          |   2 -
 .../gserver/tests/proto_files_compressed.txt  |   2 -
 .../gserver/tests/test_ProtoDataProvider.cpp  | 732 --------------
 8 files changed, 1 insertion(+), 1862 deletions(-)
 delete mode 100644 paddle/gserver/dataproviders/ProtoDataProvider.cpp
 delete mode 100644 paddle/gserver/dataproviders/ProtoDataProvider.h
 delete mode 100644 paddle/gserver/tests/proto_files.txt
 delete mode 100644 paddle/gserver/tests/proto_files_compressed.txt
 delete mode 100644 paddle/gserver/tests/test_ProtoDataProvider.cpp

diff --git a/paddle/gserver/CMakeLists.txt b/paddle/gserver/CMakeLists.txt
index 5f39167afc..b02902543b 100644
--- a/paddle/gserver/CMakeLists.txt
+++ b/paddle/gserver/CMakeLists.txt
@@ -73,7 +73,6 @@ if(MOBILE_INFERENCE)
     list(REMOVE_ITEM GSERVER_SOURCES
          dataproviders/DataProvider.cpp
          dataproviders/MultiDataProvider.cpp
-         dataproviders/ProtoDataProvider.cpp
          dataproviders/PyDataProvider2.cpp
          dataproviders/PyDataProvider.cpp)
 
diff --git a/paddle/gserver/dataproviders/DataProvider.cpp b/paddle/gserver/dataproviders/DataProvider.cpp
index 0478256f9c..106cf5b622 100644
--- a/paddle/gserver/dataproviders/DataProvider.cpp
+++ b/paddle/gserver/dataproviders/DataProvider.cpp
@@ -16,8 +16,8 @@ limitations under the License. */
 
 #include <unistd.h>
 #include <algorithm>
-#include "ProtoDataProvider.h"
 #include "paddle/utils/Logging.h"
+#include "paddle/utils/Stat.h"
 #include "paddle/utils/StringUtil.h"
 #include "paddle/utils/Util.h"
 
@@ -164,8 +164,6 @@ DataProvider* DataProvider::create(const DataConfig& config,
 
 REGISTER_DATA_PROVIDER(simple, SimpleDataProvider);
 REGISTER_DATA_PROVIDER(dummy, DummyDataProvider);
-REGISTER_DATA_PROVIDER(proto, ProtoDataProvider);
-REGISTER_DATA_PROVIDER(proto_sequence, ProtoSequenceDataProvider);
 
 int64_t DataProvider::getNextBatch(int64_t size, DataBatch* batch) {
   int64_t batchSize = doubleBuffer_ ? getNextBatchFromBuffer(size, batch)
diff --git a/paddle/gserver/dataproviders/ProtoDataProvider.cpp b/paddle/gserver/dataproviders/ProtoDataProvider.cpp
deleted file mode 100644
index c6f5cab191..0000000000
--- a/paddle/gserver/dataproviders/ProtoDataProvider.cpp
+++ /dev/null
@@ -1,932 +0,0 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#include "ProtoDataProvider.h"
-#include <algorithm>
-#include <fstream>
-#include <istream>
-#include "paddle/utils/StringUtil.h"
-#include "paddle/utils/Util.h"
-
-#include "DataProviderGroup.h"
-#include "paddle/utils/Logging.h"
-
-DEFINE_double(memory_threshold_on_load_data,
-              1.0,
-              "stop loading data when memory is not sufficient");
-
-namespace paddle {
-
-REGISTER_DATA_PROVIDER(proto_group, DataProviderGroup<ProtoDataProvider>);
-REGISTER_DATA_PROVIDER(proto_sequence_group,
-                       DataProviderGroup<ProtoSequenceDataProvider>);
-
-ProtoDataProvider::ProtoDataProvider(const DataConfig& config,
-                                     bool useGpu,
-                                     bool loadDataAll)
-    : DataProvider(config, useGpu), sampleNums_(0), currentSequenceIndex_(0) {
-  if (loadDataAll) {
-    loadData(config_.files());
-  }
-}
-
-void ProtoDataProvider::loadData(const std::vector<std::string>& fileList) {
-  for (auto& file : fileList) {
-    if (FLAGS_memory_threshold_on_load_data < 1.0) {
-      double memUsage = getMemoryUsage();
-      if (memUsage > FLAGS_memory_threshold_on_load_data) {
-        LOG(INFO) << "memUsage is " << memUsage << ", > "
-                  << FLAGS_memory_threshold_on_load_data
-                  << " therefore SKIP ALL REMAINING file.";
-        break;
-      }
-    }
-    LOG(INFO) << "load data file " << file;
-    loadDataFile(file);
-  }
-
-  if (sequenceStartPositions_.size() == sampleNums_) {
-    // This means that each sample is one sequence
-    shuffledSequenceIds_.swap(sequenceStartPositions_);
-  } else {
-    sequenceStartPositions_.push_back(sampleNums_);
-    shuffledSequenceIds_.reserve(sequenceStartPositions_.size() - 1);
-    for (size_t i = 0; i < sequenceStartPositions_.size() - 1; ++i) {
-      shuffledSequenceIds_.push_back(i);
-    }
-  }
-
-  LOG(INFO) << "read done, num of instance=" << sampleNums_;
-  showDataStats();
-}
-
-void ProtoDataProvider::loadData(const std::string& fileName) {
-  std::vector<std::string> fileList;
-  loadFileList(fileName, fileList);
-  loadData(fileList);
-}
-
-void ProtoDataProvider::checkDataHeader(const DataHeader& header) {
-  if (header_.slot_defs_size()) {
-    // header_ is already set. Need to check consistency.
-    CHECK_EQ(header_.slot_defs_size(), header.slot_defs_size())
-        << "Different header";
-    for (int i = 0; i < header.slot_defs_size(); ++i) {
-      CHECK_EQ(header_.slot_defs(i).type(), header.slot_defs(i).type());
-      CHECK_EQ(header_.slot_defs(i).dim(), header.slot_defs(i).dim());
-    }
-    return;
-  }
-
-  // header_ is not set before
-  CHECK(header.slot_defs_size()) << "Invalid header: no slot is defined";
-  int i;
-  for (i = 0; i < header.slot_defs_size(); ++i) {
-    if (header.slot_defs(i).type() == SlotDef::INDEX ||
-        header.slot_defs(i).type() == SlotDef::VAR_MDIM_INDEX) {
-      break;
-    }
-    constexpr int kBufLen = 100;
-    char buf[kBufLen];
-    snprintf(buf, kBufLen, "slot%d_nnz", i);
-    nnzStats_.push_back(getStat(buf));
-  }
-  numVecSlots_ = i;
-
-  // Check that INDEX slots are after VECTOR slots
-  for (int i = numVecSlots_; i < header.slot_defs_size(); ++i) {
-    CHECK(header.slot_defs(i).type() == SlotDef::INDEX ||
-          header.slot_defs(i).type() == SlotDef::VAR_MDIM_INDEX);
-  }
-
-  slots_.clear();
-  slots_.reserve(header.slot_defs_size());
-  for (int i = 0; i < header.slot_defs_size(); ++i) {
-    slots_.emplace_back();
-    slots_.back().type = header.slot_defs(i).type();
-    slots_.back().dim = header.slot_defs(i).dim();
-    if (SlotDef::VECTOR_SPARSE_NON_VALUE == header.slot_defs(i).type() ||
-        SlotDef::VECTOR_SPARSE_VALUE == header.slot_defs(i).type()) {
-      slots_.back().indices.push_back(0);
-    }
-  }
-
-  header_ = header;
-}
-
-void ProtoDataProvider::checkSample(const DataSample& sample) {
-  CHECK_EQ(numVecSlots_, sample.vector_slots_size());
-  CHECK(header_.slot_defs_size() == numVecSlots_ + sample.id_slots_size() ||
-        header_.slot_defs_size() == numVecSlots_ + sample.var_id_slots_size());
-  for (int i = 0; i < numVecSlots_; ++i) {
-    uint32_t dim = header_.slot_defs(i).dim();
-    switch (header_.slot_defs(i).type()) {
-      case SlotDef::VECTOR_DENSE: {
-        CHECK_EQ(static_cast<int>(dim), sample.vector_slots(i).values_size());
-        CHECK_EQ(0, sample.vector_slots(i).ids_size());
-        break;
-      }
-      case SlotDef::VECTOR_SPARSE_NON_VALUE: {
-        if (0 == sample.vector_slots(i).ids_size()) {
-          break;
-        }
-        CHECK_LT(0, sample.vector_slots(i).ids_size());
-        CHECK_EQ(0, sample.vector_slots(i).values_size());
-        auto maxId = *std::max_element(sample.vector_slots(i).ids().begin(),
-                                       sample.vector_slots(i).ids().end());
-        CHECK_GT(dim, maxId);
-        break;
-      }
-      case SlotDef::VECTOR_SPARSE_VALUE: {
-        if (0 == sample.vector_slots(i).ids_size()) {
-          CHECK_EQ(0, sample.vector_slots(i).values_size());
-          break;
-        }
-        CHECK_LT(0, sample.vector_slots(i).values_size());
-        CHECK_GE(static_cast<int>(dim), sample.vector_slots(i).values_size());
-        CHECK_EQ(sample.vector_slots(i).values_size(),
-                 sample.vector_slots(i).ids_size());
-        auto maxId = *std::max_element(sample.vector_slots(i).ids().begin(),
-                                       sample.vector_slots(i).ids().end());
-        CHECK_GT(dim, maxId);
-        break;
-      }
-      case SlotDef::VAR_MDIM_DENSE: {
-        if (static_cast<int>(dim) != 0) {
-          CHECK_EQ(static_cast<int>(dim), sample.vector_slots(i).values_size());
-          if (sample.vector_slots(i).dims_size() != 0) {
-            int totalDim = sample.vector_slots(i).dims(0);
-            for (int j = 1; j < sample.vector_slots(i).dims_size(); ++j) {
-              totalDim *= sample.vector_slots(i).dims(j);
-            }
-            CHECK_EQ(static_cast<int>(dim), totalDim);
-          }
-        } else {
-          CHECK_NE(sample.vector_slots(i).dims_size(), 0);
-          int totalDim = sample.vector_slots(i).dims(0);
-          for (int j = 1; j < sample.vector_slots(i).dims_size(); ++j) {
-            totalDim *= sample.vector_slots(i).dims(j);
-          }
-          CHECK_EQ(totalDim, sample.vector_slots(i).values_size());
-        }
-        break;
-      }
-      case SlotDef::STRING: {
-        CHECK_EQ(static_cast<int>(1), sample.vector_slots(i).strs_size());
-        CHECK_EQ(0, sample.vector_slots(i).ids_size());
-        CHECK_EQ(0, sample.vector_slots(i).values_size());
-        break;
-      }
-      default:
-        LOG(FATAL) << "BUG: Should not reach here";
-    }
-  }
-  for (int i = numVecSlots_; i < header_.slot_defs_size(); ++i) {
-    if (header_.slot_defs(i).type() != SlotDef::VAR_MDIM_INDEX) {
-      uint32_t id = sample.id_slots(i - numVecSlots_);
-      if (id == -1U) continue;
-      CHECK_LT(id, header_.slot_defs(i).dim());
-    } else {
-      for (int j = 0; j < sample.var_id_slots(i - numVecSlots_).ids_size();
-           ++j) {
-        uint32_t id = sample.var_id_slots(i - numVecSlots_).ids(j);
-        CHECK_LT(id, header_.slot_defs(i).dim());
-      }
-    }
-  }
-}
-
-void ProtoDataProvider::loadDataFile(const std::string& fileName) {
-  std::ifstream is(fileName);
-  CHECK(is) << "Fail to open " << fileName;
-  bool dataCompression = str::endsWith(fileName, ".gz");
-  std::unique_ptr<ProtoReader> reader(new ProtoReader(&is, dataCompression));
-  CHECK(reader) << "Fail to create proto data input stream";
-
-  DataHeader header;
-  CHECK(reader->read(&header));
-  checkDataHeader(header);
-
-  DataSample sample;
-  do {
-    if (!reader->read(&sample)) {
-      break;
-    }
-    checkSample(sample);
-    if (sample.is_beginning()) {
-      sequenceStartPositions_.push_back(sampleNums_);
-    }
-    fillSlots(sample);
-    ++sampleNums_;
-  } while (true);
-
-  CHECK(is.eof()) << "Fail to read file";
-  reader.reset(nullptr);
-  is.close();
-}
-
-// checkSample has done before, no check here
-void ProtoDataProvider::fillSlots(const DataSample& sample) {
-  for (size_t i = 0; i < slots_.size(); ++i) {
-    auto& slot = slots_[i];
-    int dim = slot.dim;
-    switch (slot.type) {
-      case SlotDef::VECTOR_DENSE: {
-        size_t oldSize = slot.denseData.size();
-        slot.denseData.resize(oldSize + dim);
-        const float* values = sample.vector_slots(i).values().data();
-#ifdef PADDLE_TYPE_DOUBLE
-        std::copy(values, values + dim, slot.denseData.begin() + oldSize);
-#else
-        memcpy(slot.denseData.data() + oldSize, values, sizeof(real) * dim);
-#endif
-        break;
-      }
-      case SlotDef::VECTOR_SPARSE_NON_VALUE: {
-        int slotSize = sample.vector_slots(i).ids_size();
-        int subSlotSize = 0;
-        int id = 0;  // the slot id
-        // find whether this vector_slots has subseq. If not has subseq,
-        // subSlotSize = 0.
-        for (id = 0; id < sample.subseq_slots_size(); id++) {
-          if (sample.subseq_slots(id).slot_id() == i) {
-            subSlotSize = sample.subseq_slots(id).lens_size();
-            break;
-          }
-        }
-        if (subSlotSize && slot.subIndices.size() == 0UL) {
-          // If has subSeq, the first element of subIndices = 0.
-          slot.subIndices.push_back(0);
-        }
-        if (slotSize == 0UL) {
-          // if has no id, new indices = old indices.
-          slot.indices.push_back(slot.indices.back());
-          // if has subSeq, new subIndices = old subIndices.
-          if (slot.subIndices.size()) {
-            slot.subIndices.push_back(slot.subIndices.back());
-          }
-          break;
-        }
-        slot.sparseNonValueData.resize(slot.indices.back() + slotSize);
-        const unsigned int* ids = sample.vector_slots(i).ids().data();
-        memcpy(slot.sparseNonValueData.data() + slot.indices.back(),
-               ids,
-               sizeof(*ids) * slotSize);
-        slot.indices.push_back(slot.indices.back() + slotSize);
-        if (subSlotSize) {
-          for (int ii = 0; ii < subSlotSize; ++ii) {
-            slot.subIndices.push_back(slot.subIndices.back() +
-                                      sample.subseq_slots(id).lens(ii));
-          }
-        }
-        break;
-      }
-      case SlotDef::VECTOR_SPARSE_VALUE: {
-        if (0 == sample.vector_slots(i).ids_size()) {
-          slot.indices.push_back(slot.indices.back());
-          break;
-        }
-        int slotSize = sample.vector_slots(i).ids_size();
-        slot.sparseFloatValueData.resize(slot.indices.back() + slotSize);
-        const unsigned int* ids = sample.vector_slots(i).ids().data();
-        const float* values = sample.vector_slots(i).values().data();
-        for (int ii = 0; ii < slotSize; ++ii) {
-          slot.sparseFloatValueData[slot.indices.back() + ii].col = ids[ii];
-          slot.sparseFloatValueData[slot.indices.back() + ii].value =
-              values[ii];
-        }
-        slot.indices.push_back(slot.indices.back() + slotSize);
-        break;
-      }
-      case SlotDef::INDEX: {
-        slot.indexData.push_back(sample.id_slots(i - numVecSlots_));
-        break;
-      }
-      case SlotDef::VAR_MDIM_DENSE: {
-        size_t oldSize = slot.varDenseData.size();
-        slot.varDenseData.resize(oldSize + 1);
-        size_t varDim = sample.vector_slots(i).values_size();
-        slot.varDenseData[oldSize].data.resize(varDim);
-        const float* values = sample.vector_slots(i).values().data();
-#ifdef PADDLE_TYPE_DOUBLE
-        std::copy(
-            values, values + varDim, slot.varDenseData[oldSize].data.data());
-#else
-        memcpy(slot.varDenseData[oldSize].data.data(),
-               values,
-               sizeof(real) * varDim);
-#endif
-        slot.varDenseData[oldSize].dims.resize(
-            sample.vector_slots(i).dims_size());
-        memcpy(slot.varDenseData[oldSize].dims.data(),
-               sample.vector_slots(i).dims().data(),
-               sizeof(uint32_t) * sample.vector_slots(i).dims_size());
-        break;
-      }
-      case SlotDef::VAR_MDIM_INDEX: {
-        size_t oldSize = slot.varIndices.size();
-        slot.varIndices.resize(oldSize + 1);
-        size_t varDim = sample.var_id_slots(i - numVecSlots_).ids_size();
-        slot.varIndices[oldSize].resize(varDim);
-        memcpy(slot.varIndices[oldSize].data(),
-               sample.var_id_slots(i - numVecSlots_).ids().data(),
-               sizeof(uint32_t) * varDim);
-        break;
-      }
-      case SlotDef::STRING: {
-        slot.strData.push_back(sample.vector_slots(i).strs(0));
-        break;
-      }
-    }
-  }
-}
-
-void ProtoDataProvider::showDataStats() {
-  std::ostringstream oss;
-  for (size_t i = 0; i < slots_.size(); ++i) {
-    auto& slot = slots_[i];
-    if (slot.type == SlotDef::VECTOR_SPARSE_NON_VALUE) {
-      size_t nnz = slot.sparseNonValueData.size();
-      oss << "slot" << i << ":avgNNZ=" << ((double)nnz / sampleNums_) << "; ";
-    } else if (slot.type == SlotDef::VECTOR_SPARSE_VALUE) {
-      size_t nnz = slot.sparseFloatValueData.size();
-      oss << "slot" << i << ":avgNNZ=" << ((double)nnz / sampleNums_) << "; ";
-    }
-  }
-  LOG(INFO) << oss.str();
-}
-
-void ProtoDataProvider::reset() {
-  currentSequenceIndex_ = 0;
-  if (!skipShuffle_) {
-    shuffle();
-  }
-
-  DataProvider::reset();
-}
-
-void ProtoDataProvider::shuffle() {
-  std::shuffle(shuffledSequenceIds_.begin(),
-               shuffledSequenceIds_.end(),
-               ThreadLocalRandomEngine::get());
-}
-
-/*
-  Loop through sequences starting from currentSequenceIndex_
-  for at most size samples. For each sequence ranging from [begin, end),
-  op(begin, end) will be called.
-
-  return the number of sequences scanned
-*/
-template <class Op>
-int64_t ProtoDataProvider::sequenceLoop(Op op, int64_t size) {
-  int64_t sz = 0;
-  size_t i;
-  size_t sequenceCount = shuffledSequenceIds_.size();
-  if (usageRatio_ < 1.0f) {
-    sequenceCount = static_cast<int64_t>(sequenceCount * usageRatio_);
-  }
-  for (i = currentSequenceIndex_; i < sequenceCount; ++i) {
-    size_t id = shuffledSequenceIds_[i];
-    int64_t begin = sequenceStartPositions_[id];
-    int64_t end = sequenceStartPositions_[id + 1];
-    int64_t len = end - begin;
-    if (sz + len > size && sz > 0) break;
-    sz += len;
-    op(begin, end);
-  }
-  return i - currentSequenceIndex_;
-}
-
-/*
-  Loop through sequences starting from currentSequenceIndex_
-  for at most size samples. For each sample of each sequence at position
-  pos, op(pos) will be called.
-
-  return the number of sequences scanned
-*/
-template <class Op>
-int64_t ProtoDataProvider::sampleLoop(Op op, int64_t size) {
-  if (iidData()) {
-    size = std::min<int64_t>(sampleNums_ - currentSequenceIndex_, size);
-    for (int64_t i = currentSequenceIndex_; i < currentSequenceIndex_ + size;
-         ++i) {
-      size_t pos = shuffledSequenceIds_[i];
-      op(pos);
-    }
-    return size;
-  } else {
-    auto f = [op](int64_t begin, int64_t end) {
-      for (int64_t pos = begin; pos < end; ++pos) {
-        op(pos);
-      }
-    };
-    return sequenceLoop(f, size);
-  }
-}
-
-/*
-  Loop through sub-sequences starting from currentSequenceIndex_
-  for at most size samples. For each sample of each sub-sequence at position
-  pos, op(pos) will be called.
-
-  return the number of sub-sequences scanned
-*/
-template <class Op>
-int64_t ProtoDataProvider::subSampleLoop(Op op, int64_t size, int slot) {
-  CHECK(iidData()) << "subSampleLoop only accepts iid data";
-  size = std::min<int64_t>(sampleNums_ - currentSequenceIndex_, size);
-  int subSize = 0;
-  for (int64_t i = currentSequenceIndex_; i < currentSequenceIndex_ + size;
-       ++i) {
-    size_t pos = shuffledSequenceIds_[i];
-    int64_t* indexs = slots_[slot].indices.data();
-    int64_t* subIndexs = slots_[slot].subIndices.data();
-    int64_t subSeqStart = 0;
-    int64_t subSeqEnd = 0;
-    for (int j = 0; j < (int)slots_[slot].subIndices.size(); j++) {
-      if (subIndexs[j] == indexs[pos]) {
-        subSeqStart = j;
-        if (subIndexs[pos] == subIndexs[pos + 1]) {
-          subSeqEnd = j + 1;
-          break;
-        }
-      } else if (subIndexs[j] == indexs[pos + 1]) {
-        subSeqEnd = j;
-        break;
-      }
-    }
-    for (int j = subSeqStart; j < subSeqEnd; j++) {
-      op(j);
-    }
-    subSize += subSeqEnd - subSeqStart;
-  }
-  return subSize;
-}
-
-int64_t ProtoDataProvider::getNextBatchInternal(int64_t size,
-                                                DataBatch* batch) {
-  int64_t numSequences = 0;  // actual number of sequences in the batch
-
-  // the number of sequences scanned, including those skipped because too long
-  int64_t numScannedSeqs = 0;
-  std::lock_guard<RWLock> guard(lock_);
-  if (iidData()) {
-    size = std::min<int64_t>(getSize() - currentSequenceIndex_, size);
-    numScannedSeqs = numSequences = size;
-  } else {
-    int64_t sz = 0;
-    auto op = [&sz, &numSequences](int64_t begin, int64_t end) {
-      ++numSequences;
-      sz += end - begin;
-    };
-    numScannedSeqs = sequenceLoop(op, size);
-    VLOG_IF(1, numScannedSeqs > numSequences)
-        << numScannedSeqs - numSequences
-        << " sequences are skipped because longer than " << size;
-    size = sz;
-  }
-  if (size <= 0) return 0;
-
-  DataBatch& cpuBatch = *cpuBatch_;
-  std::vector<Argument>& cpuArguments = cpuBatch.getStreams();
-  cpuBatch.setSize(size);
-  cpuArguments.resize(header_.slot_defs_size());
-
-  if (!iidData()) {
-    ICpuGpuVector::resizeOrCreate(cpuArguments[0].sequenceStartPositions,
-                                  numSequences + 1,
-                                  /* useGpu= */ false);
-    int* buf = cpuArguments[0].sequenceStartPositions->getMutableData(false);
-    int pos = 0;
-    int i = 0;
-    auto op = [buf, &pos, &i](int64_t begin, int64_t end) {
-      buf[i] = pos;
-      pos += end - begin;
-      ++i;
-    };
-    sequenceLoop(op, size);
-    buf[i] = size;
-    for (size_t slot = 1; slot < cpuArguments.size(); ++slot) {
-      cpuArguments[slot].sequenceStartPositions =
-          cpuArguments[0].sequenceStartPositions;
-    }
-  }
-
-  for (int slot = 0; slot < header_.slot_defs_size(); ++slot) {
-    size_t dim = header_.slot_defs(slot).dim();
-    SlotDef::SlotType slotType = header_.slot_defs(slot).type();
-
-    std::vector<int64_t> dataPos;
-    dataPos.reserve(size);
-    auto op = [this, &dataPos](int64_t pos) { dataPos.push_back(pos); };
-    sampleLoop(op, size);
-
-    switch (slotType) {
-      case SlotDef::VECTOR_DENSE: {
-        Matrix::resizeOrCreate(cpuArguments[slot].value,
-                               size,
-                               dim,
-                               false,   // trans = false
-                               false);  // useGpu = false
-        real* buf = cpuArguments[slot].value->getData();
-        for (int i = 0; i < size; ++i) {
-          memcpy(buf + i * dim,
-                 slots_[slot].denseData.data() + dataPos[i] * dim,
-                 sizeof(real) * dim);
-        }
-        break;
-      }
-      case SlotDef::VECTOR_SPARSE_NON_VALUE: {
-        if (!(cpuArguments[slot].value)) {
-          cpuArguments[slot].value =
-              Matrix::createSparseMatrix(size,
-                                         dim,
-                                         size /*DEFAULT_AVG_WIDTH = 1*/,
-                                         NO_VALUE,
-                                         SPARSE_CSR,
-                                         false,
-                                         useGpu_);
-        }
-        auto mat = cpuArguments[slot].value;
-        mat->resize(size, dim);
-        if (std::dynamic_pointer_cast<GpuSparseMatrix>(mat)) {
-          std::dynamic_pointer_cast<GpuSparseMatrix>(mat)->copyFrom(
-              dataPos.data(),
-              slots_[slot].indices.data(),
-              slots_[slot].sparseNonValueData.data(),
-              HPPL_STREAM_1);
-        } else if (std::dynamic_pointer_cast<CpuSparseMatrix>(mat)) {
-          std::dynamic_pointer_cast<CpuSparseMatrix>(mat)->copyFrom(
-              dataPos.data(),
-              slots_[slot].indices.data(),
-              slots_[slot].sparseNonValueData.data());
-        } else {
-          LOG(FATAL) << "Not Supported";
-        }
-        size_t numElements = 0;
-        for (auto pos : dataPos) {
-          numElements +=
-              slots_[slot].indices[pos + 1] - slots_[slot].indices[pos];
-        }
-        nnzStats_[slot]->addSample(numElements);
-
-        break;
-      }
-      case SlotDef::VECTOR_SPARSE_VALUE: {
-        if (!(cpuArguments[slot].value)) {
-          cpuArguments[slot].value =
-              Matrix::createSparseMatrix(size,
-                                         dim,
-                                         size /*DEFAULT_AVG_WIDTH = 1*/,
-                                         FLOAT_VALUE,
-                                         SPARSE_CSR,
-                                         false,
-                                         useGpu_);
-        }
-        auto mat = cpuArguments[slot].value;
-        mat->resize(size, dim);
-        if (std::dynamic_pointer_cast<GpuSparseMatrix>(mat)) {
-          std::dynamic_pointer_cast<GpuSparseMatrix>(mat)->copyFrom(
-              dataPos.data(),
-              slots_[slot].indices.data(),
-              slots_[slot].sparseFloatValueData.data(),
-              HPPL_STREAM_1);
-        } else if (std::dynamic_pointer_cast<CpuSparseMatrix>(mat)) {
-          std::dynamic_pointer_cast<CpuSparseMatrix>(mat)->copyFrom(
-              dataPos.data(),
-              slots_[slot].indices.data(),
-              slots_[slot].sparseFloatValueData.data());
-        } else {
-          LOG(FATAL) << "Not Supported";
-        }
-        break;
-      }
-      case SlotDef::INDEX: {
-        IVector::resizeOrCreate(cpuArguments[slot].ids,
-                                size,
-                                /*  useGpu= */ false);
-        int* buf = cpuArguments[slot].ids->getData();
-        for (int i = 0; i < size; ++i) {
-          buf[i] = slots_[slot].indexData[dataPos[i]];
-        }
-        break;
-      }
-      case SlotDef::VAR_MDIM_DENSE: {
-        CHECK_EQ(size, 1);
-        auto mat = cpuArguments[slot].value;
-        size_t totalDim = slots_[slot].varDenseData[dataPos[0]].data.size();
-
-        CHECK_EQ(slots_[slot].varDenseData[dataPos[0]].dims.size(), size_t(3));
-        size_t height, width, depth, oldWidth;
-        /* dims[2] is depth, will be changed to dims[0] in future */
-        depth = slots_[slot].varDenseData[dataPos[0]].dims[2];
-        height = slots_[slot].varDenseData[dataPos[0]].dims[1];
-        width = slots_[slot].varDenseData[dataPos[0]].dims[0];
-        oldWidth = width;
-        /* process the undesirable sample */
-        if (oldWidth < height) {
-          width = height;
-        }
-        cpuArguments[slot].setFrameHeight(height);
-        cpuArguments[slot].setFrameWidth(width);
-
-        if (oldWidth < height) {
-          totalDim = width * height * depth;
-        }
-        Matrix::resizeOrCreate(cpuArguments[slot].value,
-                               size,
-                               totalDim,
-                               false,   // trans = false
-                               false);  // useGpu = false
-        real* buf = cpuArguments[slot].value->getData();
-        cpuArguments[slot].value->zeroMem();
-        if (oldWidth < height) {
-          real* srcBuf = slots_[slot].varDenseData[dataPos[0]].data.data();
-          for (size_t i = 0; i < depth; i++) {
-            for (size_t j = 0; j < height; j++) {
-              for (size_t k = 0; k < oldWidth; k++) {
-                buf[i * height * width + j * width + k] =
-                    srcBuf[i * height * oldWidth + j * oldWidth + k];
-              }
-            }
-          }
-        } else {
-          memcpy(buf,
-                 slots_[slot].varDenseData[dataPos[0]].data.data(),
-                 sizeof(real) * totalDim);
-        }
-        ICpuGpuVector::resizeOrCreate(cpuArguments[slot].sequenceStartPositions,
-                                      size + 1, /* size == 1 currently */
-                                      /* useGpu= */ false);
-        int* bufStarts =
-            cpuArguments[slot].sequenceStartPositions->getMutableData(false);
-        bufStarts[0] = 0;
-        bufStarts[1] = 1;
-        break;
-      }
-      case SlotDef::VAR_MDIM_INDEX: {
-        CHECK_EQ(size, 1);
-        size_t totalDim = slots_[slot].varIndices[dataPos[0]].size();
-        IVector::resizeOrCreate(cpuArguments[slot].ids,
-                                totalDim,
-                                /*  useGpu= */ false);
-        int* buf = cpuArguments[slot].ids->getData();
-        memcpy(buf,
-               slots_[slot].varIndices[dataPos[0]].data(),
-               sizeof(int) * totalDim);
-
-        ICpuGpuVector::resizeOrCreate(cpuArguments[slot].sequenceStartPositions,
-                                      size + 1, /* size == 1 currently */
-                                      /* useGpu= */ false);
-        int* bufStarts =
-            cpuArguments[slot].sequenceStartPositions->getMutableData(false);
-        bufStarts[0] = 0;
-        /* we expand the convolutinal feature map to a sequence data,
-         * so there should be a corresponding sequence labels */
-        bufStarts[1] = totalDim;
-        break;
-      }
-      case SlotDef::STRING: {
-        if (cpuArguments[slot].strs) {
-          cpuArguments[slot].strs->resize(size);
-        } else {
-          cpuArguments[slot].strs =
-              std::make_shared<std::vector<std::string>>(size);
-        }
-        for (int i = 0; i < size; ++i) {
-          (*cpuArguments[slot].strs)[i] = slots_[slot].strData[dataPos[i]];
-        }
-        break;
-      }
-    }
-  }
-
-  if (useGpu_) {
-    std::vector<Argument>& cpuArguments = cpuBatch.getStreams();
-    DataBatch& gpuBatch = *gpuBatch_;
-    std::vector<Argument>& gpuArguments = gpuBatch.getStreams();
-    gpuArguments.resize(cpuArguments.size());
-    gpuBatch.setSize(size);
-    for (int i = 0; i < header_.slot_defs_size(); ++i) {
-      SlotDef::SlotType slotType = header_.slot_defs(i).type();
-      if (SlotDef::VECTOR_SPARSE_VALUE == slotType ||
-          SlotDef::VECTOR_SPARSE_NON_VALUE == slotType) {
-        gpuArguments[i] = cpuArguments[i];
-        gpuArguments[i].sequenceStartPositions =
-            cpuArguments[i].sequenceStartPositions;
-      } else {
-        gpuArguments[i].resizeAndCopyFrom(
-            cpuArguments[i], useGpu_, HPPL_STREAM_1);
-      }
-    }
-    hl_stream_synchronize(HPPL_STREAM_1);
-    *batch = gpuBatch;
-  } else {
-    *batch = cpuBatch;
-  }
-
-  currentSequenceIndex_ += numScannedSeqs;
-
-  return batch->getSize();
-}
-
-ProtoSequenceDataProvider::ProtoSequenceDataProvider(const DataConfig& config,
-                                                     bool useGpu,
-                                                     bool loadDataAll)
-    : ProtoDataProvider(config, useGpu, loadDataAll) {}
-
-int64_t ProtoSequenceDataProvider::getNextBatchInternal(int64_t size,
-                                                        DataBatch* batch) {
-  CHECK(iidData()) << "ProtoSequenceDataProvider only accepts iid data";
-  int64_t numSequences = 0;  // actual number of sequences in the batch
-
-  // the number of sequences scanned, including those skipped because too long
-  int64_t numScannedSeqs = 0;
-  std::lock_guard<RWLock> guard(lock_);
-  size = std::min<int64_t>(getSize() - currentSequenceIndex_, size);
-  numScannedSeqs = numSequences = size;
-  if (size <= 0) return 0;
-
-  DataBatch& cpuBatch = *cpuBatch_;
-  std::vector<Argument>& cpuArguments = cpuBatch.getStreams();
-  cpuBatch.setSize(size);
-  cpuArguments.resize(header_.slot_defs_size());
-
-  for (int slot = 0; slot < header_.slot_defs_size(); ++slot) {
-    SlotDef::SlotType slotType = header_.slot_defs(slot).type();
-
-    std::vector<int64_t> dataPos;
-    dataPos.reserve(size);
-    auto op = [this, &dataPos](int64_t pos) { dataPos.push_back(pos); };
-    sampleLoop(op, size);
-
-    // current slot: sequenceStartPositions
-    ICpuGpuVector::resizeOrCreate(cpuArguments[slot].sequenceStartPositions,
-                                  size + 1,
-                                  /* useGpu= */ false);
-
-    switch (slotType) {
-      case SlotDef::VECTOR_SPARSE_VALUE:
-      case SlotDef::VAR_MDIM_DENSE:
-      case SlotDef::VAR_MDIM_INDEX: {
-        LOG(FATAL) << "ProtoSequenceDataProvider only support"
-                   << " VECTOR_DENSE, VECTOR_SPARSE_NON_VALUE and INDEX slots";
-        break;
-      }
-      case SlotDef::VECTOR_SPARSE_NON_VALUE: {
-        // copy to IDS, not value
-        // pointers used in current slot
-        sparse_non_value_t* data = slots_[slot].sparseNonValueData.data();
-        int64_t* indexs = slots_[slot].indices.data();
-        int64_t* seqs = dataPos.data();
-
-        // current slot: i need size instances. what is the total length?
-        int totalFeatureInCurrentSlot = 0;
-        for (int ins = 0; ins < size; ins++) {
-          int64_t currInsId = seqs[ins];
-          totalFeatureInCurrentSlot +=
-              indexs[currInsId + 1] - indexs[currInsId];
-          // special: if current instance has NO feature in current slot
-          if (indexs[currInsId + 1] == indexs[currInsId]) {
-            totalFeatureInCurrentSlot++;
-          }
-        }
-        // done
-
-        // current slot: ids
-        IVector::resizeOrCreate(cpuArguments[slot].ids,
-                                totalFeatureInCurrentSlot,
-                                /* useGpu= */ false);
-
-        // where to write
-        int* currPosOfArgumentId = cpuArguments[slot].ids->getData();
-        int* currPosOfArgumentSeqStart =
-            cpuArguments[slot].sequenceStartPositions->getMutableData(false);
-        int allSequenceLength = 0;
-        currPosOfArgumentSeqStart[0] = 0;
-        // for each instance, copy data and fill sequence positions
-        for (int instance = 0; instance < size; instance++) {
-          int64_t currInstanceId = seqs[instance];
-          int64_t currInstanceLength =
-              indexs[currInstanceId + 1] - indexs[currInstanceId];
-          sparse_non_value_t* currInstanceData = data + indexs[currInstanceId];
-          // write sequenceStartPositions
-          allSequenceLength += currInstanceLength;
-          currPosOfArgumentSeqStart[instance + 1] = allSequenceLength;
-          // copy features
-          for (int featCopier = 0; featCopier < currInstanceLength;
-               featCopier++) {
-            currPosOfArgumentId[featCopier] = currInstanceData[featCopier].col;
-          }
-          currPosOfArgumentId += currInstanceLength;
-          // special: if current instance has NO feature in current slot
-          if (currInstanceLength == 0) {
-            allSequenceLength++;
-            currPosOfArgumentSeqStart[instance + 1] = allSequenceLength;
-            currPosOfArgumentId[0] = -1;
-            currPosOfArgumentId++;
-          }
-          // done
-        }
-        if (slots_[slot].subIndices.size()) {
-          std::vector<int64_t> dataSubPos;
-          auto op = [this, &dataSubPos](int64_t pos) {
-            dataSubPos.push_back(pos);
-          };
-          int subSize = subSampleLoop(op, size, slot);
-          ICpuGpuVector::resizeOrCreate(
-              cpuArguments[slot].subSequenceStartPositions, subSize + 1, false);
-          int* currPosOfArgumentSubSeqStart =
-              cpuArguments[slot].subSequenceStartPositions->getMutableData(
-                  false);
-          int64_t* subSeqs = dataSubPos.data();
-          int64_t* subIndexs = slots_[slot].subIndices.data();
-          int allSubSequenceLength = 0;
-          currPosOfArgumentSubSeqStart[0] = 0;
-          // for each instance, compute sub-sequence number
-          for (int instance = 0; instance < subSize; instance++) {
-            int64_t currSubInstanceId = subSeqs[instance];
-            int64_t currSubInstanceLength =
-                subIndexs[currSubInstanceId + 1] - subIndexs[currSubInstanceId];
-            // write subSequenceStartPositions
-            allSubSequenceLength += currSubInstanceLength;
-            currPosOfArgumentSubSeqStart[instance + 1] = allSubSequenceLength;
-            // special: if current instance has NO feature in current slot
-            if (currSubInstanceLength == 0) {
-              allSubSequenceLength++;
-              currPosOfArgumentSubSeqStart[instance + 1] = allSubSequenceLength;
-            }
-          }
-          cpuArguments[slot].checkSubset();
-        }
-        break;
-      }
-      case SlotDef::INDEX: {
-        // label slot
-        IVector::resizeOrCreate(cpuArguments[slot].ids,
-                                size,
-                                /* useGpu= */ false);
-        // fill labels
-        int* buf = cpuArguments[slot].ids->getData();
-        for (int i = 0; i < size; ++i) {
-          buf[i] = slots_[slot].indexData[dataPos[i]];
-        }
-        // label HAS sequence structure
-        cpuArguments[slot].sequenceStartPositions->fillSequence(false);
-        break;
-      }
-      case SlotDef::VECTOR_DENSE: {
-        // copy values
-        size_t dim = header_.slot_defs(slot).dim();
-        Matrix::resizeOrCreate(cpuArguments[slot].value,
-                               size,
-                               dim,
-                               false,   // trans = false
-                               false);  // useGpu = false
-        real* buf = cpuArguments[slot].value->getData();
-        for (int i = 0; i < size; ++i) {
-          memcpy(buf + i * dim,
-                 slots_[slot].denseData.data() + dataPos[i] * dim,
-                 sizeof(real) * dim);
-        }
-        // sequence structure
-        cpuArguments[slot].sequenceStartPositions->fillSequence(false);
-        break;
-      }
-      default: { LOG(FATAL) << "should not reach here"; }
-    }
-  }
-
-  if (useGpu_) {
-    std::vector<Argument>& cpuArguments = cpuBatch.getStreams();
-    DataBatch& gpuBatch = *gpuBatch_;
-    std::vector<Argument>& gpuArguments = gpuBatch.getStreams();
-    gpuArguments.resize(cpuArguments.size());
-    gpuBatch.setSize(size);
-    for (size_t i = 0; i < cpuArguments.size(); ++i) {
-      gpuArguments[i].resizeAndCopyFrom(
-          cpuArguments[i], useGpu_, HPPL_STREAM_1);
-    }
-    hl_stream_synchronize(HPPL_STREAM_1);
-    *batch = gpuBatch;
-  } else {
-    *batch = cpuBatch;
-  }
-
-  currentSequenceIndex_ += numScannedSeqs;
-  return batch->getSize();
-}
-
-}  // namespace paddle
diff --git a/paddle/gserver/dataproviders/ProtoDataProvider.h b/paddle/gserver/dataproviders/ProtoDataProvider.h
deleted file mode 100644
index 7dd45e0622..0000000000
--- a/paddle/gserver/dataproviders/ProtoDataProvider.h
+++ /dev/null
@@ -1,179 +0,0 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#pragma once
-
-#include <vector>
-
-#include "DataFormat.pb.h"
-#include "paddle/utils/Stat.h"
-
-#include "DataProvider.h"
-#include "ProtoReader.h"
-
-namespace paddle {
-
-/**
- * @brief Provider data from protobuf data file with each sample
- * specified by proto message
- *
- * DataSample defined in DataFormat.proto.
- *
- * The file format is
- *
- *    header
- *
- *    sample1
- *
- *    sample2
- *
- *    ...
- *
- *    sampleN
- *
- * @note: In the data file, each message is prefixed with its length.
- * The read/write of the protbuf are implemented in ProtoReader.h
- */
-class ProtoDataProvider : public DataProvider {
-public:
-  ProtoDataProvider(const DataConfig& config,
-                    bool useGpu,
-                    bool loadDataAll = true);
-  virtual void reset();
-
-  /**
-   * @note this size includes the sequences which are skipped because they
-   * are longer than the batch size.
-   */
-  virtual int64_t getSize() {
-    int64_t size = sampleNums_;
-    if (usageRatio_ < 1.0f) {
-      size = static_cast<int64_t>(size * usageRatio_);
-    }
-    return size;
-  }
-  virtual void shuffle();
-
-  void loadData(const std::vector<std::string>& fileList);
-
-  virtual int64_t getNextBatchInternal(int64_t size, DataBatch* batch);
-
-protected:
-  /**
-   * @brief load protobuf data from a list of file
-   * @param[in]  fileName  file name of a file which contains
-   * a list of file names
-   */
-  void loadData(const std::string& fileName);
-
-  /**
-   * @brief load protobuf data from file
-   * @param[in]  fileName   data file name
-   */
-  void loadDataFile(const std::string& fileName);
-  /** @brief check data header of each data sample
-   *  @param[in] header     data header read from protobuf data
-   */
-  void checkDataHeader(const DataHeader& header);
-  /**
-   * @brief fill protobuf data into slot_,
-   * slot_ is a vector of ProtoSlot in memory.
-   * @param[in]  sample     data sample read from protobuf data
-   */
-  void fillSlots(const DataSample& sample);
-
-  /**
-   * @brief return true if each sample is one sequence, i.e., independent
-   * of other samples.
-   */
-  inline bool iidData() const { return sequenceStartPositions_.empty(); }
-
-  /**
-   * @brief check that sample is consistent with header_
-   */
-  void checkSample(const DataSample& sample);
-
-  template <class Op>
-  int64_t sequenceLoop(Op op, int64_t size);
-
-  template <class Op>
-  int64_t sampleLoop(Op op, int64_t size);
-
-  template <class Op>
-  int64_t subSampleLoop(Op op, int64_t size, int slot);
-
-  void showDataStats();
-
-protected:
-  struct ProtoVarSlot {
-    std::vector<real> data;
-    std::vector<int> dims;
-  };
-
-  struct ProtoSlot {
-    SlotDef::SlotType type;
-    int dim;
-    std::vector<int> indexData;
-    std::vector<real> denseData;
-    std::vector<sparse_non_value_t> sparseNonValueData;
-    std::vector<sparse_float_value_t> sparseFloatValueData;
-    std::vector<int64_t> indices;
-    std::vector<int64_t> subIndices;
-
-    std::vector<ProtoVarSlot> varDenseData;
-    std::vector<std::vector<int>> varIndices;
-    std::vector<std::string> strData;
-  };
-  DataHeader header_;
-  int numVecSlots_;
-
-  std::vector<ProtoSlot> slots_;
-  size_t sampleNums_;
-
-  /**
-   * The starting position of each sequence in samples.
-   * The last element should be num of samples.
-   * If empty, each sample is one sequence.
-   */
-  std::vector<size_t> sequenceStartPositions_;
-
-  int64_t currentSequenceIndex_;
-
-  // The size should be the number of sequences.
-  std::vector<size_t> shuffledSequenceIds_;
-
-  ThreadLocalD<DataBatch> cpuBatch_;
-  ThreadLocalD<DataBatch> gpuBatch_;
-
-  RWLock lock_;
-  std::vector<StatPtr> nnzStats_;  // stats for number of none-zeros entries
-};
-
-/**
- * @brief Special use for Proto data: instances should contain sparse-non-value
- * slots
- * and label.
- *
- * @note ProtoSequenceDataProvider treats each SPARSE SLOT as a SEQUENCE
- */
-class ProtoSequenceDataProvider : public ProtoDataProvider {
-public:
-  ProtoSequenceDataProvider(const DataConfig& config,
-                            bool useGpu,
-                            bool loadDataAll = true);
-  ~ProtoSequenceDataProvider() {}
-  virtual int64_t getNextBatchInternal(int64_t size, DataBatch* batch);
-};
-
-}  // namespace paddle
diff --git a/paddle/gserver/tests/CMakeLists.txt b/paddle/gserver/tests/CMakeLists.txt
index aa94ee406e..232fa01568 100644
--- a/paddle/gserver/tests/CMakeLists.txt
+++ b/paddle/gserver/tests/CMakeLists.txt
@@ -58,17 +58,6 @@ if(NOT WITH_DOUBLE)
 endif()
 
 if(NOT MOBILE_INFERENCE)
-################### test_ProtoDataProvider ############
-    add_unittest_without_exec(test_ProtoDataProvider
-        test_ProtoDataProvider.cpp)
-
-    # test_ProtoDataProvider will mkdir as same name,
-    # so if WORKING_DIRECTORY is default directory, then
-    # mkdir will get error.
-    add_test(NAME test_ProtoDataProvider
-        COMMAND ${CMAKE_CURRENT_BINARY_DIR}/test_ProtoDataProvider
-        WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle)
-
 ################## test_Evaluator #######################
     add_unittest(test_Evaluator
         test_Evaluator.cpp)
diff --git a/paddle/gserver/tests/proto_files.txt b/paddle/gserver/tests/proto_files.txt
deleted file mode 100644
index 691b38c794..0000000000
--- a/paddle/gserver/tests/proto_files.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-./test_ProtoDataProvider/data1.bin
-./test_ProtoDataProvider/data2.bin
diff --git a/paddle/gserver/tests/proto_files_compressed.txt b/paddle/gserver/tests/proto_files_compressed.txt
deleted file mode 100644
index 7413c81e18..0000000000
--- a/paddle/gserver/tests/proto_files_compressed.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-./test_ProtoDataProvider/data1.bin.gz
-./test_ProtoDataProvider/data2.bin.gz
diff --git a/paddle/gserver/tests/test_ProtoDataProvider.cpp b/paddle/gserver/tests/test_ProtoDataProvider.cpp
deleted file mode 100644
index af6472619d..0000000000
--- a/paddle/gserver/tests/test_ProtoDataProvider.cpp
+++ /dev/null
@@ -1,732 +0,0 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#include <memory>
-#include <string>
-
-#include <gtest/gtest.h>
-
-#include "paddle/gserver/dataproviders/ProtoDataProvider.h"
-#include "paddle/utils/Util.h"
-
-#include "paddle/testing/TestUtil.h"
-
-using namespace std;  // NOLINT
-
-std::vector<string> protoFiles{
-    "./test_ProtoDataProvider/data1.bin", "./test_ProtoDataProvider/data2.bin",
-};
-std::vector<string> protoFilesCompressed{
-    "./test_ProtoDataProvider/data1.bin.gz",
-    "./test_ProtoDataProvider/data2.bin.gz",
-};
-
-const char* kTestDir = "./test_ProtoDataProvider";
-const char kProtoFileList[] = "gserver/tests/proto_files.txt";
-const char kProtoFileListCompressed[] =
-    "gserver/tests/proto_files_compressed.txt";
-const int kSpraseMatrixDim = 1024;
-
-using namespace paddle;  // NOLINT
-
-void prepareData(DataBatch* batch,
-                 const int* numPerSlotType,
-                 bool iid,
-                 bool useGpu) {
-  batch->clear();
-  int64_t size = uniformRandom(100) + 10;
-  batch->setSize(size);
-
-  ICpuGpuVectorPtr sequenceStartPositions;
-  ICpuGpuVectorPtr subSequenceStartPositions;
-  if (!iid) {
-    int numSeqs = uniformRandom(10) + 1;
-    sequenceStartPositions =
-        ICpuGpuVector::create(numSeqs + 1, /* useGpu= */ false);
-    int* buf = sequenceStartPositions->getMutableData(false);
-    subSequenceStartPositions =
-        ICpuGpuVector::create(numSeqs + 1, /* useGpu= */ false);
-    int* subBuf = subSequenceStartPositions->getMutableData(false);
-    int64_t pos = 0;
-    int maxLen = 2 * size / numSeqs;
-    for (int i = 0; i < numSeqs; ++i) {
-      int len =
-          uniformRandom(min<int64_t>(maxLen, size - pos - numSeqs + i)) + 1;
-      buf[i] = pos;
-      subBuf[i] = pos;
-      pos += len;
-      VLOG(1) << " len=" << len;
-    }
-    buf[numSeqs] = size;
-    subBuf[numSeqs] = size;
-  }
-
-  vector<Argument>& arguments = batch->getStreams();
-  for (int i = 0; i < numPerSlotType[SlotDef::VECTOR_DENSE]; ++i) {
-    int64_t dim = rand() % 10 + 4;  // NOLINT rand_r
-    MatrixPtr mat = Matrix::create(size, dim, /* trans= */ false, false);
-    mat->randomizeUniform();
-    Argument arg;
-    arg.value = mat;
-    arg.sequenceStartPositions = sequenceStartPositions;
-    arguments.push_back(arg);
-  }
-  for (int i = 0; i < numPerSlotType[SlotDef::VECTOR_SPARSE_NON_VALUE]; ++i) {
-    MatrixPtr mat =
-        makeRandomSparseMatrix(size, kSpraseMatrixDim, false, useGpu);
-    Argument arg;
-    arg.value = mat;
-    arg.sequenceStartPositions = sequenceStartPositions;
-    arg.subSequenceStartPositions = subSequenceStartPositions;
-    arguments.push_back(arg);
-  }
-  for (int i = 0; i < numPerSlotType[SlotDef::VECTOR_SPARSE_VALUE]; ++i) {
-    MatrixPtr mat =
-        makeRandomSparseMatrix(size, kSpraseMatrixDim, true, useGpu);
-    Argument arg;
-    arg.value = mat;
-    arg.sequenceStartPositions = sequenceStartPositions;
-    arguments.push_back(arg);
-  }
-  for (int i = 0; i < numPerSlotType[SlotDef::STRING]; ++i) {
-    int64_t dim = rand() % 10 + 4;  // NOLINT rand_r
-    SVectorPtr vec = std::make_shared<std::vector<std::string>>();
-    for (int j = 0; j < size; ++j) {
-      vec->push_back(randStr(dim));
-    }
-    Argument arg;
-    arg.strs = vec;
-    arg.sequenceStartPositions = sequenceStartPositions;
-    arguments.push_back(arg);
-  }
-  for (int i = 0; i < numPerSlotType[SlotDef::INDEX]; ++i) {
-    int64_t dim = rand() % 10 + 4;  // NOLINT rand_r
-    IVectorPtr vec = IVector::create(size, /* useGpu= */ false);
-    int* buf = vec->getData();
-    for (int j = 0; j < size; ++j) {
-      buf[j] = uniformRandom(dim);
-    }
-    Argument arg;
-    arg.ids = vec;
-    arg.sequenceStartPositions = sequenceStartPositions;
-    arguments.push_back(arg);
-  }
-}
-
-inline int getSlotDim(const Argument& arg) {
-  if (arg.value) {
-    return arg.value->getWidth();
-  } else if (arg.ids) {
-    return arg.ids->getMax() + 1;
-  } else if (arg.strs) {
-    return 1;
-  }
-  LOG(FATAL) << "Invalid argument";
-  return 0;
-}
-
-inline SlotDef::SlotType getSlotType(const Argument& arg) {
-  if (arg.value) {
-    auto& m = *arg.value;
-    auto& type = typeid(m);
-    if (type == typeid(CpuMatrix) || type == typeid(GpuMatrix)) {
-      return SlotDef::VECTOR_DENSE;
-    }
-    if (type == typeid(CpuSparseMatrix)) {
-      auto valueType =
-          std::dynamic_pointer_cast<CpuSparseMatrix>(arg.value)->getValueType();
-      if (NO_VALUE == valueType) {
-        return SlotDef::VECTOR_SPARSE_NON_VALUE;
-      } else {
-        return SlotDef::VECTOR_SPARSE_VALUE;
-      }
-    }
-    if (type == typeid(GpuSparseMatrix)) {
-      auto valueType =
-          std::dynamic_pointer_cast<GpuSparseMatrix>(arg.value)->getValueType();
-      if (NO_VALUE == valueType) {
-        return SlotDef::VECTOR_SPARSE_NON_VALUE;
-      } else {
-        return SlotDef::VECTOR_SPARSE_VALUE;
-      }
-    }
-
-    LOG(FATAL) << "Unknown matrix type";
-  }
-  if (arg.ids) return SlotDef::INDEX;
-  if (arg.strs) return SlotDef::STRING;
-  LOG(FATAL) << "Invalid argument";
-  return SlotDef::VECTOR_DENSE;
-}
-
-void getColRow(const Argument& arg,
-               int64_t pos,
-               bool useGpu,
-               int* colNum,
-               const int** rowCols,
-               const real** rowValues) {
-  SlotDef::SlotType type = getSlotType(arg);
-  GpuSparseMatrixPtr matGpu;
-  CpuSparseMatrixPtr matCpu;
-  if (useGpu) {
-    matGpu = dynamic_pointer_cast<GpuSparseMatrix>(arg.value);
-    ASSERT_TRUE(matGpu != NULL);
-  } else {
-    matCpu = dynamic_pointer_cast<CpuSparseMatrix>(arg.value);
-    ASSERT_TRUE(matCpu != NULL);
-  }
-  *colNum = useGpu ? matGpu->getColNum(pos) : matCpu->getColNum(pos);
-  *rowCols = useGpu ? matGpu->getRowCols(pos) : matCpu->getRowCols(pos);
-  if (type == SlotDef::VECTOR_SPARSE_VALUE) {
-    *rowValues = useGpu ? matGpu->getRowValues(pos) : matCpu->getRowValues(pos);
-  } else {
-    *rowValues = NULL;
-  }
-}
-
-void makeSample(const vector<Argument>& arguments,
-                int64_t pos,
-                bool isBeginning,
-                DataSample* sample,
-                bool useGpu) {
-  sample->set_is_beginning(isBeginning);
-  int slotid = 0;
-  for (auto& arg : arguments) {
-    SlotDef::SlotType type = getSlotType(arg);
-    int64_t dim = getSlotDim(arg);
-    switch (type) {
-      case SlotDef::VECTOR_DENSE: {
-        VectorSlot* vecSlot = sample->add_vector_slots();
-        auto values = vecSlot->mutable_values();
-        values->Reserve(dim);
-        for (int i = 0; i < dim; ++i) {
-          values->AddAlreadyReserved(
-              static_cast<float>(arg.value->getElement(pos, i)));
-        }
-        break;
-      }
-      case SlotDef::INDEX: {
-        sample->add_id_slots(arg.ids->get(pos));
-        break;
-      }
-      case SlotDef::VECTOR_SPARSE_NON_VALUE: {
-        VectorSlot* vecSlot = sample->add_vector_slots();
-        auto ids = vecSlot->mutable_ids();
-        int colNum;
-        const int* rowCols;
-        const real* rowValues;  // nullptr
-        getColRow(arg, pos, useGpu, &colNum, &rowCols, &rowValues);
-        ids->Reserve(colNum);
-        for (int i = 0; i < colNum; ++i) {
-          ids->AddAlreadyReserved(rowCols[i]);
-        }
-        SubseqSlot* subseqSlot = sample->add_subseq_slots();  // subseq
-        subseqSlot->set_slot_id(slotid);
-        auto lens = subseqSlot->mutable_lens();
-        lens->Add(colNum);
-        break;
-      }
-      case SlotDef::VECTOR_SPARSE_VALUE: {
-        VectorSlot* vecSlot = sample->add_vector_slots();
-        auto values = vecSlot->mutable_values();
-        auto ids = vecSlot->mutable_ids();
-        int colNum;
-        const int* rowCols;
-        const real* rowValues;
-        getColRow(arg, pos, useGpu, &colNum, &rowCols, &rowValues);
-        ids->Reserve(colNum);
-        values->Reserve(colNum);
-        for (int i = 0; i < colNum; ++i) {
-          ids->AddAlreadyReserved(rowCols[i]);
-          values->AddAlreadyReserved(rowValues[i]);
-        }
-        break;
-      }
-      case SlotDef::VAR_MDIM_DENSE:
-      case SlotDef::VAR_MDIM_INDEX: {
-        LOG(FATAL) << "Not implemented";
-        break;
-      }
-      case SlotDef::STRING: {
-        VectorSlot* vecSlot = sample->add_vector_slots();
-        vecSlot->add_strs((*arg.strs)[pos]);
-        break;
-      }
-    }
-    slotid++;
-  }
-}
-
-void writeData(const DataBatch& batch, bool useGpu, bool dataCompression) {
-  DataHeader header;
-  const vector<Argument>& arguments = batch.getStreams();
-  for (auto& argument : arguments) {
-    SlotDef* slotDef = header.add_slot_defs();
-    slotDef->set_type(getSlotType(argument));
-    slotDef->set_dim(getSlotDim(argument));
-  }
-  VLOG(1) << "header=" << header.DebugString();
-
-  int64_t totalSeqs = batch.getNumSequences();
-  int64_t seq = 0;
-  ICpuGpuVectorPtr sequenceStartPositions = arguments[0].sequenceStartPositions;
-  int64_t numWritten = 0;
-  vector<string> curProtoFiles =
-      dataCompression ? protoFilesCompressed : protoFiles;
-  for (size_t i = 0; i < curProtoFiles.size(); ++i) {
-    int64_t numSeqs = totalSeqs * (i + 1) / curProtoFiles.size() -
-                      totalSeqs * i / curProtoFiles.size();
-    ofstream os(curProtoFiles[i]);
-    CHECK(os) << "Fail to open " << curProtoFiles[i];
-    unique_ptr<ProtoWriter> writer(new ProtoWriter(&os, dataCompression));
-    CHECK(writer->write(header));
-    for (int j = 0; j < numSeqs; ++j, ++seq) {
-      int64_t begin = seq;
-      int64_t end = seq + 1;
-      if (sequenceStartPositions) {
-        begin = sequenceStartPositions->getElement(seq);
-        end = sequenceStartPositions->getElement(seq + 1);
-      }
-      for (int pos = begin; pos < end; ++pos) {
-        DataSample sample;
-        makeSample(arguments, pos, pos == begin, &sample, useGpu);
-        CHECK(writer->write(sample));
-        ++numWritten;
-      }
-    }
-
-    writer.reset(nullptr);
-    os.close();
-  }
-  CHECK_EQ(arguments[0].getBatchSize(), numWritten);
-}
-
-// check that the sample at pos1 in args1 is same as the sample at pos2 in args2
-void checkSample(const vector<Argument>& args1,
-                 int64_t pos1,
-                 const vector<Argument>& args2,
-                 int64_t pos2,
-                 bool useGpu) {
-  EXPECT_EQ(args1.size(), args2.size());
-  VLOG(1) << " pos1=" << pos1 << " pos2=" << pos2;
-
-  for (size_t i = 0; i < args1.size(); ++i) {
-    auto type = getSlotType(args1[i]);
-    int dim = getSlotDim(args1[i]);
-    EXPECT_EQ(type, getSlotType(args2[i]));
-    if (type == SlotDef::INDEX) {
-      EXPECT_GE(dim, getSlotDim(args2[i]));
-    } else {
-      EXPECT_EQ(dim, getSlotDim(args2[i]));
-    }
-    switch (type) {
-      case SlotDef::VECTOR_DENSE: {
-        for (int j = 0; j < dim; ++j) {
-          EXPECT_EQ(static_cast<float>(args1[i].value->getElement(pos1, j)),
-                    static_cast<float>(args2[i].value->getElement(pos2, j)));
-        }
-        break;
-      }
-      case SlotDef::INDEX: {
-        EXPECT_EQ(args1[i].ids->get(pos1), args2[i].ids->get(pos2));
-        break;
-      }
-      case SlotDef::VECTOR_SPARSE_NON_VALUE:
-      case SlotDef::VECTOR_SPARSE_VALUE: {
-        int colNum1, colNum2;
-        const int *rowCols1, *rowCols2;
-        const real *rowValues1, *rowValues2;
-        getColRow(args1[i], pos1, useGpu, &colNum1, &rowCols1, &rowValues1);
-        getColRow(args2[i], pos2, useGpu, &colNum2, &rowCols2, &rowValues2);
-        EXPECT_EQ(colNum1, colNum2);
-        for (int j = 0; j < colNum1; ++j) {
-          EXPECT_EQ(rowCols1[j], rowCols2[j]);
-          if (type == SlotDef::VECTOR_SPARSE_VALUE) {
-            EXPECT_EQ(rowValues1[j], rowValues2[j]);
-          }
-        }
-        break;
-      }
-      case SlotDef::VAR_MDIM_DENSE:
-      case SlotDef::VAR_MDIM_INDEX: {
-        LOG(FATAL) << "Not implemented";
-        break;
-      }
-      case SlotDef::STRING: {
-        EXPECT_EQ((*args1[i].strs)[pos1], (*args2[i].strs)[pos2]);
-        break;
-      }
-    }
-  }
-}
-
-void testProtoDataProvider(int* numPerSlotType,
-                           bool iid,
-                           bool async,
-                           bool useGpu,
-                           bool dataCompression,
-                           int numConstantSlots = 0) {
-  mkDir(kTestDir);
-  DataBatch data;
-
-  prepareData(&data, numPerSlotType, iid, useGpu);
-  writeData(data, useGpu, dataCompression);
-
-  DataConfig config;
-  config.set_type("proto");
-  config.set_files(dataCompression ? kProtoFileListCompressed : kProtoFileList);
-  config.set_async_load_data(async);
-
-  for (int i = 0; i < numConstantSlots; ++i) {
-    config.add_constant_slots(i + 11);
-    MatrixPtr w = Matrix::create(data.getSize(),
-                                 1,
-                                 /* trans= */ false,
-                                 /* useGpu= */ false);
-    w->assign(config.constant_slots(i));
-    data.appendData(w);
-  }
-
-  unique_ptr<DataProvider> dataProvider(DataProvider::create(config, useGpu));
-  dataProvider->setSkipShuffle();
-
-  EXPECT_EQ(data.getSize(), dataProvider->getSize());
-
-  int64_t batchSize = 10;
-  DataBatch batch;
-
-  size_t seq1 = 0;
-  vector<Argument>& args1 = data.getStreams();
-  ICpuGpuVectorPtr sequenceStartPositions1 = args1[0].sequenceStartPositions;
-
-  dataProvider->reset();
-
-  while (dataProvider->getNextBatch(batchSize, &batch) > 0) {
-    CHECK_EQ(data.getNumStreams(), batch.getNumStreams());
-    vector<Argument>& args2 = batch.getStreams();
-    ICpuGpuVectorPtr sequenceStartPositions2 = args2[0].sequenceStartPositions;
-    for (auto& arg : args2) {
-      EXPECT_EQ(iid, !arg.sequenceStartPositions);
-    }
-    size_t numSeqs = batch.getNumSequences();
-    VLOG(1) << "numSeqs=" << numSeqs;
-    for (size_t seq2 = 0; seq2 < numSeqs; ++seq1, ++seq2) {
-      int64_t begin1 = seq1;
-      int64_t end1 = seq1 + 1;
-      if (sequenceStartPositions1) {
-        begin1 = sequenceStartPositions1->getElement(seq1);
-        end1 = sequenceStartPositions1->getElement(seq1 + 1);
-        EXPECT_LT(seq1, sequenceStartPositions1->getSize() - 1);
-      }
-
-      int64_t begin2 = seq2;
-      int64_t end2 = seq2 + 1;
-      if (sequenceStartPositions2) {
-        begin2 = sequenceStartPositions2->getElement(seq2);
-        end2 = sequenceStartPositions2->getElement(seq2 + 1);
-      }
-      VLOG(1) << " begin1=" << begin1 << " end1=" << end1
-              << " begin2=" << begin2 << " end2=" << end2;
-      EXPECT_EQ(end1 - begin1, end2 - begin2);
-      for (int i = 0; i < end1 - begin1; ++i) {
-        checkSample(args1, begin1 + i, args2, begin2 + i, useGpu);
-      }
-    }
-  }
-
-  EXPECT_EQ(seq1, (size_t)data.getNumSequences());
-  rmDir(kTestDir);
-}
-
-TEST(ProtoDataProvider, test) {
-  int numSlotsArray[] = {0, 3};
-  int numTwoArray[] = {0, 1};
-  int numSlotsArraySize = sizeof(numSlotsArray) / sizeof(numSlotsArray[0]);
-  const int numSlot = 5;
-  int combination[numSlot] = {0};
-  int k = numSlot - 1;
-  while (k >= 0) {
-    int numDenseVecSlots = numSlotsArray[combination[0]];
-    int numSparseNonValueVecSlots = numSlotsArray[combination[1]];
-    int numSparseValueVectorSlots = numSlotsArray[combination[2]];
-    int numStrSlots = numSlotsArray[combination[3]];
-    int numIdSlots = numSlotsArray[combination[4]];
-    // while loop : traverse all cases
-    k = numSlot - 1;
-    while (k >= 0) {
-      if (combination[k] < (numSlotsArraySize - 1)) {
-        ++combination[k];
-        break;
-      } else {
-        combination[k] = 0;
-        --k;
-      }
-    }
-    if (numDenseVecSlots + numSparseNonValueVecSlots +
-            numSparseValueVectorSlots + numStrSlots + numIdSlots <
-        1)
-      continue;
-    for (int iid : numTwoArray) {
-      for (int async : numTwoArray) {
-        for (int useGpu : numTwoArray) {
-          for (int dataCompression : numTwoArray) {
-            if (async && useGpu) {
-              // Currently in async mode, useGpu is not supported
-              continue;
-            }
-#ifndef PADDLE_WITH_CUDA
-            if (useGpu) {
-              continue;
-            }
-#endif
-            LOG(INFO) << " numDenseVecSlots=" << numDenseVecSlots
-                      << " numSparseNonValueVecSlots="
-                      << numSparseNonValueVecSlots
-                      << " numSparseValueVectorSlots="
-                      << numSparseValueVectorSlots
-                      << " numStrSlots=" << numStrSlots
-                      << " numIdSlots=" << numIdSlots << " iid=" << iid
-                      << " async=" << async << " useGpu=" << useGpu
-                      << " dataCompression=" << dataCompression;
-            int numPerSlotType[SlotDef::SlotType_ARRAYSIZE] = {0};
-            numPerSlotType[SlotDef::VECTOR_DENSE] = numDenseVecSlots;
-            numPerSlotType[SlotDef::VECTOR_SPARSE_NON_VALUE] =
-                numSparseNonValueVecSlots;
-            numPerSlotType[SlotDef::VECTOR_SPARSE_VALUE] =
-                numSparseValueVectorSlots;
-            numPerSlotType[SlotDef::INDEX] = numIdSlots;
-            numPerSlotType[SlotDef::STRING] = numStrSlots;
-            testProtoDataProvider(
-                numPerSlotType, iid, async, useGpu, dataCompression);
-          }  // end for (int dataCompression : numTwoArray)
-        }    // end for (int useGpu : numTwoArray)
-      }      // end for (int async : numTwoArray)
-    }        // end for (int iid : numTwoArray)
-  }          // end for (while, traverse all slots)
-}
-
-TEST(ProtoDataProvider, constant_slots) {
-  int numSlotsArray[] = {0, 3};
-  int numTwoArray[] = {0, 1};
-  for (int numDenseVecSlots : numSlotsArray) {
-    for (int numSparseNonValueVecSlots : numSlotsArray) {
-      if (numDenseVecSlots + numSparseNonValueVecSlots < 1) continue;
-      for (int numConstantSlots : {1, 2}) {
-        for (int useGpu : numTwoArray) {
-          for (int dataCompression : numTwoArray) {
-#ifndef PADDLE_WITH_CUDA
-            if (useGpu) {
-              continue;
-            }
-#endif
-            LOG(INFO) << " numDenseVecSlots=" << numDenseVecSlots
-                      << " numSparseNonValueVecSlots="
-                      << numSparseNonValueVecSlots
-                      << " numConstantSlogs=" << numConstantSlots
-                      << " useGpu=" << useGpu
-                      << " dataCompression=" << dataCompression;
-            int numPerSlotType[SlotDef::SlotType_ARRAYSIZE] = {0};
-            numPerSlotType[SlotDef::VECTOR_DENSE] = numDenseVecSlots;
-            numPerSlotType[SlotDef::VECTOR_SPARSE_NON_VALUE] =
-                numSparseNonValueVecSlots;
-            numPerSlotType[SlotDef::VECTOR_SPARSE_VALUE] = 1;
-            numPerSlotType[SlotDef::INDEX] = 1;
-            testProtoDataProvider(numPerSlotType,
-                                  /* iid= */ true,
-                                  /* async= */ false,
-                                  useGpu,
-                                  dataCompression,
-                                  numConstantSlots);
-          }  // end for (int dataCompression : numTwoArray)
-        }    // end for (int useGpu : numTwoArray)
-      }      // end for (int numConstantSlots : {1, 2})
-    }        // end for (int numSparseNonValueVecSlots : numSlotsArray)
-  }          // end for (int numDenseVecSlots : numSlotsArray)
-}
-
-void checkSampleSequence(const vector<Argument>& args1,
-                         const vector<Argument>& args2,
-                         int64_t offset,
-                         int64_t numSeqs,
-                         bool useGpu) {
-  // check slot num are equal
-  EXPECT_EQ(args1.size(), args2.size());
-  for (size_t i = 0; i < args1.size(); i++) {
-    auto type = getSlotType(args1[i]);
-    // check for args2: sequenceStartPositions vs numSeqs
-    // (1) size
-    EXPECT_EQ(args2[i].sequenceStartPositions->getSize(), (size_t)numSeqs + 1);
-    // (2) content
-    auto checkArgContent = [&](const Argument& args, int numSeqs) {
-      for (int j = 0; j <= numSeqs; j++) {
-        int start_pos = args.sequenceStartPositions->getElement(j);
-        EXPECT_EQ(start_pos, j);
-      }
-    };
-    switch (type) {
-      case SlotDef::INDEX: {
-        // args1: for label
-        checkArgContent(args2[i], numSeqs);
-        // check for args2: ids are equal to args1[offset]
-        // (1) size
-        EXPECT_EQ(args2[i].ids->getSize(), (size_t)numSeqs);
-        // (2) content
-        for (int j = 0; j < numSeqs; j++) {
-          EXPECT_EQ(args2[i].ids->get(j), args1[i].ids->get(offset + j));
-        }
-        break;
-      }
-      case SlotDef::VECTOR_SPARSE_NON_VALUE: {
-        // args1: for sparse_non_value
-        // args2 should put sparse indexes in ids
-        int colNum1;
-        const int* rowCols1;
-        const real* rowValues1;  // nullptr
-        int totalLength = 0;
-        for (int j = 0; j < numSeqs; j++) {
-          getColRow(
-              args1[i], offset + j, useGpu, &colNum1, &rowCols1, &rowValues1);
-          // (1) lengths
-          EXPECT_EQ(totalLength,
-                    args2[i].sequenceStartPositions->getElement(j));
-          EXPECT_EQ(totalLength,
-                    args2[i].subSequenceStartPositions->getElement(j));
-          // (2) content
-          for (int k = 0; k < colNum1; k++) {
-            EXPECT_EQ(rowCols1[k], args2[i].ids->get(totalLength + k));
-          }
-          totalLength += colNum1;
-          if (colNum1 == 0) {
-            // special case here: we will put a "-1" into ids when column num is
-            // zero. see ProtoSequenceDataProvider::getNextBatchInternal.
-            EXPECT_EQ(-1, args2[i].ids->get(totalLength));
-            totalLength++;
-          }
-        }
-        EXPECT_EQ(totalLength,
-                  args2[i].sequenceStartPositions->getElement(numSeqs));
-        EXPECT_EQ(totalLength,
-                  args2[i].subSequenceStartPositions->getElement(numSeqs));
-        break;
-      }
-      case SlotDef::VECTOR_DENSE: {
-        // args1: for dense vector
-        checkArgContent(args2[i], numSeqs);
-        // check for args2: values are equal to args1[offset]
-        // (1) size
-        EXPECT_EQ(args2[i].value->getHeight(), (size_t)numSeqs);
-        EXPECT_EQ(args2[i].value->getWidth(), (size_t)getSlotDim(args1[i]));
-        // (2) content
-        for (int j = 0; j < numSeqs; j++) {
-          for (size_t k = 0; k < args2[i].value->getWidth(); k++) {
-            EXPECT_EQ(
-                static_cast<float>(args1[i].value->getElement(j + offset, k)),
-                static_cast<float>(args2[i].value->getElement(j, k)));
-          }
-        }
-        break;
-      }
-      default: { EXPECT_EQ(true, false) << "should not reach here"; }
-    }
-  }
-}
-
-void testProtoSequenceDataProvider(int* numPerSlotType,
-                                   bool async,
-                                   bool useGpu) {
-  mkDir(kTestDir);
-  DataBatch data;
-
-  prepareData(&data,
-              numPerSlotType,
-              /* iid */ true,
-              useGpu);
-  writeData(data, useGpu, /* dataCompression */ false);
-
-  DataConfig config;
-  config.set_type("proto_sequence");
-  config.set_files(kProtoFileList);
-  config.set_async_load_data(async);
-
-  unique_ptr<DataProvider> dataProvider(DataProvider::create(config, useGpu));
-  dataProvider->setSkipShuffle();
-
-  EXPECT_EQ(data.getSize(), dataProvider->getSize());
-
-  int64_t batchSize = 10;
-  DataBatch batch;
-
-  vector<Argument>& args1 = data.getStreams();
-  ICpuGpuVectorPtr sequenceStartPositions1 = args1[0].sequenceStartPositions;
-
-  dataProvider->reset();
-
-  size_t args1Offset = 0;
-  while (dataProvider->getNextBatch(batchSize, &batch) > 0) {
-    CHECK_EQ(data.getNumStreams(), batch.getNumStreams());
-    vector<Argument>& args2 = batch.getStreams();
-    ICpuGpuVectorPtr sequenceStartPositions2 = args2[0].sequenceStartPositions;
-    for (auto& arg : args1) {
-      // args1 should not has sequence
-      EXPECT_EQ(true, !arg.sequenceStartPositions);
-    }
-    for (auto& arg : args2) {
-      // args2 should has sequence
-      EXPECT_NE(true, !arg.sequenceStartPositions);
-    }
-    size_t numSeqs = batch.getNumSequences();
-    checkSampleSequence(args1, args2, args1Offset, numSeqs, useGpu);
-    args1Offset += numSeqs;
-  }
-
-  EXPECT_EQ(args1Offset, (size_t)data.getNumSequences());
-  rmDir(kTestDir);
-}
-
-TEST(ProtoSequenceDataProvider, test) {
-  int numSlotsArray[] = {0, 3};
-  int numTwoArray[] = {0, 1};
-  for (int numSparseNonValueVecSlots : numSlotsArray) {
-    for (int numIdSlots : numSlotsArray) {
-      for (int numDenseVecSlots : numSlotsArray) {
-        if (numDenseVecSlots + numSparseNonValueVecSlots + numIdSlots < 1)
-          continue;
-        for (int async : numTwoArray) {
-          for (int useGpu : numTwoArray) {
-            if (async && useGpu) {
-              // Currently in async mode, useGpu is not supported
-              continue;
-            }
-#ifndef PADDLE_WITH_CUDA
-            if (useGpu) {
-              continue;
-            }
-#endif
-            LOG(INFO) << " numDenseVecSlots=" << numDenseVecSlots
-                      << " numSparseNonValueVecSlots="
-                      << numSparseNonValueVecSlots
-                      << " numIdSlots=" << numIdSlots << " async=" << async
-                      << " useGpu=" << useGpu;
-            int numPerSlotType[SlotDef::SlotType_ARRAYSIZE] = {0};
-            numPerSlotType[SlotDef::VECTOR_DENSE] = numDenseVecSlots;
-            numPerSlotType[SlotDef::VECTOR_SPARSE_NON_VALUE] =
-                numSparseNonValueVecSlots;
-            numPerSlotType[SlotDef::INDEX] = numIdSlots;
-            testProtoSequenceDataProvider(numPerSlotType, async, useGpu);
-          }  // end for (int useGpu : numTwoArray)
-        }    // end for (int async : numTwoArray)
-      }      // end for (int numDenseVecSlots : numSlotsArray)
-    }        // end for (int numIdSlots : numSlotsArray)
-  }          // end for (int numSparseNonValueVecSlots : numSlotsArray)
-}

From bbeb826f42aa02796347019159301d696a0e7da8 Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Fri, 3 Nov 2017 16:03:55 +0800
Subject: [PATCH 02/43] remove ProtoDataProvider in test_PyDataProvider.cpp

---
 .../test_pydata_provider_wrapper.proto_data   | Bin 121 -> 0 bytes
 .../test_pydata_provider_wrapper.protolist    |   1 -
 paddle/trainer/tests/testPyDataWrapper.py     |  24 -----
 .../tests/test_PyDataProviderWrapper.cpp      |  96 ------------------
 4 files changed, 121 deletions(-)
 delete mode 100644 paddle/trainer/tests/pydata_provider_wrapper_dir/test_pydata_provider_wrapper.proto_data
 delete mode 100644 paddle/trainer/tests/pydata_provider_wrapper_dir/test_pydata_provider_wrapper.protolist

diff --git a/paddle/trainer/tests/pydata_provider_wrapper_dir/test_pydata_provider_wrapper.proto_data b/paddle/trainer/tests/pydata_provider_wrapper_dir/test_pydata_provider_wrapper.proto_data
deleted file mode 100644
index f189b21e86a50d70d317b5e43aa2d6e05af5e774..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 121
zcmb2+V&PyE-~y5i0!%=X3Cv~_U<9(61(<b&ScRAwnOTLnxHvo>MmaDr2sjAwadD)j
zwK*6Y8#@Rwv9JlTC^01_N-;8k6|*yQfE0ri2JM(<4^nJwEbPE_o!L={iIEkgI4Mbr
GkqH2v@C}Oq

diff --git a/paddle/trainer/tests/pydata_provider_wrapper_dir/test_pydata_provider_wrapper.protolist b/paddle/trainer/tests/pydata_provider_wrapper_dir/test_pydata_provider_wrapper.protolist
deleted file mode 100644
index 6b406dff0b..0000000000
--- a/paddle/trainer/tests/pydata_provider_wrapper_dir/test_pydata_provider_wrapper.protolist
+++ /dev/null
@@ -1 +0,0 @@
-./trainer/tests/pydata_provider_wrapper_dir/test_pydata_provider_wrapper.proto_data
diff --git a/paddle/trainer/tests/testPyDataWrapper.py b/paddle/trainer/tests/testPyDataWrapper.py
index 2c29a27433..a76eeeacb9 100644
--- a/paddle/trainer/tests/testPyDataWrapper.py
+++ b/paddle/trainer/tests/testPyDataWrapper.py
@@ -20,28 +20,6 @@ import random
 import json
 import string
 
-
-@provider(slots=[
-    SparseNonValueSlot(10), DenseSlot(2), SparseValueSlot(10), StringSlot(1),
-    IndexSlot(3)
-])
-def processNonSequenceData(obj, filename):
-    with open(filename, "rb") as f:
-        for line in f:
-            slots_str = line.split(';')
-            index = int(slots_str[0])
-            non_values = map(int, slots_str[1].split()[1:])
-            dense = map(float, slots_str[2].split()[1:])
-            strs = slots_str[4].strip().split(' ', 1)[1]
-
-            def __values_mapper__(s):
-                s = s.split(":")
-                return int(s[0]), float(s[1])
-
-            values = map(__values_mapper__, slots_str[3].split()[1:])
-            yield [non_values, dense, values, strs, index]
-
-
 SPARSE_ID_LIMIT = 1000
 SPARSE_ID_COUNT = 100
 SEQUENCE_LIMIT = 50
@@ -146,8 +124,6 @@ def processSubSeqAndGenerateData(obj, name):
 
 
 if __name__ == "__main__":
-    pvd = processNonSequenceData("test.txt")
-    print pvd.getNextBatch(100)
     pvd = processSeqAndGenerateData("_")
     print pvd.getNextBatch(100)
     pvd = processSubSeqAndGenerateData("_")
diff --git a/paddle/trainer/tests/test_PyDataProviderWrapper.cpp b/paddle/trainer/tests/test_PyDataProviderWrapper.cpp
index 66ec65e340..92dc8aa9ec 100644
--- a/paddle/trainer/tests/test_PyDataProviderWrapper.cpp
+++ b/paddle/trainer/tests/test_PyDataProviderWrapper.cpp
@@ -25,45 +25,9 @@ limitations under the License. */
 #include <unordered_set>
 #include "picojson.h"
 
-void checkEqual(const paddle::Argument& expect, const paddle::Argument& actual);
 void checkValue(std::vector<paddle::Argument>& arguments, picojson::array& arr);
 const std::string kDir = "./trainer/tests/pydata_provider_wrapper_dir/";
 
-TEST(PyDataProviderWrapper, NoSequenceData) {
-  paddle::DataConfig conf;
-  conf.set_type("py");
-  conf.set_load_data_module(std::string("testPyDataWrapper"));
-  conf.set_load_data_object(std::string("processNonSequenceData"));
-  conf.set_async_load_data(false);
-  conf.clear_files();
-  conf.set_files(kDir + "test_pydata_provider_wrapper.list");
-  paddle::DataProviderPtr provider(paddle::DataProvider::create(conf, false));
-  provider->setSkipShuffle();
-  provider->reset();
-  paddle::DataBatch batchFromPy;
-  provider->getNextBatch(100, &batchFromPy);
-
-  paddle::DataConfig conf2;
-  conf2.set_type("proto");
-  conf2.set_async_load_data(false);
-  conf2.clear_files();
-  conf2.set_files(kDir + "test_pydata_provider_wrapper.protolist");
-
-  provider.reset(paddle::DataProvider::create(conf2, false));
-  provider->setSkipShuffle();
-  provider->reset();
-  paddle::DataBatch batchFromProto;
-  provider->getNextBatch(100, &batchFromProto);
-
-  std::vector<paddle::Argument>& pyArguments = batchFromPy.getStreams();
-  std::vector<paddle::Argument>& protoArguments = batchFromProto.getStreams();
-  EXPECT_EQ(pyArguments.size(), protoArguments.size());
-
-  for (size_t i = 0; i < pyArguments.size(); ++i) {
-    checkEqual(protoArguments[i], pyArguments[i]);
-  }
-}
-
 TEST(PyDataProviderWrapper, SequenceData) {
   paddle::DataConfig conf;
   conf.set_type("py");
@@ -148,66 +112,6 @@ int main(int argc, char** argv) {
   return RUN_ALL_TESTS();
 }
 
-void checkEqual(const paddle::Argument& expect,
-                const paddle::Argument& actual) {
-  if (expect.value) {
-    EXPECT_TRUE(actual.value != nullptr);
-    paddle::Matrix* e = expect.value.get();
-    paddle::Matrix* a = actual.value.get();
-    EXPECT_EQ(e->getWidth(), a->getWidth());
-    EXPECT_EQ(e->getHeight(), a->getHeight());
-    if (dynamic_cast<paddle::CpuSparseMatrix*>(e)) {
-      paddle::CpuSparseMatrix* se = dynamic_cast<paddle::CpuSparseMatrix*>(e);
-      paddle::CpuSparseMatrix* sa = dynamic_cast<paddle::CpuSparseMatrix*>(a);
-      EXPECT_EQ(se->getFormat(), sa->getFormat());
-      EXPECT_EQ(se->getElementCnt(), sa->getElementCnt());
-      size_t rowSize = se->getFormat() == paddle::SPARSE_CSC
-                           ? se->getElementCnt()
-                           : se->getHeight() + 1;
-      size_t colSize = se->getFormat() == paddle::SPARSE_CSC
-                           ? se->getWidth() + 1
-                           : se->getElementCnt();
-      for (size_t i = 0; i < rowSize; ++i) {
-        EXPECT_EQ(se->getRows()[i], sa->getRows()[i]);
-      }
-      for (size_t i = 0; i < colSize; ++i) {
-        EXPECT_EQ(se->getCols()[i], sa->getCols()[i]);
-      }
-      if (se->getValueType() == paddle::FLOAT_VALUE) {
-        EXPECT_EQ(paddle::FLOAT_VALUE, sa->getValueType());
-        for (size_t i = 0; i < se->getElementCnt(); ++i) {
-          EXPECT_EQ(se->getValue()[i], sa->getValue()[i]);
-        }
-      }
-    } else if (dynamic_cast<paddle::CpuMatrix*>(e)) {
-      EXPECT_EQ(e->getElementCnt(), a->getElementCnt());
-      for (size_t i = 0; i < e->getElementCnt(); ++i) {
-        EXPECT_EQ(e->getData()[i], a->getData()[i]);
-      }
-    }
-  }
-
-  if (expect.ids) {
-    EXPECT_TRUE(actual.ids != nullptr);
-    paddle::VectorT<int>* e = expect.ids.get();
-    paddle::VectorT<int>* a = actual.ids.get();
-    EXPECT_EQ(e->getSize(), a->getSize());
-    for (size_t i = 0; i < e->getSize(); ++i) {
-      EXPECT_EQ(e->getData()[i], a->getData()[i]);
-    }
-  }
-
-  if (expect.strs) {
-    EXPECT_TRUE(actual.strs != nullptr);
-    std::vector<std::string>* e = expect.strs.get();
-    std::vector<std::string>* a = actual.strs.get();
-    EXPECT_EQ(e->size(), a->size());
-    for (size_t i = 0; i < e->size(); ++i) {
-      EXPECT_EQ((*e)[i], (*a)[i]);
-    }
-  }
-}
-
 void checkValue(std::vector<paddle::Argument>& arguments,
                 picojson::array& arr) {
   // CHECK SLOT 0, Sparse Value.

From ebb22e5cb7a9548f5015f46323bb611cc7c328c8 Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Fri, 3 Nov 2017 16:49:17 +0800
Subject: [PATCH 03/43] remove ProtoData in sample_trainer_config_opt_*.conf

---
 paddle/trainer/tests/mnist.list               |   1 -
 paddle/trainer/tests/mnist_bin_part           | Bin 3861383 -> 0 bytes
 .../tests/sample_trainer_config_opt_a.conf    |   8 ++++++--
 .../tests/sample_trainer_config_opt_b.conf    |   8 ++++++--
 4 files changed, 12 insertions(+), 5 deletions(-)
 delete mode 100644 paddle/trainer/tests/mnist.list
 delete mode 100644 paddle/trainer/tests/mnist_bin_part

diff --git a/paddle/trainer/tests/mnist.list b/paddle/trainer/tests/mnist.list
deleted file mode 100644
index 703e87753d..0000000000
--- a/paddle/trainer/tests/mnist.list
+++ /dev/null
@@ -1 +0,0 @@
-trainer/tests/mnist_bin_part
diff --git a/paddle/trainer/tests/mnist_bin_part b/paddle/trainer/tests/mnist_bin_part
deleted file mode 100644
index 08b93a0ebb5698bdafbc36c3c757918a50bab621..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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diff --git a/paddle/trainer/tests/sample_trainer_config_opt_a.conf b/paddle/trainer/tests/sample_trainer_config_opt_a.conf
index b1744db8d6..8ece96f595 100644
--- a/paddle/trainer/tests/sample_trainer_config_opt_a.conf
+++ b/paddle/trainer/tests/sample_trainer_config_opt_a.conf
@@ -15,12 +15,16 @@
 from paddle.trainer_config_helpers import *
 
 ################################### Data Configuration ###################################
-TrainData(ProtoData(files = "trainer/tests/mnist.list"))
+TrainData(SimpleData(
+            files = "trainer/tests/sample_filelist.txt",
+            feat_dim = 3,
+            context_len = 0,
+            buffer_capacity = 1000000))
 ################################### Algorithm Configuration ###################################
 settings(batch_size = 1000,
          learning_method = MomentumOptimizer(momentum=0.5, sparse=False))
 ################################### Network Configuration ###################################
-data = data_layer(name ="input", size=784)
+data = data_layer(name ="input", size=3)
 
 fc1 = fc_layer(input=data, size=800,
                bias_attr=True,
diff --git a/paddle/trainer/tests/sample_trainer_config_opt_b.conf b/paddle/trainer/tests/sample_trainer_config_opt_b.conf
index b1744db8d6..8ece96f595 100644
--- a/paddle/trainer/tests/sample_trainer_config_opt_b.conf
+++ b/paddle/trainer/tests/sample_trainer_config_opt_b.conf
@@ -15,12 +15,16 @@
 from paddle.trainer_config_helpers import *
 
 ################################### Data Configuration ###################################
-TrainData(ProtoData(files = "trainer/tests/mnist.list"))
+TrainData(SimpleData(
+            files = "trainer/tests/sample_filelist.txt",
+            feat_dim = 3,
+            context_len = 0,
+            buffer_capacity = 1000000))
 ################################### Algorithm Configuration ###################################
 settings(batch_size = 1000,
          learning_method = MomentumOptimizer(momentum=0.5, sparse=False))
 ################################### Network Configuration ###################################
-data = data_layer(name ="input", size=784)
+data = data_layer(name ="input", size=3)
 
 fc1 = fc_layer(input=data, size=800,
                bias_attr=True,

From 3a507b44bdf41f082145e8c028adfb976c8571ac Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Mon, 13 Nov 2017 17:55:08 +0800
Subject: [PATCH 04/43] add conv3d_trans_cudnn_op

---
 paddle/operators/CMakeLists.txt               | 33 +++++++++++--------
 ...cudnn_op.cc => conv_transpose_cudnn_op.cc} | 11 +++++++
 ...cudnn_op.cu => conv_transpose_cudnn_op.cu} |  5 +++
 3 files changed, 36 insertions(+), 13 deletions(-)
 rename paddle/operators/{conv2d_transpose_cudnn_op.cc => conv_transpose_cudnn_op.cc} (82%)
 rename paddle/operators/{conv2d_transpose_cudnn_op.cu => conv_transpose_cudnn_op.cu} (97%)

diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt
index 709f7de2e4..71740b8b0c 100644
--- a/paddle/operators/CMakeLists.txt
+++ b/paddle/operators/CMakeLists.txt
@@ -55,6 +55,18 @@ function(op_library TARGET)
         set(pybind_flag 1)
     endif()
 
+    if ("${TARGET}" STREQUAL "compare_op")
+        set(pybind_flag 1)
+        file(APPEND ${pybind_file} "USE_OP(less_than);\nUSE_OP(equal);\n")
+    endif()
+
+    # conv_op contains several operators
+    if ("${TARGET}" STREQUAL "conv_op")
+        set(pybind_flag 1)
+        # It's enough to just adding one operator to pybind
+        file(APPEND ${pybind_file} "USE_OP(conv2d);\n")
+    endif()
+
     # pool_op contains several operators
     if ("${TARGET}" STREQUAL "pool_op")
         set(pybind_flag 1)
@@ -62,9 +74,11 @@ function(op_library TARGET)
         file(APPEND ${pybind_file} "USE_OP(pool2d);\n")
     endif()
 
-    if ("${TARGET}" STREQUAL "compare_op")
+    # pool_cudnn_op contains several operators
+    if ("${TARGET}" STREQUAL "pool_cudnn_op")
         set(pybind_flag 1)
-        file(APPEND ${pybind_file} "USE_OP(less_than);\nUSE_OP(equal);\n")
+        # It's enough to just adding one operator to pybind
+        file(APPEND ${pybind_file} "USE_OP(pool2d_cudnn);\n")
     endif()
 
     # pool_with_index_op contains several operators
@@ -74,25 +88,18 @@ function(op_library TARGET)
         file(APPEND ${pybind_file} "USE_OP(max_pool2d_with_index);\n")
     endif()
 
-    # conv_op contains several operators
-    if ("${TARGET}" STREQUAL "conv_op")
-        set(pybind_flag 1)
-        # It's enough to just adding one operator to pybind
-        file(APPEND ${pybind_file} "USE_OP(conv2d);\n")
-    endif()
-
     # conv_transpose_op contains several operators
     if ("${TARGET}" STREQUAL "conv_transpose_op")
         set(pybind_flag 1)
         # It's enough to just adding one operator to pybind
         file(APPEND ${pybind_file} "USE_OP(conv2d_transpose);\n")
     endif()
-    
-    # pool_cudnn_op contains several operators
-    if ("${TARGET}" STREQUAL "pool_cudnn_op")
+
+    # conv_transpose_cudnn_op contains two operators
+    if ("${TARGET}" STREQUAL "conv_transpose_cudnn_op")
         set(pybind_flag 1)
         # It's enough to just adding one operator to pybind
-        file(APPEND ${pybind_file} "USE_OP(pool2d_cudnn);\n")
+        file(APPEND ${pybind_file} "USE_OP(conv2d_transpose_cudnn);\n")
     endif()
 
     # save_restore_op contains several operators
diff --git a/paddle/operators/conv2d_transpose_cudnn_op.cc b/paddle/operators/conv_transpose_cudnn_op.cc
similarity index 82%
rename from paddle/operators/conv2d_transpose_cudnn_op.cc
rename to paddle/operators/conv_transpose_cudnn_op.cc
index fce1357ce5..7ec3319cd0 100644
--- a/paddle/operators/conv2d_transpose_cudnn_op.cc
+++ b/paddle/operators/conv_transpose_cudnn_op.cc
@@ -48,3 +48,14 @@ REGISTER_OP_CPU_KERNEL(
 REGISTER_OP_CPU_KERNEL(
     conv2d_transpose_cudnn_grad,
     ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>);
+
+REGISTER_OP(conv3d_transpose_cudnn, ops::ConvTransposeOp,
+            ops::CudnnConv3DTransposeOpMaker, conv3d_transpose_cudnn_grad,
+            ops::ConvTransposeOpGrad);
+
+REGISTER_OP_CPU_KERNEL(
+    conv3d_transpose_cudnn,
+    ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    conv3d_transpose_cudnn_grad,
+    ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>);
diff --git a/paddle/operators/conv2d_transpose_cudnn_op.cu b/paddle/operators/conv_transpose_cudnn_op.cu
similarity index 97%
rename from paddle/operators/conv2d_transpose_cudnn_op.cu
rename to paddle/operators/conv_transpose_cudnn_op.cu
index 694526ec01..cd31896f2c 100644
--- a/paddle/operators/conv2d_transpose_cudnn_op.cu
+++ b/paddle/operators/conv_transpose_cudnn_op.cu
@@ -237,3 +237,8 @@ REGISTER_OP_GPU_KERNEL(conv2d_transpose_cudnn,
                        ops::CudnnConvTransposeOpKernel<float>);
 REGISTER_OP_GPU_KERNEL(conv2d_transpose_cudnn_grad,
                        ops::CudnnConvTransposeGradOpKernel<float>);
+
+REGISTER_OP_GPU_KERNEL(conv3d_transpose_cudnn,
+                       ops::CudnnConvTransposeOpKernel<float>);
+REGISTER_OP_GPU_KERNEL(conv3d_transpose_cudnn_grad,
+                       ops::CudnnConvTransposeGradOpKernel<float>);

From 6fb4bb8efea3c21ef33b8568069c1cbc2a38a381 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Mon, 13 Nov 2017 17:58:44 +0800
Subject: [PATCH 05/43] add conv3d_trans_cudnn_op unit test

---
 paddle/operators/conv_transpose_cudnn_op.cc   | 19 ++++++++++++++++++-
 .../tests/test_conv3d_transpose_op.py         |  6 ++++++
 2 files changed, 24 insertions(+), 1 deletion(-)

diff --git a/paddle/operators/conv_transpose_cudnn_op.cc b/paddle/operators/conv_transpose_cudnn_op.cc
index 7ec3319cd0..dbd1bc3c3b 100644
--- a/paddle/operators/conv_transpose_cudnn_op.cc
+++ b/paddle/operators/conv_transpose_cudnn_op.cc
@@ -23,7 +23,24 @@ class CudnnConv2DTransposeOpMaker : public Conv2DTransposeOpMaker {
                               framework::OpAttrChecker* op_checker)
       : Conv2DTransposeOpMaker(proto, op_checker) {
     AddAttr<std::vector<int>>("dilations", "dilations of convolution operator.")
-        .SetDefault(std::vector<int>{1, 1});
+        .SetDefault({1, 1});
+    AddAttr<int>("workspace_size_MB",
+                 "workspace size for cudnn, in MB, "
+                 "workspace is a section of GPU memory which will be "
+                 "allocated/freed each time the operator runs, larger "
+                 "workspace size can increase performance but also requires "
+                 "better hardward. This size should be carefully setted.")
+        .SetDefault(4096);
+  }
+};
+
+class CudnnConv3DTransposeOpMaker : public Conv3DTransposeOpMaker {
+ public:
+  CudnnConv3DTransposeOpMaker(framework::OpProto* proto,
+                              framework::OpAttrChecker* op_checker)
+      : Conv3DTransposeOpMaker(proto, op_checker) {
+    AddAttr<std::vector<int>>("dilations", "dilations of convolution operator.")
+        .SetDefault({1, 1, 1});
     AddAttr<int>("workspace_size_MB",
                  "workspace size for cudnn, in MB, "
                  "workspace is a section of GPU memory which will be "
diff --git a/python/paddle/v2/framework/tests/test_conv3d_transpose_op.py b/python/paddle/v2/framework/tests/test_conv3d_transpose_op.py
index 132fe79314..73ee260c5a 100644
--- a/python/paddle/v2/framework/tests/test_conv3d_transpose_op.py
+++ b/python/paddle/v2/framework/tests/test_conv3d_transpose_op.py
@@ -93,5 +93,11 @@ class TestConv3dTransposeOp(OpTest):
         self.op_type = "conv3d_transpose"
 
 
+# ------------ test_cudnn ------------
+class TestCudnn(TestConv3dTransposeOp):
+    def init_op_type(self):
+        self.op_type = "conv3d_transpose_cudnn"
+
+
 if __name__ == '__main__':
     unittest.main()

From b103072dc805ec74727fae37492a5e6d184e6992 Mon Sep 17 00:00:00 2001
From: guosheng <guosheng@baidu.com>
Date: Sat, 11 Nov 2017 10:00:29 +0800
Subject: [PATCH 06/43] Fix data order of H0 in GRU Operator

---
 paddle/operators/gru_op.h                     | 49 +++++++++++++------
 .../paddle/v2/framework/tests/test_gru_op.py  | 18 ++++---
 2 files changed, 44 insertions(+), 23 deletions(-)

diff --git a/paddle/operators/gru_op.h b/paddle/operators/gru_op.h
index ba90ec9816..b2cf358994 100644
--- a/paddle/operators/gru_op.h
+++ b/paddle/operators/gru_op.h
@@ -14,6 +14,7 @@
 
 #pragma once
 
+#include "paddle/operators/lstm_op.h"
 #include "paddle/operators/math/gru_compute.h"
 #include "paddle/operators/math/math_function.h"
 #include "paddle/operators/math/sequence2batch.h"
@@ -24,20 +25,12 @@
 namespace paddle {
 namespace operators {
 
-using Tensor = framework::Tensor;
-using LoDTensor = framework::LoDTensor;
-
-template <typename T, int MajorType = Eigen::RowMajor,
-          typename IndexType = Eigen::DenseIndex>
-using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
-
 template <typename Place, typename T>
 class GRUKernel : public framework::OpKernel<T> {
  public:
   void BatchCompute(const framework::ExecutionContext& context) const {
     auto* input = context.Input<LoDTensor>("Input");
     auto* h0 = context.Input<Tensor>("H0");
-    const T* h0_data = h0 ? h0->data<T>() : nullptr;
     auto* weight = context.Input<Tensor>("Weight");
     const T* weight_data = weight->data<T>();
     auto* bias = context.Input<Tensor>("Bias");
@@ -74,7 +67,18 @@ class GRUKernel : public framework::OpKernel<T> {
     gru_value.gateWeight = const_cast<T*>(weight_data);
     gru_value.stateWeight =
         const_cast<T*>(weight_data + 2 * frame_size * frame_size);
-    gru_value.prevOutValue = const_cast<T*>(h0_data);
+    Tensor ordered_h0;
+    const size_t* order = batch_gate->lod()[2].data();
+    if (h0) {
+      // Since the batch computing for GRU reorders the input sequences
+      // according to their length. The initialized cell state also needs
+      // to reorder.
+      ReorderInitState<Place, T>(context.device_context(), *h0, order,
+                                 &ordered_h0, true);
+      gru_value.prevOutValue = ordered_h0.data<T>();
+    } else {
+      gru_value.prevOutValue = nullptr;
+    }
     auto batch_starts = batch_gate->lod()[0];
     size_t num_batch = batch_starts.size() - 1;
     for (size_t n = 0; n < num_batch; n++) {
@@ -110,7 +114,6 @@ class GRUGradKernel : public framework::OpKernel<T> {
  public:
   void BatchCompute(const framework::ExecutionContext& context) const {
     auto* h0 = context.Input<Tensor>("H0");
-    const T* h0_data = h0 ? h0->data<T>() : nullptr;
     auto* weight = context.Input<Tensor>("Weight");
     const T* weight_data = weight->data<T>();
     auto* batch_gate = context.Input<LoDTensor>("BatchGate");
@@ -143,6 +146,16 @@ class GRUGradKernel : public framework::OpKernel<T> {
     zero(context.device_context(), &batch_reset_hidden_prev_grad,
          static_cast<T>(0.0));
 
+    Tensor ordered_h0, ordered_h0_grad;
+    const size_t* order = batch_gate->lod()[2].data();
+    if (h0) {
+      ReorderInitState<Place, T>(context.device_context(), *h0, order,
+                                 &ordered_h0, true);
+    }
+    if (h0_grad) {
+      ordered_h0_grad.mutable_data<T>(h0_grad->dims(), context.GetPlace());
+    }
+
     bool is_reverse = context.Attr<bool>("is_reverse");
     batch_hidden_grad.set_lod(batch_hidden->lod());
     to_batch(context.device_context(), *hidden_grad, batch_hidden_grad, false,
@@ -185,11 +198,13 @@ class GRUGradKernel : public framework::OpKernel<T> {
           batch_reset_hidden_prev_grad.Slice(bstart, bend);
       gru_grad.resetOutputGrad = reset_hidden_prev_grad_t.data<T>();
       if (n == 0) {
-        gru_value.prevOutValue = const_cast<T*>(h0_data);
-        if (h0_grad) {
-          T* h0_grad_data = h0_grad->mutable_data<T>(context.GetPlace());
-          zero(context.device_context(), h0_grad, static_cast<T>(0.0));
-          gru_grad.prevOutGrad = h0_grad_data;
+        if (h0) {
+          gru_value.prevOutValue = ordered_h0.data<T>();
+        } else {
+          gru_value.prevOutValue = nullptr;
+        }
+        if (h0 && h0_grad) {
+          gru_grad.prevOutGrad = ordered_h0_grad.data<T>();
         } else {
           gru_grad.prevOutGrad = nullptr;
         }
@@ -220,6 +235,10 @@ class GRUGradKernel : public framework::OpKernel<T> {
       auto place = context.GetEigenDevice<Place>();
       d_b.device(place) = d_g.sum(Eigen::array<int, 1>({{0}}));
     }
+    if (h0 && h0_grad) {
+      ReorderInitState<Place, T>(context.device_context(), ordered_h0_grad,
+                                 order, h0_grad, false);
+    }
   }
 
   void Compute(const framework::ExecutionContext& context) const override {
diff --git a/python/paddle/v2/framework/tests/test_gru_op.py b/python/paddle/v2/framework/tests/test_gru_op.py
index b2474cff94..2bb78d10e0 100644
--- a/python/paddle/v2/framework/tests/test_gru_op.py
+++ b/python/paddle/v2/framework/tests/test_gru_op.py
@@ -6,7 +6,8 @@ from test_lstm_op import identity, sigmoid, tanh, relu
 
 
 class TestGRUOp(OpTest):
-    batch_size = 9
+    lod = [[0, 2, 6, 9]]
+    batch_size = lod[0][-1]
     frame_size = 5
     activate = {
         'identity': identity,
@@ -35,7 +36,7 @@ class TestGRUOp(OpTest):
                            seq_starts[sorted_seqs[i]] + batch_idx)
                 idx_in_seq.append(idx)
             idx_in_seq_list.append(idx_in_seq)
-        return idx_in_seq_list
+        return idx_in_seq_list, sorted_seqs
 
     def gru_step(self, x, h_p, w, b):
         batch_size = x.shape[0]
@@ -66,8 +67,8 @@ class TestGRUOp(OpTest):
         batch_hidden = self.outputs['BatchHidden']
         hidden = self.outputs['Hidden']
         idx_in_seq_list = self.idx_in_seq_list
-        h_p = self.inputs['H0'] if self.inputs.has_key('H0') else np.zeros(
-            (len(idx_in_seq_list[0]), self.frame_size))
+        h_p = self.inputs['H0'][self.sorted_seqs] if self.inputs.has_key(
+            'H0') else np.zeros((len(idx_in_seq_list[0]), self.frame_size))
         num_batch = len(idx_in_seq_list)
         end_idx = 0
         for batch_idx in range(num_batch):
@@ -84,8 +85,9 @@ class TestGRUOp(OpTest):
         return batch_gate, batch_reset_hidden_prev, hidden
 
     def set_data(self):
-        lod = [[0, 2, 6, self.batch_size]]
-        self.idx_in_seq_list = self.seq_to_batch(lod, self.is_reverse)
+        lod = self.lod
+        self.idx_in_seq_list, self.sorted_seqs = self.seq_to_batch(
+            lod, self.is_reverse)
         batch_size = self.batch_size
         frame_size = self.frame_size
         input = np.random.rand(batch_size, frame_size * 3).astype('float64')
@@ -146,8 +148,8 @@ class TestGRUOpReverse(TestGRUOp):
     def set_confs(self):
         self.is_reverse = True
         self.attrs = {
-            'activation': 'identity',
-            'gate_activation': 'sigmoid',
+            'activation': 'tanh',
+            'gate_activation': 'tanh',
             'is_reverse': self.is_reverse
         }
 

From cd6d69a95fb1c71aed1d4ada065d91baa61ddffa Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Tue, 14 Nov 2017 20:36:47 +0800
Subject: [PATCH 07/43] modify the test config for test_CompareSparse.cpp

---
 paddle/gserver/tests/CMakeLists.txt           |  12 ++
 paddle/gserver/tests/sequence_lstm.conf       |  64 ++++++++
 .../tests/test_CompareSparse.cpp              |   3 +-
 paddle/trainer/tests/CMakeLists.txt           |  10 --
 .../sample_trainer_config_compare_sparse.conf | 154 ------------------
 5 files changed, 77 insertions(+), 166 deletions(-)
 create mode 100644 paddle/gserver/tests/sequence_lstm.conf
 rename paddle/{trainer => gserver}/tests/test_CompareSparse.cpp (98%)
 delete mode 100644 paddle/trainer/tests/sample_trainer_config_compare_sparse.conf

diff --git a/paddle/gserver/tests/CMakeLists.txt b/paddle/gserver/tests/CMakeLists.txt
index 232fa01568..0ce7ee208b 100644
--- a/paddle/gserver/tests/CMakeLists.txt
+++ b/paddle/gserver/tests/CMakeLists.txt
@@ -95,3 +95,15 @@ add_test(NAME test_PyDataProvider2
    COMMAND .set_python_path.sh -d ${PADDLE_SOURCE_DIR}/paddle/gserver/tests:${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/test_PyDataProvider2
         WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle
 )
+
+################# test_CompareSparse ##################
+add_unittest_without_exec(test_CompareSparse
+    test_CompareSparse.cpp)
+if(NOT ON_TRAVIS)
+  add_test(NAME test_CompareSparse
+    COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d
+          ${PADDLE_SOURCE_DIR}/python:${PADDLE_SOURCE_DIR}/paddle/gserver/tests
+              ./.set_port.sh -p port -n 6
+                  ${CMAKE_CURRENT_BINARY_DIR}/test_CompareSparse
+    WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
+endif()
diff --git a/paddle/gserver/tests/sequence_lstm.conf b/paddle/gserver/tests/sequence_lstm.conf
new file mode 100644
index 0000000000..f49a827f22
--- /dev/null
+++ b/paddle/gserver/tests/sequence_lstm.conf
@@ -0,0 +1,64 @@
+#!/usr/bin/env python
+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddle.trainer_config_helpers import *
+
+######################## data source ################################
+dict_path = 'gserver/tests/Sequence/tour_dict_phrase.dict'
+dict_file = dict()
+for line_count, line in enumerate(open(dict_path, "r")):
+    dict_file[line.strip()] = line_count
+
+define_py_data_sources2(
+    train_list='gserver/tests/Sequence/train.list',
+    test_list=None,
+    module='sequenceGen',
+    obj='process',
+    args={"dict_file": dict_file})
+
+settings(batch_size=5)
+######################## network configure ################################
+dict_dim = len(open(dict_path, 'r').readlines())
+word_dim = 128
+hidden_dim = 256
+label_dim = 3
+sparse_update = get_config_arg("sparse_update", bool, False)
+
+data = data_layer(name="word", size=dict_dim)
+
+emb = embedding_layer(
+    input=data,
+    size=word_dim,
+    param_attr=ParamAttr(sparse_update=sparse_update))
+
+with mixed_layer(size=hidden_dim * 4) as lstm_input:
+    lstm_input += full_matrix_projection(input=emb)
+
+lstm = lstmemory(
+    input=lstm_input,
+    act=TanhActivation(),
+    gate_act=SigmoidActivation(),
+    state_act=TanhActivation())
+
+lstm_last = last_seq(input=lstm)
+
+with mixed_layer(
+        size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
+    output += full_matrix_projection(input=lstm_last)
+
+outputs(
+    classification_cost(
+        input=output, label=data_layer(
+            name="label", size=1)))
diff --git a/paddle/trainer/tests/test_CompareSparse.cpp b/paddle/gserver/tests/test_CompareSparse.cpp
similarity index 98%
rename from paddle/trainer/tests/test_CompareSparse.cpp
rename to paddle/gserver/tests/test_CompareSparse.cpp
index 5f1834bd73..c6e07650fc 100644
--- a/paddle/trainer/tests/test_CompareSparse.cpp
+++ b/paddle/gserver/tests/test_CompareSparse.cpp
@@ -22,8 +22,7 @@ limitations under the License. */
 using namespace paddle;  // NOLINT
 using namespace std;     // NOLINT
 
-static const string& configFile1 =
-    "trainer/tests/sample_trainer_config_compare_sparse.conf";
+static const string& configFile1 = "gserver/tests/sequence_lstm.conf";
 
 DECLARE_bool(use_gpu);
 DECLARE_string(config);
diff --git a/paddle/trainer/tests/CMakeLists.txt b/paddle/trainer/tests/CMakeLists.txt
index f01ad4142d..441df2b57b 100644
--- a/paddle/trainer/tests/CMakeLists.txt
+++ b/paddle/trainer/tests/CMakeLists.txt
@@ -47,16 +47,6 @@ add_test(NAME test_CompareTwoOpts
             --num_passes=1 --need_high_accuracy=0
     WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
 
-################# test_CompareSparse ##################
-add_unittest_without_exec(test_CompareSparse
-    test_CompareSparse.cpp)
-if(NOT ON_TRAVIS)
-  add_test(NAME test_CompareSparse
-    COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python/
-              ./.set_port.sh -p port -n 6
-                  ${CMAKE_CURRENT_BINARY_DIR}/test_CompareSparse
-    WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
-endif()
 ################# test_recurrent_machine_generation ###############
 add_unittest_without_exec(test_recurrent_machine_generation
     test_recurrent_machine_generation.cpp)
diff --git a/paddle/trainer/tests/sample_trainer_config_compare_sparse.conf b/paddle/trainer/tests/sample_trainer_config_compare_sparse.conf
deleted file mode 100644
index 92f32a18c0..0000000000
--- a/paddle/trainer/tests/sample_trainer_config_compare_sparse.conf
+++ /dev/null
@@ -1,154 +0,0 @@
-#edit-mode: -*- python -*-
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-#Todo(luotao02) This config is only used for unitest. It is out of date now, and will be updated later.
-
-# Note: when making change to this file, please make sure
-# sample_trainer_config_rnn.conf is changed accordingly so that the uniitest
-# for comparing these two nets can pass (test_CompareTwoNets)
-
-default_initial_std(0.1)
-default_device(0)
-
-word_dim = 999
-l1 = 0
-l2 = 0
-
-model_type("nn")
-
-sparse_update = get_config_arg("sparse_update", bool, False)
-
-TrainData(ProtoData(        
-            type = "proto_sequence",
-            files = ('trainer/tests/train_sparse.list'), 
-            ))
-
-Settings(
-    algorithm='sgd',
-    batch_size=100,
-    learning_rate=0.0001,
-    learning_rate_decay_a=4e-08,
-    learning_rate_decay_b=0.0,
-    learning_rate_schedule='poly',
-)
-
-
-wordvec_dim = 32
-layer2_dim = 16
-layer3_dim = 16
-hidden_dim = 32
-
-slot_names = ["qb", "qw", "tb", "tw"]
-
-def ltr_network(network_name,
-                word_dim=word_dim,
-                wordvec_dim=wordvec_dim,
-                layer2_dim=layer2_dim,
-                layer3_dim=layer3_dim,
-                hidden_dim=hidden_dim,
-                slot_names=slot_names,
-                l1=l1,
-                l2=l2):
-
-    slotnum = len(slot_names)
-    for i in xrange(slotnum):
-        Inputs(slot_names[i] + network_name)
-    for i in xrange(slotnum):
-        Layer(
-            name = slot_names[i] + network_name,
-            type = "data",
-            size = word_dim,
-            device = -1,
-        )
-        Layer(
-            name = slot_names[i] + "_embedding_" + network_name,
-            type = "mixed",
-            size = wordvec_dim,
-            bias = False,
-            device = -1,
-            inputs = TableProjection(slot_names[i] + network_name,
-                                     parameter_name = "embedding.w0",
-                                     decay_rate_l1=l1,
-                                     sparse_remote_update = True,
-                                     sparse_update = sparse_update,
-                                     ),
-        )
-        Layer(
-            name = slot_names[i] + "_rnn1_" + network_name,
-            type = "recurrent",
-            active_type = "tanh",
-            bias = Bias(initial_std = 0,
-                        parameter_name = "rnn1.bias"),
-            inputs = Input(slot_names[i] + "_embedding_" + network_name,
-                           parameter_name = "rnn1.w0")
-        )
-        Layer(
-            name = slot_names[i] + "_rnnlast_" + network_name,
-            type = "seqlastins",
-            inputs = [
-                slot_names[i] + "_rnn1_" + network_name,
-            ],
-        )
-
-    Layer(
-        name = "layer2_" + network_name,
-        type = "fc",
-        active_type = "tanh",
-        size = layer2_dim,
-        bias = Bias(parameter_name = "layer2.bias"),
-        inputs = [Input(slot_name + "_rnnlast_" + network_name, 
-                        parameter_name = "_layer2_" + slot_name + ".w", 
-                        decay_rate = l2, 
-                        initial_smart = True) for slot_name in slot_names]
-    )
-    Layer(
-        name = "layer3_" + network_name,
-        type = "fc",
-        active_type = "tanh",
-        size = layer3_dim,
-        bias = Bias(parameter_name = "layer3.bias"),
-        inputs = [
-            Input("layer2_" + network_name, 
-                  parameter_name = "_layer3.w", 
-                  decay_rate = l2, 
-                  initial_smart = True),
-        ]
-    )
-    Layer(
-        name = "output_" + network_name,
-        type = "fc",
-        size = 1,
-        bias = False,
-        inputs = [
-                  Input("layer3_" + network_name,
-                       parameter_name = "_layerO.w"),
-                 ],
-        )
-
-
-ltr_network("left")
-ltr_network("right")
-Inputs("label")
-Layer(
-    name = "label",
-    type = "data",
-    size = 1,
-    )
-Outputs("cost", "qb_rnnlast_left")
-Layer(
-    name = "cost",
-    type = "rank-cost",
-    inputs = ["output_left", "output_right", "label"],
-    )

From 74912c7d4ed83c78c4c3076d306fae3923c5432f Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Wed, 15 Nov 2017 15:37:40 +0800
Subject: [PATCH 08/43] fix data layout

---
 paddle/operators/conv_transpose_cudnn_op.cu | 20 +++++++++++++-------
 paddle/platform/cudnn_helper.h              | 13 +++++++------
 2 files changed, 20 insertions(+), 13 deletions(-)

diff --git a/paddle/operators/conv_transpose_cudnn_op.cu b/paddle/operators/conv_transpose_cudnn_op.cu
index cd31896f2c..00e0ec255d 100644
--- a/paddle/operators/conv_transpose_cudnn_op.cu
+++ b/paddle/operators/conv_transpose_cudnn_op.cu
@@ -54,15 +54,21 @@ class CudnnConvTransposeOpKernel : public framework::OpKernel<T> {
     ScopedTensorDescriptor output_desc;
     ScopedFilterDescriptor filter_desc;
     ScopedConvolutionDescriptor conv_desc;
-    DataLayout layout = DataLayout::kNCHW;
+    DataLayout layout;
+
+    if (strides.size() == 2U) {
+      layout = DataLayout::kNCHW;
+    } else {
+      layout = DataLayout::kNCDHW;
+    }
 
-    // N, M, H, W
+    // (N, M, H, W) or (N, M, D, H, W)
     cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
         layout, framework::vectorize2int(input->dims()));
-    // N, C, O_h, O_w
+    // (N, C, O_h, O_w) or (N, C, O_d, O_h, O_w)
     cudnnTensorDescriptor_t cudnn_output_desc = output_desc.descriptor<T>(
         layout, framework::vectorize2int(output->dims()));
-    // M, C, K_h, K_w
+    // (M, C, K_h, K_w) or (M, C, K_d, K_h, K_w)
     cudnnFilterDescriptor_t cudnn_filter_desc = filter_desc.descriptor<T>(
         layout, framework::vectorize2int(filter->dims()));
     cudnnConvolutionDescriptor_t cudnn_conv_desc =
@@ -136,13 +142,13 @@ class CudnnConvTransposeGradOpKernel : public framework::OpKernel<T> {
     ScopedConvolutionDescriptor conv_desc;
     DataLayout layout = DataLayout::kNCHW;
 
-    // Input: (N, M, H, W)
+    // Input: (N, M, H, W) or (N, M, D, H, W)
     cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
         layout, framework::vectorize2int(input->dims()));
-    // Output: (N, C, O_H, O_W)
+    // Output: (N, C, O_h, O_w) or (N, C, O_d, O_h, O_w)
     cudnnTensorDescriptor_t cudnn_output_desc = output_desc.descriptor<T>(
         layout, framework::vectorize2int(output_grad->dims()));
-    // Filter (M, C, K_H, K_W)
+    // Filter (M, C, K_h, K_w) or (M, C, K_d K_h, K_w)
     cudnnFilterDescriptor_t cudnn_filter_desc = filter_desc.descriptor<T>(
         layout, framework::vectorize2int(filter->dims()));
 
diff --git a/paddle/platform/cudnn_helper.h b/paddle/platform/cudnn_helper.h
index ce3421a3cb..8d75fceae8 100644
--- a/paddle/platform/cudnn_helper.h
+++ b/paddle/platform/cudnn_helper.h
@@ -1,11 +1,8 @@
 /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
-
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
-
     http://www.apache.org/licenses/LICENSE-2.0
-
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
@@ -63,9 +60,10 @@ inline const char* cudnnGetErrorString(cudnnStatus_t status) {
     }                                                             \
   } while (false)
 
-enum class DataLayout {
+enum class DataLayout {  // Not use
   kNHWC,
   kNCHW,
+  kNCDHW,
   kNCHW_VECT_C,
 };
 
@@ -107,12 +105,15 @@ class CudnnDataType<double> {
   }
 };
 
-inline cudnnTensorFormat_t GetCudnnTensorFormat(const DataLayout& order) {
+inline cudnnTensorFormat_t GetCudnnTensorFormat(
+    const DataLayout& order) {  // Not use
   switch (order) {
     case DataLayout::kNHWC:
       return CUDNN_TENSOR_NHWC;
     case DataLayout::kNCHW:
       return CUDNN_TENSOR_NCHW;
+    case DataLayout::kNCDHW:
+      return CUDNN_TENSOR_NCHW;  // TODO(chengduoZH) : add CUDNN_TENSOR_NCDHW
     default:
       PADDLE_THROW("Unknown cudnn equivalent for order");
   }
@@ -139,7 +140,7 @@ class ScopedTensorDescriptor {
       strides[i] = dims[i + 1] * strides[i + 1];
     }
     // Update tensor descriptor dims setting if groups > 1
-    // FIXME(typhoonzero): Assume using NCHW order
+    // FIXME(typhoonzero): Assume using NCHW or NCDHW order
     std::vector<int> dims_with_group(dims.begin(), dims.end());  // copy
     if (groups > 1) {
       dims_with_group[1] = dims_with_group[1] / groups;

From 212f6eae774438693231cc90ae0a81a561331dc1 Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Wed, 15 Nov 2017 18:04:11 +0800
Subject: [PATCH 09/43] modify the test config for test_CompareTwoNets.cpp

---
 paddle/gserver/tests/CMakeLists.txt           |   9 +
 paddle/gserver/tests/sequence_recurrent.py    |  56 ++++++
 .../gserver/tests/sequence_recurrent_group.py |  70 +++++++
 .../tests/test_CompareTwoNets.cpp             |  11 +-
 paddle/trainer/tests/CMakeLists.txt           |   8 -
 .../tests/sample_trainer_config_qb_rnn.conf   | 154 ---------------
 .../tests/sample_trainer_config_rnn.conf      | 180 ------------------
 7 files changed, 142 insertions(+), 346 deletions(-)
 create mode 100644 paddle/gserver/tests/sequence_recurrent.py
 create mode 100644 paddle/gserver/tests/sequence_recurrent_group.py
 rename paddle/{trainer => gserver}/tests/test_CompareTwoNets.cpp (95%)
 delete mode 100644 paddle/trainer/tests/sample_trainer_config_qb_rnn.conf
 delete mode 100644 paddle/trainer/tests/sample_trainer_config_rnn.conf

diff --git a/paddle/gserver/tests/CMakeLists.txt b/paddle/gserver/tests/CMakeLists.txt
index 45edef017e..09e1b949c2 100644
--- a/paddle/gserver/tests/CMakeLists.txt
+++ b/paddle/gserver/tests/CMakeLists.txt
@@ -111,3 +111,12 @@ if(NOT ON_TRAVIS)
                   ${CMAKE_CURRENT_BINARY_DIR}/test_CompareSparse
     WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
 endif()
+
+################ test_CompareTwoNets ######################
+add_unittest_without_exec(test_CompareTwoNets
+    test_CompareTwoNets.cpp)
+add_test(NAME test_CompareTwoNets
+  COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d
+        ${PADDLE_SOURCE_DIR}/python:${PADDLE_SOURCE_DIR}/paddle/gserver/tests
+        ${CMAKE_CURRENT_BINARY_DIR}/test_CompareTwoNets
+    WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
diff --git a/paddle/gserver/tests/sequence_recurrent.py b/paddle/gserver/tests/sequence_recurrent.py
new file mode 100644
index 0000000000..4895df186b
--- /dev/null
+++ b/paddle/gserver/tests/sequence_recurrent.py
@@ -0,0 +1,56 @@
+#!/usr/bin/env python
+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddle.trainer_config_helpers import *
+
+######################## data source ################################
+dict_path = 'gserver/tests/Sequence/tour_dict_phrase.dict'
+dict_file = dict()
+for line_count, line in enumerate(open(dict_path, "r")):
+    dict_file[line.strip()] = line_count
+
+define_py_data_sources2(
+    train_list='gserver/tests/Sequence/train.list',
+    test_list=None,
+    module='sequenceGen',
+    obj='process',
+    args={"dict_file": dict_file})
+
+settings(batch_size=5)
+######################## network configure ################################
+dict_dim = len(open(dict_path, 'r').readlines())
+word_dim = 128
+hidden_dim = 128
+label_dim = 3
+
+# This config is designed to be equivalent with sequence_recurrent_group.py
+
+data = data_layer(name="word", size=dict_dim)
+
+emb = embedding_layer(
+    input=data, size=word_dim, param_attr=ParamAttr(name="emb"))
+
+recurrent = recurrent_layer(input=emb, bias_attr=False, act=SoftmaxActivation())
+
+recurrent_last = last_seq(input=recurrent)
+
+with mixed_layer(
+        size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
+    output += full_matrix_projection(input=recurrent_last)
+
+outputs(
+    classification_cost(
+        input=output, label=data_layer(
+            name="label", size=1)))
diff --git a/paddle/gserver/tests/sequence_recurrent_group.py b/paddle/gserver/tests/sequence_recurrent_group.py
new file mode 100644
index 0000000000..a1d54542e3
--- /dev/null
+++ b/paddle/gserver/tests/sequence_recurrent_group.py
@@ -0,0 +1,70 @@
+#!/usr/bin/env python
+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddle.trainer_config_helpers import *
+
+######################## data source ################################
+dict_path = 'gserver/tests/Sequence/tour_dict_phrase.dict'
+dict_file = dict()
+for line_count, line in enumerate(open(dict_path, "r")):
+    dict_file[line.strip()] = line_count
+
+define_py_data_sources2(
+    train_list='gserver/tests/Sequence/train.list',
+    test_list=None,
+    module='sequenceGen',
+    obj='process',
+    args={"dict_file": dict_file})
+
+settings(batch_size=5)
+######################## network configure ################################
+dict_dim = len(open(dict_path, 'r').readlines())
+word_dim = 128
+hidden_dim = 128
+label_dim = 3
+
+# This config is designed to be equivalent with sequence_recurrent.py
+
+data = data_layer(name="word", size=dict_dim)
+
+emb = embedding_layer(
+    input=data, size=word_dim, param_attr=ParamAttr(name="emb"))
+
+
+def step(y):
+    mem = memory(name="rnn_state", size=hidden_dim)
+    with mixed_layer(
+            name="rnn_state",
+            size=hidden_dim,
+            bias_attr=False,
+            act=SoftmaxActivation()) as out:
+        out += identity_projection(input=y)
+        out += full_matrix_projection(
+            input=mem, param_attr=ParamAttr(name="___recurrent_layer_0__"))
+    return out
+
+
+recurrent = recurrent_group(name="rnn", step=step, input=emb)
+
+recurrent_last = last_seq(input=recurrent)
+
+with mixed_layer(
+        size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
+    output += full_matrix_projection(input=recurrent_last)
+
+outputs(
+    classification_cost(
+        input=output, label=data_layer(
+            name="label", size=1)))
diff --git a/paddle/trainer/tests/test_CompareTwoNets.cpp b/paddle/gserver/tests/test_CompareTwoNets.cpp
similarity index 95%
rename from paddle/trainer/tests/test_CompareTwoNets.cpp
rename to paddle/gserver/tests/test_CompareTwoNets.cpp
index 94f65e545d..801d960756 100644
--- a/paddle/trainer/tests/test_CompareTwoNets.cpp
+++ b/paddle/gserver/tests/test_CompareTwoNets.cpp
@@ -30,8 +30,6 @@ DECLARE_bool(use_gpu);
 DECLARE_string(config);
 DECLARE_string(nics);
 
-DEFINE_string(config_file_a, "", "config of one network to compare");
-DEFINE_string(config_file_b, "", "config of another network to compare");
 DEFINE_bool(need_high_accuracy,
             false,
             "whether need to run in double accuracy");
@@ -42,6 +40,10 @@ DEFINE_double(
 DECLARE_bool(thread_local_rand_use_global_seed);
 DECLARE_int32(seed);
 
+static const string& config_file_a = "gserver/tests/sequence_recurrent.py";
+static const string& config_file_b =
+    "gserver/tests/sequence_recurrent_group.py";
+
 struct ComData {
   vector<Argument> outArgs;
   vector<ParameterPtr> parameters;
@@ -66,6 +68,7 @@ void calcGradient(ComData& data, const string configFile) {
   DataBatch dataBatch;
   int32_t batchSize = trainer.getConfig().opt_config().batch_size();
 
+  trainer.getDataProvider()->reset();
   trainer.getDataProvider()->setSkipShuffle();
   trainer.getDataProvider()->getNextBatch(batchSize, &dataBatch);
 
@@ -167,11 +170,11 @@ void compareGradient(ComData& comDataA, ComData& comDataB) {
 
 TEST(Trainer, create) {
   ComData dataA;
-  calcGradient(dataA, FLAGS_config_file_a);
+  calcGradient(dataA, config_file_a);
   LOG(INFO) << "\n\nforwardBackward of Network A is finished\n\n";
 
   ComData dataB;
-  calcGradient(dataB, FLAGS_config_file_b);
+  calcGradient(dataB, config_file_b);
   LOG(INFO) << "\n\nforwardBackward of the Network B is finished\n\n";
 
   compareGradient(dataA, dataB);
diff --git a/paddle/trainer/tests/CMakeLists.txt b/paddle/trainer/tests/CMakeLists.txt
index 441df2b57b..3168f3c0ff 100644
--- a/paddle/trainer/tests/CMakeLists.txt
+++ b/paddle/trainer/tests/CMakeLists.txt
@@ -28,14 +28,6 @@ if(WITH_PYTHON)
           ${PADDLE_SOURCE_DIR}/paddle/.set_port.sh -p port ${CMAKE_CURRENT_BINARY_DIR}/test_TrainerOnePass
       WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
 endif()
-################ test_CompareTwoNets ######################
-add_unittest_without_exec(test_CompareTwoNets
-    test_CompareTwoNets.cpp)
-add_test(NAME test_CompareTwoNets
-  COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python/
-        ${CMAKE_CURRENT_BINARY_DIR}/test_CompareTwoNets
-            --config_file_a=trainer/tests/sample_trainer_config_qb_rnn.conf --config_file_b=trainer/tests/sample_trainer_config_rnn.conf
-    WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
 
 ############### test_CompareTwoOpts ###################
 add_unittest_without_exec(test_CompareTwoOpts
diff --git a/paddle/trainer/tests/sample_trainer_config_qb_rnn.conf b/paddle/trainer/tests/sample_trainer_config_qb_rnn.conf
deleted file mode 100644
index d19222360c..0000000000
--- a/paddle/trainer/tests/sample_trainer_config_qb_rnn.conf
+++ /dev/null
@@ -1,154 +0,0 @@
-#edit-mode: -*- python -*-
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-#Todo(luotao02) This config is only used for unitest. It is out of date now, and will be updated later.
-
-# Note: when making change to this file, please make sure
-# sample_trainer_config_rnn.conf is changed accordingly so that the uniitest
-# for comparing these two nets can pass (test_CompareTwoNets)
-
-default_initial_std(0.1)
-default_device(0)
-
-word_dim = 1451594
-l1 = 0
-l2 = 0
-
-model_type("nn")
-
-sparse_update = get_config_arg("sparse_update", bool, False)
-
-TrainData(ProtoData(        
-            type = "proto_sequence",
-            files = ('trainer/tests/train.list'), 
-            ))
-
-Settings(
-    algorithm='sgd',
-    batch_size=100,
-    learning_rate=0.0001,
-    learning_rate_decay_a=4e-08,
-    learning_rate_decay_b=0.0,
-    learning_rate_schedule='poly',
-)
-
-
-wordvec_dim = 128
-layer2_dim = 96
-layer3_dim = 96
-hidden_dim = 128
-
-slot_names = ["qb", "qw", "tb", "tw"]
-
-def ltr_network(network_name,
-                word_dim=word_dim,
-                wordvec_dim=wordvec_dim,
-                layer2_dim=layer2_dim,
-                layer3_dim=layer3_dim,
-                hidden_dim=hidden_dim,
-                slot_names=slot_names,
-                l1=l1,
-                l2=l2):
-
-    slotnum = len(slot_names)
-    for i in xrange(slotnum):
-        Inputs(slot_names[i] + network_name)
-    for i in xrange(slotnum):
-        Layer(
-            name = slot_names[i] + network_name,
-            type = "data",
-            size = word_dim,
-            device = -1,
-        )
-        Layer(
-            name = slot_names[i] + "_embedding_" + network_name,
-            type = "mixed",
-            size = wordvec_dim,
-            bias = False,
-            device = -1,
-            inputs = TableProjection(slot_names[i] + network_name,
-                                     parameter_name = "embedding.w0",
-                                     decay_rate_l1=l1,
-                                     sparse_remote_update = True,
-                                     sparse_update = sparse_update,
-                                     ),
-        )
-        Layer(
-            name = slot_names[i] + "_rnn1_" + network_name,
-            type = "recurrent",
-            active_type = "tanh",
-            bias = Bias(initial_std = 0,
-                        parameter_name = "rnn1.bias"),
-            inputs = Input(slot_names[i] + "_embedding_" + network_name,
-                           parameter_name = "rnn1.w0")
-        )
-        Layer(
-            name = slot_names[i] + "_rnnlast_" + network_name,
-            type = "seqlastins",
-            inputs = [
-                slot_names[i] + "_rnn1_" + network_name,
-            ],
-        )
-
-    Layer(
-        name = "layer2_" + network_name,
-        type = "fc",
-        active_type = "tanh",
-        size = layer2_dim,
-        bias = Bias(parameter_name = "layer2.bias"),
-        inputs = [Input(slot_name + "_rnnlast_" + network_name, 
-                        parameter_name = "_layer2_" + slot_name + ".w", 
-                        decay_rate = l2, 
-                        initial_smart = True) for slot_name in slot_names]
-    )
-    Layer(
-        name = "layer3_" + network_name,
-        type = "fc",
-        active_type = "tanh",
-        size = layer3_dim,
-        bias = Bias(parameter_name = "layer3.bias"),
-        inputs = [
-            Input("layer2_" + network_name, 
-                  parameter_name = "_layer3.w", 
-                  decay_rate = l2, 
-                  initial_smart = True),
-        ]
-    )
-    Layer(
-        name = "output_" + network_name,
-        type = "fc",
-        size = 1,
-        bias = False,
-        inputs = [
-                  Input("layer3_" + network_name,
-                       parameter_name = "_layerO.w"),
-                 ],
-        )
-
-
-ltr_network("left")
-ltr_network("right")
-Inputs("label")
-Layer(
-    name = "label",
-    type = "data",
-    size = 1,
-    )
-Outputs("cost", "qb_rnnlast_left")
-Layer(
-    name = "cost",
-    type = "rank-cost",
-    inputs = ["output_left", "output_right", "label"],
-    )
diff --git a/paddle/trainer/tests/sample_trainer_config_rnn.conf b/paddle/trainer/tests/sample_trainer_config_rnn.conf
deleted file mode 100644
index b720d4d5a6..0000000000
--- a/paddle/trainer/tests/sample_trainer_config_rnn.conf
+++ /dev/null
@@ -1,180 +0,0 @@
-#edit-mode: -*- python -*-
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-#Todo(luotao02) This config is only used for unitest. It is out of date now, and will be updated later.
-
-# Note: when making change to this file, please make sure
-# sample_trainer_config_qb_rnn.conf is changed accordingly so that the uniitest
-# for comparing these two nets can pass (test_CompareTwoNets)
-
-default_initial_std(0.1)
-default_device(0)
-
-word_dim = 1451594
-l1 = 0
-l2 = 0
-
-model_type("recurrent_nn")
-
-sparse_update = get_config_arg("sparse_update", bool, False)
-
-TrainData(ProtoData(
-            type = "proto_sequence",
-            files = ('trainer/tests/train.list'), 
-            ))
-
-Settings(
-    algorithm='sgd',
-    batch_size=100,
-    learning_rate=0.0001,
-    learning_rate_decay_a=4e-08,
-    learning_rate_decay_b=0.0,
-    learning_rate_schedule='poly',
-)
-
-
-wordvec_dim = 128
-layer2_dim = 96
-layer3_dim = 96
-hidden_dim = 128
-
-slot_names = ["qb", "qw", "tb", "tw"]
-
-def SimpleRecurrentLayer(name, 
-                         size, 
-                         active_type, 
-                         bias, 
-                         input_layer_name, 
-                         parameter_name,
-                         seq_reversed = False):
-    RecurrentLayerGroupBegin(name + "_layer_group", 
-                             in_links=[input_layer_name], 
-                             out_links=[name],
-                             seq_reversed=seq_reversed)
-    memory_name = Memory(name=name, size=size)
-    Layer(
-        name = name,
-        type = "mixed",
-        size = size,
-        active_type = active_type,
-        bias = bias,
-        inputs = [IdentityProjection(input_layer_name),
-                  FullMatrixProjection(memory_name,
-                                       parameter_name = parameter_name,
-                                       ),
-                  ]
-        )
-    RecurrentLayerGroupEnd(name + "_layer_group")
-
-
-def ltr_network(network_name,
-                word_dim=word_dim,
-                wordvec_dim=wordvec_dim,
-                layer2_dim=layer2_dim,
-                layer3_dim=layer3_dim,
-                hidden_dim=hidden_dim,
-                slot_names=slot_names,
-                l1=l1,
-                l2=l2):
-
-    slotnum = len(slot_names)
-    for i in xrange(slotnum):
-        Inputs(slot_names[i] + network_name)
-    for i in xrange(slotnum):
-        Layer(
-            name = slot_names[i] + network_name,
-            type = "data",
-            size = word_dim,
-            device = -1,
-        )
-        Layer(
-            name = slot_names[i] + "_embedding_" + network_name,
-            type = "mixed",
-            size = wordvec_dim,
-            bias = False,
-            device = -1,
-            inputs = TableProjection(slot_names[i] + network_name,
-                                     parameter_name = "embedding.w0",
-                                     decay_rate_l1=l1,
-                                     sparse_remote_update = True,
-                                     sparse_update = sparse_update,
-                                     ),
-        )
-        SimpleRecurrentLayer(
-            name = slot_names[i] + "_rnn1_" + network_name,
-            size = hidden_dim,
-            active_type = "tanh",
-            bias = Bias(initial_std = 0,
-                        parameter_name = "rnn1.bias"),
-            input_layer_name = slot_names[i] + "_embedding_" + network_name,
-            parameter_name = "rnn1.w0",
-            )
-        Layer(
-            name = slot_names[i] + "_rnnlast_" + network_name,
-            type = "seqlastins",
-            inputs = [
-                slot_names[i] + "_rnn1_" + network_name,
-            ],
-        )
-    Layer(
-        name = "layer2_" + network_name,
-        type = "fc",
-        active_type = "tanh",
-        size = layer2_dim,
-        bias = Bias(parameter_name = "layer2.bias"),
-        inputs = [Input(slot_name + "_rnnlast_" + network_name, 
-                        parameter_name = "_layer2_" + slot_name + ".w", 
-                        decay_rate = l2, 
-                        initial_smart = True) for slot_name in slot_names]
-    )
-    Layer(
-        name = "layer3_" + network_name,
-        type = "fc",
-        active_type = "tanh",
-        size = layer3_dim,
-        bias = Bias(parameter_name = "layer3.bias"),
-        inputs = [
-            Input("layer2_" + network_name, 
-                  parameter_name = "_layer3.w", 
-                  decay_rate = l2, 
-                  initial_smart = True),
-        ]
-    )
-    Layer(
-        name = "output_" + network_name,
-        type = "fc",
-        size = 1,
-        bias = False,
-        inputs = [
-                  Input("layer3_" + network_name,
-                       parameter_name = "_layerO.w"),
-                 ],
-        )
-
-
-ltr_network("left")
-ltr_network("right")
-Inputs("label")
-Layer(
-    name = "label",
-    type = "data",
-    size = 1,
-    )
-Outputs("cost", "qb_rnnlast_left")
-Layer(
-    name = "cost",
-    type = "rank-cost",
-    inputs = ["output_left", "output_right", "label"],
-    )

From 75426e013a8af9a327a1c47008719053a4df8dff Mon Sep 17 00:00:00 2001
From: guosheng <guosheng@baidu.com>
Date: Thu, 16 Nov 2017 11:24:08 +0800
Subject: [PATCH 10/43] Refine GRU Operator

---
 paddle/operators/gru_op.h                       | 1 +
 python/paddle/v2/framework/tests/test_gru_op.py | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/paddle/operators/gru_op.h b/paddle/operators/gru_op.h
index b2cf358994..9fb60e20d1 100644
--- a/paddle/operators/gru_op.h
+++ b/paddle/operators/gru_op.h
@@ -154,6 +154,7 @@ class GRUGradKernel : public framework::OpKernel<T> {
     }
     if (h0_grad) {
       ordered_h0_grad.mutable_data<T>(h0_grad->dims(), context.GetPlace());
+      zero(context.device_context(), &ordered_h0_grad, static_cast<T>(0.0));
     }
 
     bool is_reverse = context.Attr<bool>("is_reverse");
diff --git a/python/paddle/v2/framework/tests/test_gru_op.py b/python/paddle/v2/framework/tests/test_gru_op.py
index 2bb78d10e0..fa2c5a53ec 100644
--- a/python/paddle/v2/framework/tests/test_gru_op.py
+++ b/python/paddle/v2/framework/tests/test_gru_op.py
@@ -149,7 +149,7 @@ class TestGRUOpReverse(TestGRUOp):
         self.is_reverse = True
         self.attrs = {
             'activation': 'tanh',
-            'gate_activation': 'tanh',
+            'gate_activation': 'sigmoid',
             'is_reverse': self.is_reverse
         }
 

From 9acfba82a37d06aeafaaacccc30b6e2df56354ed Mon Sep 17 00:00:00 2001
From: tensor-tang <jian.j.tang@intel.com>
Date: Thu, 16 Nov 2017 11:46:31 +0800
Subject: [PATCH 11/43] add input index choice for mkldnn_concat

---
 paddle/gserver/layers/MKLDNNLayer.cpp | 7 +++++--
 paddle/gserver/layers/MKLDNNLayer.h   | 5 ++++-
 2 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/paddle/gserver/layers/MKLDNNLayer.cpp b/paddle/gserver/layers/MKLDNNLayer.cpp
index e75ac5ba46..0d063a89cc 100644
--- a/paddle/gserver/layers/MKLDNNLayer.cpp
+++ b/paddle/gserver/layers/MKLDNNLayer.cpp
@@ -138,8 +138,11 @@ void MKLDNNLayer::backward(const UpdateCallback& callback) {
   }
 }
 
-void MKLDNNLayer::reshapeInput(int& batchsize, int& height, int& width) {
-  const Argument& input = inputLayers_[0]->getOutput();
+void MKLDNNLayer::reshapeInput(int& batchsize,
+                               int& height,
+                               int& width,
+                               size_t inputIdx) {
+  const Argument& input = inputLayers_[inputIdx]->getOutput();
   batchsize = input.getBatchSize();
   int h = input.getFrameHeight();
   int w = input.getFrameWidth();
diff --git a/paddle/gserver/layers/MKLDNNLayer.h b/paddle/gserver/layers/MKLDNNLayer.h
index 7479c34c92..4c42df1bee 100644
--- a/paddle/gserver/layers/MKLDNNLayer.h
+++ b/paddle/gserver/layers/MKLDNNLayer.h
@@ -178,7 +178,10 @@ protected:
   /**
    * reshape the input image sizes and input batchsize
    */
-  void reshapeInput(int& batchsize, int& height, int& width);
+  void reshapeInput(int& batchsize,
+                    int& height,
+                    int& width,
+                    size_t inputIdx = 0);
 
   /**
    * reshape output image sizes

From c66b5ce2c11ecc00b1211c7ae9c762880c6ed4e4 Mon Sep 17 00:00:00 2001
From: tensor-tang <jian.j.tang@intel.com>
Date: Thu, 16 Nov 2017 11:55:12 +0800
Subject: [PATCH 12/43] add mkldnn concat layer

---
 paddle/gserver/layers/MKLDNNConcatLayer.cpp | 190 ++++++++++++++++++++
 paddle/gserver/layers/MKLDNNConcatLayer.h   | 129 +++++++++++++
 2 files changed, 319 insertions(+)
 create mode 100644 paddle/gserver/layers/MKLDNNConcatLayer.cpp
 create mode 100644 paddle/gserver/layers/MKLDNNConcatLayer.h

diff --git a/paddle/gserver/layers/MKLDNNConcatLayer.cpp b/paddle/gserver/layers/MKLDNNConcatLayer.cpp
new file mode 100644
index 0000000000..64946508d2
--- /dev/null
+++ b/paddle/gserver/layers/MKLDNNConcatLayer.cpp
@@ -0,0 +1,190 @@
+/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "MKLDNNConcatLayer.h"
+
+using namespace mkldnn;  // NOLINT
+typedef memory::format format;
+
+namespace paddle {
+
+REGISTER_LAYER(mkldnn_concat, MKLDNNConcatLayer);
+
+bool MKLDNNConcatLayer::init(const LayerMap& layerMap,
+                             const ParameterMap& parameterMap) {
+  if (!MKLDNNLayer::init(layerMap, parameterMap)) {
+    return false;
+  }
+  CHECK_GT(inputLayers_.size(), 1UL);
+  CHECK(!biasParameter_);
+  return true;
+}
+
+void MKLDNNConcatLayer::reshape(
+    int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
+  reshapeInput(bs, ih, iw);
+  ic = inputLayers_[0]->getSize() / ih / iw;
+  CHECK_EQ((size_t)ic * ih * iw, inputLayers_[0]->getSize());
+  CHECK_EQ(inputElemenCnt_, (size_t)bs * ic * ih * iw);
+  CHECK_GT(inputLayers_.size(), 1UL);
+  channels_.resize(inputLayers_.size());
+  channels_[0] = ic;
+  oc = ic;
+  for (size_t i = 1; i < inputLayers_.size(); i++) {
+    int batchsize, height, witdh;
+    reshapeInput(batchsize, height, witdh, i);
+    CHECK_EQ(bs, batchsize);
+    CHECK_EQ(ih, height);
+    CHECK_EQ(iw, witdh);
+
+    channels_[i] = inputLayers_[i]->getSize() / height / witdh;
+    CHECK_EQ((size_t)channels_[i] * height * witdh, inputLayers_[i]->getSize());
+    oc += channels_[i];
+  }
+  oh = ih;
+  ow = iw;
+  reshapeOutput(oh, ow);
+  resizeOutput(bs, oc * oh * ow);
+}
+
+void MKLDNNConcatLayer::resetFwd(std::vector<primitive>& pipeline,
+                                 MKLDNNMatrixPtr& in,
+                                 MKLDNNMatrixPtr& wgt,
+                                 MKLDNNMatrixPtr& bias,
+                                 MKLDNNMatrixPtr& out) {
+  resetFwdBuffers(inVals_, out);
+  in = inVals_[0];
+
+  std::shared_ptr<concat::primitive_desc> fwdPD;
+  resetFwdPD(fwdPD, inVals_, out);
+
+  resetFwdPipeline(pipeline, fwdPD, inVals_, out);
+}
+
+void MKLDNNConcatLayer::resetBwd(std::vector<primitive>& pipeline,
+                                 MKLDNNMatrixPtr& in,
+                                 MKLDNNMatrixPtr& wgt,
+                                 MKLDNNMatrixPtr& bias,
+                                 MKLDNNMatrixPtr& out) {
+  resetBwdBuffers(inGrads_, out);
+  in = inGrads_[0];
+
+  resetBwdPipeline(pipeline, bwds_, inGrads_, out);
+}
+
+void MKLDNNConcatLayer::resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
+                                        MKLDNNMatrixPtr& out) {
+  inputs.resize(inputLayers_.size());
+  bool has8c = false, has16c = false, hasnc = false;
+  for (size_t i = 0; i < inputs.size(); i++) {
+    resetInValue(inputs[i], nullptr, i);
+    CHECK(inputs[i]);
+    auto dm = inputs[i]->getDims();
+    // inputs format can be different, but ndims must equal
+    CHECK(i == 0 || dm.size() == inputs[0]->getDims().size());
+    CHECK_EQ(bs_, dm[0]);
+    CHECK_EQ(channels_[i], dm[1]);
+    if (dm.size() > 2) {
+      CHECK_EQ(ih_, dm[2]);
+      CHECK_EQ(iw_, dm[3]);
+    }
+    if (inputs[i]->getFormat() == format::nc) {
+      hasnc = true;
+    }
+    if (inputs[i]->getFormat() == format::nChw8c) {
+      has8c = true;
+    }
+    if (inputs[i]->getFormat() == format::nChw16c) {
+      has16c = true;
+    }
+  }
+
+  format outFmt;
+  if (has16c && oc_ % 16 == 0) {
+    outFmt = format::nChw16c;
+  } else if (has8c && oc_ % 8 == 0) {
+    outFmt = format::nChw8c;
+  } else if (hasnc) {
+    CHECK(oh_ == 1 && ow_ == 1);
+    outFmt = format::nc;
+  } else {
+    outFmt = format::nchw;
+  }
+  memory::dims outDims =
+      hasnc ? memory::dims{bs_, oc_} : memory::dims{bs_, oc_, oh_, ow_};
+  auto outPD = MKLDNNMatrix::createPrimitiveDesc(outDims, outFmt, engine_);
+  resetOutValue(out, outPD);
+}
+
+void MKLDNNConcatLayer::resetFwdPD(std::shared_ptr<concat::primitive_desc>& pd,
+                                   std::vector<MKLDNNMatrixPtr>& inputs,
+                                   MKLDNNMatrixPtr out) {
+  std::vector<memory::primitive_desc> srcPDs;
+  for (size_t i = 0; i < inputs.size(); i++) {
+    srcPDs.push_back(inputs[i]->getPrimitiveDesc());
+  }
+  CHECK(out);
+  pd.reset(new concat::primitive_desc(out->getMemoryDesc(), axis_, srcPDs));
+  CHECK_PRIMITIVE_DESC_EQ(out, pd->dst_primitive_desc());
+}
+
+void MKLDNNConcatLayer::resetFwdPipeline(
+    std::vector<primitive>& pipeline,
+    std::shared_ptr<concat::primitive_desc>& pd,
+    std::vector<MKLDNNMatrixPtr>& inputs,
+    MKLDNNMatrixPtr& out) {
+  std::vector<primitive::at> srcs;
+  for (size_t i = 0; i < inputs.size(); i++) {
+    srcs.push_back(*(inputs[i]));
+  }
+  fwd_.reset(new concat(*pd, srcs, *out));
+  pipeline.push_back(*fwd_);
+}
+
+void MKLDNNConcatLayer::resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
+                                        MKLDNNMatrixPtr& out) {
+  CHECK(outVal_);
+  resetOutGrad(out, outVal_->getPrimitiveDesc());
+  CHECK(out);
+
+  inputs.resize(inputLayers_.size());
+  for (size_t i = 0; i < inputs.size(); i++) {
+    CHECK(inVals_[i]);
+    resetInGrad(inputs[i], inVals_[i]->getPrimitiveDesc(), i);
+    CHECK_PRIMITIVE_DESC_EQ(inputs[i], inVals_[i]->getPrimitiveDesc());
+  }
+}
+
+void MKLDNNConcatLayer::resetBwdPipeline(
+    std::vector<mkldnn::primitive>& pipeline,
+    std::vector<std::shared_ptr<mkldnn::primitive>>& prims,
+    std::vector<MKLDNNMatrixPtr>& inputs,
+    MKLDNNMatrixPtr& out) {
+  // reset the backward primitives
+  memory::dims offsets = {0, 0, 0, 0};
+  prims.resize(inputs.size());
+  CHECK_EQ(inputs.size(), channels_.size());
+  for (size_t i = 0; i < inputs.size(); i++) {
+    auto viewPD = view::primitive_desc(
+        out->getPrimitiveDesc(), inputs[i]->getDims(), offsets);
+    auto bwdPD = reorder::primitive_desc(viewPD.dst_primitive_desc(),
+                                         inputs[i]->getPrimitiveDesc());
+    prims[i].reset(new reorder(bwdPD, *out, *(inputs[i])));
+    offsets[axis_] += channels_[i];
+    // push to pipeline
+    pipeline.push_back(*prims[i]);
+  }
+}
+
+}  // namespace paddle
diff --git a/paddle/gserver/layers/MKLDNNConcatLayer.h b/paddle/gserver/layers/MKLDNNConcatLayer.h
new file mode 100644
index 0000000000..ad70ec0ceb
--- /dev/null
+++ b/paddle/gserver/layers/MKLDNNConcatLayer.h
@@ -0,0 +1,129 @@
+/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include "MKLDNNLayer.h"
+#include "mkldnn.hpp"
+
+namespace paddle {
+
+/**
+ * @brief A subclass of MKLDNNLayer Concatenate layer.
+ *
+ * The config file api is mkldnn_concat
+ */
+class MKLDNNConcatLayer : public MKLDNNLayer {
+protected:
+  std::vector<MKLDNNMatrixPtr> inVals_;
+  std::vector<MKLDNNMatrixPtr> inGrads_;
+  std::vector<std::shared_ptr<mkldnn::primitive>> bwds_;
+  // input channel numbers
+  std::vector<int> channels_;
+
+  // concat_dimension in MKLDNN
+  // if axis_ == 0, concat batchsize
+  // if axis_ == 1, concat channel (default)
+  int axis_;
+
+public:
+  explicit MKLDNNConcatLayer(const LayerConfig& config)
+      : MKLDNNLayer(config), axis_(1) {}
+
+  ~MKLDNNConcatLayer() {}
+
+  bool init(const LayerMap& layerMap,
+            const ParameterMap& parameterMap) override;
+
+  void reshape(
+      int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
+
+  void resetFwd(std::vector<mkldnn::primitive>& pipeline,
+                MKLDNNMatrixPtr& in,
+                MKLDNNMatrixPtr& wgt,
+                MKLDNNMatrixPtr& bias,
+                MKLDNNMatrixPtr& out) override;
+
+  void resetBwd(std::vector<mkldnn::primitive>& pipeline,
+                MKLDNNMatrixPtr& in,
+                MKLDNNMatrixPtr& wgt,
+                MKLDNNMatrixPtr& bias,
+                MKLDNNMatrixPtr& out) override;
+
+  void printSizeInfo() override {
+    CHECK_EQ(channels_.size(), inputLayers_.size());
+    for (size_t i = 0; i < channels_.size(); ++i) {
+      VLOG(MKLDNN_SIZES) << "Input " << i << ", " << inputLayers_[i]->getName()
+                         << ": " << bs_ << ", " << channels_[i] << ", " << ih_
+                         << ", " << iw_;
+    }
+    VLOG(MKLDNN_SIZES) << "Output: " << bs_ << ", " << oc_ << ", " << oh_
+                       << ", " << ow_;
+  }
+
+  void printValueFormat() override {
+    for (size_t i = 0; i < inVals_.size(); ++i) {
+      VLOG(MKLDNN_FMTS) << "Input " << i << inputLayers_[i]->getName() << ": "
+                        << inVals_[i]->getFormat() << " >>>";
+    }
+    if (outVal_) {
+      VLOG(MKLDNN_FMTS) << outVal_->getFormat() << " >>> ";
+    }
+    if (extOutVal_) {
+      VLOG(MKLDNN_FMTS) << extOutVal_->getFormat();
+    }
+  }
+
+  void printGradFormat() override {
+    if (extOutGrad_) {
+      VLOG(MKLDNN_FMTS) << extOutGrad_->getFormat();
+    }
+    if (outGrad_) {
+      VLOG(MKLDNN_FMTS) << outGrad_->getFormat() << " <<< ";
+    }
+    for (size_t i = 0; i < inGrads_.size(); ++i) {
+      VLOG(MKLDNN_FMTS) << "Input " << i << inputLayers_[i]->getName() << ": "
+                        << inGrads_[i]->getFormat() << "<<<";
+    }
+  }
+
+protected:
+  /**
+   * Forward functions: reset buffers(inputs, output, bias),
+   *                    reset primitive descriptor,
+   *                    reset pipeline.
+   */
+  void resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
+                       MKLDNNMatrixPtr& out);
+  void resetFwdPD(std::shared_ptr<mkldnn::concat::primitive_desc>& pd,
+                  std::vector<MKLDNNMatrixPtr>& inputs,
+                  MKLDNNMatrixPtr out);
+  void resetFwdPipeline(std::vector<mkldnn::primitive>& pipeline,
+                        std::shared_ptr<mkldnn::concat::primitive_desc>& pd,
+                        std::vector<MKLDNNMatrixPtr>& inputs,
+                        MKLDNNMatrixPtr& out);
+
+  /**
+   * Backward functions: reset buffers(inputs, output, bias)
+   *                     reset primitives and pipeline
+   */
+  void resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
+                       MKLDNNMatrixPtr& out);
+  void resetBwdPipeline(std::vector<mkldnn::primitive>& pipeline,
+                        std::vector<std::shared_ptr<mkldnn::primitive>>& prims,
+                        std::vector<MKLDNNMatrixPtr>& inputs,
+                        MKLDNNMatrixPtr& out);
+};
+
+}  // namespace paddle

From 40a486d86520a74bbcfcbfe94ef51fa34a8c1226 Mon Sep 17 00:00:00 2001
From: tensor-tang <jian.j.tang@intel.com>
Date: Thu, 16 Nov 2017 16:21:39 +0800
Subject: [PATCH 13/43] add mkldnn_concat unit test

---
 paddle/gserver/tests/test_MKLDNN.cpp | 41 ++++++++++++++++++++++++++++
 1 file changed, 41 insertions(+)

diff --git a/paddle/gserver/tests/test_MKLDNN.cpp b/paddle/gserver/tests/test_MKLDNN.cpp
index a859e34c89..42644e9601 100644
--- a/paddle/gserver/tests/test_MKLDNN.cpp
+++ b/paddle/gserver/tests/test_MKLDNN.cpp
@@ -313,6 +313,47 @@ TEST(MKLDNNLayer, AddtoLayer) {
   testAddtoLayer({4, 12, 1, 1}, 3);
 }
 
+static void getMKLDNNConcatConfig(TestConfig& cfg,
+                                  const std::vector<testImageDesc>& inputs) {
+  CHECK_GE(inputs.size(), 2) << "at least two inputs";
+  int oc = inputs[0].ic;
+  for (size_t i = 1; i < inputs.size(); ++i) {
+    CHECK_EQ(inputs[i].bs, inputs[0].bs);
+    CHECK_EQ(inputs[i].ih, inputs[0].ih);
+    CHECK_EQ(inputs[i].iw, inputs[0].iw);
+    oc += inputs[i].ic;
+  }
+  cfg.biasSize = 0;
+  cfg.layerConfig.set_type("mkldnn_concat");
+  cfg.layerConfig.set_size(oc * inputs[0].ih * inputs[0].iw);
+  cfg.layerConfig.set_active_type("relu");
+  for (size_t i = 0; i < inputs.size(); ++i) {
+    std::stringstream ss;
+    ss << "layer_" << i;
+    cfg.inputDefs.push_back(
+        {INPUT_DATA,
+         ss.str(),
+         (size_t)(inputs[i].ic) * inputs[i].ih * inputs[i].iw,
+         0});
+    LayerInputConfig* input = cfg.layerConfig.add_inputs();
+    ImageConfig* img_conf = input->mutable_image_conf();
+    img_conf->set_channels(inputs[i].ic);
+    img_conf->set_img_size_y(inputs[i].ih);
+    img_conf->set_img_size(inputs[i].iw);
+  }
+}
+
+void testConcatLayer(const std::vector<testImageDesc>& inputs) {
+  TestConfig dnnConfig;
+  getMKLDNNConcatConfig(dnnConfig, inputs);
+  RUN_MKLDNN_TEST_LAYER(dnnConfig, "concat", inputs[0])
+}
+
+TEST(MKLDNNLayer, ConcatLayer) {
+  testConcatLayer({{64, 128, 1, 1}, {64, 32, 1, 1}, {64, 64, 1, 1}});
+  testConcatLayer({{32, 100, 8, 8}, {32, 10, 8, 8}});
+}
+
 void testActivation(std::string actType, const testImageDesc& pm) {
   // TODO(TJ): remove me when paddle support elu activation
   if (actType == "mkldnn_elu") {

From 0321e1f861c7d02086a510c2b32bc60112c9b64a Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Thu, 16 Nov 2017 16:52:39 +0800
Subject: [PATCH 14/43] Fix bilinear_tensor_product_op in debug mode.

---
 paddle/operators/bilinear_tensor_product_op.h | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/paddle/operators/bilinear_tensor_product_op.h b/paddle/operators/bilinear_tensor_product_op.h
index ffa4f43a32..ee9636e9fb 100644
--- a/paddle/operators/bilinear_tensor_product_op.h
+++ b/paddle/operators/bilinear_tensor_product_op.h
@@ -63,6 +63,7 @@ class BilinearTensorProductKernel : public framework::OpKernel<T> {
       math::gemm<Place, T>(ctx.device_context(), CblasNoTrans, CblasNoTrans,
                            batch_size, y_dim, x_dim, 1, x->data<T>(),
                            weight_mat.data<T>(), 0, left_mul.data<T>());
+      Eigen::array<int, 2> shape({{static_cast<int>(out->dims()[0]), 1}});
       output_col_vec.device(place) =
           (left_mul_mat * y_mat).sum(Eigen::DSizes<int, 1>(1));
     }
@@ -174,7 +175,7 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> {
     // Caculate the gradient of Input(Bias).
     if (d_bias) {
       d_bias->mutable_data<T>(ctx.GetPlace());
-      auto d_bias_mat = EigenMatrix<T>::From(*d_bias);
+      auto d_bias_mat = framework::EigenVector<T>::Flatten(*d_bias);
       d_bias_mat.device(place) = d_out_mat.sum(Eigen::DSizes<int, 1>(0));
     }
   }

From 49ac0480e60c5e5ecb122b0c3d8e54bb99232061 Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Thu, 16 Nov 2017 16:55:25 +0800
Subject: [PATCH 15/43] fix a lot of warnings -Wunused-but-set-variable and
 Wunused-variable when compile openBlas

---
 cmake/external/openblas.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cmake/external/openblas.cmake b/cmake/external/openblas.cmake
index 324e29f931..2253807981 100644
--- a/cmake/external/openblas.cmake
+++ b/cmake/external/openblas.cmake
@@ -29,7 +29,7 @@ IF(NOT ${CBLAS_FOUND})
         "${CBLAS_INSTALL_DIR}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}openblas${CMAKE_STATIC_LIBRARY_SUFFIX}"
         CACHE FILEPATH "openblas library." FORCE)
 
-    SET(OPENBLAS_CC "${CMAKE_C_COMPILER}")
+    SET(OPENBLAS_CC "${CMAKE_C_COMPILER} -Wno-unused-but-set-variable -Wno-unused-variable")
 
     IF(CMAKE_CROSSCOMPILING)
         SET(OPTIONAL_ARGS HOSTCC=${HOST_C_COMPILER})

From 19c989ac159dc831248edc694654d309956ad3e9 Mon Sep 17 00:00:00 2001
From: tensor-tang <jian.j.tang@intel.com>
Date: Thu, 16 Nov 2017 17:24:19 +0800
Subject: [PATCH 16/43] fix error and pass unit test

---
 paddle/gserver/layers/MKLDNNConcatLayer.cpp | 18 +++++++++++++++---
 paddle/gserver/layers/MKLDNNConcatLayer.h   |  8 ++++----
 2 files changed, 19 insertions(+), 7 deletions(-)

diff --git a/paddle/gserver/layers/MKLDNNConcatLayer.cpp b/paddle/gserver/layers/MKLDNNConcatLayer.cpp
index 64946508d2..c9099297cc 100644
--- a/paddle/gserver/layers/MKLDNNConcatLayer.cpp
+++ b/paddle/gserver/layers/MKLDNNConcatLayer.cpp
@@ -40,7 +40,9 @@ void MKLDNNConcatLayer::reshape(
   CHECK_GT(inputLayers_.size(), 1UL);
   channels_.resize(inputLayers_.size());
   channels_[0] = ic;
-  oc = ic;
+  // need change the output channel, so use oc_ instead
+  // TODO(TJ): change API, use &oc
+  oc_ = ic;
   for (size_t i = 1; i < inputLayers_.size(); i++) {
     int batchsize, height, witdh;
     reshapeInput(batchsize, height, witdh, i);
@@ -50,12 +52,12 @@ void MKLDNNConcatLayer::reshape(
 
     channels_[i] = inputLayers_[i]->getSize() / height / witdh;
     CHECK_EQ((size_t)channels_[i] * height * witdh, inputLayers_[i]->getSize());
-    oc += channels_[i];
+    oc_ += channels_[i];
   }
   oh = ih;
   ow = iw;
   reshapeOutput(oh, ow);
-  resizeOutput(bs, oc * oh * ow);
+  resizeOutput(bs, oc_ * oh * ow);
 }
 
 void MKLDNNConcatLayer::resetFwd(std::vector<primitive>& pipeline,
@@ -88,6 +90,9 @@ void MKLDNNConcatLayer::resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
   inputs.resize(inputLayers_.size());
   bool has8c = false, has16c = false, hasnc = false;
   for (size_t i = 0; i < inputs.size(); i++) {
+    // resetInValue will use ic_ so temporary change as current input's channel
+    // TODO(TJ): change ic_ as vector then can remove channels_
+    ic_ = channels_[i];
     resetInValue(inputs[i], nullptr, i);
     CHECK(inputs[i]);
     auto dm = inputs[i]->getDims();
@@ -109,6 +114,8 @@ void MKLDNNConcatLayer::resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
       has16c = true;
     }
   }
+  // change back, ic_ always save the input 0 size
+  ic_ = channels_[0];
 
   format outFmt;
   if (has16c && oc_ % 16 == 0) {
@@ -161,9 +168,14 @@ void MKLDNNConcatLayer::resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
   inputs.resize(inputLayers_.size());
   for (size_t i = 0; i < inputs.size(); i++) {
     CHECK(inVals_[i]);
+    // resetInGrad will use inVal_
+    // TODO(TJ): change move inVals_ to MKLDNNLayer ans remove inVal_
+    inVal_ = inVals_[i];
     resetInGrad(inputs[i], inVals_[i]->getPrimitiveDesc(), i);
     CHECK_PRIMITIVE_DESC_EQ(inputs[i], inVals_[i]->getPrimitiveDesc());
   }
+  // change back, inVal_ always save the input 0
+  inVal_ = inVals_[0];
 }
 
 void MKLDNNConcatLayer::resetBwdPipeline(
diff --git a/paddle/gserver/layers/MKLDNNConcatLayer.h b/paddle/gserver/layers/MKLDNNConcatLayer.h
index ad70ec0ceb..d5749d327e 100644
--- a/paddle/gserver/layers/MKLDNNConcatLayer.h
+++ b/paddle/gserver/layers/MKLDNNConcatLayer.h
@@ -74,8 +74,8 @@ public:
 
   void printValueFormat() override {
     for (size_t i = 0; i < inVals_.size(); ++i) {
-      VLOG(MKLDNN_FMTS) << "Input " << i << inputLayers_[i]->getName() << ": "
-                        << inVals_[i]->getFormat() << " >>>";
+      VLOG(MKLDNN_FMTS) << "Input " << i << ", " << inputLayers_[i]->getName()
+                        << ": " << inVals_[i]->getFormat() << " >>>";
     }
     if (outVal_) {
       VLOG(MKLDNN_FMTS) << outVal_->getFormat() << " >>> ";
@@ -93,8 +93,8 @@ public:
       VLOG(MKLDNN_FMTS) << outGrad_->getFormat() << " <<< ";
     }
     for (size_t i = 0; i < inGrads_.size(); ++i) {
-      VLOG(MKLDNN_FMTS) << "Input " << i << inputLayers_[i]->getName() << ": "
-                        << inGrads_[i]->getFormat() << "<<<";
+      VLOG(MKLDNN_FMTS) << "Input " << i << ", " << inputLayers_[i]->getName()
+                        << ": " << inGrads_[i]->getFormat() << "<<<";
     }
   }
 

From 739858c8899c33f1116cf5c599b13229a28659aa Mon Sep 17 00:00:00 2001
From: tensor-tang <jian.j.tang@intel.com>
Date: Thu, 16 Nov 2017 17:26:39 +0800
Subject: [PATCH 17/43] add python interface for mkldnn_concat

---
 python/paddle/trainer/config_parser.py | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py
index 43d02bf70e..7ffb9d279a 100644
--- a/python/paddle/trainer/config_parser.py
+++ b/python/paddle/trainer/config_parser.py
@@ -3488,11 +3488,17 @@ def ExpressionLayer(name, inputs, **xargs):
 
 @config_layer('concat')
 class ConcatenateLayer(LayerBase):
+    layer_type = 'concat'
+
     def __init__(self, name, inputs, bias=False, **xargs):
         config_assert(inputs, 'inputs cannot be empty')
         config_assert(not bias, 'ConcatenateLayer cannot support bias.')
+        use_mkldnn = bool(int(g_command_config_args.get("use_mkldnn", 0)))
+        if self.layer_type == "mkldnn_concat":
+            config_assert(use_mkldnn, "mkldnn_concat only support MKLDNN")
+        self.layer_type = 'mkldnn_concat' if use_mkldnn else 'concat'
         super(ConcatenateLayer, self).__init__(
-            name, 'concat', 0, inputs=inputs, **xargs)
+            name, self.layer_type, 0, inputs=inputs, **xargs)
         size = 0
         for input_index in xrange(len(self.inputs)):
             assert self.get_input_layer(0).height == self.get_input_layer(
@@ -3512,6 +3518,11 @@ class ConcatenateLayer(LayerBase):
         self.set_layer_size(size)
 
 
+@config_layer('mkldnn_concat')
+class MKLDNNConcatLayer(ConcatenateLayer):
+    layer_type = 'mkldnn_concat'
+
+
 # like concat layer, but each input layer was processed by a Projection.
 @config_layer('concat2')
 class ConcatenateLayer2(LayerBase):

From 01fa4cc73d37671eaec9e58dfc4d2bc76df9a15e Mon Sep 17 00:00:00 2001
From: qingqing01 <dangqingqing@baidu.com>
Date: Thu, 16 Nov 2017 17:32:32 +0800
Subject: [PATCH 18/43] Remove the unused code.

---
 paddle/operators/bilinear_tensor_product_op.h | 1 -
 1 file changed, 1 deletion(-)

diff --git a/paddle/operators/bilinear_tensor_product_op.h b/paddle/operators/bilinear_tensor_product_op.h
index ee9636e9fb..1113a4c6f3 100644
--- a/paddle/operators/bilinear_tensor_product_op.h
+++ b/paddle/operators/bilinear_tensor_product_op.h
@@ -63,7 +63,6 @@ class BilinearTensorProductKernel : public framework::OpKernel<T> {
       math::gemm<Place, T>(ctx.device_context(), CblasNoTrans, CblasNoTrans,
                            batch_size, y_dim, x_dim, 1, x->data<T>(),
                            weight_mat.data<T>(), 0, left_mul.data<T>());
-      Eigen::array<int, 2> shape({{static_cast<int>(out->dims()[0]), 1}});
       output_col_vec.device(place) =
           (left_mul_mat * y_mat).sum(Eigen::DSizes<int, 1>(1));
     }

From 24819df05538980e5d705ef159b51d885ab2a207 Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Thu, 16 Nov 2017 17:35:44 +0800
Subject: [PATCH 19/43] Fix cos_sim_op in debug mode.

---
 paddle/operators/cos_sim_op.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/paddle/operators/cos_sim_op.h b/paddle/operators/cos_sim_op.h
index 68c56f531f..62a4e484ec 100644
--- a/paddle/operators/cos_sim_op.h
+++ b/paddle/operators/cos_sim_op.h
@@ -132,7 +132,7 @@ class CosSimGradKernel : public framework::OpKernel<T> {
       // compute dy
       if (out_grad_y) {
         out_grad_y->mutable_data<T>(context.GetPlace());
-        auto dy = EigenMatrix<T>::Reshape(*out_grad_y, 1);
+        auto dy = EigenVector<T>::Flatten(*out_grad_y);
         auto grad = x / norm_prod_bcast - z_bcast * y_bcast / y_snorm_bcast;
         dy.device(place) = (dz_bcast * grad).sum(Eigen::array<int, 1>({{0}}));
       }

From 18f0c40a97ee81f222731dd16aff719e8cbbd27d Mon Sep 17 00:00:00 2001
From: "Yang Yang(Tony)" <yangyang62@baidu.com>
Date: Thu, 16 Nov 2017 02:19:18 -0800
Subject: [PATCH 20/43] feature/while_grad_op (#5554)

* first commit

* Python API for while op

* Python Unittest for simple while_op forward

* fix out to be list

* Fix UT

* VarType

* Fix several bugs

* Fix bug

* Fix bug

* Fix Bug

* Fix bug

* Fix unittest

* Remove debug log

* Add comments

* add PADDLE_ENFORCE

* while_grad_op first commit

* Add `BlockDescBind::FindRecursiveOrCreateVar()` and fix bugs

* not sure how to setdim of while outputs

* push for test

* add executor vlog

* fix bug of while_op cond

* Several enhancement for code

1. Backward always infer shape & infer var type. Since there are RENAME
variables will be created when creating backward operator, but their
shape & var types are not inferenced.
2. Never use SomePtr-> directly, since every pointer could be nullptr if
it is a function return value. Add `detail::Ref` to cast pointer to
reference safely.
3. Enhance error message for backward.
4. Infer data type of variable in `sum` and `tensor_write`

* Fix bugs of while_op gradient

* Fix several bugs of while_op grad

* fix fill zeros like

* fix 3 >= 3

* fix place holder shouldn't be null

* fail on sum op

* Fix SumOp of TensorList

* clean up

* pass while test

* fix test_array_write_read

* pass sum op

* Support int/int64 for fill_constant_batch_size_like

* Fix compile
---
 paddle/framework/backward.cc                  |  68 +++++--
 paddle/framework/data_type.h                  |   2 +
 paddle/framework/ddim.cc                      |   3 +-
 paddle/framework/executor.cc                  |   1 +
 paddle/framework/op_desc.cc                   |  24 ++-
 paddle/framework/op_desc.h                    |   4 +
 paddle/framework/operator.cc                  |  13 --
 paddle/framework/scope.cc                     |   3 +-
 paddle/framework/shape_inference.h            |   7 +-
 paddle/operators/array_operator.h             |   1 +
 paddle/operators/detail/safe_ref.h            |  31 +++
 .../fill_constant_batch_size_like_op.cc       |   5 +-
 .../fill_constant_batch_size_like_op.cu.cc    |   5 +-
 paddle/operators/fill_zeros_like_op.cc        |   7 +-
 paddle/operators/fill_zeros_like_op.cu.cc     |   7 +-
 paddle/operators/math/math_function.cc        |   2 +
 paddle/operators/math/math_function.cu        |   2 +
 paddle/operators/sum_op.cc                    |  37 +++-
 .../operators/tensor_array_read_write_op.cc   |  24 ++-
 paddle/operators/while_op.cc                  | 184 +++++++++++++++---
 python/paddle/v2/fluid/framework.py           |  35 +++-
 python/paddle/v2/fluid/net_drawer.py          |   4 +
 python/paddle/v2/fluid/tests/test_while_op.py |  13 +-
 23 files changed, 378 insertions(+), 104 deletions(-)
 create mode 100644 paddle/operators/detail/safe_ref.h

diff --git a/paddle/framework/backward.cc b/paddle/framework/backward.cc
index b3b9c45ded..00d9dd238e 100644
--- a/paddle/framework/backward.cc
+++ b/paddle/framework/backward.cc
@@ -270,6 +270,19 @@ static bool AllGradInSet(const std::vector<std::string>& names,
       return false;
     }
   }
+  if (VLOG_IS_ON(10)) {
+    std::ostringstream sout;
+    sout << "All input {";
+    for (auto& name : names) {
+      sout << name << ",";
+    }
+    sout << "} is in {";
+    for (auto& name : set) {
+      sout << name << ",";
+    }
+    sout << "}";
+    VLOG(10) << sout.str();
+  }
   return true;
 }
 
@@ -290,14 +303,12 @@ static void CreateGradVarInBlock(
   auto ops = block_desc->AllOps();
   for (size_t op_index = grad_op_start_index; op_index < ops.size();
        ++op_index) {
-    bool need_infer_shape = false;
     std::unordered_set<std::string> new_vars;
     ForEachVarName(ops[op_index]->Outputs(),
                    [&](const std::string& grad_var_name) {
                      if (block_desc->HasVar(grad_var_name)) {
                        return false;
                      }
-                     need_infer_shape = true;
                      auto var = block_desc->Var(grad_var_name);
                      new_vars.insert(var->Name());
                      auto it = param_name_map.find(grad_var_name);
@@ -311,23 +322,21 @@ static void CreateGradVarInBlock(
                      grad_record.op_idx_ = static_cast<int>(op_index);
                      return false; /* not break */
                    });
-    if (need_infer_shape) {
-      ops[op_index]->InferVarType(block_desc);
-      for (auto& arg : ops[op_index]->OutputArgumentNames()) {
-        if (new_vars.find(arg) == new_vars.end()) {
-          continue;
-        }
-        auto pname = FwdName(arg);
-        auto* param = block_desc->FindVarRecursive(pname);
-        auto* grad = block_desc->FindVar(arg);
-        if (param == nullptr) {
-          grad->SetDataType(DataType::FP32);
-        } else {
-          grad->SetDataType(param->GetDataType());
-        }
+    ops[op_index]->InferVarType(block_desc);
+    for (auto& arg : ops[op_index]->OutputArgumentNames()) {
+      if (new_vars.find(arg) == new_vars.end()) {
+        continue;
+      }
+      auto pname = FwdName(arg);
+      auto* param = block_desc->FindVarRecursive(pname);
+      auto* grad = block_desc->FindVar(arg);
+      if (param == nullptr) {
+        grad->SetDataType(DataType::FP32);
+      } else {
+        grad->SetDataType(param->GetDataType());
       }
-      ops[op_index]->InferShape(*block_desc);
     }
+    ops[op_index]->InferShape(*block_desc);
   }
 }
 
@@ -387,6 +396,7 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
     ProgramDescBind& program_desc, int block_idx,
     std::unordered_set<std::string>* no_grad_vars,
     std::unordered_map<std::string, std::string>* grad_to_var) {
+  VLOG(5) << "MakeBlockBackward";
   BlockDescBind* cur_block = program_desc.MutableBlock(block_idx);
   std::vector<OpDescBind*> op_descs = cur_block->AllOps();
   std::unordered_map<std::string, std::vector<size_t>> dup_out_ops;
@@ -394,9 +404,10 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
   std::vector<std::unique_ptr<OpDescBind>> backward_descs;
 
   for (auto it = op_descs.rbegin(); it != op_descs.rend(); ++it) {
+    VLOG(5) << "Making backward " << (*it)->Type() << " op";
     std::vector<std::unique_ptr<OpDescBind>> op_grads;
 
-    if ((*it)->Type() == "recurrent") {
+    if ((*it)->Type() == "recurrent" || (*it)->Type() == "while") {
       int step_block_idx = (*it)->GetBlockAttr("step_block");
       BlockDescBind* backward_block = CreateStepBlock(
           program_desc, no_grad_vars, grad_to_var, step_block_idx);
@@ -410,6 +421,15 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
       op_grads = MakeOpGrad(*it, no_grad_vars, grad_to_var);
     }
 
+    if (VLOG_IS_ON(10)) {
+      std::ostringstream sout;
+      sout << "Made ";
+      for (auto& op_grad : op_grads) {
+        sout << op_grad->Type() << " ";
+      }
+      VLOG(10) << sout.str();
+    }
+
     for (const auto& desc : op_grads) {
       for (const std::string& out_name : desc->OutputArgumentNames()) {
         if (out_name.find("@GRAD") == std::string::npos) {
@@ -425,6 +445,8 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
         op_grads.begin(), op_grads.end(), std::back_inserter(backward_descs),
         [](std::unique_ptr<OpDescBind>& ptr) { return std::move(ptr); });
   }
+
+  VLOG(5) << "Appending Sums";
   // Check whether some variables are written more than once
   std::list<std::pair<size_t, std::unique_ptr<OpDescBind>>> pending_sum_ops;
   for (const auto& dup : dup_out_ops) {
@@ -432,16 +454,22 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
     const std::vector<size_t> dup_op = dup.second;
     if (out_name != kEmptyVarName && dup_op.size() > 1) {
       std::vector<std::string> sum_op_inputs;
+      std::string next_g_name = out_name;
       for (size_t i = 0; i < dup_op.size(); ++i) {
+        VLOG(10) << backward_descs[dup_op[i]]->Type() << " has " << out_name
+                 << " duplicated";
         std::string new_name = out_name + "@RENAME@" + std::to_string(i);
-        backward_descs[dup_op[i]]->Rename(out_name, new_name);
+        backward_descs[dup_op[i]]->RenameOutput(out_name, new_name);
+        backward_descs[dup_op[i]]->RenameInput(out_name, next_g_name);
         sum_op_inputs.emplace_back(new_name);
+        next_g_name = sum_op_inputs.back();
       }
       std::unique_ptr<OpDescBind> sum_op(new OpDescBind(
           "sum", {{"X", sum_op_inputs}}, {{"Out", {out_name}}}, {}));
       pending_sum_ops.push_back({dup_op.back(), std::move(sum_op)});
     }
   }
+
   pending_sum_ops.sort(
       [](const std::pair<size_t, std::unique_ptr<OpDescBind>>& a,
          const std::pair<size_t, std::unique_ptr<OpDescBind>>& b) {
@@ -452,6 +480,8 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
                           std::move(p.second));
   }
 
+  VLOG(5) << "MakeBlockBackward Finished";
+
   return backward_descs;
 }
 
diff --git a/paddle/framework/data_type.h b/paddle/framework/data_type.h
index 3ec88d7a72..be144d8fc0 100644
--- a/paddle/framework/data_type.h
+++ b/paddle/framework/data_type.h
@@ -29,6 +29,8 @@ inline DataType ToDataType(std::type_index type) {
     return DataType::INT32;
   } else if (typeid(int64_t).hash_code() == type.hash_code()) {
     return DataType::INT64;
+  } else if (typeid(bool).hash_code() == type.hash_code()) {
+    return DataType::BOOL;
   } else {
     PADDLE_THROW("Not supported");
   }
diff --git a/paddle/framework/ddim.cc b/paddle/framework/ddim.cc
index 53b899a239..8b6f42b82d 100644
--- a/paddle/framework/ddim.cc
+++ b/paddle/framework/ddim.cc
@@ -60,8 +60,7 @@ void make_ddim(DDim& ddim, const int64_t* dims, int n) {
       ddim = make_dim<9>(dims);
       break;
     default:
-      throw std::invalid_argument(
-          "Dynamic dimensions must have between [1, 9] dimensions.");
+      PADDLE_THROW("Dynamic dimensions must have between [1, 9] dimensions.");
   }
 }
 
diff --git a/paddle/framework/executor.cc b/paddle/framework/executor.cc
index 2fcf41d69f..adedd8cb0e 100644
--- a/paddle/framework/executor.cc
+++ b/paddle/framework/executor.cc
@@ -120,6 +120,7 @@ void Executor::Run(const ProgramDescBind& pdesc, Scope* scope, int block_id,
 
   for (auto& op_desc : block.AllOps()) {
     auto op = paddle::framework::OpRegistry::CreateOp(*op_desc);
+    VLOG(10) << op->DebugString();
     op->Run(*local_scope, *device);
   }
   if (create_local_scope) {
diff --git a/paddle/framework/op_desc.cc b/paddle/framework/op_desc.cc
index 39c8def82e..48cd131550 100644
--- a/paddle/framework/op_desc.cc
+++ b/paddle/framework/op_desc.cc
@@ -235,6 +235,23 @@ void OpDescBind::Rename(const std::string &old_name,
   need_update_ = true;
 }
 
+void OpDescBind::RenameOutput(const std::string &old_name,
+                              const std::string &new_name) {
+  for (auto &output : outputs_) {
+    std::replace(output.second.begin(), output.second.end(), old_name,
+                 new_name);
+  }
+  need_update_ = true;
+}
+
+void OpDescBind::RenameInput(const std::string &old_name,
+                             const std::string &new_name) {
+  for (auto &input : inputs_) {
+    std::replace(input.second.begin(), input.second.end(), old_name, new_name);
+  }
+  need_update_ = true;
+}
+
 struct SetAttrDescVisitor : public boost::static_visitor<void> {
   explicit SetAttrDescVisitor(OpDesc::Attr *attr) : attr_(attr) {}
   mutable OpDesc::Attr *attr_;
@@ -448,7 +465,12 @@ const std::vector<std::string> &CompileTimeInferShapeContext::Outputs(
 DDim CompileTimeInferShapeContext::GetDim(const std::string &name) const {
   auto var = block_.FindVarRecursive(name);
   PADDLE_ENFORCE(var != nullptr, "Cannot find variable %s", name);
-  return framework::make_ddim(var->Shape());
+  try {
+    return framework::make_ddim(var->Shape());
+  } catch (...) {
+    VLOG(5) << "GetDim of variable " << name << " error";
+    std::rethrow_exception(std::current_exception());
+  }
 }
 
 void CompileTimeInferShapeContext::SetDim(const std::string &name,
diff --git a/paddle/framework/op_desc.h b/paddle/framework/op_desc.h
index e3e96441bb..da032319af 100644
--- a/paddle/framework/op_desc.h
+++ b/paddle/framework/op_desc.h
@@ -73,6 +73,10 @@ class OpDescBind {
 
   void Rename(const std::string &old_name, const std::string &new_name);
 
+  void RenameOutput(const std::string &old_name, const std::string &new_name);
+
+  void RenameInput(const std::string &old_name, const std::string &new_name);
+
   // Only be used in C++
   const AttributeMap &GetAttrMap() const;
 
diff --git a/paddle/framework/operator.cc b/paddle/framework/operator.cc
index 3276f8af39..93467ab8ac 100644
--- a/paddle/framework/operator.cc
+++ b/paddle/framework/operator.cc
@@ -403,19 +403,6 @@ class RuntimeInferShapeContext : public InferShapeContext {
 
 void OperatorWithKernel::Run(const Scope& scope,
                              const platform::DeviceContext& dev_ctx) const {
-  if (VLOG_IS_ON(1)) {
-    auto inputs = this->InputVars();
-    auto outputs = this->OutputVars(true);
-    std::ostringstream sout;
-    sout << "Run operator " << this->Type() << " From [";
-    std::ostream_iterator<std::string> out_it(sout, ",");
-    std::copy(inputs.begin(), inputs.end(), out_it);
-    sout << "] to [";
-    std::copy(outputs.begin(), outputs.end(), out_it);
-    sout << "]";
-    VLOG(1) << sout.str();
-  }
-
   RuntimeInferShapeContext infer_shape_ctx(*this, scope);
   this->InferShape(&infer_shape_ctx);
 
diff --git a/paddle/framework/scope.cc b/paddle/framework/scope.cc
index 9428b8a07e..9ad6272c99 100644
--- a/paddle/framework/scope.cc
+++ b/paddle/framework/scope.cc
@@ -38,11 +38,12 @@ Scope& Scope::NewScope() const {
 Variable* Scope::Var(const std::string& name) {
   auto iter = vars_.find(name);
   if (iter != vars_.end()) {
+    VLOG(3) << "Get existing variable " << name;
     return iter->second;
   }
   Variable* v = new Variable();
   vars_[name] = v;
-  VLOG(3) << "Create variable " << name << " on scope";
+  VLOG(3) << "Create variable " << name;
   v->name_ = &(vars_.find(name)->first);
   return v;
 }
diff --git a/paddle/framework/shape_inference.h b/paddle/framework/shape_inference.h
index 7d36ead2ca..05dc47f06a 100644
--- a/paddle/framework/shape_inference.h
+++ b/paddle/framework/shape_inference.h
@@ -53,6 +53,10 @@ class InferShapeContext {
 
   virtual bool IsRuntime() const = 0;
 
+  // Note: In while op, we need this to be public
+  void SetDims(const std::vector<std::string> &names,
+               const std::vector<framework::DDim> &dims);
+
  protected:
   virtual framework::DDim GetDim(const std::string &name) const = 0;
   virtual void SetDim(const std::string &name, const framework::DDim &dim) = 0;
@@ -60,9 +64,6 @@ class InferShapeContext {
   std::vector<framework::DDim> GetDims(
       const std::vector<std::string> &names) const;
 
-  void SetDims(const std::vector<std::string> &names,
-               const std::vector<framework::DDim> &dims);
-
   std::vector<VarDesc::VarType> GetVarTypes(
       const std::vector<std::string> &names) const;
 
diff --git a/paddle/operators/array_operator.h b/paddle/operators/array_operator.h
index 666043e824..233a81198e 100644
--- a/paddle/operators/array_operator.h
+++ b/paddle/operators/array_operator.h
@@ -42,6 +42,7 @@ class ArrayOp : public framework::OperatorBase {
     } else {
       offset = static_cast<size_t>(*i_tensor.data<int64_t>());
     }
+    VLOG(10) << " Offset = " << offset;
     return offset;
   }
 };
diff --git a/paddle/operators/detail/safe_ref.h b/paddle/operators/detail/safe_ref.h
new file mode 100644
index 0000000000..b71af17309
--- /dev/null
+++ b/paddle/operators/detail/safe_ref.h
@@ -0,0 +1,31 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#pragma once
+
+namespace paddle {
+namespace operators {
+namespace detail {
+/**
+ * Get Reference From Pointer with check. The error message is printf format,
+ * and passed by `args`
+ */
+template <typename T, typename... ARGS>
+inline T &Ref(T *ptr, ARGS &&... args) {
+  PADDLE_ENFORCE(ptr != nullptr, args...);
+  return *ptr;
+}
+}  // namespace detail
+}  // namespace operators
+}  // namespace paddle
diff --git a/paddle/operators/fill_constant_batch_size_like_op.cc b/paddle/operators/fill_constant_batch_size_like_op.cc
index 85871ebbfc..985b5d1e86 100644
--- a/paddle/operators/fill_constant_batch_size_like_op.cc
+++ b/paddle/operators/fill_constant_batch_size_like_op.cc
@@ -101,4 +101,7 @@ REGISTER_OPERATOR(fill_constant_batch_size_like,
 REGISTER_OP_CPU_KERNEL(
     fill_constant_batch_size_like,
     ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUPlace, float>,
-    ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUPlace, double>);
+    ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUPlace, double>,
+    ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUPlace, int>,
+    ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUPlace,
+                                           int64_t>);
diff --git a/paddle/operators/fill_constant_batch_size_like_op.cu.cc b/paddle/operators/fill_constant_batch_size_like_op.cu.cc
index 87e3697e28..9e7a1eeab8 100644
--- a/paddle/operators/fill_constant_batch_size_like_op.cu.cc
+++ b/paddle/operators/fill_constant_batch_size_like_op.cu.cc
@@ -19,4 +19,7 @@ namespace ops = paddle::operators;
 REGISTER_OP_GPU_KERNEL(
     fill_constant_batch_size_like,
     ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::GPUPlace, float>,
-    ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::GPUPlace, double>);
+    ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::GPUPlace, double>,
+    ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::GPUPlace, int>,
+    ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::GPUPlace,
+                                           int64_t>);
diff --git a/paddle/operators/fill_zeros_like_op.cc b/paddle/operators/fill_zeros_like_op.cc
index 8ab39d4fb0..95fb5932b8 100644
--- a/paddle/operators/fill_zeros_like_op.cc
+++ b/paddle/operators/fill_zeros_like_op.cc
@@ -54,5 +54,8 @@ namespace ops = paddle::operators;
 REGISTER_OP_WITHOUT_GRADIENT(fill_zeros_like, ops::FillZerosLikeOp,
                              ops::FillZerosLikeOpMaker);
 REGISTER_OP_CPU_KERNEL(
-    fill_zeros_like,
-    ops::FillZerosLikeKernel<paddle::platform::CPUPlace, float>);
+    fill_zeros_like, ops::FillZerosLikeKernel<paddle::platform::CPUPlace, int>,
+    ops::FillZerosLikeKernel<paddle::platform::CPUPlace, int64_t>,
+    ops::FillZerosLikeKernel<paddle::platform::CPUPlace, float>,
+    ops::FillZerosLikeKernel<paddle::platform::CPUPlace, double>,
+    ops::FillZerosLikeKernel<paddle::platform::CPUPlace, bool>);
diff --git a/paddle/operators/fill_zeros_like_op.cu.cc b/paddle/operators/fill_zeros_like_op.cu.cc
index 2adb40cf90..1501a17441 100644
--- a/paddle/operators/fill_zeros_like_op.cu.cc
+++ b/paddle/operators/fill_zeros_like_op.cu.cc
@@ -17,5 +17,8 @@
 
 namespace ops = paddle::operators;
 REGISTER_OP_GPU_KERNEL(
-    fill_zeros_like,
-    ops::FillZerosLikeKernel<paddle::platform::GPUPlace, float>);
+    fill_zeros_like, ops::FillZerosLikeKernel<paddle::platform::GPUPlace, int>,
+    ops::FillZerosLikeKernel<paddle::platform::GPUPlace, int64_t>,
+    ops::FillZerosLikeKernel<paddle::platform::GPUPlace, float>,
+    ops::FillZerosLikeKernel<paddle::platform::GPUPlace, double>,
+    ops::FillZerosLikeKernel<paddle::platform::GPUPlace, bool>);
diff --git a/paddle/operators/math/math_function.cc b/paddle/operators/math/math_function.cc
index 5ee0917886..2e333a8cde 100644
--- a/paddle/operators/math/math_function.cc
+++ b/paddle/operators/math/math_function.cc
@@ -250,6 +250,8 @@ void axpy<platform::CPUPlace, double>(const platform::DeviceContext& context,
 template struct SetConstant<platform::CPUPlace, float>;
 template struct SetConstant<platform::CPUPlace, double>;
 template struct SetConstant<platform::CPUPlace, int>;
+template struct SetConstant<platform::CPUPlace, int64_t>;
+template struct SetConstant<platform::CPUPlace, bool>;
 
 #define DEFINE_CPU_TRANS(RANK)                                \
   template struct Transpose<platform::CPUPlace, float, RANK>; \
diff --git a/paddle/operators/math/math_function.cu b/paddle/operators/math/math_function.cu
index 38c04b97f9..58356a4b77 100644
--- a/paddle/operators/math/math_function.cu
+++ b/paddle/operators/math/math_function.cu
@@ -256,6 +256,8 @@ void axpy<platform::GPUPlace, double>(const platform::DeviceContext& context,
 template struct SetConstant<platform::GPUPlace, float>;
 template struct SetConstant<platform::GPUPlace, double>;
 template struct SetConstant<platform::GPUPlace, int>;
+template struct SetConstant<platform::GPUPlace, int64_t>;
+template struct SetConstant<platform::GPUPlace, bool>;
 
 #define DEFINE_GPU_TRANS(RANK)                                \
   template struct Transpose<platform::GPUPlace, float, RANK>; \
diff --git a/paddle/operators/sum_op.cc b/paddle/operators/sum_op.cc
index 9837f325e3..c2b7632b28 100644
--- a/paddle/operators/sum_op.cc
+++ b/paddle/operators/sum_op.cc
@@ -12,6 +12,7 @@ limitations under the License. */
 #include "paddle/operators/sum_op.h"
 #include <vector>
 #include "paddle/framework/var_type_inference.h"
+#include "paddle/operators/detail/safe_ref.h"
 
 namespace paddle {
 namespace operators {
@@ -59,13 +60,16 @@ class SumOp : public framework::OperatorWithKernel {
               x_vars[0]->Get<framework::SelectedRows>().value().type()),
           ctx.device_context());
     } else if (x_vars[0]->IsType<framework::LoDTensorArray>()) {
-      auto& array = x_vars[0]->Get<framework::LoDTensorArray>();
-      for (auto& each : array) {
-        if (each.numel() != 0) {
-          return framework::OpKernelType(framework::ToDataType(each.type()),
-                                         ctx.device_context());
+      for (auto& x_var : x_vars) {
+        auto& array = x_var->Get<framework::LoDTensorArray>();
+        for (auto& each : array) {
+          if (each.numel() != 0) {
+            return framework::OpKernelType(framework::ToDataType(each.type()),
+                                           ctx.device_context());
+          }
         }
       }
+      PADDLE_THROW("Cannot find the input data type by all input data");
     }
     PADDLE_THROW("Unexpected branch. Input type is %s",
                  x_vars[0]->Type().name());
@@ -96,6 +100,11 @@ class SumOpVarTypeInference : public framework::VarTypeInference {
     auto& inputs = op_desc.Input("X");
     auto var_type = framework::VarDesc::SELECTED_ROWS;
 
+    for (auto& name : op_desc.Input("X")) {
+      VLOG(10) << name << " "
+               << block->FindRecursiveOrCreateVar(name)->GetType();
+    }
+
     bool any_input_is_lod_tensor = std::any_of(
         inputs.begin(), inputs.end(), [block](const std::string& name) {
           return block->FindRecursiveOrCreateVar(name)->GetType() ==
@@ -103,7 +112,7 @@ class SumOpVarTypeInference : public framework::VarTypeInference {
         });
 
     auto is_tensor_array = [block](const std::string& name) {
-      return block->FindRecursiveOrCreateVar(name)->GetType() ==
+      return detail::Ref(block->FindRecursiveOrCreateVar(name)).GetType() ==
              framework::VarDesc::LOD_TENSOR_ARRAY;
     };
 
@@ -113,14 +122,26 @@ class SumOpVarTypeInference : public framework::VarTypeInference {
         std::all_of(inputs.begin(), inputs.end(), is_tensor_array);
 
     if (any_input_is_tensor_array) {
-      PADDLE_ENFORCE(all_inputs_are_tensor_array);
+      if (!all_inputs_are_tensor_array) {
+        std::ostringstream os;
+        for (auto& each : inputs) {
+          os << "    " << each << " type is "
+             << detail::Ref(block->FindRecursiveOrCreateVar(each)).GetType()
+             << "\n";
+        }
+        PADDLE_ENFORCE(all_inputs_are_tensor_array,
+                       "Not all inputs are tensor array:\n%s", os.str());
+      }
       var_type = framework::VarDesc::LOD_TENSOR_ARRAY;
     } else if (any_input_is_lod_tensor) {
       var_type = framework::VarDesc::LOD_TENSOR;
     }
 
     auto out_var_name = op_desc.Output("Out").front();
-    block->FindRecursiveOrCreateVar(out_var_name)->SetType(var_type);
+    auto& out_var = detail::Ref(block->FindRecursiveOrCreateVar(out_var_name));
+    out_var.SetType(var_type);
+    auto& in_var = detail::Ref(block->FindVarRecursive(inputs.front()));
+    out_var.SetDataType(in_var.GetDataType());
   }
 };
 
diff --git a/paddle/operators/tensor_array_read_write_op.cc b/paddle/operators/tensor_array_read_write_op.cc
index 62e15604c4..ae1b48d7a8 100644
--- a/paddle/operators/tensor_array_read_write_op.cc
+++ b/paddle/operators/tensor_array_read_write_op.cc
@@ -12,7 +12,7 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 #include "paddle/operators/array_operator.h"
-
+#include "paddle/operators/detail/safe_ref.h"
 namespace paddle {
 namespace operators {
 
@@ -33,6 +33,8 @@ class WriteToArrayOp : public ArrayOp {
     auto *out =
         scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensorArray>();
     if (offset >= out->size()) {
+      VLOG(10) << "Resize " << Output("Out") << " from " << out->size()
+               << " to " << offset + 1;
       out->resize(offset + 1);
     }
     auto *out_tensor = &out->at(offset);
@@ -85,11 +87,15 @@ class WriteToArrayInferVarType : public framework::VarTypeInference {
  public:
   void operator()(const framework::OpDescBind &op_desc,
                   framework::BlockDescBind *block) const override {
-    for (auto &out_var : op_desc.OutputArgumentNames()) {
-      VLOG(10) << "Set Variable " << out_var << " as LOD_TENSOR_ARRAY";
-      block->FindRecursiveOrCreateVar(out_var)->SetType(
-          framework::VarDesc::LOD_TENSOR_ARRAY);
-    }
+    auto x_name = op_desc.Input("X")[0];
+    auto out_name = op_desc.Output("Out")[0];
+    VLOG(10) << "Set Variable " << out_name << " as LOD_TENSOR_ARRAY";
+    auto &out = detail::Ref(block->FindRecursiveOrCreateVar(out_name),
+                            "Cannot found %s", out_name);
+    out.SetType(framework::VarDesc::LOD_TENSOR_ARRAY);
+    auto &x =
+        detail::Ref(block->FindVarRecursive(x_name), "Cannot found %s", x_name);
+    out.SetDataType(x.GetDataType());
   }
 };
 
@@ -107,11 +113,11 @@ class ReadFromArrayOp : public ArrayOp {
     auto &x_array = x->Get<framework::LoDTensorArray>();
     auto *out = scope.FindVar(Output("Out"));
     PADDLE_ENFORCE(out != nullptr, "Out must be set");
-    auto *out_tesnor = out->GetMutable<framework::LoDTensor>();
+    auto *out_tensor = out->GetMutable<framework::LoDTensor>();
     size_t offset = GetOffset(scope, dev_ctx);
     PADDLE_ENFORCE_LT(offset, x_array.size());
-    out_tesnor->CopyFrom(x_array[offset], dev_ctx.GetPlace(), dev_ctx);
-    out_tesnor->set_lod(x_array[offset].lod());
+    out_tensor->CopyFrom(x_array[offset], dev_ctx.GetPlace(), dev_ctx);
+    out_tensor->set_lod(x_array[offset].lod());
   }
 };
 
diff --git a/paddle/operators/while_op.cc b/paddle/operators/while_op.cc
index 4ca6c8507a..dcc59f5ff2 100644
--- a/paddle/operators/while_op.cc
+++ b/paddle/operators/while_op.cc
@@ -14,8 +14,10 @@
 
 #include <vector>
 #include "paddle/framework/executor.h"
+#include "paddle/framework/lod_tensor_array.h"
 #include "paddle/framework/op_registry.h"
 #include "paddle/framework/operator.h"
+#include "paddle/operators/detail/safe_ref.h"
 
 namespace paddle {
 namespace operators {
@@ -26,8 +28,9 @@ using LoDTensor = framework::LoDTensor;
 constexpr char kStepBlock[] = "step_block";
 constexpr char kCondition[] = "Condition";
 constexpr char kStepScopes[] = "StepScopes";
-constexpr char kParamGrads[] = "X@Grad";
 constexpr char kParameters[] = "X";
+constexpr char kParamGrads[] = "X@GRAD";
+constexpr char kOutputs[] = "Out";
 
 class WhileOp : public framework::OperatorBase {
  public:
@@ -71,9 +74,9 @@ class WhileOpMaker : public framework::OpProtoAndCheckerMaker {
         kCondition,
         "(Bool) An scalar. When it's False, the While Op will be terminated.")
         .AsDuplicable();
-    AddOutput("Out",
+    AddOutput(kOutputs,
               "A set of variables, which will be assigned with values "
-              "generated by perators inside the block of While Op.")
+              "generated by the operators inside the block of While Op.")
         .AsDuplicable();
     AddOutput(kStepScopes,
               "(StepScopeVar) A vector of local scope, which size equals the "
@@ -104,17 +107,64 @@ class WhileGradOp : public framework::OperatorBase {
     auto *step_scopes =
         scope.FindVar(Input(kStepScopes))->GetMutable<StepScopeVar>();
 
+    auto outside_og_names = Inputs(framework::GradVarName(kOutputs));
+    auto inside_og_names =
+        Attr<std::vector<std::string>>("original_output_grad");
+
+    PADDLE_ENFORCE_EQ(outside_og_names.size(), inside_og_names.size());
+
     for (auto cur_scope_iter = step_scopes->rbegin();
          cur_scope_iter != step_scopes->rend(); ++cur_scope_iter) {
+      VLOG(3) << "Start backward at time_step "
+              << cur_scope_iter - step_scopes->rbegin();
+      framework::Scope &cur_scope = **cur_scope_iter;
+      // Link OG from outside to inside
+      for (size_t i = 0; i < outside_og_names.size(); ++i) {
+        auto outside_og_name = outside_og_names[i];
+        auto inside_og_name = inside_og_names[i];
+        VLOG(10) << "Linking outside " << outside_og_name << " --> inside "
+                 << inside_og_name;
+        auto &og_outside = detail::Ref(scope.FindVar(outside_og_name));
+        auto &og_inside = detail::Ref(cur_scope.Var(inside_og_name));
+        if (og_outside.Type().hash_code() ==
+            typeid(framework::LoDTensor).hash_code()) {
+          auto &outside_tensor = og_outside.Get<framework::LoDTensor>();
+          auto &inside_tensor =
+              detail::Ref(og_inside.GetMutable<framework::LoDTensor>());
+          inside_tensor.set_lod(outside_tensor.lod());
+          inside_tensor.ShareDataWith(outside_tensor);
+        } else if (og_outside.Type().hash_code() ==
+                   typeid(framework::LoDTensorArray).hash_code()) {
+          auto &outside_array = og_outside.Get<framework::LoDTensorArray>();
+          auto &inside_array =
+              detail::Ref(og_inside.GetMutable<framework::LoDTensorArray>());
+          VLOG(10) << outside_og_name << " size = " << outside_array.size();
+          inside_array.resize(outside_array.size());
+
+          for (size_t j = 0; j < inside_array.size(); ++j) {
+            VLOG(10) << j << " " << outside_array[j].numel();
+            if (outside_array[j].numel() != 0) {
+              inside_array[j].set_lod(outside_array[j].lod());
+              inside_array[j].ShareDataWith(outside_array[j]);
+            } else {
+              PADDLE_ENFORCE_EQ(inside_array[j].numel(), 0);
+            }
+          }
+        }
+      }
+
       executor.Run(*program, *cur_scope_iter, block->ID(), false);
 
       auto &pg_names = Outputs(kParamGrads);
       auto &p_names = Inputs(kParameters);
       PADDLE_ENFORCE_EQ(pg_names.size(), p_names.size());
-      for (size_t prog_id = 0; prog_id < pg_names.size(); ++prog_id) {
-        auto inside_grad_name = framework::GradVarName(p_names[prog_id]);
+      for (size_t param_id = 0; param_id < pg_names.size(); ++param_id) {
+        if (pg_names[param_id] == framework::kEmptyVarName) {
+          continue;  // iterator doesn't have gradient
+        }
+        auto inside_grad_name = framework::GradVarName(p_names[param_id]);
 
-        //  // TODO(tonyyang-savil: Not sure we need the following
+        //  // TODO(tonyyang-svail): Not sure we need the following
         //  // If does not compute gradient of that variable inside rnn,
         //  just
         //  // continue
@@ -126,7 +176,7 @@ class WhileGradOp : public framework::OperatorBase {
         // zero gradient variable in step 0
         if (cur_scope_iter == step_scopes->rbegin()) {
           auto *var = (*cur_scope_iter)->FindVar(inside_grad_name);
-          PADDLE_ENFORCE_NOT_NULL(var);
+          PADDLE_ENFORCE_NOT_NULL(var, "Can not find var %s", inside_grad_name);
           if (var->IsType<LoDTensor>()) {
             auto &inside_tensor = var->Get<framework::LoDTensor>();
             framework::AttributeMap attrs;
@@ -135,27 +185,18 @@ class WhileGradOp : public framework::OperatorBase {
             attrs["value"] = 0.0f;
 
             auto zero_op = framework::OpRegistry::CreateOp(
-                "fill_constant", {}, {{"Out", {pg_names[prog_id]}}}, attrs);
+                "fill_constant", {}, {{"Out", {pg_names[param_id]}}}, attrs);
             zero_op->Run(scope, dev_ctx);
           }
         }
 
         // sum gradient
-        auto *outside_var = scope.FindVar(pg_names[prog_id]);
-        PADDLE_ENFORCE_NOT_NULL(outside_var);
-        auto &outside_tensor = *outside_var->GetMutable<framework::LoDTensor>();
-
-        std::string result_var_name;
-        auto *local_result_var = (*cur_scope_iter)->Var(&result_var_name);
-        auto &local_result_tensor =
-            *local_result_var->GetMutable<framework::LoDTensor>();
-
-        local_result_tensor.ShareDataWith(outside_tensor);
-
+        auto new_inside_name = cur_scope.Rename(inside_grad_name);
         auto sum_op = framework::OpRegistry::CreateOp(
-            "sum", {{"X", {result_var_name, inside_grad_name}}},
-            {{"Out", {result_var_name}}}, {});
-        sum_op->Run(**cur_scope_iter, dev_ctx);
+            "sum", {{"X", {pg_names[param_id], new_inside_name}}},
+            {{"Out", {pg_names[param_id]}}}, {});
+        sum_op->Run(cur_scope, dev_ctx);
+        cur_scope.Rename(new_inside_name, inside_grad_name);
       }
     }
   }
@@ -169,29 +210,110 @@ class WhileGradOpDescMaker : public framework::SingleGradOpDescMaker {
   virtual std::unique_ptr<framework::OpDescBind> Apply() const {
     auto *grad = new framework::OpDescBind();
     grad->SetType("while_grad");
-    for (auto &input_param : this->InputNames()) {
-      grad->SetInput(input_param, this->Input(input_param));
-      grad->SetOutput(framework::GradVarName(input_param),
-                      this->InputGrad(input_param));
+    grad->SetInput(kParameters, Input(kParameters));
+    grad->SetOutput(
+        framework::GradVarName(kParameters),
+        InputGrad(kParameters, /*do not drop empty gradient*/ false));
+    grad->SetInput(kOutputs, Output(kOutputs));
+
+    // OG should be re-calculated by step blocks, since many outputs of while op
+    // do not need to calculate gradients.
+    std::unordered_set<std::string> block_ins;
+    {
+      for (auto &p : Input(kParameters)) {
+        block_ins.insert(p);
+      }
+      for (auto &o : Output(kOutputs)) {
+        block_ins.insert(o);
+      }
     }
+    std::unordered_set<std::string> extra_inputs;
+    for (size_t i = 0; i < grad_block_[0]->OpSize(); ++i) {
+      for (auto &input_name : grad_block_[0]->Op(i)->InputArgumentNames()) {
+        if (block_ins.find(input_name) != block_ins.end()) {
+          continue;
+        }
+        extra_inputs.insert(input_name);
+      }
 
-    for (auto &output_param : this->OutputNames()) {
-      grad->SetInput(output_param, this->Output(output_param));
-      if (output_param != kStepScopes) {
-        grad->SetInput(framework::GradVarName(output_param),
-                       this->OutputGrad(output_param));
+      for (auto &output_name : grad_block_[0]->Op(i)->OutputArgumentNames()) {
+        block_ins.insert(output_name);
       }
     }
+
+    std::vector<std::string> extra_inputs_list;
+    extra_inputs_list.resize(extra_inputs.size());
+    std::copy(extra_inputs.begin(), extra_inputs.end(),
+              extra_inputs_list.begin());
+    grad->SetInput(framework::GradVarName(kOutputs), extra_inputs_list);
+    grad->SetInput(kStepScopes, Output(kStepScopes));
     grad->SetAttrMap(this->Attrs());
     grad->SetBlockAttr(kStepBlock, *grad_block_[0]);
+    // record the original output gradient names, since the gradient name of
+    // while operator could be renamed.
+    grad->SetAttr("original_output_grad", extra_inputs_list);
 
     return std::unique_ptr<framework::OpDescBind>(grad);
   }
 };
 
+class WhileGradOpVarTypeInference : public framework::VarTypeInference {
+ public:
+  void operator()(const framework::OpDescBind &op_desc,
+                  framework::BlockDescBind *block) const override {
+    auto p_names = op_desc.Input(kParameters);
+    auto pg_names = op_desc.Output(framework::GradVarName(kParameters));
+
+    for (size_t i = 0; i < p_names.size(); ++i) {
+      auto &p_var = detail::Ref(block->FindVarRecursive(p_names[i]));
+      auto *g_var = block->FindVarRecursive(pg_names[i]);
+      if (g_var != nullptr) {  // Gradient could be @EMPTY@
+        VLOG(5) << "Setting " << pg_names[i] << " following " << p_names[i]
+                << " type: " << p_var.GetType();
+        g_var->SetType(p_var.GetType());
+        g_var->SetDataType(p_var.GetDataType());
+      }
+    }
+  }
+};
+
+class WhileGradOpShapeInference : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext *ctx) const override {
+    ctx->HasInputs(kParameters);
+    ctx->HasOutputs(framework::GradVarName(kParameters));
+    ctx->HasInputs(kOutputs);
+    ctx->HasInputs(framework::GradVarName(kOutputs));
+
+    auto p_names = ctx->Inputs(kParameters);
+    auto pg_names = ctx->Outputs(kParamGrads);
+    auto dims = ctx->GetInputsDim(kParameters);
+    auto var_types = ctx->GetInputsVarType(kParameters);
+    std::vector<std::string> names_to_set;
+    std::vector<framework::DDim> dims_to_set;
+    for (size_t i = 0; i < p_names.size(); ++i) {
+      if (pg_names[i] == framework::kEmptyVarName) {
+        continue;
+      }
+      if (var_types[i] == framework::VarDesc::LOD_TENSOR) {
+        names_to_set.push_back(pg_names[i]);
+        dims_to_set.push_back(dims[i]);
+      } else if (var_types[i] == framework::VarDesc::LOD_TENSOR_ARRAY) {
+        // not sure how to set the dim of LOD_TENSOR_ARRAY
+        names_to_set.push_back(pg_names[i]);
+        dims_to_set.push_back(dims[i]);
+      }
+    }
+    ctx->SetDims(names_to_set, dims_to_set);
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 REGISTER_OPERATOR(while, paddle::operators::WhileOp,
                   paddle::operators::WhileOpMaker,
                   paddle::operators::WhileGradOpDescMaker);
+REGISTER_OPERATOR(while_grad, paddle::operators::WhileGradOp,
+                  paddle::operators::WhileGradOpShapeInference,
+                  paddle::operators::WhileGradOpVarTypeInference);
diff --git a/python/paddle/v2/fluid/framework.py b/python/paddle/v2/fluid/framework.py
index f20567243a..a6eca2d719 100644
--- a/python/paddle/v2/fluid/framework.py
+++ b/python/paddle/v2/fluid/framework.py
@@ -12,9 +12,9 @@ def unique_name(prefix):
     return "_".join([prefix, str(uid)])
 
 
-def _debug_string_(proto):
+def _debug_string_(proto, throw_on_error=True):
     error_fields = list()
-    if not proto.IsInitialized(error_fields):
+    if not proto.IsInitialized(error_fields) and throw_on_error:
         raise ValueError("{0} are not initialized\nThe message is {1}".format(
             error_fields, proto))
     return proto.__str__()
@@ -101,9 +101,12 @@ class Variable(object):
         self.stop_gradient = stop_gradient
 
     def __str__(self):
+        return self.to_string(True)
+
+    def to_string(self, throw_on_error):
         protostr = self.desc.serialize_to_string()
         proto = framework_pb2.VarDesc.FromString(str(protostr))
-        return _debug_string_(proto)
+        return _debug_string_(proto, throw_on_error)
 
     __repr__ = __str__
 
@@ -291,10 +294,13 @@ class Operator(object):
             self.desc.infer_var_type(self.block.desc)
             self.desc.infer_shape(self.block.desc)
 
-    def __str__(self):
+    def to_string(self, throw_on_error):
         protostr = self.desc.serialize_to_string()
         proto = framework_pb2.OpDesc.FromString(str(protostr))
-        return _debug_string_(proto)
+        return _debug_string_(proto, throw_on_error)
+
+    def __str__(self):
+        return self.to_string(True)
 
     __repr__ = __str__
 
@@ -349,9 +355,12 @@ class Block(object):
         self.program = program
 
     def __str__(self):
+        return self.to_string(True)
+
+    def to_string(self, throw_on_error):
         protostr = self.desc.serialize_to_string()
         proto = framework_pb2.BlockDesc.FromString(str(protostr))
-        return _debug_string_(proto)
+        return _debug_string_(proto, throw_on_error)
 
     __repr__ = __str__
 
@@ -454,9 +463,12 @@ class Program(object):
         self.current_block_idx = 0
 
     def __str__(self):
+        return self.to_string(True)
+
+    def to_string(self, throw_on_error):
         protostr = self.desc.serialize_to_string()
         proto = framework_pb2.ProgramDesc.FromString(str(protostr))
-        return _debug_string_(proto)
+        return _debug_string_(proto, throw_on_error)
 
     def clone(self):
         p = Program()
@@ -512,7 +524,14 @@ class Program(object):
         assert isinstance(target, Variable)
         if no_grad_set is None:
             no_grad_set = set()
-        param_to_grad_info = self.desc.append_backward(target.desc, no_grad_set)
+        try:
+            param_to_grad_info = self.desc.append_backward(target.desc,
+                                                           no_grad_set)
+        except Exception as e:
+            raise core.EnforceNotMet(
+                str(e) + "\nCurrent protobuf is\n{0}".format(
+                    self.to_string(False)))
+
         self.sync_with_cpp()
         return param_to_grad_info
 
diff --git a/python/paddle/v2/fluid/net_drawer.py b/python/paddle/v2/fluid/net_drawer.py
index 17ad547c2b..94fdd5e389 100644
--- a/python/paddle/v2/fluid/net_drawer.py
+++ b/python/paddle/v2/fluid/net_drawer.py
@@ -66,10 +66,13 @@ def parse_graph(program, graph, var_dict, **kwargs):
             if not var_dict.has_key(var):
                 var_dict[var] = "Feed"
 
+    temp_id = 0
     proto = framework_pb2.ProgramDesc.FromString(
         program.desc.serialize_to_string())
     for block in proto.blocks:
         for op in block.ops:
+            op.type = op.type + "_" + str(temp_id)
+            temp_id += 1
             graph.node(**draw_node(op))
             for o in op.outputs:
                 for arg in o.arguments:
@@ -78,6 +81,7 @@ def parse_graph(program, graph, var_dict, **kwargs):
                 for arg in e.arguments:
                     if var_dict.has_key(arg):
                         graph.edge(**draw_edge(var_dict, op, e, arg))
+        break  # only plot the first block
 
 
 def draw_graph(startup_program, main_program, **kwargs):
diff --git a/python/paddle/v2/fluid/tests/test_while_op.py b/python/paddle/v2/fluid/tests/test_while_op.py
index 0f01acb3b9..84b432333f 100644
--- a/python/paddle/v2/fluid/tests/test_while_op.py
+++ b/python/paddle/v2/fluid/tests/test_while_op.py
@@ -2,6 +2,7 @@ import unittest
 import paddle.v2.fluid.layers as layers
 from paddle.v2.fluid.executor import Executor
 import paddle.v2.fluid.core as core
+from paddle.v2.fluid.backward import append_backward_ops
 import numpy
 
 
@@ -16,7 +17,7 @@ class TestWhileOp(unittest.TestCase):
         i = layers.zeros(shape=[1], dtype='int64')
         i.stop_gradient = True
         init = layers.zeros(shape=[10], dtype='float32')
-        mem_array = layers.array_write(init, i=i)
+        mem_array = layers.array_write(x=init, i=i)
         data_array = layers.array_write(x=d0, i=i)
 
         i = layers.increment(i)
@@ -29,17 +30,23 @@ class TestWhileOp(unittest.TestCase):
         i.stop_gradient = True
 
         array_len = layers.fill_constant(shape=[1], dtype='int64', value=3)
+        array_len.stop_gradient = True
         cond = layers.less_than(x=i, y=array_len)
 
         while_op = layers.While(cond=cond)
         with while_op.block():
             d = layers.array_read(array=data_array, i=i)
             prev = layers.array_read(array=mem_array, i=i)
-            i = layers.increment(x=i, in_place=True)
             result = layers.sums(input=[d, prev])
+
+            i = layers.increment(x=i, in_place=True)
             layers.array_write(result, i=i, array=mem_array)
             layers.less_than(x=i, y=array_len, cond=cond)
-        sum_result = layers.array_read(mem_array, i=array_len)
+
+        sum_result = layers.array_read(array=mem_array, i=i)
+        loss = layers.mean(x=sum_result)
+
+        append_backward_ops(loss)
 
         cpu = core.CPUPlace()
         exe = Executor(cpu)

From b34f21bdf787a01aa271af260dadacbcda0165a6 Mon Sep 17 00:00:00 2001
From: hedaoyuan <hedaoyuan@github.com>
Date: Thu, 16 Nov 2017 19:25:51 +0800
Subject: [PATCH 21/43] The pool_limit_size need be zero in mobile inference.

---
 paddle/math/Storage.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/paddle/math/Storage.cpp b/paddle/math/Storage.cpp
index 4adaaef983..a2ef731ecb 100644
--- a/paddle/math/Storage.cpp
+++ b/paddle/math/Storage.cpp
@@ -17,9 +17,13 @@ limitations under the License. */
 #include "paddle/utils/StringUtil.h"
 #include "paddle/utils/Util.h"
 
+#ifndef PADDLE_MOBILE_INFERENCE
 DEFINE_int32(pool_limit_size,
              536870912,
              "maximum memory size managed by a memory pool, default is 512M");
+#else
+DEFINE_int32(pool_limit_size, 0, "default is 0");
+#endif
 
 namespace paddle {
 

From 186581d2cc598621df2d1f3941160d1f1051bf06 Mon Sep 17 00:00:00 2001
From: Yan Chunwei <yanchunwei@outlook.com>
Date: Thu, 16 Nov 2017 20:18:31 +0800
Subject: [PATCH 22/43] add is empty op (#5639)

---
 paddle/operators/is_empty_op.cc               | 67 +++++++++++++++++++
 .../v2/framework/tests/test_is_empty_op.py    | 43 ++++++++++++
 2 files changed, 110 insertions(+)
 create mode 100644 paddle/operators/is_empty_op.cc
 create mode 100644 python/paddle/v2/framework/tests/test_is_empty_op.py

diff --git a/paddle/operators/is_empty_op.cc b/paddle/operators/is_empty_op.cc
new file mode 100644
index 0000000000..54fecf44e8
--- /dev/null
+++ b/paddle/operators/is_empty_op.cc
@@ -0,0 +1,67 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#include "paddle/framework/op_registry.h"
+#include "paddle/framework/operator.h"
+
+namespace paddle {
+namespace operators {
+
+constexpr char kInput[] = "X";
+constexpr char kOutput[] = "Out";
+
+class IsEmptyOp : public framework::OperatorBase {
+ public:
+  IsEmptyOp(const std::string &type, const framework::VariableNameMap &inputs,
+            const framework::VariableNameMap &outputs,
+            const framework::AttributeMap &attrs)
+      : OperatorBase(type, inputs, outputs, attrs) {}
+
+  void Run(const framework::Scope &scope,
+           const platform::DeviceContext &dev_ctx) const override {
+    // get input
+    auto *var = scope.FindVar(Input(kInput));
+    PADDLE_ENFORCE_NOT_NULL(var);
+    auto &tensor = var->Get<framework::LoDTensor>();
+    // get output
+    auto *out = scope.FindVar(Output(kOutput));
+    PADDLE_ENFORCE_NOT_NULL(out);
+    auto *out_tensor = out->GetMutable<framework::LoDTensor>();
+
+    out_tensor->Resize({1});
+    out_tensor->mutable_data<bool>(platform::CPUPlace())[0] =
+        framework::product(tensor.dims()) == 0;
+  }
+};
+
+class IsEmptyOpProtoMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  IsEmptyOpProtoMaker(framework::OpProto *proto,
+                      framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput(kInput, "(Tensor) Tensor which is to be checked.");
+    AddOutput(kOutput, "(Tensor) a boolean Tensor that indicate empty or not.");
+    AddComment(R"DOC(
+IsEmpty Operator which checks whether a tensor is empty.
+
+It will just return product(tensor.ddims()) > 0;
+              )DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+REGISTER_OP_WITHOUT_GRADIENT(is_empty, paddle::operators::IsEmptyOp,
+                             paddle::operators::IsEmptyOpProtoMaker);
diff --git a/python/paddle/v2/framework/tests/test_is_empty_op.py b/python/paddle/v2/framework/tests/test_is_empty_op.py
new file mode 100644
index 0000000000..129d1c1944
--- /dev/null
+++ b/python/paddle/v2/framework/tests/test_is_empty_op.py
@@ -0,0 +1,43 @@
+import unittest
+import numpy as np
+from paddle.v2.framework.op import Operator
+import paddle.v2.framework.core as core
+
+
+def create_tensor(scope, name, np_data):
+    tensor = scope.var(name).get_tensor()
+    tensor.set_dims(np_data.shape)
+    tensor.set(np_data, core.CPUPlace())
+    return tensor
+
+
+class TestIsEmptyOp(unittest.TestCase):
+    def setUp(self):
+        self.scope = core.Scope()
+        # create input variables
+        np_data0 = np.array([0, 1, 2])
+        create_tensor(self.scope, "X0", np_data0)
+
+        np_data1 = np.array([1])
+        t = create_tensor(self.scope, "X1", np_data1)
+        t.set_dims([0])
+
+        # create output variables
+        self.scope.var("out")
+
+    def test_no_empty(self):
+        self.one_case("X0", False)
+
+    def test_empty(self):
+        self.one_case("X1", True)
+
+    def one_case(self, input, target):
+        op = Operator(type="is_empty", X=input, Out="out")
+        ctx = core.DeviceContext.create(core.CPUPlace())
+        op.run(self.scope, ctx)
+        out = self.scope.var("out").get_tensor()
+        self.assertEqual(np.array(out)[0], target)
+
+
+if __name__ == "__main__":
+    unittest.main()

From 3d080f3ad53d10e858a1bcd6c34a8ff07c56d7b0 Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Thu, 16 Nov 2017 22:34:54 +0800
Subject: [PATCH 23/43] =?UTF-8?q?Refine=20cmake=20about=20CUDA=20to=20auto?=
 =?UTF-8?q?matically=C2=A0detect=20GPU=20arch=20by=20default.=201.=20Autom?=
 =?UTF-8?q?atically=20detect=20GPU=20arch=20by=20default.=202.=20Specify?=
 =?UTF-8?q?=20-DCUDA=5FARCH=5FNAME=3DAll=20when=20releasing=20PaddlePaddle?=
 =?UTF-8?q?=20new=20version?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 CMakeLists.txt    |   5 +-
 cmake/cuda.cmake  | 219 ++++++++++++++++++++++++++++++++++++++++++++++
 cmake/flags.cmake |  55 ------------
 3 files changed, 220 insertions(+), 59 deletions(-)
 create mode 100644 cmake/cuda.cmake

diff --git a/CMakeLists.txt b/CMakeLists.txt
index fd3582a1bc..fba5c58dc4 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -158,10 +158,7 @@ set(EXTERNAL_LIBS
 )
 
 if(WITH_GPU)
-    list(APPEND EXTERNAL_LIBS ${CUDA_LIBRARIES} ${CUDA_rt_LIBRARY})
-    if(NOT WITH_DSO)
-        list(APPEND EXTERNAL_LIBS ${CUDNN_LIBRARY} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_curand_LIBRARY} ${NCCL_LIBRARY})
-    endif(NOT WITH_DSO)
+  include(cuda)
 endif(WITH_GPU)
 
 if(WITH_MKLDNN)
diff --git a/cmake/cuda.cmake b/cmake/cuda.cmake
new file mode 100644
index 0000000000..5d0840f273
--- /dev/null
+++ b/cmake/cuda.cmake
@@ -0,0 +1,219 @@
+if(NOT WITH_GPU)
+    return()
+endif()
+
+set(paddle_known_gpu_archs "20 21(20) 30 35 50 52 60 61 70")
+set(paddle_known_gpu_archs7 "20 21(20) 30 35 50 52")
+set(paddle_known_gpu_archs8 "20 21(20) 30 35 50 52 60 61")
+
+######################################################################################
+# A function for automatic detection of GPUs installed  (if autodetection is enabled)
+# Usage:
+#   detect_installed_gpus(out_variable)
+function(detect_installed_gpus out_variable)
+  if(NOT CUDA_gpu_detect_output)
+    set(cufile ${PROJECT_BINARY_DIR}/detect_cuda_archs.cu)
+
+    file(WRITE ${cufile} ""
+      "#include <cstdio>\n"
+      "int main() {\n"
+      "  int count = 0;\n"
+      "  if (cudaSuccess != cudaGetDeviceCount(&count)) return -1;\n"
+      "  if (count == 0) return -1;\n"
+      "  for (int device = 0; device < count; ++device) {\n"
+      "    cudaDeviceProp prop;\n"
+      "    if (cudaSuccess == cudaGetDeviceProperties(&prop, device))\n"
+      "      std::printf(\"%d.%d \", prop.major, prop.minor);\n"
+      "  }\n"
+      "  return 0;\n"
+      "}\n")
+
+    execute_process(COMMAND "${CUDA_NVCC_EXECUTABLE}" "-ccbin=${CUDA_HOST_COMPILER}"
+                    "--run" "${cufile}"
+                    WORKING_DIRECTORY "${PROJECT_BINARY_DIR}/CMakeFiles/"
+                    RESULT_VARIABLE nvcc_res OUTPUT_VARIABLE nvcc_out
+                    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
+
+    if(nvcc_res EQUAL 0)
+      # only keep the last line of nvcc_out
+      STRING(REGEX REPLACE ";" "\\\\;" nvcc_out "${nvcc_out}")
+      STRING(REGEX REPLACE "\n" ";" nvcc_out "${nvcc_out}")
+      list(GET nvcc_out -1 nvcc_out)
+      string(REPLACE "2.1" "2.1(2.0)" nvcc_out "${nvcc_out}")
+      set(CUDA_gpu_detect_output ${nvcc_out} CACHE INTERNAL "Returned GPU architetures from caffe_detect_gpus tool" FORCE)
+    endif()
+  endif()
+
+  if(NOT CUDA_gpu_detect_output)
+    message(STATUS "Automatic GPU detection failed. Building for all known architectures.")
+    set(${out_variable} ${paddle_known_gpu_archs} PARENT_SCOPE)
+  else()
+    set(${out_variable} ${CUDA_gpu_detect_output} PARENT_SCOPE)
+  endif()
+endfunction()
+
+
+########################################################################
+# Function for selecting GPU arch flags for nvcc based on CUDA_ARCH_NAME
+# Usage:
+#   select_nvcc_arch_flags(out_variable)
+function(select_nvcc_arch_flags out_variable)
+  # List of arch names
+  set(archs_names "Kepler" "Maxwell" "Pascal" "All" "Manual")
+  set(archs_name_default "All")
+  if(NOT CMAKE_CROSSCOMPILING)
+    list(APPEND archs_names "Auto")
+    set(archs_name_default "Auto")
+  endif()
+
+  # set CUDA_ARCH_NAME strings (so it will be seen as dropbox in CMake-Gui)
+  set(CUDA_ARCH_NAME ${archs_name_default} CACHE STRING "Select target NVIDIA GPU achitecture.")
+  set_property( CACHE CUDA_ARCH_NAME PROPERTY STRINGS "" ${archs_names} )
+  mark_as_advanced(CUDA_ARCH_NAME)
+
+  # verify CUDA_ARCH_NAME value
+  if(NOT ";${archs_names};" MATCHES ";${CUDA_ARCH_NAME};")
+    string(REPLACE ";" ", " archs_names "${archs_names}")
+    message(FATAL_ERROR "Only ${archs_names} architeture names are supported.")
+  endif()
+
+  if(${CUDA_ARCH_NAME} STREQUAL "Manual")
+    set(CUDA_ARCH_BIN ${paddle_known_gpu_archs} CACHE STRING "Specify 'real' GPU architectures to build binaries for, BIN(PTX) format is supported")
+    set(CUDA_ARCH_PTX "50"                     CACHE STRING "Specify 'virtual' PTX architectures to build PTX intermediate code for")
+    mark_as_advanced(CUDA_ARCH_BIN CUDA_ARCH_PTX)
+  else()
+    unset(CUDA_ARCH_BIN CACHE)
+    unset(CUDA_ARCH_PTX CACHE)
+  endif()
+
+  if(${CUDA_ARCH_NAME} STREQUAL "Kepler")
+    set(cuda_arch_bin "30 35")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Maxwell")
+    set(cuda_arch_bin "50")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Pascal")
+    set(cuda_arch_bin "60 61")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Volta")
+    set(cuda_arch_bin "70")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "All")
+    set(cuda_arch_bin ${paddle_known_gpu_archs})
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Auto")
+    detect_installed_gpus(cuda_arch_bin)
+  else()  # (${CUDA_ARCH_NAME} STREQUAL "Manual")
+    set(cuda_arch_bin ${CUDA_ARCH_BIN})
+  endif()
+
+  # remove dots and convert to lists
+  string(REGEX REPLACE "\\." "" cuda_arch_bin "${cuda_arch_bin}")
+  string(REGEX REPLACE "\\." "" cuda_arch_ptx "${CUDA_ARCH_PTX}")
+  string(REGEX MATCHALL "[0-9()]+" cuda_arch_bin "${cuda_arch_bin}")
+  string(REGEX MATCHALL "[0-9]+"   cuda_arch_ptx "${cuda_arch_ptx}")
+  list(REMOVE_DUPLICATES cuda_arch_bin)
+  list(REMOVE_DUPLICATES cuda_arch_ptx)
+
+  set(nvcc_flags "")
+  set(nvcc_archs_readable "")
+
+  # Tell NVCC to add binaries for the specified GPUs
+  foreach(arch ${cuda_arch_bin})
+    if(arch MATCHES "([0-9]+)\\(([0-9]+)\\)")
+      # User explicitly specified PTX for the concrete BIN
+      list(APPEND nvcc_flags -gencode arch=compute_${CMAKE_MATCH_2},code=sm_${CMAKE_MATCH_1})
+      list(APPEND nvcc_archs_readable sm_${CMAKE_MATCH_1})
+    else()
+      # User didn't explicitly specify PTX for the concrete BIN, we assume PTX=BIN
+      list(APPEND nvcc_flags -gencode arch=compute_${arch},code=sm_${arch})
+      list(APPEND nvcc_archs_readable sm_${arch})
+    endif()
+  endforeach()
+
+  # Tell NVCC to add PTX intermediate code for the specified architectures
+  foreach(arch ${cuda_arch_ptx})
+    list(APPEND nvcc_flags -gencode arch=compute_${arch},code=compute_${arch})
+    list(APPEND nvcc_archs_readable compute_${arch})
+  endforeach()
+
+  string(REPLACE ";" " " nvcc_archs_readable "${nvcc_archs_readable}")
+  set(${out_variable}          ${nvcc_flags}          PARENT_SCOPE)
+  set(${out_variable}_readable ${nvcc_archs_readable} PARENT_SCOPE)
+endfunction()
+
+if(NOT CUDA_FOUND)
+  return()
+endif()
+
+message(STATUS "CUDA detected: " ${CUDA_VERSION})
+if (${CUDA_VERSION} LESS 7.0)
+  set(paddle_known_gpu_archs ${paddle_known_gpu_archs})
+elseif (${CUDA_VERSION} LESS 8.0) # CUDA 7.x
+  set(paddle_known_gpu_archs ${paddle_known_gpu_archs7})
+  list(APPEND CUDA_NVCC_FLAGS "-D_MWAITXINTRIN_H_INCLUDED")
+  list(APPEND CUDA_NVCC_FLAGS "-D__STRICT_ANSI__")
+elseif (${CUDA_VERSION} LESS 9.0) # CUDA 8.x
+  set(paddle_known_gpu_archs ${paddle_known_gpu_archs8})
+  list(APPEND CUDA_NVCC_FLAGS "-D_MWAITXINTRIN_H_INCLUDED")
+  list(APPEND CUDA_NVCC_FLAGS "-D__STRICT_ANSI__")
+  # CUDA 8 may complain that sm_20 is no longer supported. Suppress the
+  # warning for now.
+  list(APPEND CUDA_NVCC_FLAGS "-Wno-deprecated-gpu-targets")
+endif()
+
+include_directories(${CUDA_INCLUDE_DIRS})
+list(APPEND EXTERNAL_LIBS ${CUDA_LIBRARIES} ${CUDA_rt_LIBRARY})
+if(NOT WITH_DSO)
+    list(APPEND EXTERNAL_LIBS ${CUDNN_LIBRARY} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_curand_LIBRARY} ${NCCL_LIBRARY})
+endif(NOT WITH_DSO)
+
+# find libcuda.so and lbnvrtc.so
+# For libcuda.so, we will find it under lib, lib64, and then the
+# stubs folder, in case we are building on a system that does not
+# have cuda driver installed. On windows, we also search under the
+# folder lib/x64.
+
+find_library(CUDA_CUDA_LIB cuda
+    PATHS ${CUDA_TOOLKIT_ROOT_DIR}
+    PATH_SUFFIXES lib lib64 lib/stubs lib64/stubs lib/x64)
+find_library(CUDA_NVRTC_LIB nvrtc
+    PATHS ${CUDA_TOOLKIT_ROOT_DIR}
+    PATH_SUFFIXES lib lib64 lib/x64)
+
+# setting nvcc arch flags
+select_nvcc_arch_flags(NVCC_FLAGS_EXTRA)
+list(APPEND CUDA_NVCC_FLAGS ${NVCC_FLAGS_EXTRA})
+message(STATUS "Added CUDA NVCC flags for: ${NVCC_FLAGS_EXTRA_readable}")
+
+if(CUDA_CUDA_LIB)
+    # message(STATUS "Found libcuda: ${CUDA_CUDA_LIB}")
+    list(APPEND Caffe2_DEPENDENCY_LIBS ${CUDA_CUDA_LIB})
+else()
+    message(FATAL_ERROR "Cannot find libcuda.so.")
+endif()
+if(CUDA_NVRTC_LIB)
+  # message(STATUS "Found libnvrtc: ${CUDA_NVRTC_LIB}")
+  list(APPEND Caffe2_DEPENDENCY_LIBS ${CUDA_NVRTC_LIB})
+else()
+    message(FATAL_ERROR "Cannot find libnvrtc.so.")
+endif()
+
+# Set C++11 support
+set(CUDA_PROPAGATE_HOST_FLAGS OFF)
+
+# Release/Debug flags set by cmake. Such as -O3 -g -DNDEBUG etc.
+# So, don't set these flags here.
+list(APPEND CUDA_NVCC_FLAGS "-std=c++11")
+list(APPEND CUDA_NVCC_FLAGS "--use_fast_math")
+list(APPEND CUDA_NVCC_FLAGS "-Xcompiler -fPIC")
+# Set :expt-relaxed-constexpr to suppress Eigen warnings
+list(APPEND CUDA_NVCC_FLAGS "--expt-relaxed-constexpr")
+
+if(CMAKE_BUILD_TYPE  STREQUAL "Debug")
+    list(APPEND CUDA_NVCC_FLAGS  ${CMAKE_CXX_FLAGS_DEBUG})
+elseif(CMAKE_BUILD_TYPE  STREQUAL "Release")
+    list(APPEND CUDA_NVCC_FLAGS  ${CMAKE_CXX_FLAGS_RELEASE})
+elseif(CMAKE_BUILD_TYPE  STREQUAL "RelWithDebInfo")
+    list(APPEND CUDA_NVCC_FLAGS  ${CMAKE_CXX_FLAGS_RELWITHDEBINFO})
+elseif(CMAKE_BUILD_TYPE  STREQUAL "MinSizeRel")
+    list(APPEND CUDA_NVCC_FLAGS  ${CMAKE_CXX_FLAGS_MINSIZEREL})
+endif()
+
+mark_as_advanced(CUDA_BUILD_CUBIN CUDA_BUILD_EMULATION CUDA_VERBOSE_BUILD)
+mark_as_advanced(CUDA_SDK_ROOT_DIR CUDA_SEPARABLE_COMPILATION)
diff --git a/cmake/flags.cmake b/cmake/flags.cmake
index 4593ae6180..2b125cef6a 100644
--- a/cmake/flags.cmake
+++ b/cmake/flags.cmake
@@ -149,58 +149,3 @@ endforeach()
 foreach(flag ${GPU_COMMON_FLAGS})
     safe_set_nvflag(${flag})
 endforeach()
-
-
-set(CUDA_PROPAGATE_HOST_FLAGS OFF)
-
-# Release/Debug flags set by cmake. Such as -O3 -g -DNDEBUG etc.
-# So, don't set these flags here.
-LIST(APPEND CUDA_NVCC_FLAGS -std=c++11)
-LIST(APPEND CUDA_NVCC_FLAGS --use_fast_math)
-
-if(CMAKE_BUILD_TYPE  STREQUAL "Debug")
-    LIST(APPEND CUDA_NVCC_FLAGS  ${CMAKE_CXX_FLAGS_DEBUG})
-elseif(CMAKE_BUILD_TYPE  STREQUAL "Release")
-    LIST(APPEND CUDA_NVCC_FLAGS  ${CMAKE_CXX_FLAGS_RELEASE})
-elseif(CMAKE_BUILD_TYPE  STREQUAL "RelWithDebInfo")
-    LIST(APPEND CUDA_NVCC_FLAGS  ${CMAKE_CXX_FLAGS_RELWITHDEBINFO})
-elseif(CMAKE_BUILD_TYPE  STREQUAL "MinSizeRel")
-    LIST(APPEND CUDA_NVCC_FLAGS  ${CMAKE_CXX_FLAGS_MINSIZEREL})
-endif()
-
-function(specify_cuda_arch cuda_version cuda_arch)
-    if(${cuda_version} VERSION_GREATER "8.0")
-        foreach(capability 61 62)
-          if(${cuda_arch} STREQUAL ${capability})
-            list(APPEND __arch_flags " -gencode arch=compute_${cuda_arch},code=sm_${cuda_arch}")
-          endif()
-        endforeach()
-    elseif(${cuda_version} VERSION_GREATER "7.0" and ${cuda_arch} STREQUAL "53")
-        list(APPEND __arch_flags " -gencode arch=compute_${cuda_arch},code=sm_${cuda_arch}")
-    endif()
-endfunction()
-
-# Common gpu architectures: Kepler, Maxwell
-foreach(capability 30 35 50)
-      list(APPEND __arch_flags " -gencode arch=compute_${capability},code=sm_${capability}")
-endforeach()
-
-if (CUDA_VERSION VERSION_GREATER "7.0" OR CUDA_VERSION VERSION_EQUAL "7.0")
-      list(APPEND __arch_flags " -gencode arch=compute_52,code=sm_52")
-endif()
-
-# Modern gpu architectures: Pascal
-if (CUDA_VERSION VERSION_GREATER "8.0" OR CUDA_VERSION VERSION_EQUAL "8.0")
-      list(APPEND __arch_flags " -gencode arch=compute_60,code=sm_60")
-      list(APPEND CUDA_NVCC_FLAGS --expt-relaxed-constexpr)
-endif()
-
-# Custom gpu architecture
-set(CUDA_ARCH)
-
-if(CUDA_ARCH)
-  specify_cuda_arch(${CUDA_VERSION} ${CUDA_ARCH})
-endif()
-
-set(CUDA_NVCC_FLAGS ${__arch_flags} ${CUDA_NVCC_FLAGS})
-

From 8496eab45a23852cd9941227041bcf0fb289c29a Mon Sep 17 00:00:00 2001
From: tensor-tang <jian.j.tang@intel.com>
Date: Thu, 16 Nov 2017 22:45:51 +0800
Subject: [PATCH 24/43] make mklml necessary when with_mkldnn

---
 CMakeLists.txt              |  6 +++++-
 cmake/configure.cmake       | 29 ++++++++---------------------
 cmake/external/mkldnn.cmake | 13 ++++++-------
 cmake/util.cmake            |  4 ++--
 4 files changed, 21 insertions(+), 31 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index fd3582a1bc..5209c40e0f 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -164,8 +164,12 @@ if(WITH_GPU)
     endif(NOT WITH_DSO)
 endif(WITH_GPU)
 
+if(WITH_MKLML)
+    list(APPEND EXTERNAL_LIBS ${MKLML_IOMP_LIB})
+endif()
+
 if(WITH_MKLDNN)
-    list(APPEND EXTERNAL_LIBS ${MKLDNN_LIB} ${MKLDNN_IOMP_LIB})
+    list(APPEND EXTERNAL_LIBS ${MKLDNN_LIB})
 endif()
 
 if(USE_NNPACK)
diff --git a/cmake/configure.cmake b/cmake/configure.cmake
index 24ddb24399..e550ec2856 100644
--- a/cmake/configure.cmake
+++ b/cmake/configure.cmake
@@ -76,27 +76,14 @@ else()
     include_directories(${CUDA_TOOLKIT_INCLUDE})
 endif(NOT WITH_GPU)
 
-if(WITH_MKLDNN)
-    add_definitions(-DPADDLE_USE_MKLDNN)
-    if (WITH_MKLML AND MKLDNN_IOMP_DIR)
-        message(STATUS "Enable Intel OpenMP at ${MKLDNN_IOMP_DIR}")
-        set(OPENMP_FLAGS "-fopenmp")
-        set(CMAKE_C_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
-        set(CMAKE_CXX_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
-        set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OPENMP_FLAGS}")
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OPENMP_FLAGS}")
-    else()
-        find_package(OpenMP)
-        if(OPENMP_FOUND)
-            set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
-            set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
-        else()
-            message(WARNING "Can not find OpenMP."
-                 "Some performance features in MKLDNN may not be available")
-        endif()
-    endif()
-
-endif(WITH_MKLDNN)
+if (WITH_MKLML AND MKLML_IOMP_LIB)
+    message(STATUS "Enable Intel OpenMP with ${MKLML_IOMP_LIB}")
+    set(OPENMP_FLAGS "-fopenmp")
+    set(CMAKE_C_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
+    set(CMAKE_CXX_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
+    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OPENMP_FLAGS}")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OPENMP_FLAGS}")
+endif()
 
 set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${SIMD_FLAG}")
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${SIMD_FLAG}")
diff --git a/cmake/external/mkldnn.cmake b/cmake/external/mkldnn.cmake
index 5a06825beb..b80b6b90c0 100644
--- a/cmake/external/mkldnn.cmake
+++ b/cmake/external/mkldnn.cmake
@@ -40,10 +40,9 @@ INCLUDE_DIRECTORIES(${MKLDNN_INC_DIR})
 
 IF(${CBLAS_PROVIDER} STREQUAL "MKLML")
     SET(MKLDNN_DEPENDS   ${MKLML_PROJECT})
-    SET(MKLDNN_MKLROOT   ${MKLML_ROOT})
-    SET(MKLDNN_IOMP_LIB  ${MKLML_IOMP_LIB})
-    SET(MKLDNN_IOMP_DIR  ${MKLML_LIB_DIR})
-    MESSAGE(STATUS "Build MKLDNN with ${MKLDNN_MKLROOT}")
+    MESSAGE(STATUS "Build MKLDNN with MKLML ${MKLML_ROOT}")
+ELSE()
+    MESSAGE(FATAL_ERROR "Should enable MKLML when build MKLDNN")
 ENDIF()
 
 SET(MKLDNN_CFLAG "${CMAKE_C_FLAGS} -Wno-error=strict-overflow")
@@ -57,15 +56,15 @@ ExternalProject_Add(
     PREFIX              ${MKLDNN_SOURCES_DIR}
     UPDATE_COMMAND      ""
     CMAKE_ARGS          -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR}
-    CMAKE_ARGS          -DMKLROOT=${MKLDNN_MKLROOT}
+    CMAKE_ARGS          -DMKLROOT=${MKLML_ROOT}
     CMAKE_ARGS          -DCMAKE_C_FLAGS=${MKLDNN_CFLAG}
     CMAKE_ARGS          -DCMAKE_CXX_FLAGS=${MKLDNN_CXXFLAG}
     CMAKE_CACHE_ARGS    -DCMAKE_INSTALL_PREFIX:PATH=${MKLDNN_INSTALL_DIR}
-                        -DMKLROOT:PATH=${MKLDNN_MKLROOT}
+                        -DMKLROOT:PATH=${MKLML_ROOT}
 )
 
 ADD_LIBRARY(mkldnn SHARED IMPORTED GLOBAL)
 SET_PROPERTY(TARGET mkldnn PROPERTY IMPORTED_LOCATION ${MKLDNN_LIB})
 ADD_DEPENDENCIES(mkldnn ${MKLDNN_PROJECT})
-MESSAGE(STATUS "Mkldnn library: ${MKLDNN_LIB}")
+MESSAGE(STATUS "MKLDNN library: ${MKLDNN_LIB}")
 LIST(APPEND external_project_dependencies mkldnn)
diff --git a/cmake/util.cmake b/cmake/util.cmake
index 117ab7f49c..ad905ab55b 100644
--- a/cmake/util.cmake
+++ b/cmake/util.cmake
@@ -115,8 +115,8 @@ function(link_paddle_exe TARGET_NAME)
         target_link_libraries(${TARGET_NAME} log)
     endif(ANDROID)
 
-    if(WITH_MKLDNN AND WITH_MKLML AND MKLDNN_IOMP_DIR)
-      target_link_libraries(${TARGET_NAME} "-L${MKLDNN_IOMP_DIR} -liomp5 -Wl,--as-needed")
+    if(WITH_MKLML AND MKLML_LIB_DIR AND MKLML_IOMP_LIB)
+      target_link_libraries(${TARGET_NAME} "-L${MKLML_LIB_DIR} -liomp5 -Wl,--as-needed")
     endif()
 
     add_dependencies(${TARGET_NAME} ${external_project_dependencies})

From 363f690d79aebc5b09bdeb5794ee70c968963e49 Mon Sep 17 00:00:00 2001
From: tensor-tang <jian.j.tang@intel.com>
Date: Thu, 16 Nov 2017 23:14:37 +0800
Subject: [PATCH 25/43] expose only one WITH_MKL to user, covering WITH_MKLDNN
 and WITH_MKLML

---
 CMakeLists.txt                        | 20 ++++++++++++++------
 doc/design/mkldnn/README.MD           |  8 ++++----
 doc/howto/dev/write_docs_cn.rst       |  2 +-
 paddle/gserver/layers/MKLDNNLayer.cpp |  2 +-
 paddle/scripts/docker/README.md       |  3 +--
 paddle/scripts/docker/build.sh        |  6 ++----
 paddle/scripts/submit_local.sh.in     | 10 +++++-----
 paddle/scripts/travis/build_doc.sh    |  2 +-
 8 files changed, 29 insertions(+), 24 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 5209c40e0f..9e30dff70f 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -36,8 +36,7 @@ include(simd)
 ################################ Configurations #######################################
 option(WITH_GPU         "Compile PaddlePaddle with NVIDIA GPU"          ${CUDA_FOUND})
 option(WITH_AVX         "Compile PaddlePaddle with AVX intrinsics"      ${AVX_FOUND})
-option(WITH_MKLDNN      "Compile PaddlePaddle with mkl-dnn support."    ${AVX_FOUND})
-option(WITH_MKLML       "Compile PaddlePaddle with mklml package."      ${AVX_FOUND})
+option(WITH_MKL         "Compile PaddlePaddle with MKL support."        ${AVX_FOUND})
 option(WITH_DSO         "Compile PaddlePaddle with dynamic linked CUDA" ON)
 option(WITH_TESTING     "Compile PaddlePaddle with unit testing"        ON)
 option(WITH_SWIG_PY     "Compile PaddlePaddle with inference api"       ON)
@@ -82,10 +81,8 @@ if(ANDROID OR IOS)
         "Disable PYTHON when cross-compiling for Android and iOS" FORCE)
     set(WITH_RDMA OFF CACHE STRING
         "Disable RDMA when cross-compiling for Android and iOS" FORCE)
-    set(WITH_MKLDNN OFF CACHE STRING
-        "Disable MKLDNN when cross-compiling for Android and iOS" FORCE)
-    set(WITH_MKLML OFF CACHE STRING
-        "Disable MKLML package when cross-compiling for Android and iOS" FORCE)
+    set(WITH_MKL OFF CACHE STRING
+        "Disable MKL when cross-compiling for Android and iOS" FORCE)
 
     # Compile PaddlePaddle mobile inference library
     if (NOT WITH_C_API)
@@ -111,6 +108,17 @@ else()
     set(THIRD_PARTY_BUILD_TYPE Release)
 endif()
 
+if(WITH_MKL)
+    set(WITH_MKLML ON)
+    set(WITH_MKLDNN ${AVX2_FOUND})
+    if(NOT WITH_MKLDNN)
+        message(WARNING "Do not have AVX2 intrinsics and disabled MKL-DNN")
+    endif()
+else()
+    set(WITH_MKLML OFF)
+    set(WITH_MKLDNN OFF)
+endif()
+
 ########################################################################################
 
 include(external/mklml)     # download mklml package
diff --git a/doc/design/mkldnn/README.MD b/doc/design/mkldnn/README.MD
index 16236763a7..ec6d468183 100644
--- a/doc/design/mkldnn/README.MD
+++ b/doc/design/mkldnn/README.MD
@@ -36,13 +36,13 @@ Figure 1. PaddlePaddle on IA.
 我们把集成方案大致分为了如下几个方面。
 
 ### CMake
-我们会在`CMakeLists.txt`中会添加`WITH_MKLDNN`的选项，当设置这个值为`ON`的时候会启用编译MKL-DNN功能。同时会自动开启OpenMP用于提高MKL-DNN的性能。
+我们会在`CMakeLists.txt`中会给用户添加一个`WITH_MKL`的开关，他是负责`WITH_MKLML`和`WITH_MKLDNN`的总开关。
 
-同时，我们会引入`WITH_MKLML`选项，用于选择是否使用MKL-DNN自带的MKLML安装包。这个安装包可以独立于MKL-DNN使用，但是建议在开启MKL-DNN的同时也打开MKLML的开关，这样才能发挥最好的性能。
+当打开`WITH_MKL`时，会开启MKLML的功能，作为PaddlePaddle的CBLAS和LAPACK库，同时会开启Intel OpenMP用于提高MKLML的性能。 如果系统支持AVX2指令集及以上，同时会开启MKL-DNN功能。
 
-所以，我们会在`cmake/external`目录新建`mkldnn.cmake`和`mklml.cmake`文件，它们会在编译PaddlePaddle的时候下载对应的软件包，并放到PaddlePaddle的third party目录中。
+当关闭`WITH_MKL`时，MKLML和MKL-DNN功能会同时关闭。
 
-**备注**：当`WITH_MKLML=ON`的时候，会优先使用这个包作为PaddlePaddle的CBLAS和LAPACK库，所以会稍微改动`cmake/cblas.cmake`中的逻辑。
+所以，我们会在`cmake/external`目录新建`mkldnn.cmake`和`mklml.cmake`文件，它们会在编译PaddlePaddle的时候下载对应的软件包，并放到PaddlePaddle的third party目录中。
 
 ### Layers
 所有MKL-DNN相关的C++ layers，都会按照PaddlePaddle的目录结构存放在
diff --git a/doc/howto/dev/write_docs_cn.rst b/doc/howto/dev/write_docs_cn.rst
index 731a63f945..61f3a22354 100644
--- a/doc/howto/dev/write_docs_cn.rst
+++ b/doc/howto/dev/write_docs_cn.rst
@@ -34,7 +34,7 @@ PaddlePaddle的文档构建有两种方式。
     cd TO_YOUR_PADDLE_CLONE_PATH
     mkdir -p build
     cd build
-    cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKLDNN=OFF -DWITH_MKLML=OFF -DWITH_DOC=ON
+    cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKL=OFF -DWITH_DOC=ON
     make gen_proto_py
     make paddle_docs paddle_docs_cn
 
diff --git a/paddle/gserver/layers/MKLDNNLayer.cpp b/paddle/gserver/layers/MKLDNNLayer.cpp
index e75ac5ba46..2125155c6c 100644
--- a/paddle/gserver/layers/MKLDNNLayer.cpp
+++ b/paddle/gserver/layers/MKLDNNLayer.cpp
@@ -22,7 +22,7 @@ namespace paddle {
 bool MKLDNNLayer::init(const LayerMap& layerMap,
                        const ParameterMap& parameterMap) {
   CHECK(FLAGS_use_mkldnn) << "MkldnnLayers only support use_mkldnn."
-                          << "Please set WITH_MKLDNN=ON "
+                          << "Please set WITH_MKL=ON "
                           << "and set use_mkldnn=True";
   CHECK(!useGpu_) << "Do not support GPU yet";
 
diff --git a/paddle/scripts/docker/README.md b/paddle/scripts/docker/README.md
index b5fd68839d..f3a6f1dba7 100644
--- a/paddle/scripts/docker/README.md
+++ b/paddle/scripts/docker/README.md
@@ -57,8 +57,7 @@ Users can specify the following Docker build arguments with either "ON" or "OFF"
 | `WITH_GPU` | OFF | Generates NVIDIA CUDA GPU code and relies on CUDA libraries. |
 | `WITH_AVX` | OFF | Set to "ON" to enable AVX support. |
 | `WITH_TESTING` | ON | Build unit tests binaries. |
-| `WITH_MKLDNN` | ON | Build with [Intel® MKL DNN](https://github.com/01org/mkl-dnn) support. |
-| `WITH_MKLML` | ON | Build with [Intel® MKL](https://software.intel.com/en-us/mkl) support. |
+| `WITH_MKL` | ON | Build with [Intel® MKL](https://software.intel.com/en-us/mkl) and [Intel® MKL-DNN](https://github.com/01org/mkl-dnn) support. |
 | `WITH_GOLANG` | ON | Build fault-tolerant parameter server written in go. |
 | `WITH_SWIG_PY` | ON | Build with SWIG python API support. |
 | `WITH_C_API` | OFF | Build capi libraries for inference. |
diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh
index e9c89eee1a..595d25fd48 100644
--- a/paddle/scripts/docker/build.sh
+++ b/paddle/scripts/docker/build.sh
@@ -34,8 +34,7 @@ function cmake_gen() {
         ${PYTHON_FLAGS}
         -DWITH_DOC=OFF
         -DWITH_GPU=${WITH_GPU:-OFF}
-        -DWITH_MKLDNN=${WITH_MKLDNN:-ON}
-        -DWITH_MKLML=${WITH_MKLML:-ON}
+        -DWITH_MKL=${WITH_MKL:-ON}
         -DWITH_AVX=${WITH_AVX:-OFF}
         -DWITH_GOLANG=${WITH_GOLANG:-ON}
         -DWITH_SWIG_PY=ON
@@ -56,8 +55,7 @@ EOF
         ${PYTHON_FLAGS} \
         -DWITH_DOC=OFF \
         -DWITH_GPU=${WITH_GPU:-OFF} \
-        -DWITH_MKLDNN=${WITH_MKLDNN:-ON} \
-        -DWITH_MKLML=${WITH_MKLML:-ON} \
+        -DWITH_MKL=${WITH_MKL:-ON} \
         -DWITH_AVX=${WITH_AVX:-OFF} \
         -DWITH_GOLANG=${WITH_GOLANG:-ON} \
         -DWITH_SWIG_PY=${WITH_SWIG_PY:-ON} \
diff --git a/paddle/scripts/submit_local.sh.in b/paddle/scripts/submit_local.sh.in
index b9a49526a7..d71cb84df3 100755
--- a/paddle/scripts/submit_local.sh.in
+++ b/paddle/scripts/submit_local.sh.in
@@ -18,8 +18,8 @@ function version(){
         echo "PaddlePaddle @PADDLE_VERSION@, compiled with"
         echo "    with_avx: @WITH_AVX@"
         echo "    with_gpu: @WITH_GPU@"
+        echo "    with_mkl: @WITH_MKL@"
         echo "    with_mkldnn: @WITH_MKLDNN@"
-        echo "    with_mklml: @WITH_MKLML@"
         echo "    with_double: @WITH_DOUBLE@"
         echo "    with_python: @WITH_PYTHON@"
         echo "    with_rdma: @WITH_RDMA@"
@@ -45,8 +45,8 @@ function ver2num() {
 
 function cpu_config() {
   # auto set KMP_AFFINITY and OMP_DYNAMIC from Hyper Threading Status
-  # only when MKLDNN or MKLML enabled
-  if [ "@WITH_MKLDNN@" == "OFF" ] && [ "@WITH_MKLML@" == "OFF"]; then
+  # only when MKL enabled
+  if [ "@WITH_MKL@" == "OFF" ]; then
     return 0
   fi
   ht=`lscpu |grep "per core"|awk -F':' '{print $2}'|xargs`
@@ -70,8 +70,8 @@ function cpu_config() {
 function threads_config() {
   # auto set OMP_NUM_THREADS and MKL_NUM_THREADS
   # according to trainer_count and total processors
-  # only when MKLDNN or MKLML enabled
-  if [ "@WITH_MKLDNN@" == "OFF" ] && [ "@WITH_MKLML@" == "OFF"]; then
+  # only when MKL enabled
+  if [ "@WITH_MKL@" == "OFF" ]; then
     return 0
   fi
   processors=`grep "processor" /proc/cpuinfo|sort -u|wc -l`
diff --git a/paddle/scripts/travis/build_doc.sh b/paddle/scripts/travis/build_doc.sh
index 973b2736e5..28d82343ed 100755
--- a/paddle/scripts/travis/build_doc.sh
+++ b/paddle/scripts/travis/build_doc.sh
@@ -6,7 +6,7 @@ mkdir -p $TRAVIS_BUILD_DIR/build
 cd $TRAVIS_BUILD_DIR/build
 
 # Compile Documentation only.
-cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKLDNN=OFF -DWITH_MKLML=OFF -DWITH_DOC=ON
+cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKL=OFF -DWITH_DOC=ON
 make -j `nproc` gen_proto_py
 make -j `nproc` paddle_docs paddle_docs_cn
 

From defd7ec6412e0c9d4a5761a9500f22f5b58cf438 Mon Sep 17 00:00:00 2001
From: tensor-tang <jian.j.tang@intel.com>
Date: Thu, 16 Nov 2017 23:35:01 +0800
Subject: [PATCH 26/43] mkldnn only need one trainer

---
 paddle/trainer/Trainer.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/paddle/trainer/Trainer.cpp b/paddle/trainer/Trainer.cpp
index b68e29cd5e..65ca217470 100644
--- a/paddle/trainer/Trainer.cpp
+++ b/paddle/trainer/Trainer.cpp
@@ -137,6 +137,10 @@ void Trainer::init(const std::shared_ptr<TrainerConfigHelper>& config,
     }
   }
 
+  if (FLAGS_trainer_count > 1) {
+    CHECK(!FLAGS_use_mkldnn) << "MKLDNN only need 1 trainer";
+  }
+
   if (testing) {
     LOG(INFO) << "trainer: in testing mode";
     if (config_->getOptConfig().use_sparse_remote_updater() ||

From c808fbbfcbaaf5c08f6254bfdb860f5dac76a627 Mon Sep 17 00:00:00 2001
From: Yiqun Liu <liuyiqun01@baidu.com>
Date: Fri, 17 Nov 2017 10:15:40 +0800
Subject: [PATCH 27/43] Support the build for multiple architectures at one
 cmake command (iOS). (#5677)

* Support the build for multiple architectures at one cmake command (iOS).

* Update the documentations.
---
 cmake/cross_compiling/ios.cmake                |  8 +++-----
 cmake/external/openblas.cmake                  | 13 ++++++-------
 cmake/external/warpctc.cmake                   |  4 ++++
 doc/mobile/cross_compiling_for_android_cn.md   |  2 +-
 doc/mobile/cross_compiling_for_ios_cn.md       | 12 ++++++------
 doc/mobile/cross_compiling_for_raspberry_cn.md |  2 +-
 paddle/cuda/include/hl_gpu.h                   |  2 ++
 7 files changed, 23 insertions(+), 20 deletions(-)

diff --git a/cmake/cross_compiling/ios.cmake b/cmake/cross_compiling/ios.cmake
index 310450f7d0..d3f5bf6852 100644
--- a/cmake/cross_compiling/ios.cmake
+++ b/cmake/cross_compiling/ios.cmake
@@ -76,11 +76,9 @@ set(IOS_PLATFORM ${IOS_PLATFORM} CACHE STRING "Type of iOS Platform")
 # Set the architecture for iOS
 if(NOT DEFINED IOS_ARCH)
   if(IOS_PLATFORM STREQUAL "OS")
-    # FIXME(liuyiqun): support "armv7;armv7s;arm64" future
-    set(IOS_ARCH "arm64")
+    set(IOS_ARCH "armv7;armv7s;arm64")
   elseif(IOS_PLATFORM STREQUAL "SIMULATOR")
-    # FIXME(liuyiqun): support "i386;x86_64" future
-    set(IOS_ARCH "x86_64")
+    set(IOS_ARCH "i386;x86_64")
   endif()
 endif()
 set(CMAKE_OSX_ARCHITECTURES ${IOS_ARCH} CACHE string  "Build architecture for iOS")
@@ -248,7 +246,7 @@ set(IOS_COMPILER_FLAGS "${XCODE_IOS_PLATFORM_VERSION_FLAGS} ${XCODE_IOS_BITCODE_
 
 # Hidden visibilty is required for cxx on iOS 
 set(CMAKE_C_FLAGS "${IOS_COMPILER_FLAGS} ${CMAKE_C_FLAGS}" CACHE STRING "C flags")
-set(CMAKE_CXX_FLAGS "${IOS_COMPILER_FLAGS} -fvisibility-inlines-hidden ${CMAKE_CXX_FLAGS}" CACHE STRING "CXX flags")
+set(CMAKE_CXX_FLAGS "${IOS_COMPILER_FLAGS} -fvisibility=hidden -fvisibility-inlines-hidden ${CMAKE_CXX_FLAGS}" CACHE STRING "CXX flags")
 
 set(IOS_LINK_FLAGS "${XCODE_IOS_PLATFORM_VERSION_FLAGS} -Wl,-search_paths_first")
 
diff --git a/cmake/external/openblas.cmake b/cmake/external/openblas.cmake
index 2253807981..4c4f59656d 100644
--- a/cmake/external/openblas.cmake
+++ b/cmake/external/openblas.cmake
@@ -45,15 +45,14 @@ IF(NOT ${CBLAS_FOUND})
                 SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV8 BINARY=64 USE_THREAD=0)
             ENDIF()
         ELSEIF(IOS)
-            # FIXME(liuyiqun): support multiple architectures
-            SET(OPENBLAS_COMMIT "b5c96fcfcdc82945502a2303116a64d89985daf5")
-            SET(OPENBLAS_CC "${OPENBLAS_CC} ${CMAKE_C_FLAGS} -isysroot ${CMAKE_OSX_SYSROOT}")
-            IF(CMAKE_OSX_ARCHITECTURES MATCHES "armv7")
-                SET(OPENBLAS_CC "${OPENBLAS_CC} -arch armv7")
-                SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV7 ARM_SOFTFP_ABI=1 USE_THREAD=0)
-            ELSEIF(CMAKE_OSX_ARCHITECTURES MATCHES "arm64")
+            IF(CMAKE_OSX_ARCHITECTURES MATCHES "arm64")
+                SET(OPENBLAS_COMMIT "b5c96fcfcdc82945502a2303116a64d89985daf5")
+                SET(OPENBLAS_CC "${OPENBLAS_CC} ${CMAKE_C_FLAGS} -isysroot ${CMAKE_OSX_SYSROOT}")
                 SET(OPENBLAS_CC "${OPENBLAS_CC} -arch arm64")
                 SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV8 BINARY=64 USE_THREAD=0 CROSS_SUFFIX=${CROSS_SUFFIX})
+            ELSE()
+                MESSAGE(FATAL_ERROR "OpenBLAS only support arm64 architectures on iOS. "
+                       "You can set IOS_USE_VECLIB_FOR_BLAS=ON or USE_EIGEN_FOR_BLAS=ON to use other blas library instead.")
             ENDIF()
         ELSEIF(RPI)
             # use hardfp
diff --git a/cmake/external/warpctc.cmake b/cmake/external/warpctc.cmake
index 8bd0582228..a8e1aca49c 100644
--- a/cmake/external/warpctc.cmake
+++ b/cmake/external/warpctc.cmake
@@ -12,6 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+IF(MOBILE_INFERENCE)
+    return()
+ENDIF()
+
 INCLUDE(ExternalProject)
 
 SET(WARPCTC_SOURCES_DIR ${THIRD_PARTY_PATH}/warpctc)
diff --git a/doc/mobile/cross_compiling_for_android_cn.md b/doc/mobile/cross_compiling_for_android_cn.md
index 882066f237..424d7718c6 100644
--- a/doc/mobile/cross_compiling_for_android_cn.md
+++ b/doc/mobile/cross_compiling_for_android_cn.md
@@ -1,4 +1,4 @@
-# 构建Android平台上的PaddlePaddle库
+# Android平台编译指南
 
 用户可通过如下两种方式，交叉编译Android平台上适用的PaddlePaddle库：
 - 基于Docker容器的编译方式
diff --git a/doc/mobile/cross_compiling_for_ios_cn.md b/doc/mobile/cross_compiling_for_ios_cn.md
index cda636a67d..9da48e7f21 100644
--- a/doc/mobile/cross_compiling_for_ios_cn.md
+++ b/doc/mobile/cross_compiling_for_ios_cn.md
@@ -1,4 +1,4 @@
-# 构建iOS平台上的PaddlePaddle库
+# iOS平台编译指南
 交叉编译iOS平台上适用的PaddlePaddle库，需要在MacOS系统上进行。本文的将介绍在MacOS上，从源码交叉编译iOS平台上适用的PaddlePaddle库。
 
 ## 准备交叉编译环境
@@ -25,7 +25,7 @@ iOS平台可选配置参数：
 - `IOS_PLATFORM`，可设置为`OS/SIMULATOR`，默认值为`OS`。
   - `OS`，构建目标为`arm`架构的iPhone或者iPad等物理设备。
   - `SIMULATOR`，构建目标为`x86`架构的模拟器平台。
-- `IOS_ARCH`，目标架构。针对不同的`IOS_PLATFORM`，可设置的目标架构如下表所示：
+- `IOS_ARCH`，目标架构。针对不同的`IOS_PLATFORM`，可设置的目标架构如下表所示，默认编译所有架构：
 
     <table class="docutils">
     <colgroup>
@@ -41,11 +41,11 @@ iOS平台可选配置参数：
     <tbody valign="top">
       <tr class="row-even">
       <td>OS</td>
-      <td>armv7, armv7s, arm64 (默认)</td>
+      <td>armv7, armv7s, arm64 </td>
     </tr>
     <tr class="row-odd">
       <td>SIMULATOR</td>
-      <td>i386, x86_64 (默认)</td>
+      <td>i386, x86_64 </td>
     </tr>
     </tbody>
     </table>
@@ -66,7 +66,7 @@ iOS平台可选配置参数：
 ```bash
 cmake -DCMAKE_SYSTEM_NAME=iOS \
       -DIOS_PLATFORM=OS \
-      -DIOS_ARCH="arm64" \
+      -DIOS_ARCH="armv7;arm64" \
       -DIOS_ENABLE_BITCODE=ON \
       -DIOS_USE_VECLIB_FOR_BLAS=ON \
       -DCMAKE_INSTALL_PREFIX=your/path/to/install \
@@ -112,6 +112,6 @@ $ make install
 - `lib`目录，其中包含PaddlePaddle的C-API静态库
 - `third_party`目录，其中包含所依赖的所有第三方库
 
-注意，不同架构的PaddlePaddle库建议安装到不同的目录下，然后使用`lipo`工具将多个静态库合并成一个支持多个架构的fat库。
+注意，如果PaddlePaddle库需要同时支持真机和模拟器，则需要分别编译真机和模拟器版本，然后使用`lipo`工具合并fat库。
 
 自此，PaddlePaddle库已经安装完成，用户可将合成的fat库用于深度学习相关的iOS App中，调用方法见C-API文档。
diff --git a/doc/mobile/cross_compiling_for_raspberry_cn.md b/doc/mobile/cross_compiling_for_raspberry_cn.md
index 6e983645fa..f8ef9dc803 100644
--- a/doc/mobile/cross_compiling_for_raspberry_cn.md
+++ b/doc/mobile/cross_compiling_for_raspberry_cn.md
@@ -1,4 +1,4 @@
-# 构建Raspberry Pi平台上的PaddlePaddle库
+# Raspberry Pi平台编译指南
 
 通常有两个方法来构建基于 Rasspberry Pi 的版本：
 
diff --git a/paddle/cuda/include/hl_gpu.h b/paddle/cuda/include/hl_gpu.h
index ede2670882..4ab8de80d1 100644
--- a/paddle/cuda/include/hl_gpu.h
+++ b/paddle/cuda/include/hl_gpu.h
@@ -25,7 +25,9 @@ limitations under the License. */
 #include "hl_matrix.h"
 #include "hl_sequence.h"
 #include "hl_sparse.h"
+#ifndef PADDLE_MOBILE_INFERENCE
 #include "hl_warpctc_wrap.h"
+#endif
 
 #ifdef HPPL_STUB_FUNC
 #include "stub/hl_aggregate_stub.h"

From 23a674c98aee5eaf00280d6952d2cc3dec40b495 Mon Sep 17 00:00:00 2001
From: tensor-tang <jian.j.tang@intel.com>
Date: Fri, 17 Nov 2017 10:33:19 +0800
Subject: [PATCH 28/43] switch the flag

---
 paddle/trainer/Trainer.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/paddle/trainer/Trainer.cpp b/paddle/trainer/Trainer.cpp
index 65ca217470..88e684849d 100644
--- a/paddle/trainer/Trainer.cpp
+++ b/paddle/trainer/Trainer.cpp
@@ -137,8 +137,8 @@ void Trainer::init(const std::shared_ptr<TrainerConfigHelper>& config,
     }
   }
 
-  if (FLAGS_trainer_count > 1) {
-    CHECK(!FLAGS_use_mkldnn) << "MKLDNN only need 1 trainer";
+  if (FLAGS_use_mkldnn) {
+    CHECK_EQ(FLAGS_trainer_count, 1UL) << "MKLDNN only need 1 trainer";
   }
 
   if (testing) {

From d13c3a98ceffa807a8fb4e8d2971acf0235afa06 Mon Sep 17 00:00:00 2001
From: Yancey1989 <yancey1989@gmail.com>
Date: Fri, 17 Nov 2017 10:36:03 +0800
Subject: [PATCH 29/43] fix no framework proto file

---
 paddle/operators/math/CMakeLists.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/paddle/operators/math/CMakeLists.txt b/paddle/operators/math/CMakeLists.txt
index b9417f1d7f..002b68fecf 100644
--- a/paddle/operators/math/CMakeLists.txt
+++ b/paddle/operators/math/CMakeLists.txt
@@ -1,7 +1,7 @@
 add_subdirectory(detail)
 
 if(WITH_GPU)
-    nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc im2col.cu DEPS cblas device_context)
+    nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc im2col.cu DEPS cblas device_context framework_proto)
     nv_test(math_function_gpu_test SRCS math_function_test.cu DEPS math_function tensor)
     nv_library(selected_rows_functor SRCS selected_rows_functor.cc selected_rows_functor.cu DEPS selected_rows math_function)
     nv_test(selected_rows_functor_gpu_test SRCS selected_rows_functor_test.cu DEPS selected_rows_functor)
@@ -15,7 +15,7 @@ if(WITH_GPU)
     nv_library(lstm_compute SRCS lstm_compute.cc lstm_compute.cu DEPS device_context activation_functions)
     nv_library(gru_compute SRCS gru_compute.cc gru_compute.cu DEPS device_context activation_functions math_function)
 else()
-    cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context)
+    cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context framework_proto)
     cc_library(selected_rows_functor SRCS selected_rows_functor.cc DEPS selected_rows math_function)
     cc_library(softmax SRCS softmax.cc DEPS device_context)
     cc_library(cross_entropy SRCS cross_entropy.cc DEPS device_context)

From eb070476996bb5d26de5969b4d98892f104dcb42 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Thu, 16 Nov 2017 19:58:49 +0800
Subject: [PATCH 30/43] add padding

---
 paddle/operators/conv_transpose_op.cc         |  7 +---
 paddle/operators/conv_transpose_op.h          |  6 +--
 paddle/operators/math/im2col.cu               |  4 +-
 .../paddle/v2/fluid/tests/test_conv2d_op.py   | 40 +++++++++++++------
 .../fluid/tests/test_conv2d_transpose_op.py   | 26 +++++++++---
 .../fluid/tests/test_conv3d_transpose_op.py   | 31 ++++++++++----
 6 files changed, 77 insertions(+), 37 deletions(-)

diff --git a/paddle/operators/conv_transpose_op.cc b/paddle/operators/conv_transpose_op.cc
index 13ac0cd54c..310e3f5c93 100644
--- a/paddle/operators/conv_transpose_op.cc
+++ b/paddle/operators/conv_transpose_op.cc
@@ -30,11 +30,6 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
   std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
   std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
 
-  for (size_t i = 0; i < paddings.size(); ++i) {
-    PADDLE_ENFORCE_EQ(paddings[i], 0,
-                      "No Padding allowed in conv transpose op.");
-  }
-
   PADDLE_ENFORCE(in_dims.size() == 4 || in_dims.size() == 5,
                  "ConvTransposeOp intput should be 4-D or 5-D tensor.");
   PADDLE_ENFORCE_EQ(in_dims.size(), filter_dims.size(),
@@ -52,7 +47,7 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
 
   std::vector<int64_t> output_shape({in_dims[0], filter_dims[1]});
   for (size_t i = 0; i < strides.size(); ++i) {
-    output_shape.push_back((in_dims[i + 2] - 1) * strides[i] +
+    output_shape.push_back((in_dims[i + 2] - 1) * strides[i] - 2 * paddings[i] +
                            filter_dims[i + 2]);
   }
   ctx->SetOutputDim("Output", framework::make_ddim(output_shape));
diff --git a/paddle/operators/conv_transpose_op.h b/paddle/operators/conv_transpose_op.h
index 4b2bd60437..ab336ad23c 100644
--- a/paddle/operators/conv_transpose_op.h
+++ b/paddle/operators/conv_transpose_op.h
@@ -62,7 +62,6 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
     Tensor* output = context.Output<Tensor>("Output");
 
     std::vector<int> strides = context.Attr<std::vector<int>>("strides");
-    // Actually, no paddings and groups allowed in conv transpose.
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
     // TODO(Zhuoyuan): Paddings can be added in future.
     // groups will alway be disabled in conv2dtranspose.
@@ -148,8 +147,8 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
       } else if (filter_shape_vec.size() == 3) {
         // col2vol: col_matrix -> dy
         // from (c * k_d * k_h * k_w, d * h * w) to (c, o_d, o_h, o_w)
-        col2vol(context.device_context(), col, dilations, strides,
-                std::vector<int>{0, 0, 0}, &output_batch);
+        col2vol(context.device_context(), col, dilations, strides, paddings,
+                &output_batch);
       }
     }
   }
@@ -173,7 +172,6 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
     if ((!input_grad) && (!filter_grad)) return;
 
     std::vector<int> strides = context.Attr<std::vector<int>>("strides");
-    // Actually, no paddings and groups allowed in conv transpose.
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
 
     const int batch_size = static_cast<int>(input->dims()[0]);
diff --git a/paddle/operators/math/im2col.cu b/paddle/operators/math/im2col.cu
index 347df7a0ff..bf78942439 100644
--- a/paddle/operators/math/im2col.cu
+++ b/paddle/operators/math/im2col.cu
@@ -119,8 +119,8 @@ __global__ void col2im(int n, const T* data_col, int im_height, int im_width,
 
   if (index < n) {
     T val = 0;
-    int w = index % im_width;
-    int h = (index / im_width) % im_height;
+    int w = index % im_width + padding_width;
+    int h = (index / im_width) % im_height + padding_height;
     int c = index / (im_width * im_height);
 
     // compute the start and end of the output
diff --git a/python/paddle/v2/fluid/tests/test_conv2d_op.py b/python/paddle/v2/fluid/tests/test_conv2d_op.py
index 907b52c405..2240dc73cd 100644
--- a/python/paddle/v2/fluid/tests/test_conv2d_op.py
+++ b/python/paddle/v2/fluid/tests/test_conv2d_op.py
@@ -110,13 +110,30 @@ class TestConv2dOp(OpTest):
         self.op_type = "conv2d"
 
 
+class TestWithPad(TestConv2dOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] / self.groups
+        self.filter_size = [6, f_c, 3, 3]
+
+
+class TestWithStride(TestConv2dOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [2, 2]
+        self.input_size = [2, 3, 6, 6]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] / self.groups
+        self.filter_size = [6, f_c, 3, 3]
+
+
 class TestWithGroup(TestConv2dOp):
     def init_group(self):
         self.groups = 3
 
-    def init_op_type(self):
-        self.op_type = "conv2d"
-
 
 class TestWith1x1(TestConv2dOp):
     def init_test_case(self):
@@ -127,15 +144,9 @@ class TestWith1x1(TestConv2dOp):
         f_c = self.input_size[1] / self.groups
         self.filter_size = [6, f_c, 1, 1]
 
-    def init_dilation(self):
-        self.dilations = [1, 1]
-
     def init_group(self):
         self.groups = 3
 
-    def init_op_type(self):
-        self.op_type = "conv2d"
-
 
 class TestWithDilation(TestConv2dOp):
     def init_test_case(self):
@@ -152,14 +163,19 @@ class TestWithDilation(TestConv2dOp):
     def init_group(self):
         self.groups = 3
 
+
+#----------------Conv2dCudnn----------------
+class TestCudnn(TestConv2dOp):
     def init_op_type(self):
-        self.op_type = "conv2d"
+        self.op_type = "conv_cudnn"
 
 
-#----------------Conv2dCudnn----------------
+class TestCudnnWithPad(TestWithPad):
+    def init_op_type(self):
+        self.op_type = "conv_cudnn"
 
 
-class TestCudnn(TestConv2dOp):
+class TestCudnnWithStride(TestWithStride):
     def init_op_type(self):
         self.op_type = "conv_cudnn"
 
diff --git a/python/paddle/v2/fluid/tests/test_conv2d_transpose_op.py b/python/paddle/v2/fluid/tests/test_conv2d_transpose_op.py
index 54349c018c..d7b1f2f2a3 100644
--- a/python/paddle/v2/fluid/tests/test_conv2d_transpose_op.py
+++ b/python/paddle/v2/fluid/tests/test_conv2d_transpose_op.py
@@ -4,9 +4,7 @@ from op_test import OpTest
 
 
 def conv2dtranspose_forward_naive(input_, filter_, conv2dtranspose_param):
-    # [2, 3, 5, 5]
     in_n, in_c, in_h, in_w = input_.shape
-    # [3, 6, 3, 3]
     f_c, out_c, f_h, f_w = filter_.shape
     assert in_c == f_c
 
@@ -29,6 +27,7 @@ def conv2dtranspose_forward_naive(input_, filter_, conv2dtranspose_param):
                     j1, j2 = j * stride[0], j * stride[0] + f_w
                     out[n, k, i1:i2, j1:j2] += tmp_out
 
+    out = out[:, :, pad[0]:out_h - pad[0], pad[1]:out_w - pad[1]]
     return out
 
 
@@ -36,8 +35,6 @@ class TestConv2dTransposeOp(OpTest):
     def setUp(self):
         # init as conv transpose
         self.init_op_type()
-
-        # [2, 3, 5, 5] -> kernel [3, 6, 3, 3] -> output [2, 6, 7, 7]
         self.init_test_case()
 
         conv2dtranspose_param = {'stride': self.stride, 'pad': self.pad}
@@ -55,7 +52,6 @@ class TestConv2dTransposeOp(OpTest):
         self.outputs = {'Output': output}
 
     def test_check_output(self):
-        print 'check output here for', self.op_type
         self.check_output()
 
     def test_check_grad_no_input(self):
@@ -88,6 +84,26 @@ class TestConv2dTransposeOp(OpTest):
         self.op_type = "conv2d_transpose"
 
 
+class TestWithPad(TestConv2dTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 3, 3]
+
+
+class TestWithStride(TestConv2dTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [2, 2]
+        self.dilations = [1, 1]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 3, 3]
+
+
 # ------------ test_cudnn ------------
 class TestCudnn(TestConv2dTransposeOp):
     def init_op_type(self):
diff --git a/python/paddle/v2/fluid/tests/test_conv3d_transpose_op.py b/python/paddle/v2/fluid/tests/test_conv3d_transpose_op.py
index 132fe79314..59a32c4082 100644
--- a/python/paddle/v2/fluid/tests/test_conv3d_transpose_op.py
+++ b/python/paddle/v2/fluid/tests/test_conv3d_transpose_op.py
@@ -4,9 +4,7 @@ from op_test import OpTest
 
 
 def conv3dtranspose_forward_naive(input_, filter_, conv3dtranspose_param):
-    # [2, 3, 5, 5, 5]
     in_n, in_c, in_d, in_h, in_w = input_.shape
-    # [3, 6, 3, 3, 3]
     f_c, out_c, f_d, f_h, f_w = filter_.shape
     assert in_c == f_c
 
@@ -14,7 +12,6 @@ def conv3dtranspose_forward_naive(input_, filter_, conv3dtranspose_param):
     out_d = (in_d - 1) * stride[0] + f_d
     out_h = (in_h - 1) * stride[1] + f_h
     out_w = (in_w - 1) * stride[2] + f_w
-
     out = np.zeros((in_n, out_c, out_d, out_h, out_w))
 
     for n in range(in_n):
@@ -33,6 +30,8 @@ def conv3dtranspose_forward_naive(input_, filter_, conv3dtranspose_param):
                         j1, j2 = j * stride[2], j * stride[2] + f_w
                         out[n, k, d1:d2, i1:i2, j1:j2] += tmp_out
 
+    out = out[:, :, pad[0]:out_d - pad[0], pad[1]:out_h - pad[1], pad[2]:out_w -
+              pad[2]]
     return out
 
 
@@ -40,8 +39,6 @@ class TestConv3dTransposeOp(OpTest):
     def setUp(self):
         # init as conv transpose
         self.init_op_type()
-
-        # [2, 3, 5, 5, 5] -> kernel [3, 6, 3, 3, 3] -> output [2, 6, 7, 7, 7]
         self.init_test_case()
 
         conv3dtranspose_param = {'stride': self.stride, 'pad': self.pad}
@@ -49,7 +46,6 @@ class TestConv3dTransposeOp(OpTest):
         filter_ = np.random.random(self.filter_size).astype("float32")
         output = conv3dtranspose_forward_naive(
             input_, filter_, conv3dtranspose_param).astype("float32")
-        # print 'deconv output py', output, output.shape
 
         self.inputs = {'Input': input_, 'Filter': filter_}
         self.attrs = {
@@ -60,7 +56,6 @@ class TestConv3dTransposeOp(OpTest):
         self.outputs = {'Output': output}
 
     def test_check_output(self):
-        print 'check output here'
         self.check_output()
 
     def test_check_grad(self):
@@ -85,7 +80,7 @@ class TestConv3dTransposeOp(OpTest):
         self.pad = [0, 0, 0]
         self.stride = [1, 1, 1]
         self.dilations = [1, 1, 1]
-        self.input_size = [2, 3, 5, 5, 5]  # NCHW
+        self.input_size = [2, 3, 5, 5, 5]  # NCDHW
         f_c = self.input_size[1]
         self.filter_size = [f_c, 6, 3, 3, 3]
 
@@ -93,5 +88,25 @@ class TestConv3dTransposeOp(OpTest):
         self.op_type = "conv3d_transpose"
 
 
+class TestWithPad(TestConv3dTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 1, 1]
+        self.stride = [1, 1, 1]
+        self.dilations = [1, 1, 1]
+        self.input_size = [2, 3, 5, 5, 5]  # NCDHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 3, 3, 3]
+
+
+class TestWithStride(TestConv3dTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 1, 1]
+        self.stride = [2, 2, 2]
+        self.dilations = [1, 1, 1]
+        self.input_size = [2, 3, 5, 5, 5]  # NCDHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 3, 3, 3]
+
+
 if __name__ == '__main__':
     unittest.main()

From 0ce38b77f2312390a61c61fbd05ec4b72347fea6 Mon Sep 17 00:00:00 2001
From: Qiao Longfei <qiaolongfei@baidu.com>
Date: Thu, 16 Nov 2017 22:16:22 -0600
Subject: [PATCH 31/43] correct optimizer import (#5699)

---
 .../v2/fluid/tests/book/test_fit_a_line.py    |  29 ++---
 .../book/test_image_classification_train.py   | 100 ++++--------------
 .../tests/book/test_recognize_digits_conv.py  |  29 ++---
 .../tests/book/test_recognize_digits_mlp.py   |  35 ++----
 .../tests/book/test_recommender_system.py     |  99 +++++------------
 .../book/test_understand_sentiment_conv.py    |  11 +-
 .../test_understand_sentiment_dynamic_lstm.py |  10 +-
 .../book/test_understand_sentiment_lstm.py    |   9 +-
 .../v2/fluid/tests/book/test_word2vec.py      |  49 +++------
 9 files changed, 101 insertions(+), 270 deletions(-)

diff --git a/python/paddle/v2/fluid/tests/book/test_fit_a_line.py b/python/paddle/v2/fluid/tests/book/test_fit_a_line.py
index ee677a2c56..a7f3bfc0ca 100644
--- a/python/paddle/v2/fluid/tests/book/test_fit_a_line.py
+++ b/python/paddle/v2/fluid/tests/book/test_fit_a_line.py
@@ -1,33 +1,22 @@
+import numpy as np
 import paddle.v2 as paddle
-import paddle.v2.fluid.layers as layers
 import paddle.v2.fluid.core as core
-import paddle.v2.fluid.optimizer as optimizer
 import paddle.v2.fluid.framework as framework
-from paddle.v2.fluid.io import save_persistables, load_persistables
+import paddle.v2.fluid.layers as layers
 from paddle.v2.fluid.executor import Executor
+from paddle.v2.fluid.io import save_persistables, load_persistables
+from paddle.v2.fluid.optimizer import SGDOptimizer
 
-import numpy as np
-
-x = layers.data(
-    name='x',
-    shape=[13],
-    data_type='float32')
+x = layers.data(name='x', shape=[13], data_type='float32')
 
-y_predict = layers.fc(input=x,
-                      size=1,
-                      act=None)
+y_predict = layers.fc(input=x, size=1, act=None)
 
-y = layers.data(
-    name='y',
-    shape=[1],
-    data_type='float32')
+y = layers.data(name='y', shape=[1], data_type='float32')
 
-cost = layers.square_error_cost(
-    input=y_predict,
-    label=y)
+cost = layers.square_error_cost(input=y_predict, label=y)
 avg_cost = layers.mean(x=cost)
 
-sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.001)
+sgd_optimizer = SGDOptimizer(learning_rate=0.001)
 opts = sgd_optimizer.minimize(avg_cost)
 
 BATCH_SIZE = 20
diff --git a/python/paddle/v2/fluid/tests/book/test_image_classification_train.py b/python/paddle/v2/fluid/tests/book/test_image_classification_train.py
index f4be835b3a..b850612550 100644
--- a/python/paddle/v2/fluid/tests/book/test_image_classification_train.py
+++ b/python/paddle/v2/fluid/tests/book/test_image_classification_train.py
@@ -1,21 +1,16 @@
 import numpy as np
 import paddle.v2 as paddle
 import paddle.v2.fluid.core as core
+import paddle.v2.fluid.framework as framework
 import paddle.v2.fluid.layers as layers
 import paddle.v2.fluid.nets as nets
-import paddle.v2.fluid.optimizer as optimizer
 from paddle.v2.fluid.executor import Executor
-import paddle.v2.fluid.framework as framework
 from paddle.v2.fluid.initializer import XavierInitializer
+from paddle.v2.fluid.optimizer import AdamOptimizer
 
 
 def resnet_cifar10(input, depth=32):
-    def conv_bn_layer(input,
-                      ch_out,
-                      filter_size,
-                      stride,
-                      padding,
-                      act='relu'):
+    def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu'):
         tmp = layers.conv2d(
             input=input,
             filter_size=filter_size,
@@ -24,9 +19,7 @@ def resnet_cifar10(input, depth=32):
             padding=padding,
             act=None,
             bias_attr=False)
-        return layers.batch_norm(
-            input=tmp,
-            act=act)
+        return layers.batch_norm(input=tmp, act=act)
 
     def shortcut(input, ch_in, ch_out, stride, program, init_program):
         if ch_in != ch_out:
@@ -35,28 +28,11 @@ def resnet_cifar10(input, depth=32):
         else:
             return input
 
-    def basicblock(input,
-                   ch_in,
-                   ch_out,
-                   stride):
-        tmp = conv_bn_layer(
-            input,
-            ch_out,
-            3,
-            stride,
-            1)
-        tmp = conv_bn_layer(
-            tmp,
-            ch_out,
-            3,
-            1,
-            1,
-            act=None)
+    def basicblock(input, ch_in, ch_out, stride):
+        tmp = conv_bn_layer(input, ch_out, 3, stride, 1)
+        tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, act=None)
         short = shortcut(input, ch_in, ch_out, stride)
-        return layers.elementwise_add(
-            x=tmp,
-            y=short,
-            act='relu')
+        return layers.elementwise_add(x=tmp, y=short, act='relu')
 
     def layer_warp(block_func, input, ch_in, ch_out, count, stride):
         tmp = block_func(input, ch_in, ch_out, stride)
@@ -67,45 +43,17 @@ def resnet_cifar10(input, depth=32):
     assert (depth - 2) % 6 == 0
     n = (depth - 2) / 6
     conv1 = conv_bn_layer(
-        input=input,
-        ch_out=16,
-        filter_size=3,
-        stride=1,
-        padding=1)
-    res1 = layer_warp(
-        basicblock,
-        conv1,
-        16,
-        16,
-        n,
-        1)
-    res2 = layer_warp(
-        basicblock,
-        res1,
-        16,
-        32,
-        n,
-        2)
-    res3 = layer_warp(
-        basicblock,
-        res2,
-        32,
-        64,
-        n,
-        2)
+        input=input, ch_out=16, filter_size=3, stride=1, padding=1)
+    res1 = layer_warp(basicblock, conv1, 16, 16, n, 1)
+    res2 = layer_warp(basicblock, res1, 16, 32, n, 2)
+    res3 = layer_warp(basicblock, res2, 32, 64, n, 2)
     pool = layers.pool2d(
-        input=res3,
-        pool_size=8,
-        pool_type='avg',
-        pool_stride=1)
+        input=res3, pool_size=8, pool_type='avg', pool_stride=1)
     return pool
 
 
 def vgg16_bn_drop(input):
-    def conv_block(input,
-                   num_filter,
-                   groups,
-                   dropouts):
+    def conv_block(input, num_filter, groups, dropouts):
         return nets.img_conv_group(
             input=input,
             pool_size=2,
@@ -123,22 +71,14 @@ def vgg16_bn_drop(input):
     conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
     conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
 
-    drop = layers.dropout(
-        x=conv5,
-        dropout_prob=0.5)
+    drop = layers.dropout(x=conv5, dropout_prob=0.5)
     fc1 = layers.fc(input=drop,
                     size=512,
                     act=None,
                     param_attr={"initializer": XavierInitializer()})
-    reshape1 = layers.reshape(
-        x=fc1,
-        shape=list(fc1.shape + (1, 1)))
-    bn = layers.batch_norm(
-        input=reshape1,
-        act='relu')
-    drop2 = layers.dropout(
-        x=bn,
-        dropout_prob=0.5)
+    reshape1 = layers.reshape(x=fc1, shape=list(fc1.shape + (1, 1)))
+    bn = layers.batch_norm(input=reshape1, act='relu')
+    drop2 = layers.dropout(x=bn, dropout_prob=0.5)
     fc2 = layers.fc(input=drop2,
                     size=512,
                     act=None,
@@ -165,8 +105,8 @@ cost = layers.cross_entropy(input=predict, label=label)
 avg_cost = layers.mean(x=cost)
 accuracy = layers.accuracy(input=predict, label=label)
 
-# optimizer = optimizer.SGDOptimizer(learning_rate=0.001)
-optimizer = optimizer.AdamOptimizer(learning_rate=0.001)
+# optimizer = SGDOptimizer(learning_rate=0.001)
+optimizer = AdamOptimizer(learning_rate=0.001)
 opts = optimizer.minimize(avg_cost)
 
 BATCH_SIZE = 128
diff --git a/python/paddle/v2/fluid/tests/book/test_recognize_digits_conv.py b/python/paddle/v2/fluid/tests/book/test_recognize_digits_conv.py
index f330ff5813..75fbaf83e8 100644
--- a/python/paddle/v2/fluid/tests/book/test_recognize_digits_conv.py
+++ b/python/paddle/v2/fluid/tests/book/test_recognize_digits_conv.py
@@ -1,22 +1,15 @@
+import numpy as np
 import paddle.v2 as paddle
-import paddle.v2.fluid.layers as layers
-import paddle.v2.fluid.nets as nets
 import paddle.v2.fluid.core as core
-import paddle.v2.fluid.optimizer as optimizer
 import paddle.v2.fluid.evaluator as evaluator
 import paddle.v2.fluid.framework as framework
+import paddle.v2.fluid.layers as layers
+import paddle.v2.fluid.nets as nets
 from paddle.v2.fluid.executor import Executor
+from paddle.v2.fluid.optimizer import AdamOptimizer
 
-import numpy as np
-
-images = layers.data(
-    name='pixel',
-    shape=[1, 28, 28],
-    data_type='float32')
-label = layers.data(
-    name='label',
-    shape=[1],
-    data_type='int64')
+images = layers.data(name='pixel', shape=[1, 28, 28], data_type='float32')
+label = layers.data(name='label', shape=[1], data_type='int64')
 conv_pool_1 = nets.simple_img_conv_pool(
     input=images,
     filter_size=5,
@@ -32,17 +25,13 @@ conv_pool_2 = nets.simple_img_conv_pool(
     pool_stride=2,
     act="relu")
 
-predict = layers.fc(input=conv_pool_2,
-                    size=10,
-                    act="softmax")
+predict = layers.fc(input=conv_pool_2, size=10, act="softmax")
 cost = layers.cross_entropy(input=predict, label=label)
 avg_cost = layers.mean(x=cost)
-optimizer = optimizer.AdamOptimizer(learning_rate=0.01, beta1=0.9, beta2=0.999)
+optimizer = AdamOptimizer(learning_rate=0.01, beta1=0.9, beta2=0.999)
 opts = optimizer.minimize(avg_cost)
 
-accuracy, acc_out = evaluator.accuracy(
-    input=predict,
-    label=label)
+accuracy, acc_out = evaluator.accuracy(input=predict, label=label)
 
 BATCH_SIZE = 50
 PASS_NUM = 3
diff --git a/python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py b/python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py
index b0164e3e36..cf10b1942e 100644
--- a/python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py
+++ b/python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py
@@ -1,19 +1,15 @@
+import numpy as np
 import paddle.v2 as paddle
-import paddle.v2.fluid.layers as layers
 import paddle.v2.fluid.core as core
-import paddle.v2.fluid.optimizer as optimizer
 import paddle.v2.fluid.framework as framework
+import paddle.v2.fluid.layers as layers
 from paddle.v2.fluid.executor import Executor
-from paddle.v2.fluid.regularizer import L2DecayRegularizer
 from paddle.v2.fluid.initializer import UniformInitializer
-
-import numpy as np
+from paddle.v2.fluid.optimizer import MomentumOptimizer
+from paddle.v2.fluid.regularizer import L2DecayRegularizer
 
 BATCH_SIZE = 128
-image = layers.data(
-    name='x',
-    shape=[784],
-    data_type='float32')
+image = layers.data(name='x', shape=[784], data_type='float32')
 
 param_attr = {
     'name': None,
@@ -22,32 +18,21 @@ param_attr = {
     'regularization': L2DecayRegularizer(0.0005 * BATCH_SIZE)
 }
 
-hidden1 = layers.fc(input=image,
-                    size=128,
-                    act='relu',
-                    param_attr=param_attr)
-hidden2 = layers.fc(input=hidden1,
-                    size=64,
-                    act='relu',
-                    param_attr=param_attr)
+hidden1 = layers.fc(input=image, size=128, act='relu', param_attr=param_attr)
+hidden2 = layers.fc(input=hidden1, size=64, act='relu', param_attr=param_attr)
 
 predict = layers.fc(input=hidden2,
                     size=10,
                     act='softmax',
                     param_attr=param_attr)
 
-label = layers.data(
-    name='y',
-    shape=[1],
-    data_type='int64')
+label = layers.data(name='y', shape=[1], data_type='int64')
 
 cost = layers.cross_entropy(input=predict, label=label)
 avg_cost = layers.mean(x=cost)
-accuracy = layers.accuracy(
-    input=predict,
-    label=label)
+accuracy = layers.accuracy(input=predict, label=label)
 
-optimizer = optimizer.MomentumOptimizer(learning_rate=0.001, momentum=0.9)
+optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
 opts = optimizer.minimize(avg_cost)
 
 train_reader = paddle.batch(
diff --git a/python/paddle/v2/fluid/tests/book/test_recommender_system.py b/python/paddle/v2/fluid/tests/book/test_recommender_system.py
index eefcb55beb..55ded3aed3 100644
--- a/python/paddle/v2/fluid/tests/book/test_recommender_system.py
+++ b/python/paddle/v2/fluid/tests/book/test_recommender_system.py
@@ -1,12 +1,11 @@
+import numpy as np
 import paddle.v2 as paddle
-import paddle.v2.fluid.layers as layers
-import paddle.v2.fluid.nets as nets
 import paddle.v2.fluid.core as core
-import paddle.v2.fluid.optimizer as optimizer
 import paddle.v2.fluid.framework as framework
+import paddle.v2.fluid.layers as layers
+import paddle.v2.fluid.nets as nets
 from paddle.v2.fluid.executor import Executor
-
-import numpy as np
+from paddle.v2.fluid.optimizer import SGDOptimizer
 
 IS_SPARSE = True
 USE_GPU = False
@@ -19,10 +18,7 @@ def get_usr_combined_features():
 
     USR_DICT_SIZE = paddle.dataset.movielens.max_user_id() + 1
 
-    uid = layers.data(
-        name='user_id',
-        shape=[1],
-        data_type='int64')
+    uid = layers.data(name='user_id', shape=[1], data_type='int64')
 
     usr_emb = layers.embedding(
         input=uid,
@@ -31,15 +27,11 @@ def get_usr_combined_features():
         param_attr={'name': 'user_table'},
         is_sparse=IS_SPARSE)
 
-    usr_fc = layers.fc(input=usr_emb,
-                       size=32)
+    usr_fc = layers.fc(input=usr_emb, size=32)
 
     USR_GENDER_DICT_SIZE = 2
 
-    usr_gender_id = layers.data(
-        name='gender_id',
-        shape=[1],
-        data_type='int64')
+    usr_gender_id = layers.data(name='gender_id', shape=[1], data_type='int64')
 
     usr_gender_emb = layers.embedding(
         input=usr_gender_id,
@@ -47,14 +39,10 @@ def get_usr_combined_features():
         param_attr={'name': 'gender_table'},
         is_sparse=IS_SPARSE)
 
-    usr_gender_fc = layers.fc(input=usr_gender_emb,
-                              size=16)
+    usr_gender_fc = layers.fc(input=usr_gender_emb, size=16)
 
     USR_AGE_DICT_SIZE = len(paddle.dataset.movielens.age_table)
-    usr_age_id = layers.data(
-        name='age_id',
-        shape=[1],
-        data_type="int64")
+    usr_age_id = layers.data(name='age_id', shape=[1], data_type="int64")
 
     usr_age_emb = layers.embedding(
         input=usr_age_id,
@@ -62,14 +50,10 @@ def get_usr_combined_features():
         is_sparse=IS_SPARSE,
         param_attr={'name': 'age_table'})
 
-    usr_age_fc = layers.fc(input=usr_age_emb,
-                           size=16)
+    usr_age_fc = layers.fc(input=usr_age_emb, size=16)
 
     USR_JOB_DICT_SIZE = paddle.dataset.movielens.max_job_id() + 1
-    usr_job_id = layers.data(
-        name='job_id',
-        shape=[1],
-        data_type="int64")
+    usr_job_id = layers.data(name='job_id', shape=[1], data_type="int64")
 
     usr_job_emb = layers.embedding(
         input=usr_job_id,
@@ -77,16 +61,12 @@ def get_usr_combined_features():
         param_attr={'name': 'job_table'},
         is_sparse=IS_SPARSE)
 
-    usr_job_fc = layers.fc(input=usr_job_emb,
-                           size=16)
+    usr_job_fc = layers.fc(input=usr_job_emb, size=16)
 
     concat_embed = layers.concat(
-        input=[usr_fc, usr_gender_fc, usr_age_fc, usr_job_fc],
-        axis=1)
+        input=[usr_fc, usr_gender_fc, usr_age_fc, usr_job_fc], axis=1)
 
-    usr_combined_features = layers.fc(input=concat_embed,
-                                      size=200,
-                                      act="tanh")
+    usr_combined_features = layers.fc(input=concat_embed, size=200, act="tanh")
 
     return usr_combined_features
 
@@ -95,10 +75,7 @@ def get_mov_combined_features():
 
     MOV_DICT_SIZE = paddle.dataset.movielens.max_movie_id() + 1
 
-    mov_id = layers.data(
-        name='movie_id',
-        shape=[1],
-        data_type='int64')
+    mov_id = layers.data(name='movie_id', shape=[1], data_type='int64')
 
     mov_emb = layers.embedding(
         input=mov_id,
@@ -107,36 +84,24 @@ def get_mov_combined_features():
         param_attr={'name': 'movie_table'},
         is_sparse=IS_SPARSE)
 
-    mov_fc = layers.fc(input=mov_emb,
-                       size=32)
+    mov_fc = layers.fc(input=mov_emb, size=32)
 
     CATEGORY_DICT_SIZE = len(paddle.dataset.movielens.movie_categories())
 
-    category_id = layers.data(
-        name='category_id',
-        shape=[1],
-        data_type='int64')
+    category_id = layers.data(name='category_id', shape=[1], data_type='int64')
 
     mov_categories_emb = layers.embedding(
-        input=category_id,
-        size=[CATEGORY_DICT_SIZE, 32],
-        is_sparse=IS_SPARSE)
+        input=category_id, size=[CATEGORY_DICT_SIZE, 32], is_sparse=IS_SPARSE)
 
     mov_categories_hidden = layers.sequence_pool(
-        input=mov_categories_emb,
-        pool_type="sum")
+        input=mov_categories_emb, pool_type="sum")
 
     MOV_TITLE_DICT_SIZE = len(paddle.dataset.movielens.get_movie_title_dict())
 
-    mov_title_id = layers.data(
-        name='movie_title',
-        shape=[1],
-        data_type='int64')
+    mov_title_id = layers.data(name='movie_title', shape=[1], data_type='int64')
 
     mov_title_emb = layers.embedding(
-        input=mov_title_id,
-        size=[MOV_TITLE_DICT_SIZE, 32],
-        is_sparse=IS_SPARSE)
+        input=mov_title_id, size=[MOV_TITLE_DICT_SIZE, 32], is_sparse=IS_SPARSE)
 
     mov_title_conv = nets.sequence_conv_pool(
         input=mov_title_emb,
@@ -146,13 +111,10 @@ def get_mov_combined_features():
         pool_type="sum")
 
     concat_embed = layers.concat(
-        input=[mov_fc, mov_categories_hidden, mov_title_conv],
-        axis=1)
+        input=[mov_fc, mov_categories_hidden, mov_title_conv], axis=1)
 
     # FIXME(dzh) : need tanh operator
-    mov_combined_features = layers.fc(input=concat_embed,
-                                      size=200,
-                                      act="tanh")
+    mov_combined_features = layers.fc(input=concat_embed, size=200, act="tanh")
 
     return mov_combined_features
 
@@ -162,18 +124,11 @@ def model():
     mov_combined_features = get_mov_combined_features()
 
     # need cos sim
-    inference = layers.cos_sim(
-        X=usr_combined_features,
-        Y=mov_combined_features)
+    inference = layers.cos_sim(X=usr_combined_features, Y=mov_combined_features)
 
-    label = layers.data(
-        name='score',
-        shape=[1],
-        data_type='float32')
+    label = layers.data(name='score', shape=[1], data_type='float32')
 
-    square_cost = layers.square_error_cost(
-        input=inference,
-        label=label)
+    square_cost = layers.square_error_cost(input=inference, label=label)
 
     avg_cost = layers.mean(x=square_cost)
 
@@ -182,7 +137,7 @@ def model():
 
 def main():
     cost = model()
-    sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.2)
+    sgd_optimizer = SGDOptimizer(learning_rate=0.2)
     opts = sgd_optimizer.minimize(cost)
 
     if USE_GPU:
diff --git a/python/paddle/v2/fluid/tests/book/test_understand_sentiment_conv.py b/python/paddle/v2/fluid/tests/book/test_understand_sentiment_conv.py
index 91fc79a987..e69b915a9c 100644
--- a/python/paddle/v2/fluid/tests/book/test_understand_sentiment_conv.py
+++ b/python/paddle/v2/fluid/tests/book/test_understand_sentiment_conv.py
@@ -1,12 +1,11 @@
+import numpy as np
 import paddle.v2 as paddle
-import paddle.v2.fluid.layers as layers
-import paddle.v2.fluid.nets as nets
 import paddle.v2.fluid.core as core
-import paddle.v2.fluid.optimizer as optimizer
 import paddle.v2.fluid.framework as framework
+import paddle.v2.fluid.layers as layers
+import paddle.v2.fluid.nets as nets
 from paddle.v2.fluid.executor import Executor
-
-import numpy as np
+from paddle.v2.fluid.optimizer import AdamOptimizer
 
 
 def convolution_net(input_dim, class_dim=2, emb_dim=32, hid_dim=32):
@@ -31,7 +30,7 @@ def convolution_net(input_dim, class_dim=2, emb_dim=32, hid_dim=32):
                            act="softmax")
     cost = layers.cross_entropy(input=prediction, label=label)
     avg_cost = layers.mean(x=cost)
-    adam_optimizer = optimizer.AdamOptimizer(learning_rate=0.002)
+    adam_optimizer = AdamOptimizer(learning_rate=0.002)
     opts = adam_optimizer.minimize(avg_cost)
     acc = layers.accuracy(input=prediction, label=label)
     return avg_cost, acc
diff --git a/python/paddle/v2/fluid/tests/book/test_understand_sentiment_dynamic_lstm.py b/python/paddle/v2/fluid/tests/book/test_understand_sentiment_dynamic_lstm.py
index 8c3d448835..65d4454250 100644
--- a/python/paddle/v2/fluid/tests/book/test_understand_sentiment_dynamic_lstm.py
+++ b/python/paddle/v2/fluid/tests/book/test_understand_sentiment_dynamic_lstm.py
@@ -1,12 +1,10 @@
+import numpy as np
 import paddle.v2 as paddle
-import paddle.v2.fluid.layers as layers
-import paddle.v2.fluid.nets as nets
 import paddle.v2.fluid.core as core
-import paddle.v2.fluid.optimizer as optimizer
 import paddle.v2.fluid.framework as framework
+import paddle.v2.fluid.layers as layers
 from paddle.v2.fluid.executor import Executor
-
-import numpy as np
+from paddle.v2.fluid.optimizer import AdamOptimizer
 
 
 def stacked_lstm_net(input_dim,
@@ -41,7 +39,7 @@ def stacked_lstm_net(input_dim,
                            act='softmax')
     cost = layers.cross_entropy(input=prediction, label=label)
     avg_cost = layers.mean(x=cost)
-    adam_optimizer = optimizer.AdamOptimizer(learning_rate=0.002)
+    adam_optimizer = AdamOptimizer(learning_rate=0.002)
     opts = adam_optimizer.minimize(avg_cost)
     acc = layers.accuracy(input=prediction, label=label)
     return avg_cost, acc
diff --git a/python/paddle/v2/fluid/tests/book/test_understand_sentiment_lstm.py b/python/paddle/v2/fluid/tests/book/test_understand_sentiment_lstm.py
index a7d791c1f3..280f6e902c 100644
--- a/python/paddle/v2/fluid/tests/book/test_understand_sentiment_lstm.py
+++ b/python/paddle/v2/fluid/tests/book/test_understand_sentiment_lstm.py
@@ -1,11 +1,10 @@
+import numpy as np
 import paddle.v2 as paddle
-import paddle.v2.fluid.layers as layers
 import paddle.v2.fluid.core as core
-import paddle.v2.fluid.optimizer as optimizer
 import paddle.v2.fluid.framework as framework
+import paddle.v2.fluid.layers as layers
 from paddle.v2.fluid.executor import Executor
-
-import numpy as np
+from paddle.v2.fluid.optimizer import AdamOptimizer
 
 
 def lstm_net(dict_dim, class_dim=2, emb_dim=32, seq_len=80, batch_size=50):
@@ -33,7 +32,7 @@ def lstm_net(dict_dim, class_dim=2, emb_dim=32, seq_len=80, batch_size=50):
     cost = layers.cross_entropy(input=prediction, label=label)
 
     avg_cost = layers.mean(x=cost)
-    adam_optimizer = optimizer.AdamOptimizer(learning_rate=0.002)
+    adam_optimizer = AdamOptimizer(learning_rate=0.002)
     opts = adam_optimizer.minimize(avg_cost)
     acc = layers.accuracy(input=prediction, label=label)
 
diff --git a/python/paddle/v2/fluid/tests/book/test_word2vec.py b/python/paddle/v2/fluid/tests/book/test_word2vec.py
index 9dcb6f2fea..afa7b28519 100644
--- a/python/paddle/v2/fluid/tests/book/test_word2vec.py
+++ b/python/paddle/v2/fluid/tests/book/test_word2vec.py
@@ -1,11 +1,10 @@
+import numpy as np
 import paddle.v2 as paddle
-import paddle.v2.fluid.layers as layers
 import paddle.v2.fluid.core as core
-import paddle.v2.fluid.optimizer as optimizer
 import paddle.v2.fluid.framework as framework
+import paddle.v2.fluid.layers as layers
 from paddle.v2.fluid.executor import Executor
-
-import numpy as np
+from paddle.v2.fluid.optimizer import SGDOptimizer
 
 PASS_NUM = 100
 EMBED_SIZE = 32
@@ -17,26 +16,11 @@ IS_SPARSE = True
 word_dict = paddle.dataset.imikolov.build_dict()
 dict_size = len(word_dict)
 
-first_word = layers.data(
-    name='firstw',
-    shape=[1],
-    data_type='int64')
-second_word = layers.data(
-    name='secondw',
-    shape=[1],
-    data_type='int64')
-third_word = layers.data(
-    name='thirdw',
-    shape=[1],
-    data_type='int64')
-forth_word = layers.data(
-    name='forthw',
-    shape=[1],
-    data_type='int64')
-next_word = layers.data(
-    name='nextw',
-    shape=[1],
-    data_type='int64')
+first_word = layers.data(name='firstw', shape=[1], data_type='int64')
+second_word = layers.data(name='secondw', shape=[1], data_type='int64')
+third_word = layers.data(name='thirdw', shape=[1], data_type='int64')
+forth_word = layers.data(name='forthw', shape=[1], data_type='int64')
+next_word = layers.data(name='nextw', shape=[1], data_type='int64')
 
 embed_first = layers.embedding(
     input=first_word,
@@ -64,19 +48,12 @@ embed_forth = layers.embedding(
     param_attr={'name': 'shared_w'})
 
 concat_embed = layers.concat(
-    input=[embed_first, embed_second, embed_third, embed_forth],
-    axis=1)
-hidden1 = layers.fc(input=concat_embed,
-                    size=HIDDEN_SIZE,
-                    act='sigmoid')
-predict_word = layers.fc(input=hidden1,
-                         size=dict_size,
-                         act='softmax')
-cost = layers.cross_entropy(
-    input=predict_word,
-    label=next_word)
+    input=[embed_first, embed_second, embed_third, embed_forth], axis=1)
+hidden1 = layers.fc(input=concat_embed, size=HIDDEN_SIZE, act='sigmoid')
+predict_word = layers.fc(input=hidden1, size=dict_size, act='softmax')
+cost = layers.cross_entropy(input=predict_word, label=next_word)
 avg_cost = layers.mean(x=cost)
-sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.001)
+sgd_optimizer = SGDOptimizer(learning_rate=0.001)
 opts = sgd_optimizer.minimize(avg_cost)
 
 train_reader = paddle.batch(

From e01b09410d7408e539f9a51f954b7378ea0c4ce9 Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Fri, 17 Nov 2017 12:30:05 +0800
Subject: [PATCH 32/43] remove test_CompareTwoOpts

---
 paddle/trainer/tests/CMakeLists.txt           |  10 -
 .../tests/sample_trainer_config_opt_a.conf    |  44 -----
 .../tests/sample_trainer_config_opt_b.conf    |  44 -----
 paddle/trainer/tests/test_CompareTwoOpts.cpp  | 184 ------------------
 4 files changed, 282 deletions(-)
 delete mode 100644 paddle/trainer/tests/sample_trainer_config_opt_a.conf
 delete mode 100644 paddle/trainer/tests/sample_trainer_config_opt_b.conf
 delete mode 100644 paddle/trainer/tests/test_CompareTwoOpts.cpp

diff --git a/paddle/trainer/tests/CMakeLists.txt b/paddle/trainer/tests/CMakeLists.txt
index 3168f3c0ff..80665551ec 100644
--- a/paddle/trainer/tests/CMakeLists.txt
+++ b/paddle/trainer/tests/CMakeLists.txt
@@ -29,16 +29,6 @@ if(WITH_PYTHON)
       WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
 endif()
 
-############### test_CompareTwoOpts ###################
-add_unittest_without_exec(test_CompareTwoOpts
-    test_CompareTwoOpts.cpp)
-add_test(NAME test_CompareTwoOpts
-  COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python/
-        ${CMAKE_CURRENT_BINARY_DIR}/test_CompareTwoOpts
-            --config_file_a=trainer/tests/sample_trainer_config_opt_a.conf --config_file_b=trainer/tests/sample_trainer_config_opt_b.conf
-            --num_passes=1 --need_high_accuracy=0
-    WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
-
 ################# test_recurrent_machine_generation ###############
 add_unittest_without_exec(test_recurrent_machine_generation
     test_recurrent_machine_generation.cpp)
diff --git a/paddle/trainer/tests/sample_trainer_config_opt_a.conf b/paddle/trainer/tests/sample_trainer_config_opt_a.conf
deleted file mode 100644
index 8ece96f595..0000000000
--- a/paddle/trainer/tests/sample_trainer_config_opt_a.conf
+++ /dev/null
@@ -1,44 +0,0 @@
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from paddle.trainer_config_helpers import *
-
-################################### Data Configuration ###################################
-TrainData(SimpleData(
-            files = "trainer/tests/sample_filelist.txt",
-            feat_dim = 3,
-            context_len = 0,
-            buffer_capacity = 1000000))
-################################### Algorithm Configuration ###################################
-settings(batch_size = 1000,
-         learning_method = MomentumOptimizer(momentum=0.5, sparse=False))
-################################### Network Configuration ###################################
-data = data_layer(name ="input", size=3)
-
-fc1 = fc_layer(input=data, size=800,
-               bias_attr=True,
-               act=SigmoidActivation())
-
-fc2 = fc_layer(input=fc1, size=800,
-               bias_attr=True,
-               act=SigmoidActivation())
-
-output = fc_layer(input=[fc1, fc2], size=10,
-                  bias_attr=True,
-                  act=SoftmaxActivation())
-
-lbl = data_layer(name ="label", size=1)
-
-cost = classification_cost(input=output, label=lbl)
-outputs(cost)
diff --git a/paddle/trainer/tests/sample_trainer_config_opt_b.conf b/paddle/trainer/tests/sample_trainer_config_opt_b.conf
deleted file mode 100644
index 8ece96f595..0000000000
--- a/paddle/trainer/tests/sample_trainer_config_opt_b.conf
+++ /dev/null
@@ -1,44 +0,0 @@
-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from paddle.trainer_config_helpers import *
-
-################################### Data Configuration ###################################
-TrainData(SimpleData(
-            files = "trainer/tests/sample_filelist.txt",
-            feat_dim = 3,
-            context_len = 0,
-            buffer_capacity = 1000000))
-################################### Algorithm Configuration ###################################
-settings(batch_size = 1000,
-         learning_method = MomentumOptimizer(momentum=0.5, sparse=False))
-################################### Network Configuration ###################################
-data = data_layer(name ="input", size=3)
-
-fc1 = fc_layer(input=data, size=800,
-               bias_attr=True,
-               act=SigmoidActivation())
-
-fc2 = fc_layer(input=fc1, size=800,
-               bias_attr=True,
-               act=SigmoidActivation())
-
-output = fc_layer(input=[fc1, fc2], size=10,
-                  bias_attr=True,
-                  act=SoftmaxActivation())
-
-lbl = data_layer(name ="label", size=1)
-
-cost = classification_cost(input=output, label=lbl)
-outputs(cost)
diff --git a/paddle/trainer/tests/test_CompareTwoOpts.cpp b/paddle/trainer/tests/test_CompareTwoOpts.cpp
deleted file mode 100644
index 383505f813..0000000000
--- a/paddle/trainer/tests/test_CompareTwoOpts.cpp
+++ /dev/null
@@ -1,184 +0,0 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#include <gtest/gtest.h>
-#include <paddle/utils/PythonUtil.h>
-#include <algorithm>
-#include <cstdlib>
-
-#include "paddle/trainer/Trainer.h"
-
-using namespace paddle;  // NOLINT
-using namespace std;     // NOLINT
-
-DECLARE_int32(gpu_id);
-
-DECLARE_bool(local);
-DECLARE_bool(use_gpu);
-
-DECLARE_string(config);
-DECLARE_string(nics);
-
-DEFINE_string(config_file_a, "", "config of one network to compare");
-DEFINE_string(config_file_b, "", "config of another network to compare");
-DEFINE_bool(need_high_accuracy,
-            true,
-            "whether need to run in double accuracy (recommended)");
-DEFINE_double(
-    max_diff_ratio,
-    0.0f,
-    "max diff ratio allowed for outputs and parameters (value/gradient)");
-
-struct ComData {
-  vector<Argument> outArgs;
-  vector<ParameterPtr> parameters;
-};
-
-void calcGradient(ComData& data, const string configFile) {
-  FLAGS_config = configFile;
-
-  FLAGS_local = true;
-  FLAGS_use_gpu = false;
-
-  FLAGS_nics = "";
-
-  *ThreadLocalRand::getSeed() = 0;
-  srand(0);
-
-  Trainer trainer;
-  trainer.init(TrainerConfigHelper::createFromFlagConfig(), false);
-
-  data.parameters = trainer.getGradientMachine()->getParameters();
-  trainer.getDataProvider()->setSkipShuffle();
-  trainer.train();
-}
-
-void checkBuffer(real* A,
-                 const char* desA,
-                 real* B,
-                 const char* desB,
-                 size_t len,
-                 size_t width = 1) {
-  int nNum = 0;
-  for (size_t i = 0; i < len; ++i) {
-    real diff = fabs(A[i] - B[i]);
-    if (diff > 0.0f &&
-        diff / std::max(fabs(A[i]), fabs(B[i])) > FLAGS_max_diff_ratio) {
-      nNum++;
-      LOG(INFO) << "Row: " << i / width << ", " << desA << " : " << A[i]
-                << "    " << desB << " : " << B[i];
-    }
-  }
-  EXPECT_EQ(0, nNum);
-  LOG(INFO) << "\n\n";
-}
-
-void compareGradient(ComData& comDataA, ComData& comDataB) {
-  vector<Argument> outArgsA = comDataA.outArgs;
-  vector<Argument> outArgsB = comDataB.outArgs;
-
-  for (size_t i = 0; i < outArgsA.size(); ++i) {
-    CpuMatrix matA(outArgsA[i].value->getHeight(),
-                   outArgsA[i].value->getWidth());
-    CpuMatrix matB(outArgsB[i].value->getHeight(),
-                   outArgsB[i].value->getWidth());
-
-    matA.copyFrom(*outArgsA[i].value);
-    matB.copyFrom(*outArgsB[i].value);
-
-    LOG(INFO) << "\n--------------------------------"
-              << " Check Network Output_" << i << ":"
-              << " -------------------------------------\n";
-    checkBuffer(matA.getData(),
-                "network A output",
-                matB.getData(),
-                "network B output",
-                matA.getElementCnt(),
-                matA.getWidth());
-  }
-
-  vector<ParameterPtr>& parametersA = comDataA.parameters;
-  vector<ParameterPtr>& parametersB = comDataB.parameters;
-
-  LOG(INFO) << "\n\n--------------------------------"
-            << " Check Gradient Machine Parameters:"
-            << " -------------------------------------\n";
-  for (size_t i = 0; i < parametersA.size(); ++i) {
-    ParameterPtr parameterA, parameterB;
-    parameterA = parametersA[i];
-    parameterB = parametersB[i];
-
-    CpuVector paraA(parameterA->getSize());
-    CpuVector paraB(parameterB->getSize());
-    paraA.copyFrom(*parameterA->getBuf(PARAMETER_VALUE));
-    paraB.copyFrom(*parameterB->getBuf(PARAMETER_VALUE));
-
-    LOG(INFO) << "\n\n----------- PARAMETER_VALUE:  " << parameterA->getName()
-              << " ; size : " << paraA.getSize() << " ------------";
-    checkBuffer(paraA.getData(),
-                "Network A",
-                paraB.getData(),
-                "Network B",
-                paraA.getSize());
-
-    CpuVector gradA(*parameterA->getBuf(PARAMETER_GRADIENT));
-    CpuVector gradB(*parameterB->getBuf(PARAMETER_GRADIENT));
-
-    LOG(INFO) << "\n\n----------- PARAMETER_GRADIENT: " << parameterA->getName()
-              << " ; size : " << gradA.getSize() << " -----------";
-    checkBuffer(gradA.getData(),
-                "Network A",
-                gradB.getData(),
-                "Network B",
-                gradA.getSize());
-  }
-}
-
-TEST(Trainer, create) {
-  ComData dataA;
-  calcGradient(dataA, FLAGS_config_file_a);
-  LOG(INFO) << "\n\ntraining of Network A is finished\n\n";
-
-  ComData dataB;
-  calcGradient(dataB, FLAGS_config_file_b);
-  LOG(INFO) << "\n\ntraining of the Network B is finished\n\n";
-
-  compareGradient(dataA, dataB);
-}
-
-int main(int argc, char** argv) {
-  paddle::initMain(argc, argv);
-  testing::InitGoogleTest(&argc, argv);
-  initPython(argc, argv);
-
-#ifndef PADDLE_TYPE_DOUBLE
-  if (FLAGS_need_high_accuracy) {
-    LOG(INFO) << "skip test due to it's need high accuracy";
-    return 0;
-  }
-  if (FLAGS_max_diff_ratio == 0.0f) {
-    FLAGS_max_diff_ratio = 2e-4;
-    LOG(INFO) << "auto set max_diff_ratio " << FLAGS_max_diff_ratio
-              << " in low accuracy mode";
-  }
-#else
-  if (FLAGS_max_diff_ratio == 0.0f) {
-    FLAGS_max_diff_ratio = 2e-7;
-    LOG(INFO) << "auto set max_diff_ratio " << FLAGS_max_diff_ratio
-              << " in high accuracy mode";
-  }
-#endif
-  int ret = RUN_ALL_TESTS();
-  return ret;
-}

From 1578c20aaf474ecbb3c3d082be9964a9fce26fa6 Mon Sep 17 00:00:00 2001
From: tensor-tang <jian.j.tang@intel.com>
Date: Fri, 17 Nov 2017 13:28:56 +0800
Subject: [PATCH 33/43] add the missing macro PADDLE_USE_MKLDNN

---
 cmake/external/mkldnn.cmake | 1 +
 1 file changed, 1 insertion(+)

diff --git a/cmake/external/mkldnn.cmake b/cmake/external/mkldnn.cmake
index b80b6b90c0..fc52d339d7 100644
--- a/cmake/external/mkldnn.cmake
+++ b/cmake/external/mkldnn.cmake
@@ -67,4 +67,5 @@ ADD_LIBRARY(mkldnn SHARED IMPORTED GLOBAL)
 SET_PROPERTY(TARGET mkldnn PROPERTY IMPORTED_LOCATION ${MKLDNN_LIB})
 ADD_DEPENDENCIES(mkldnn ${MKLDNN_PROJECT})
 MESSAGE(STATUS "MKLDNN library: ${MKLDNN_LIB}")
+add_definitions(-DPADDLE_USE_MKLDNN)
 LIST(APPEND external_project_dependencies mkldnn)

From aa2507187ef41d9c14de343751b7d6cf35a3af00 Mon Sep 17 00:00:00 2001
From: ranqiu <ranqiu@baidu.com>
Date: Fri, 17 Nov 2017 13:59:02 +0800
Subject: [PATCH 34/43] add dot_prod_layer

---
 paddle/gserver/layers/DotProdLayer.cpp        | 95 +++++++++++++++++++
 paddle/gserver/tests/test_LayerGrad.cpp       | 15 +++
 python/paddle/trainer/config_parser.py        |  9 ++
 .../paddle/trainer_config_helpers/layers.py   | 41 ++++++++
 4 files changed, 160 insertions(+)
 create mode 100644 paddle/gserver/layers/DotProdLayer.cpp

diff --git a/paddle/gserver/layers/DotProdLayer.cpp b/paddle/gserver/layers/DotProdLayer.cpp
new file mode 100644
index 0000000000..ae71a3d4eb
--- /dev/null
+++ b/paddle/gserver/layers/DotProdLayer.cpp
@@ -0,0 +1,95 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "Layer.h"
+#include "paddle/math/Matrix.h"
+#include "paddle/utils/Logging.h"
+#include "paddle/utils/Stat.h"
+
+namespace paddle {
+
+/**
+ * @brief A layer for computing the dot product of two vectors
+ * Input1: vector (batchSize * dim)
+ * Input2: vector (batchSize * dim)
+ * Output: a matrix: (batchSize * 1)
+ */
+
+class DotProdLayer : public Layer {
+public:
+  explicit DotProdLayer(const LayerConfig& config) : Layer(config) {}
+
+  ~DotProdLayer() {}
+
+  bool init(const LayerMap& layerMap,
+            const ParameterMap& parameterMap) override;
+
+  void forward(PassType passType) override;
+  void backward(const UpdateCallback& callback = nullptr) override;
+};
+
+REGISTER_LAYER(dot_prod, DotProdLayer);
+
+bool DotProdLayer::init(const LayerMap& layerMap,
+                        const ParameterMap& parameterMap) {
+  Layer::init(layerMap, parameterMap);
+
+  CHECK_EQ(inputLayers_.size(), 2U);
+  CHECK_EQ(1, getSize()) << "Dimension mismatch";
+
+  return true;
+}
+
+void DotProdLayer::forward(PassType passType) {
+  Layer::forward(passType);
+
+  MatrixPtr inV0 = getInputValue(0);
+  MatrixPtr inV1 = getInputValue(1);
+
+  size_t batchSize = inV0->getHeight();
+  CHECK_EQ(inV1->getHeight(), batchSize);
+
+  {
+    REGISTER_TIMER_INFO("FwResetTimer", getName().c_str());
+    reserveOutput(batchSize, 1);
+  }
+
+  MatrixPtr outV = getOutputValue();
+  {
+    REGISTER_TIMER_INFO("FwDotProdTimer", getName().c_str());
+    outV->sumOfProducts(*inV0, *inV1, 1, 0);
+  }
+}
+
+void DotProdLayer::backward(const UpdateCallback& callback) {
+  MatrixPtr inV0 = getInputValue(0);
+  MatrixPtr inV1 = getInputValue(1);
+  MatrixPtr outG = getOutputGrad();
+  MatrixPtr inG0 = getInputGrad(0);
+  MatrixPtr inG1 = getInputGrad(1);
+
+  {
+    REGISTER_TIMER_INFO("BwDotProdTimer", getName().c_str());
+
+    if (inG0) {
+      inG0->addRowScale(0, *inV1, *outG);
+    }
+
+    if (inG1) {
+      inG1->addRowScale(0, *inV0, *outG);
+    }
+  }
+}
+
+}  // namespace paddle
diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp
index 3517d293e3..de2db0b3f7 100644
--- a/paddle/gserver/tests/test_LayerGrad.cpp
+++ b/paddle/gserver/tests/test_LayerGrad.cpp
@@ -1081,6 +1081,21 @@ TEST(Layer, InterpolationLayer) {
   }
 }
 
+TEST(Layer, DotProdLayer) {
+  TestConfig config;
+  config.layerConfig.set_type("dot_prod");
+  config.layerConfig.set_size(1);
+
+  config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
+  config.layerConfig.add_inputs();
+  config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
+  config.layerConfig.add_inputs();
+
+  for (auto useGpu : {false, true}) {
+    testLayerGrad(config, "dot_prod", 100, false, useGpu);
+  }
+}
+
 TEST(Layer, OuterProdLayer) {
   TestConfig config;
   config.layerConfig.set_type("out_prod");
diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py
index 5bd68e211a..6d1cc5ad70 100644
--- a/python/paddle/trainer/config_parser.py
+++ b/python/paddle/trainer/config_parser.py
@@ -3209,6 +3209,15 @@ class SubNestedSequenceLayer(LayerBase):
         self.set_layer_size(size)
 
 
+@config_layer('dot_prod')
+class DotProdLayer(LayerBase):
+    def __init__(self, name, inputs, device=None):
+        super(DotProdLayer, self).__init__(
+            name, 'dot_prod', 0, inputs, device=device)
+        config_assert(len(inputs) == 2, 'DotProdLayer must have 2 inputs')
+        self.set_layer_size(1)
+
+
 @config_layer('out_prod')
 class OuterProdLayer(LayerBase):
     def __init__(self, name, inputs, device=None):
diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py
index a02eba007d..388535d53a 100644
--- a/python/paddle/trainer_config_helpers/layers.py
+++ b/python/paddle/trainer_config_helpers/layers.py
@@ -115,6 +115,7 @@ __all__ = [
     'huber_classification_cost',
     'block_expand_layer',
     'maxout_layer',
+    'dot_prod_layer',
     'out_prod_layer',
     'printer_layer',
     'print_layer',
@@ -197,6 +198,7 @@ class LayerType(object):
     SCALING_LAYER = 'scaling'
     TRANS_LAYER = 'trans'
     ROTATE_LAYER = 'rotate'
+    DOT_PROD_LAYER = 'dot_prod'
     OUT_PROD_LAYER = 'out_prod'
     FEATURE_MAP_EXPAND_LAYER = 'featmap_expand'
 
@@ -4140,6 +4142,45 @@ def maxid_layer(input, name=None, layer_attr=None):
         size=l.config.size)
 
 
+@wrap_name_default()
+def dot_prod_layer(input1, input2, name=None, layer_attr=None):
+    """
+    A layer for computing the dot product of two vectors.
+
+    The example usage is:
+
+    .. code-block:: python
+
+        dot_prod = dot_prod_layer(input1=vec1, input2=vec2)
+
+    :param name: The name of this layer. It is optional.
+    :type name: basestring
+    :param input1: The first input layer.
+    :type input: LayerOutput
+    :param input2: The second input layer.
+    :type input2: LayerOutput
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
+    :type layer_attr: ExtraLayerAttribute.
+    :return: LayerOutput object.
+    :rtype: LayerOutput
+    """
+    assert isinstance(input1, LayerOutput)
+    assert isinstance(input2, LayerOutput)
+    assert input1.size == input2.size, ("Two inputs should have the same size.")
+
+    l = Layer(
+        name=name,
+        type=LayerType.DOT_PROD_LAYER,
+        inputs=[input1.name, input2.name],
+        **ExtraLayerAttribute.to_kwargs(layer_attr))
+    return LayerOutput(
+        name=name,
+        layer_type=LayerType.DOT_PROD_LAYER,
+        parents=[input1, input2],
+        size=l.config.size)
+
+
 @wrap_name_default()
 def out_prod_layer(input1, input2, name=None, layer_attr=None):
     """

From 082bc7af56414cf3a8a156a4dbcbd4df18a61357 Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Fri, 17 Nov 2017 13:10:01 +0800
Subject: [PATCH 35/43] Use CUDA_ARCH_NAME=All in the
 paddle/scripts/docker/build.sh and remove 20 21(20) in cmake/cuda.cmake.

---
 cmake/cuda.cmake               | 38 ++++------------------------------
 paddle/scripts/docker/build.sh |  2 ++
 2 files changed, 6 insertions(+), 34 deletions(-)

diff --git a/cmake/cuda.cmake b/cmake/cuda.cmake
index 5d0840f273..9c7a52164a 100644
--- a/cmake/cuda.cmake
+++ b/cmake/cuda.cmake
@@ -2,9 +2,9 @@ if(NOT WITH_GPU)
     return()
 endif()
 
-set(paddle_known_gpu_archs "20 21(20) 30 35 50 52 60 61 70")
-set(paddle_known_gpu_archs7 "20 21(20) 30 35 50 52")
-set(paddle_known_gpu_archs8 "20 21(20) 30 35 50 52 60 61")
+set(paddle_known_gpu_archs "30 35 50 52 60 61 70")
+set(paddle_known_gpu_archs7 "30 35 50 52")
+set(paddle_known_gpu_archs8 "30 35 50 52 60 61")
 
 ######################################################################################
 # A function for automatic detection of GPUs installed  (if autodetection is enabled)
@@ -40,7 +40,7 @@ function(detect_installed_gpus out_variable)
       STRING(REGEX REPLACE "\n" ";" nvcc_out "${nvcc_out}")
       list(GET nvcc_out -1 nvcc_out)
       string(REPLACE "2.1" "2.1(2.0)" nvcc_out "${nvcc_out}")
-      set(CUDA_gpu_detect_output ${nvcc_out} CACHE INTERNAL "Returned GPU architetures from caffe_detect_gpus tool" FORCE)
+      set(CUDA_gpu_detect_output ${nvcc_out} CACHE INTERNAL "Returned GPU architetures from detect_installed_gpus tool" FORCE)
     endif()
   endif()
 
@@ -137,10 +137,6 @@ function(select_nvcc_arch_flags out_variable)
   set(${out_variable}_readable ${nvcc_archs_readable} PARENT_SCOPE)
 endfunction()
 
-if(NOT CUDA_FOUND)
-  return()
-endif()
-
 message(STATUS "CUDA detected: " ${CUDA_VERSION})
 if (${CUDA_VERSION} LESS 7.0)
   set(paddle_known_gpu_archs ${paddle_known_gpu_archs})
@@ -163,37 +159,11 @@ if(NOT WITH_DSO)
     list(APPEND EXTERNAL_LIBS ${CUDNN_LIBRARY} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_curand_LIBRARY} ${NCCL_LIBRARY})
 endif(NOT WITH_DSO)
 
-# find libcuda.so and lbnvrtc.so
-# For libcuda.so, we will find it under lib, lib64, and then the
-# stubs folder, in case we are building on a system that does not
-# have cuda driver installed. On windows, we also search under the
-# folder lib/x64.
-
-find_library(CUDA_CUDA_LIB cuda
-    PATHS ${CUDA_TOOLKIT_ROOT_DIR}
-    PATH_SUFFIXES lib lib64 lib/stubs lib64/stubs lib/x64)
-find_library(CUDA_NVRTC_LIB nvrtc
-    PATHS ${CUDA_TOOLKIT_ROOT_DIR}
-    PATH_SUFFIXES lib lib64 lib/x64)
-
 # setting nvcc arch flags
 select_nvcc_arch_flags(NVCC_FLAGS_EXTRA)
 list(APPEND CUDA_NVCC_FLAGS ${NVCC_FLAGS_EXTRA})
 message(STATUS "Added CUDA NVCC flags for: ${NVCC_FLAGS_EXTRA_readable}")
 
-if(CUDA_CUDA_LIB)
-    # message(STATUS "Found libcuda: ${CUDA_CUDA_LIB}")
-    list(APPEND Caffe2_DEPENDENCY_LIBS ${CUDA_CUDA_LIB})
-else()
-    message(FATAL_ERROR "Cannot find libcuda.so.")
-endif()
-if(CUDA_NVRTC_LIB)
-  # message(STATUS "Found libnvrtc: ${CUDA_NVRTC_LIB}")
-  list(APPEND Caffe2_DEPENDENCY_LIBS ${CUDA_NVRTC_LIB})
-else()
-    message(FATAL_ERROR "Cannot find libnvrtc.so.")
-endif()
-
 # Set C++11 support
 set(CUDA_PROPAGATE_HOST_FLAGS OFF)
 
diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh
index e9c89eee1a..8dddb2be9c 100644
--- a/paddle/scripts/docker/build.sh
+++ b/paddle/scripts/docker/build.sh
@@ -34,6 +34,7 @@ function cmake_gen() {
         ${PYTHON_FLAGS}
         -DWITH_DOC=OFF
         -DWITH_GPU=${WITH_GPU:-OFF}
+        -DCUDA_ARCH_NAME=All
         -DWITH_MKLDNN=${WITH_MKLDNN:-ON}
         -DWITH_MKLML=${WITH_MKLML:-ON}
         -DWITH_AVX=${WITH_AVX:-OFF}
@@ -56,6 +57,7 @@ EOF
         ${PYTHON_FLAGS} \
         -DWITH_DOC=OFF \
         -DWITH_GPU=${WITH_GPU:-OFF} \
+        -DCUDA_ARCH_NAME=All \
         -DWITH_MKLDNN=${WITH_MKLDNN:-ON} \
         -DWITH_MKLML=${WITH_MKLML:-ON} \
         -DWITH_AVX=${WITH_AVX:-OFF} \

From aa83e19e24d2318381bd4859588f15d43336f041 Mon Sep 17 00:00:00 2001
From: guosheng <guosheng@baidu.com>
Date: Fri, 17 Nov 2017 14:18:34 +0800
Subject: [PATCH 36/43] Remove lstm_op including in gru_op

---
 paddle/operators/gru_op.h | 26 +++++++++++++++-----------
 1 file changed, 15 insertions(+), 11 deletions(-)

diff --git a/paddle/operators/gru_op.h b/paddle/operators/gru_op.h
index a7264507bb..1b18368e0e 100644
--- a/paddle/operators/gru_op.h
+++ b/paddle/operators/gru_op.h
@@ -14,7 +14,6 @@
 
 #pragma once
 
-#include "paddle/operators/lstm_op.h"
 #include "paddle/operators/math/gru_compute.h"
 #include "paddle/operators/math/math_function.h"
 #include "paddle/operators/math/sequence2batch.h"
@@ -25,6 +24,18 @@
 namespace paddle {
 namespace operators {
 
+using LoDTensor = framework::LoDTensor;
+using Tensor = framework::Tensor;
+
+template <typename Place, typename T>
+inline void ReorderInitState(const platform::DeviceContext& ctx,
+                             const framework::Tensor& src, const size_t* index,
+                             framework::Tensor* dst, bool indexed_src) {
+  math::CopyMatrixRowsFunctor<Place, T> row_shuffle;
+  dst->mutable_data<T>(src.dims(), ctx.GetPlace());
+  row_shuffle(ctx, src, index, *dst, indexed_src);
+}
+
 template <typename Place, typename T>
 class GRUKernel : public framework::OpKernel<T> {
  public:
@@ -194,16 +205,9 @@ class GRUGradKernel : public framework::OpKernel<T> {
           batch_reset_hidden_prev_grad.Slice(bstart, bend);
       gru_grad.resetOutputGrad = reset_hidden_prev_grad_t.data<T>();
       if (n == 0) {
-        if (h0) {
-          gru_value.prevOutValue = ordered_h0.data<T>();
-        } else {
-          gru_value.prevOutValue = nullptr;
-        }
-        if (h0 && h0_grad) {
-          gru_grad.prevOutGrad = ordered_h0_grad.data<T>();
-        } else {
-          gru_grad.prevOutGrad = nullptr;
-        }
+        gru_value.prevOutValue = h0 ? ordered_h0.data<T>() : nullptr;
+        gru_grad.prevOutGrad =
+            h0 && h0_grad ? ordered_h0_grad.data<T>() : nullptr;
       } else {
         int bstart_pre = static_cast<int>(batch_starts[n - 1]);
         Tensor hidden_prev_t = batch_hidden->Slice(bstart_pre, bstart);

From a391a44dd0cc0c6dba0aa5e2e66a65689a8ccdfa Mon Sep 17 00:00:00 2001
From: QI JUN <qijun1994@hotmail.com>
Date: Fri, 17 Nov 2017 00:45:01 -0600
Subject: [PATCH 37/43] remove v2 framework (#5722)

---
 .../paddle/v2/{framework => fluid}/tests/test_is_empty_op.py  | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)
 rename python/paddle/v2/{framework => fluid}/tests/test_is_empty_op.py (92%)

diff --git a/python/paddle/v2/framework/tests/test_is_empty_op.py b/python/paddle/v2/fluid/tests/test_is_empty_op.py
similarity index 92%
rename from python/paddle/v2/framework/tests/test_is_empty_op.py
rename to python/paddle/v2/fluid/tests/test_is_empty_op.py
index 129d1c1944..ed6e3fe24f 100644
--- a/python/paddle/v2/framework/tests/test_is_empty_op.py
+++ b/python/paddle/v2/fluid/tests/test_is_empty_op.py
@@ -1,7 +1,7 @@
 import unittest
 import numpy as np
-from paddle.v2.framework.op import Operator
-import paddle.v2.framework.core as core
+from paddle.v2.fluid.op import Operator
+import paddle.v2.fluid.core as core
 
 
 def create_tensor(scope, name, np_data):

From 40450401a68215fa86be900426ee54075371149e Mon Sep 17 00:00:00 2001
From: tensor-tang <jian.j.tang@intel.com>
Date: Fri, 17 Nov 2017 11:29:32 +0800
Subject: [PATCH 38/43] change macro, can use omp when paddle use mklml

---
 paddle/parameter/ParameterUpdateFunctions.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/paddle/parameter/ParameterUpdateFunctions.cpp b/paddle/parameter/ParameterUpdateFunctions.cpp
index 8b3be062b6..1898598e49 100644
--- a/paddle/parameter/ParameterUpdateFunctions.cpp
+++ b/paddle/parameter/ParameterUpdateFunctions.cpp
@@ -30,7 +30,7 @@ void sgdUpdateCpu(real learningRate,
                   const real* grad,
                   real* momentumVec) {
   decayRate *= learningRate;
-#ifdef PADDLE_USE_MKLDNN
+#ifdef PADDLE_USE_MKLML
 #pragma omp parallel for
 #endif
   for (size_t i = 0; i < size; ++i) {

From f5df46e1a4beda7bd79e929b180ea91ee6c2ca9a Mon Sep 17 00:00:00 2001
From: tensor-tang <jian.j.tang@intel.com>
Date: Fri, 17 Nov 2017 15:32:50 +0800
Subject: [PATCH 39/43] rename all Mkldnn to MKLDNN

---
 paddle/gserver/layers/MKLDNNLayer.cpp  | 2 +-
 paddle/gserver/tests/CMakeLists.txt    | 2 +-
 paddle/gserver/tests/MKLDNNTester.h    | 2 +-
 python/paddle/trainer/config_parser.py | 6 +++---
 4 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/paddle/gserver/layers/MKLDNNLayer.cpp b/paddle/gserver/layers/MKLDNNLayer.cpp
index 2125155c6c..671e00cad3 100644
--- a/paddle/gserver/layers/MKLDNNLayer.cpp
+++ b/paddle/gserver/layers/MKLDNNLayer.cpp
@@ -21,7 +21,7 @@ namespace paddle {
 
 bool MKLDNNLayer::init(const LayerMap& layerMap,
                        const ParameterMap& parameterMap) {
-  CHECK(FLAGS_use_mkldnn) << "MkldnnLayers only support use_mkldnn."
+  CHECK(FLAGS_use_mkldnn) << "MKLDNNLayers only support use_mkldnn."
                           << "Please set WITH_MKL=ON "
                           << "and set use_mkldnn=True";
   CHECK(!useGpu_) << "Do not support GPU yet";
diff --git a/paddle/gserver/tests/CMakeLists.txt b/paddle/gserver/tests/CMakeLists.txt
index 09e1b949c2..c295ea19c9 100644
--- a/paddle/gserver/tests/CMakeLists.txt
+++ b/paddle/gserver/tests/CMakeLists.txt
@@ -29,7 +29,7 @@ gserver_test(test_KmaxSeqScore)
 gserver_test(test_Expand)
 gserver_test(test_MaxPoolingWithMaskOutput)
 
-########## test_Mkldnn layers and activations ##########
+########## test_MKLDNN layers and activations ##########
 if(WITH_MKLDNN)
     add_unittest_without_exec(test_MKLDNN
         test_MKLDNN.cpp
diff --git a/paddle/gserver/tests/MKLDNNTester.h b/paddle/gserver/tests/MKLDNNTester.h
index ca55a45bc7..9d61533c0b 100644
--- a/paddle/gserver/tests/MKLDNNTester.h
+++ b/paddle/gserver/tests/MKLDNNTester.h
@@ -23,7 +23,7 @@ limitations under the License. */
 namespace paddle {
 
 /**
- * @brief test the functionality of Mkldnnlayers
+ * @brief test the functionality of MKLDNNlayers and MKLDNNActivations
  * refer to paddle original function
  */
 class MKLDNNTester {
diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py
index 5bd68e211a..d968dfb945 100644
--- a/python/paddle/trainer/config_parser.py
+++ b/python/paddle/trainer/config_parser.py
@@ -1826,7 +1826,7 @@ class FCLayer(LayerBase):
             self.layer_type = 'mkldnn_fc'
             config_assert(
                 len(inputs) == 1,
-                "MkldnnFCLayer support one and only one input!")
+                "MKLDNNFCLayer support one and only one input!")
         super(FCLayer, self).__init__(
             name, self.layer_type, size, inputs=inputs, **xargs)
         for input_index in xrange(len(self.inputs)):
@@ -1837,7 +1837,7 @@ class FCLayer(LayerBase):
             sparse = format == "csr" or format == "csc"
             if use_mkldnn:
                 config_assert(not sparse,
-                              "MkldnnFCLayer do not support sparse format yet")
+                              "MKLDNNFCLayer do not support sparse format yet")
                 if use_mkldnn_wgt:
                     dims = [self.config.size, input_layer.size]
             if sparse:
@@ -1853,7 +1853,7 @@ class FCLayer(LayerBase):
 
 
 @config_layer('mkldnn_fc')
-class MkldnnFcLayer(FCLayer):
+class MKLDNNFcLayer(FCLayer):
     layer_type = 'mkldnn_fc'
 
 

From 2e1cd3313d502e3201551d3d443b549bc8c88cbf Mon Sep 17 00:00:00 2001
From: ranqiu <ranqiu@baidu.com>
Date: Fri, 17 Nov 2017 14:55:19 +0800
Subject: [PATCH 40/43] Update dot_prod_layer

---
 doc/api/v2/config/layer.rst                   | 10 +++++
 paddle/gserver/layers/DotProdLayer.cpp        |  6 ++-
 paddle/gserver/tests/test_LayerGrad.cpp       |  2 +-
 python/paddle/trainer/config_parser.py        |  5 ++-
 .../tests/configs/file_list.sh                |  3 +-
 .../protostr/test_dot_prod_layer.protostr     | 38 +++++++++++++++++++
 .../tests/configs/test_dot_prod_layer.py      |  7 ++++
 7 files changed, 66 insertions(+), 5 deletions(-)
 create mode 100644 python/paddle/trainer_config_helpers/tests/configs/protostr/test_dot_prod_layer.protostr
 create mode 100644 python/paddle/trainer_config_helpers/tests/configs/test_dot_prod_layer.py

diff --git a/doc/api/v2/config/layer.rst b/doc/api/v2/config/layer.rst
index 203506d7ab..b2b55ec419 100644
--- a/doc/api/v2/config/layer.rst
+++ b/doc/api/v2/config/layer.rst
@@ -335,6 +335,16 @@ bilinear_interp
 ..  autoclass:: paddle.v2.layer.bilinear_interp
     :noindex:
 
+dot_prod
+---------
+.. autoclass:: paddle.v2.layer.dot_prod
+    :noindex:
+
+out_prod
+--------
+.. autoclass:: paddle.v2.layer.out_prod
+    :noindex:
+
 power
 -----
 ..  autoclass:: paddle.v2.layer.power
diff --git a/paddle/gserver/layers/DotProdLayer.cpp b/paddle/gserver/layers/DotProdLayer.cpp
index ae71a3d4eb..9e2dbe3c3c 100644
--- a/paddle/gserver/layers/DotProdLayer.cpp
+++ b/paddle/gserver/layers/DotProdLayer.cpp
@@ -20,7 +20,7 @@ limitations under the License. */
 namespace paddle {
 
 /**
- * @brief A layer for computing the dot product of two vectors
+ * @brief A layer for computing the dot product of two vectors.
  * Input1: vector (batchSize * dim)
  * Input2: vector (batchSize * dim)
  * Output: a matrix: (batchSize * 1)
@@ -46,7 +46,8 @@ bool DotProdLayer::init(const LayerMap& layerMap,
   Layer::init(layerMap, parameterMap);
 
   CHECK_EQ(inputLayers_.size(), 2U);
-  CHECK_EQ(1, getSize()) << "Dimension mismatch";
+  CHECK_EQ(1UL, getSize())
+      << "The output dimensionality of this layer should be fixed to 1.";
 
   return true;
 }
@@ -59,6 +60,7 @@ void DotProdLayer::forward(PassType passType) {
 
   size_t batchSize = inV0->getHeight();
   CHECK_EQ(inV1->getHeight(), batchSize);
+  CHECK_EQ(inV0->getWidth(), inV1->getWidth());
 
   {
     REGISTER_TIMER_INFO("FwResetTimer", getName().c_str());
diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp
index de2db0b3f7..fb4eea6f67 100644
--- a/paddle/gserver/tests/test_LayerGrad.cpp
+++ b/paddle/gserver/tests/test_LayerGrad.cpp
@@ -1092,7 +1092,7 @@ TEST(Layer, DotProdLayer) {
   config.layerConfig.add_inputs();
 
   for (auto useGpu : {false, true}) {
-    testLayerGrad(config, "dot_prod", 100, false, useGpu);
+    testLayerGrad(config, "dot_prod", 10, false, useGpu);
   }
 }
 
diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py
index 6d1cc5ad70..fab280d1b0 100644
--- a/python/paddle/trainer/config_parser.py
+++ b/python/paddle/trainer/config_parser.py
@@ -3214,7 +3214,10 @@ class DotProdLayer(LayerBase):
     def __init__(self, name, inputs, device=None):
         super(DotProdLayer, self).__init__(
             name, 'dot_prod', 0, inputs, device=device)
-        config_assert(len(inputs) == 2, 'DotProdLayer must have 2 inputs')
+        config_assert(len(inputs) == 2, 'DotProdLayer must have 2 inputs.')
+        config_assert(
+            self.get_input_layer(0).size == self.get_input_layer(1).size,
+            "Two inputs should have the same size.")
         self.set_layer_size(1)
 
 
diff --git a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh
index 1c7451e0ab..0b269a1ff7 100755
--- a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh
+++ b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh
@@ -10,6 +10,7 @@ test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_la
 test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer
 test_kmax_seq_socre_layer test_sub_nested_seq_select_layer test_scale_shift_layer
 test_seq_slice_layer test_cross_entropy_over_beam test_roi_pool_layer test_pooling3D_layer
-test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer test_scale_sub_region_layer)
+test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer test_scale_sub_region_layer
+test_dot_prod_layer)
 
 export whole_configs=(test_split_datasource)
diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_dot_prod_layer.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_dot_prod_layer.protostr
new file mode 100644
index 0000000000..f1530c382c
--- /dev/null
+++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_dot_prod_layer.protostr
@@ -0,0 +1,38 @@
+type: "nn"
+layers {
+  name: "vector1"
+  type: "data"
+  size: 10
+  active_type: ""
+}
+layers {
+  name: "vector2"
+  type: "data"
+  size: 10
+  active_type: ""
+}
+layers {
+  name: "__dot_prod_layer_0__"
+  type: "dot_prod"
+  size: 1
+  active_type: ""
+  inputs {
+    input_layer_name: "vector1"
+  }
+  inputs {
+    input_layer_name: "vector2"
+  }
+}
+input_layer_names: "vector1"
+input_layer_names: "vector2"
+output_layer_names: "__dot_prod_layer_0__"
+sub_models {
+  name: "root"
+  layer_names: "vector1"
+  layer_names: "vector2"
+  layer_names: "__dot_prod_layer_0__"
+  input_layer_names: "vector1"
+  input_layer_names: "vector2"
+  output_layer_names: "__dot_prod_layer_0__"
+  is_recurrent_layer_group: false
+}
diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_dot_prod_layer.py b/python/paddle/trainer_config_helpers/tests/configs/test_dot_prod_layer.py
new file mode 100644
index 0000000000..e52d48dde0
--- /dev/null
+++ b/python/paddle/trainer_config_helpers/tests/configs/test_dot_prod_layer.py
@@ -0,0 +1,7 @@
+from paddle.trainer_config_helpers import *
+
+vec1 = data_layer(name='vector1', size=10)
+vec2 = data_layer(name='vector2', size=10)
+dot_product = dot_prod_layer(input1=vec1, input2=vec2)
+
+outputs(dot_product)

From c359e39b59d76abfb795e5eaf7d36bfec17c2bb9 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Fri, 17 Nov 2017 16:54:32 +0800
Subject: [PATCH 41/43] add double type kernel

---
 paddle/operators/conv_op.cc              | 12 ++++++++----
 paddle/operators/conv_op.cu.cc           | 12 ++++++++----
 paddle/operators/conv_transpose_op.cc    | 12 ++++++++----
 paddle/operators/conv_transpose_op.cu.cc | 12 ++++++++----
 4 files changed, 32 insertions(+), 16 deletions(-)

diff --git a/paddle/operators/conv_op.cc b/paddle/operators/conv_op.cc
index 687d741cb2..7a36a9b21a 100644
--- a/paddle/operators/conv_op.cc
+++ b/paddle/operators/conv_op.cc
@@ -225,11 +225,15 @@ REGISTER_OP(conv3d, ops::ConvOp, ops::Conv3DOpMaker, conv3d_grad,
             ops::ConvOpGrad);
 
 REGISTER_OP_CPU_KERNEL(conv2d,
-                       ops::GemmConvKernel<paddle::platform::CPUPlace, float>);
+                       ops::GemmConvKernel<paddle::platform::CPUPlace, float>,
+                       ops::GemmConvKernel<paddle::platform::CPUPlace, double>);
 REGISTER_OP_CPU_KERNEL(
-    conv2d_grad, ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>);
+    conv2d_grad, ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>,
+    ops::GemmConvGradKernel<paddle::platform::CPUPlace, double>);
 
 REGISTER_OP_CPU_KERNEL(conv3d,
-                       ops::GemmConvKernel<paddle::platform::CPUPlace, float>);
+                       ops::GemmConvKernel<paddle::platform::CPUPlace, float>,
+                       ops::GemmConvKernel<paddle::platform::CPUPlace, double>);
 REGISTER_OP_CPU_KERNEL(
-    conv3d_grad, ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>);
+    conv3d_grad, ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>,
+    ops::GemmConvGradKernel<paddle::platform::CPUPlace, double>);
diff --git a/paddle/operators/conv_op.cu.cc b/paddle/operators/conv_op.cu.cc
index 8e6f9da455..546451234a 100644
--- a/paddle/operators/conv_op.cu.cc
+++ b/paddle/operators/conv_op.cu.cc
@@ -17,11 +17,15 @@
 namespace ops = paddle::operators;
 
 REGISTER_OP_GPU_KERNEL(conv2d,
-                       ops::GemmConvKernel<paddle::platform::GPUPlace, float>);
+                       ops::GemmConvKernel<paddle::platform::GPUPlace, float>,
+                       ops::GemmConvKernel<paddle::platform::GPUPlace, double>);
 REGISTER_OP_GPU_KERNEL(
-    conv2d_grad, ops::GemmConvGradKernel<paddle::platform::GPUPlace, float>);
+    conv2d_grad, ops::GemmConvGradKernel<paddle::platform::GPUPlace, float>,
+    ops::GemmConvGradKernel<paddle::platform::GPUPlace, double>);
 
 REGISTER_OP_GPU_KERNEL(conv3d,
-                       ops::GemmConvKernel<paddle::platform::GPUPlace, float>);
+                       ops::GemmConvKernel<paddle::platform::GPUPlace, float>,
+                       ops::GemmConvKernel<paddle::platform::GPUPlace, double>);
 REGISTER_OP_GPU_KERNEL(
-    conv3d_grad, ops::GemmConvGradKernel<paddle::platform::GPUPlace, float>);
+    conv3d_grad, ops::GemmConvGradKernel<paddle::platform::GPUPlace, float>,
+    ops::GemmConvGradKernel<paddle::platform::GPUPlace, double>);
diff --git a/paddle/operators/conv_transpose_op.cc b/paddle/operators/conv_transpose_op.cc
index 310e3f5c93..3e55ef036a 100644
--- a/paddle/operators/conv_transpose_op.cc
+++ b/paddle/operators/conv_transpose_op.cc
@@ -185,17 +185,21 @@ REGISTER_OP(conv2d_transpose, ops::ConvTransposeOp, ops::Conv2DTransposeOpMaker,
 
 REGISTER_OP_CPU_KERNEL(
     conv2d_transpose,
-    ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>);
+    ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>,
+    ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, double>);
 REGISTER_OP_CPU_KERNEL(
     conv2d_transpose_grad,
-    ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>);
+    ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>,
+    ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, double>);
 
 REGISTER_OP(conv3d_transpose, ops::ConvTransposeOp, ops::Conv3DTransposeOpMaker,
             conv3d_transpose_grad, ops::ConvTransposeOpGrad);
 
 REGISTER_OP_CPU_KERNEL(
     conv3d_transpose,
-    ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>);
+    ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>,
+    ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, double>);
 REGISTER_OP_CPU_KERNEL(
     conv3d_transpose_grad,
-    ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>);
+    ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>,
+    ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, double>);
diff --git a/paddle/operators/conv_transpose_op.cu.cc b/paddle/operators/conv_transpose_op.cu.cc
index 401cddb379..4165eb0c7b 100644
--- a/paddle/operators/conv_transpose_op.cu.cc
+++ b/paddle/operators/conv_transpose_op.cu.cc
@@ -18,14 +18,18 @@ namespace ops = paddle::operators;
 
 REGISTER_OP_GPU_KERNEL(
     conv2d_transpose,
-    ops::GemmConvTransposeKernel<paddle::platform::GPUPlace, float>);
+    ops::GemmConvTransposeKernel<paddle::platform::GPUPlace, float>,
+    ops::GemmConvTransposeKernel<paddle::platform::GPUPlace, double>);
 REGISTER_OP_GPU_KERNEL(
     conv2d_transpose_grad,
-    ops::GemmConvTransposeGradKernel<paddle::platform::GPUPlace, float>);
+    ops::GemmConvTransposeGradKernel<paddle::platform::GPUPlace, float>,
+    ops::GemmConvTransposeGradKernel<paddle::platform::GPUPlace, double>);
 
 REGISTER_OP_GPU_KERNEL(
     conv3d_transpose,
-    ops::GemmConvTransposeKernel<paddle::platform::GPUPlace, float>);
+    ops::GemmConvTransposeKernel<paddle::platform::GPUPlace, float>,
+    ops::GemmConvTransposeKernel<paddle::platform::GPUPlace, double>);
 REGISTER_OP_GPU_KERNEL(
     conv3d_transpose_grad,
-    ops::GemmConvTransposeGradKernel<paddle::platform::GPUPlace, float>);
+    ops::GemmConvTransposeGradKernel<paddle::platform::GPUPlace, float>,
+    ops::GemmConvTransposeGradKernel<paddle::platform::GPUPlace, double>);

From 18f1f53555b33323d16861ceef7cd925fa663973 Mon Sep 17 00:00:00 2001
From: tensor-tang <jian.j.tang@intel.com>
Date: Fri, 17 Nov 2017 17:16:04 +0800
Subject: [PATCH 42/43] change message level from warning to status, and fix
 hard number in version

---
 CMakeLists.txt | 11 ++++-------
 1 file changed, 4 insertions(+), 7 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 9e30dff70f..0f25fdee54 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -108,14 +108,11 @@ else()
     set(THIRD_PARTY_BUILD_TYPE Release)
 endif()
 
-if(WITH_MKL)
-    set(WITH_MKLML ON)
-    set(WITH_MKLDNN ${AVX2_FOUND})
-    if(NOT WITH_MKLDNN)
-        message(WARNING "Do not have AVX2 intrinsics and disabled MKL-DNN")
-    endif()
+set(WITH_MKLML ${WITH_MKL})
+if (WITH_MKL AND ${AVX2_FOUND})
+    set(WITH_MKLDNN ON)
 else()
-    set(WITH_MKLML OFF)
+    message(STATUS "Do not have AVX2 intrinsics and disabled MKL-DNN")
     set(WITH_MKLDNN OFF)
 endif()
 

From 044d671e73baf912c369bca61c1a0e494ac49091 Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Fri, 17 Nov 2017 01:31:57 -0800
Subject: [PATCH 43/43] Rename 'argu' in framework.py to 'arg' (#5723)

---
 python/paddle/v2/fluid/framework.py | 45 ++++++++++++++++-------------
 1 file changed, 25 insertions(+), 20 deletions(-)

diff --git a/python/paddle/v2/fluid/framework.py b/python/paddle/v2/fluid/framework.py
index a6eca2d719..acca6ba35c 100644
--- a/python/paddle/v2/fluid/framework.py
+++ b/python/paddle/v2/fluid/framework.py
@@ -4,7 +4,10 @@ import collections
 import numpy as np
 import copy
 
-__all__ = ['Block', 'Variable', 'Program', 'Operator', 'default_startup_program', 'default_main_program']
+__all__ = [
+    'Block', 'Variable', 'Program', 'Operator', 'default_startup_program',
+    'default_main_program'
+]
 
 
 def unique_name(prefix):
@@ -232,17 +235,17 @@ class Operator(object):
                     in_proto.name)
 
                 if found:
-                    in_argus = inputs[in_proto.name]
-                    if not isinstance(in_argus, list):
-                        in_argus = [in_argus]
-                    if not in_proto.duplicable and len(in_argus) > 1:
+                    in_args = inputs[in_proto.name]
+                    if not isinstance(in_args, list):
+                        in_args = [in_args]
+                    if not in_proto.duplicable and len(in_args) > 1:
                         raise ValueError(
                             "Input %s expects only one input, but %d are given."
-                            % (in_proto.name, len(in_argus)))
-                    in_argu_names = []
-                    for argu in in_argus:
-                        in_argu_names.append(argu.name)
-                    self.desc.set_input(in_proto.name, in_argu_names)
+                            % (in_proto.name, len(in_args)))
+                    in_arg_names = []
+                    for arg in in_args:
+                        in_arg_names.append(arg.name)
+                    self.desc.set_input(in_proto.name, in_arg_names)
                 else:
                     self.desc.set_input(in_proto.name, [])
 
@@ -260,18 +263,18 @@ class Operator(object):
                         str(e) for e in given)))
 
             for out_proto in proto.outputs:
-                out_argus = outputs[out_proto.name]
-                if not isinstance(out_argus, list):
-                    out_argus = [out_argus]
-                if not out_proto.duplicable and len(out_argus) > 1:
+                out_args = outputs[out_proto.name]
+                if not isinstance(out_args, list):
+                    out_args = [out_args]
+                if not out_proto.duplicable and len(out_args) > 1:
                     raise ValueError(
                         "Output %s expects only one output, but %d are given." %
-                        (out_proto.name, len(out_argus)))
-                out_argu_names = []
-                for argu in out_argus:
-                    out_argu_names.append(argu.name)
-                    argu.op = self
-                self.desc.set_output(out_proto.name, out_argu_names)
+                        (out_proto.name, len(out_args)))
+                out_arg_names = []
+                for arg in out_args:
+                    out_arg_names.append(arg.name)
+                    arg.op = self
+                self.desc.set_output(out_proto.name, out_arg_names)
 
         if attrs is not None:
             if not isinstance(attrs, dict):
@@ -582,8 +585,10 @@ class Parameter(Variable):
 g_main_program = Program()
 g_startup_program = Program()
 
+
 def default_startup_program():
     return g_startup_program
 
+
 def default_main_program():
     return g_main_program