Merge pull request #2479 from emailweixu/mixed_input_rnn

RecurrentGroup with mixed input sequence types

paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp

@@ -214,7 +214,6 @@ void RecurrentGradientMachine::init(
     inFrameLines_[i].linkName = subModelConfig->in_links(i).link_name();
     inFrameLines_[i].inLayer =
         rootNetwork_->getLayer(subModelConfig->in_links(i).layer_name());
-    inFrameLines_[i].hasSubseq = subModelConfig->in_links(i).has_subseq();
   }
 
   outFrameLines_.resize(subModelConfig->out_links_size());
@@ -241,11 +240,8 @@ void RecurrentGradientMachine::init(
           rootNetwork_->getLayer(memoryConfig.boot_layer_name());
 
       LayerConfig scatterConfig = *agentConfig;
-      memoryFrameLines_[i].is_sequence = memoryConfig.is_sequence();
       memoryFrameLines_[i].rootAgent.reset(
-          memoryConfig.is_sequence()
-              ? new SequenceScatterAgentLayer(scatterConfig)
-              : new ScatterAgentLayer(scatterConfig));
+          new ScatterAgentLayer(scatterConfig));
       memoryFrameLines_[i].rootAgent->init(LayerMap(), parameterMap_);
 
       memoryFrameLines_[i].bootLayer = memoryFrameLines_[i].rootAgent;
@@ -267,9 +263,7 @@ void RecurrentGradientMachine::init(
     if (subModelConfig->has_generator()) {
       memoryFrameLines_[i].scatterAgents.resize(2);
       for (auto& agent : memoryFrameLines_[i].scatterAgents) {
-        agent.reset(memoryConfig.is_sequence()
-                        ? new SequenceScatterAgentLayer(*agentConfig)
-                        : new ScatterAgentLayer(*agentConfig));
+        agent.reset(new ScatterAgentLayer(*agentConfig));
         agent->init(LayerMap(), parameterMap_);
       }
     }
@@ -297,8 +291,6 @@ void RecurrentGradientMachine::init(
   if (subModelConfig->evaluator_names_size() > 0) {
     evaluator_.reset(frames_[0]->makeEvaluator());
   }
-
-  targetInfoInlinkId_ = subModelConfig->target_inlinkid();
 }
 
 void RecurrentGradientMachine::resizeOrCreateFrames(int numFrames) {
@@ -376,108 +368,102 @@ void RecurrentGradientMachine::prefetch(const std::vector<Argument>& inArgs) {
   LOG(FATAL) << "should not use this function";
 }
 
-void RecurrentGradientMachine::forward(const std::vector<Argument>& inArgs,
-                                       std::vector<Argument>* outArgs,
-                                       PassType passType) {
-  if (inFrameLines_.empty() && passType == PASS_TEST) {
-    generateSequence();
-    return;
-  }  // else forward..
-
-  const Argument& input = inFrameLines_[0].inLayer->getOutput();
-  CHECK(input.sequenceStartPositions);
-  int batchSize = input.getBatchSize();
-  size_t numSequences = input.getNumSequences();
-  const int* starts = input.sequenceStartPositions->getData(false);
-  bool hasSubseq = input.hasSubseq();
-
-  // In case of !hasSubseq or targetInfoInlinkId_ == -1, all inlinks share the
-  // same inframe info
-  bool shareInlinkInfo = !hasSubseq || targetInfoInlinkId_ == -1;
-
-  // Defaultly, share info with the first inlink
-  if (shareInlinkInfo) {
-    targetInfoInlinkId_ = 0;
-  }
-
-  // check hasSubseq in both config and input are the same
-  CHECK_EQ(hasSubseq, inFrameLines_[0].hasSubseq);
-
-  CHECK_EQ(starts[numSequences], batchSize);
-  CHECK(input.sequenceStartPositions);
-
-  // check other inputs has same sequence length and start
-  for (size_t i = 1; i < inFrameLines_.size(); ++i) {
-    const Argument& input1 = inFrameLines_[i].inLayer->getOutput();
-    CHECK_EQ((size_t)input1.getNumSequences(), numSequences);
-    // check all inputs should have same hasSubseq flag
-    CHECK_EQ(input.hasSubseq(), inFrameLines_[0].hasSubseq);
-
-    // if shareInlinkInfo, checks:
-    // 1. all inlinks have same number of total tokens
-    // 2. all inlinks have same number of tokens for each sentence of each
-    //    sample. If hasSubseq, one sample has multiple sentence, else, one
-    //    sample is one sentence
-    if (shareInlinkInfo) {
-      CHECK_EQ(input1.getBatchSize(), batchSize);
-      CHECK(std::equal(starts,
-                       starts + numSequences + 1,
-                       input1.sequenceStartPositions->getData(false)));
+void RecurrentGradientMachine::checkInputConsistency(
+    int inlinkId, const std::vector<Argument::SeqInfo>& seqInfo) {
+  if (commonSeqInfo_.empty()) {
+    commonSeqInfo_.resize(seqInfo.size());
+    for (size_t i = 0; i < seqInfo.size(); ++i) {
+      commonSeqInfo_[i].topLevelLength = seqInfo[i].topLevelLength;
+      commonSeqInfo_[i].seqId = seqInfo[i].seqId;
+    }
+  } else {
+    CHECK_EQ(commonSeqInfo_.size(), seqInfo.size())
+        << " RecurrentGroup " << subModelName_ << " input " << inlinkId
+        << " has mismatched number of sequences";
+    for (size_t i = 0; i < seqInfo.size(); ++i) {
+      CHECK_EQ(commonSeqInfo_[i].topLevelLength, seqInfo[i].topLevelLength)
+          << " RecurrentGroup " << subModelName_ << " input " << inlinkId
+          << " has mismatched sequence length";
+      CHECK_EQ(commonSeqInfo_[i].seqId, seqInfo[i].seqId)
+          << " RecurrentGroup " << subModelName_ << " input " << inlinkId
+          << " has mismatched sequence length";
     }
   }
+}
 
-  if (hasSubseq) {
-    CHECK(input.subSequenceStartPositions);
-    size_t numSubSequences = input.getNumSubSequences();
-    const int* subStarts = input.subSequenceStartPositions->getData(false);
-    CHECK_EQ(subStarts[numSubSequences], batchSize);
-    // if hasSubseq, check other inputs has same sub-sequence and sub-start
-    for (size_t i = 1; i < inFrameLines_.size(); ++i) {
-      const Argument& input1 = inFrameLines_[i].inLayer->getOutput();
-      CHECK_EQ((size_t)input1.getNumSubSequences(), numSubSequences);
-      if (shareInlinkInfo) {
-        CHECK(std::equal(subStarts,
-                         subStarts + numSubSequences + 1,
-                         input1.subSequenceStartPositions->getData(false)));
-      }
+void RecurrentGradientMachine::calcNumSequencesAtEachStep() {
+  int numSequences = commonSeqInfo_.size();
+  numSeqs_.resize(maxSequenceLength_);
+  for (int i = 0; i < numSequences; ++i) {
+    for (int j = 0; j < commonSeqInfo_[i].topLevelLength; ++j) {
+      numSeqs_[j] = i + 1;
     }
   }
+}
 
+void RecurrentGradientMachine::reorganizeInput(PassType passType) {
   info_.clear();
   info_.resize(inFrameLines_.size());
 
+  commonSeqInfo_.clear();
   seqInfos_.clear();
   seqInfos_.resize(inFrameLines_.size());
 
+  for (size_t i = 0; i < inFrameLines_.size(); i++) {
+    const Argument& input = inFrameLines_[i].inLayer->getOutput();
+    if (!input.hasSeq()) {
+      continue;
+    }
+    input.getSeqInfo(&seqInfos_[i]);
+    checkInputConsistency(i, seqInfos_[i]);
+  }
+  CHECK(!commonSeqInfo_.empty())
+      << "At least one input needs to be sequence or subsequence";
+  maxSequenceLength_ = commonSeqInfo_[0].topLevelLength;
+
+  calcNumSequencesAtEachStep();
+
+  for (size_t i = 0; i < inFrameLines_.size(); ++i) {
+    const Argument& input = inFrameLines_[i].inLayer->getOutput();
+    if (!input.hasSeq()) {
+      seqInfos_[i] = commonSeqInfo_;
+    }
+    createInFrameInfo(i, input, passType);
+  }
+
   {
     AsyncGpuBlock asyncGpuBlock;
-    // if shareInlinkInfo, only calculate info of the first inlink
-    // else, calculate info for each inlink
-    if (shareInlinkInfo) {
-      input.getSeqInfo(&seqInfos_[0]);
-      maxSequenceLength_ = seqInfos_[0][0].topLevelLength;
-      createInFrameInfo(0, input, passType);
-    } else {
-      for (size_t i = 0; i < inFrameLines_.size(); i++) {
-        const Argument& input1 = inFrameLines_[i].inLayer->getOutput();
-        input1.getSeqInfo(&seqInfos_[i]);
-        maxSequenceLength_ = seqInfos_[i][0].topLevelLength;
-        createInFrameInfo(i, input1, passType);
-      }
-    }
 
     // inFrameLine select rows in real layer one time
     for (size_t i = 0; i < inFrameLines_.size(); i++) {
-      int curInlinkId = shareInlinkInfo ? 0 : i;
       selectRowsOneTime(inFrameLines_[i].inLayer,
-                        info_[curInlinkId].allIds,
+                        info_[i].allIds,
                         &(inFrameLines_[i].outArg),
                         passType);
     }
   }
-  resizeOrCreateFrames(maxSequenceLength_);
-  resizeBootFrame(numSequences);
+}
+
+void RecurrentGradientMachine::reorganizeOutput(PassType passType) {
+  calcSequenceStartPositions();
+  for (size_t i = 0; i < outFrameLines_.size(); ++i) {
+    Info info;
+    auto& outFrameLine = outFrameLines_[i];
+    ICpuGpuVectorPtr sequenceStartPositions;
+    ICpuGpuVectorPtr subSequenceStartPositions;
+    createOutFrameInfo(
+        outFrameLine, info, sequenceStartPositions, subSequenceStartPositions);
+    auto gatherAgent =
+        dynamic_cast<GatherAgentLayer*>(outFrameLine.agentLayer.get());
+    CHECK_NOTNULL(gatherAgent);
+    gatherAgent->copyIdAndSequenceInfo(sequenceStartPositions,
+                                       subSequenceStartPositions,
+                                       info.allIds,
+                                       info.idIndex);
+  }
+}
 
+void RecurrentGradientMachine::connectFrames(PassType passType) {
   for (auto& memoryFrameLine : memoryFrameLines_) {
     if (memoryFrameLine.rootAgent) {
       auto scatterAgent =
@@ -487,8 +473,9 @@ void RecurrentGradientMachine::forward(const std::vector<Argument>& inArgs,
                                           memoryFrameLine.outArg,
                                           memoryFrameLine.allIds,
                                           /* idIndex */ 0,
-                                          memoryFrameLine.allIds->getSize());
-      if (memoryFrameLine.is_sequence) {  // memoryConfig is sequence
+                                          memoryFrameLine.allIds->getSize(),
+                                          /* handleBackward */ true);
+      if (memoryFrameLine.sequenceStartPositions) {
         int size = memoryFrameLine.sequenceStartPositions->getSize();
         scatterAgent->setSequenceStartPositions(
             memoryFrameLine.sequenceStartPositions,
@@ -501,28 +488,26 @@ void RecurrentGradientMachine::forward(const std::vector<Argument>& inArgs,
   for (auto& outFrameLine : outFrameLines_) {
     auto gatherAgent =
         dynamic_cast<GatherAgentLayer*>(outFrameLine.agentLayer.get());
-    CHECK_NOTNULL(gatherAgent);
-    gatherAgent->copyIdAndSequenceInfo(input,
-                                       info_[targetInfoInlinkId_].allIds,
-                                       info_[targetInfoInlinkId_].idIndex);
+    gatherAgent->clearRealLayers();
   }
-
   for (int i = 0; i < maxSequenceLength_; ++i) {
-    int idSize = 0;
     // connect in_links
     for (size_t j = 0; j < inFrameLines_.size(); ++j) {
-      Info& info = info_[shareInlinkInfo ? 0 : j];
+      Info& info = info_[j];
       // idSize denotes the sum number of tokens in each length i
-      idSize = info.idIndex[i + 1] - info.idIndex[i];
+      int idIndex = info.idIndex.empty() ? 0 : info.idIndex[i];
+      int idSize = info.idIndex.empty() ? numSeqs_[i]
+                                        : info.idIndex[i + 1] - info.idIndex[i];
       InFrameLine inFrameLine = inFrameLines_[j];
       auto scatterAgent =
           dynamic_cast<ScatterAgentLayer*>(inFrameLine.agents[i].get());
       scatterAgent->setRealLayerAndOutput(inFrameLine.inLayer,
                                           inFrameLine.outArg,
                                           info.allIds,
-                                          info.idIndex[i],
-                                          idSize);
-      if (hasSubseq) {
+                                          idIndex,
+                                          idSize,
+                                          i == 0);
+      if (info.sequenceStartPositions) {
         // size: the length of subsequence
         int size = info.seqStartPosIndex[i + 1] - info.seqStartPosIndex[i];
         scatterAgent->setSequenceStartPositions(
@@ -536,11 +521,6 @@ void RecurrentGradientMachine::forward(const std::vector<Argument>& inArgs,
           dynamic_cast<GatherAgentLayer*>(outFrameLine.agentLayer.get());
       gatherAgent->addRealLayer(outFrameLine.frames[i]);
     }
-    // connect memory links
-    // Adopt info_[0].idIndex because seq which has_subseq=True
-    // doesn't support Memory with !hasSubseq bootlayer;
-    // And inlinks that !hasSubSeq must have same inlink length.
-    idSize = info_[0].idIndex[i + 1] - info_[0].idIndex[i];
     for (auto& memoryFrameLine : memoryFrameLines_) {
       NeuralNetwork::connect(
           memoryFrameLine.agents[i],
@@ -548,6 +528,28 @@ void RecurrentGradientMachine::forward(const std::vector<Argument>& inArgs,
           numSeqs_[i] /*height of agent*/);
     }
   }
+}
+
+void RecurrentGradientMachine::forward(const std::vector<Argument>& inArgs,
+                                       std::vector<Argument>* outArgs,
+                                       PassType passType) {
+  /* inArgs and outArgs are not used.
+     The inputs are inFrameLines_[i].inLayer.
+     The outputs are outFramesLines_[i].agentLayer
+   */
+
+  if (inFrameLines_.empty() && passType == PASS_TEST) {
+    generateSequence();
+    return;
+  }  // else forward..
+
+  reorganizeInput(passType);
+  int numSequences = commonSeqInfo_.size();
+
+  resizeOrCreateFrames(maxSequenceLength_);
+  resizeBootFrame(numSequences);
+
+  connectFrames(passType);
 
   REGISTER_TIMER_INFO("RecurrentFwTime", "RecurrentFwTime");
   // forward
@@ -558,16 +560,12 @@ void RecurrentGradientMachine::forward(const std::vector<Argument>& inArgs,
     const std::vector<Argument> inArgs;
     std::vector<Argument> outArgs;
     frames_[i]->forward(inArgs, &outArgs, passType);
-    if (hasSubseq) {
-      for (auto& outFrameLine : outFrameLines_) {
-        CHECK(outFrameLine.frames[i]->getOutput().sequenceStartPositions)
-            << "In hierachical RNN, all out links should be from sequences.";
-      }
-    }
   }
   if (evaluator_ && passType == PASS_TEST) {
     this->eval(evaluator_.get());
   }
+
+  reorganizeOutput(passType);
 }
 
 void RecurrentGradientMachine::backward(const UpdateCallback& callback) {
@@ -634,76 +632,228 @@ void RecurrentGradientMachine::removeBeamSearchStatisticsCallbacks() {
     this->beamSearchStatistics_ = nullptr;
   }
 }
+
+namespace {
+void lenToStarts(std::vector<int>& starts) {
+  int pos = 0;
+  starts.back() = 0;
+  for (auto& start : starts) {
+    int tmp = start;
+    start = pos;
+    pos += tmp;
+  }
+  starts.back() = pos;
+}
+}
+
+void RecurrentGradientMachine::calcSequenceStartPositions() {
+  std::vector<int> starts(commonSeqInfo_.size() + 1);
+  for (auto& seqInfo : commonSeqInfo_) {
+    starts[seqInfo.seqId] = seqInfo.topLevelLength;
+  }
+  lenToStarts(starts);
+  ICpuGpuVector::resizeOrCreate(sequenceStartPositions_, starts.size(), false);
+  std::copy(starts.begin(),
+            starts.end(),
+            sequenceStartPositions_->getMutableData(false));
+}
+
+void RecurrentGradientMachine::checkOutputConsistency(
+    OutFrameLine& outFrameLine) {
+  bool hasSeq = outFrameLine.frames[0]->getOutput().hasSeq();
+  for (int i = 0; i < maxSequenceLength_; ++i) {
+    LayerPtr frame = outFrameLine.frames[i];
+    CHECK_EQ(hasSeq, frame->getOutput().hasSeq());
+    int numSequences = frame->getOutput().getNumSequences();
+    CHECK_EQ(numSeqs_[i], numSequences);
+  }
+}
+
+void RecurrentGradientMachine::createOutFrameInfo(
+    OutFrameLine& outFrameLine,
+    Info& info,
+    ICpuGpuVectorPtr& sequenceStartPositions,
+    ICpuGpuVectorPtr& subSequenceStartPositions) {
+  checkOutputConsistency(outFrameLine);
+
+  if (!outFrameLine.frames[0]->getOutput().hasSeq()) {
+    createOutFrameInfo_seq(
+        outFrameLine, info, sequenceStartPositions, subSequenceStartPositions);
+  } else {
+    createOutFrameInfo_subseq(
+        outFrameLine, info, sequenceStartPositions, subSequenceStartPositions);
+  }
+}
+
+void RecurrentGradientMachine::createOutFrameInfo_seq(
+    OutFrameLine& outFrameLine,
+    Info& info,
+    ICpuGpuVectorPtr& sequenceStartPositions,
+    ICpuGpuVectorPtr& subSequenceStartPositions) {
+  std::vector<int> allIds;
+  info.idIndex.resize(1, 0);  // first idIndex = 0
+
+  const int* starts = sequenceStartPositions_->getData(false);
+
+  for (int i = 0; i < maxSequenceLength_; ++i) {
+    LayerPtr frame = outFrameLine.frames[i];
+    size_t numSequences = frame->getOutput().getNumSequences();
+    for (size_t j = 0; j < numSequences; ++j) {
+      int seqStart = starts[commonSeqInfo_[j].seqId];
+      int seqLength = commonSeqInfo_[j].topLevelLength;
+      allIds.push_back(reversed_ ? (seqStart + seqLength - 1 - i)
+                                 : (seqStart + i));
+    }
+    info.idIndex.push_back(allIds.size());
+  }
+  sequenceStartPositions = sequenceStartPositions_;
+  copyScattedId(allIds, &info.allIds, allIds.size());
+  CHECK_EQ(info.idIndex.size(), static_cast<size_t>(maxSequenceLength_ + 1));
+}
+
+void RecurrentGradientMachine::createOutFrameInfo_subseq(
+    OutFrameLine& outFrameLine,
+    Info& info,
+    ICpuGpuVectorPtr& sequenceStartPositions,
+    ICpuGpuVectorPtr& subSequenceStartPositions) {
+  size_t numSequences = commonSeqInfo_.size();
+  std::vector<int> allIds;
+  info.idIndex.resize(1, 0);  // first idIndex = 0
+
+  const int* starts = sequenceStartPositions_->getData(false);
+  std::vector<int> subStarts(starts[numSequences] + 1);
+  for (int i = 0; i < maxSequenceLength_; ++i) {
+    LayerPtr frame = outFrameLine.frames[i];
+    size_t numSequences = frame->getOutput().getNumSequences();
+    const int* seqStarts =
+        frame->getOutput().sequenceStartPositions->getData(false);
+    for (size_t j = 0; j < numSequences; ++j) {
+      subStarts[starts[commonSeqInfo_[j].seqId] + i] =
+          seqStarts[j + 1] - seqStarts[j];
+    }
+  }
+  lenToStarts(subStarts);
+
+  for (int i = 0; i < maxSequenceLength_; ++i) {
+    LayerPtr frame = outFrameLine.frames[i];
+    size_t numSequences = frame->getOutput().getNumSequences();
+    for (size_t j = 0; j < numSequences; ++j) {
+      int pos = starts[commonSeqInfo_[j].seqId] + i;
+      int subSeqStart = subStarts[pos];
+      int subSeqEnd = subStarts[pos + 1];
+      for (int k = subSeqStart; k < subSeqEnd; ++k) {
+        allIds.push_back(k);
+      }
+    }
+    info.idIndex.push_back(allIds.size());
+  }
+
+  ICpuGpuVector::resizeOrCreate(
+      subSequenceStartPositions, subStarts.size(), false);
+  int* cpuSubSequenceStartPositions =
+      subSequenceStartPositions->getMutableData(false);
+  std::copy(subStarts.begin(), subStarts.end(), cpuSubSequenceStartPositions);
+  ICpuGpuVector::resizeOrCreate(
+      sequenceStartPositions, numSequences + 1, false);
+  int* cpuSequenceStartPositions =
+      sequenceStartPositions->getMutableData(false);
+  for (size_t i = 0; i <= numSequences; ++i) {
+    cpuSequenceStartPositions[i] = subStarts[starts[i]];
+  }
+  copyScattedId(allIds, &info.allIds, allIds.size());
+  CHECK_EQ(info.idIndex.size(), static_cast<size_t>(maxSequenceLength_ + 1));
+}
+
 /* create scattered id infomation for all realLayer of inFrameLines one time.
  * If hasSubseq, will also create scattered sequenceStartPositions infomation
  * for all realLayer of inFrameLines one time.
  */
-
 void RecurrentGradientMachine::createInFrameInfo(int inlinkId,
                                                  const Argument& input,
                                                  PassType passType) {
-  bool hasSubseq = input.hasSubseq();
-  // numSequences: # samples(sequences) in a batch
-  size_t numSequences = input.getNumSequences();
+  if (!input.hasSeq()) {
+    createInFrameInfo_nonseq(inlinkId, input, passType);
+  } else if (!input.hasSubseq()) {
+    createInFrameInfo_seq(inlinkId, input, passType);
+  } else {
+    createInFrameInfo_subseq(inlinkId, input, passType);
+  }
+}
+
+void RecurrentGradientMachine::createInFrameInfo_nonseq(int inlinkId,
+                                                        const Argument& input,
+                                                        PassType passType) {
   std::vector<int> allIds;
 
   auto& seqInfo = seqInfos_[inlinkId];
-
-  numSeqs_.clear();
   Info* inlinkInfo = &info_[inlinkId];
   inlinkInfo->idIndex.clear();
-  inlinkInfo->idIndex.push_back(0);  // first idIndex = 0
+  for (size_t i = 0; i < seqInfo.size(); ++i) {
+    allIds.push_back(seqInfo[i].seqId);
+  }
+  // copy and check scatterId
+  copyScattedId(allIds, &inlinkInfo->allIds, input.getBatchSize());
+}
 
+void RecurrentGradientMachine::createInFrameInfo_seq(int inlinkId,
+                                                     const Argument& input,
+                                                     PassType passType) {
+  std::vector<int> allIds;
+  auto& seqInfo = seqInfos_[inlinkId];
+  Info* inlinkInfo = &info_[inlinkId];
+  inlinkInfo->idIndex.resize(1, 0);  // first idIndex = 0
+
+  for (int i = 0; i < maxSequenceLength_; ++i) {
+    for (int j = 0; j < numSeqs_[i]; ++j) {
+      int seqLength = seqInfo[j].topLevelLength;
+      int seqStart = seqInfo[j].seqStart;
+      allIds.push_back(reversed_ ? (seqStart + seqLength - 1 - i)
+                                 : (seqStart + i));
+    }
+    inlinkInfo->idIndex.push_back(allIds.size());
+  }
+
+  // copy and check scatterId
+  copyScattedId(allIds, &inlinkInfo->allIds, input.getBatchSize());
+  CHECK_EQ(inlinkInfo->idIndex.size(),
+           static_cast<size_t>(maxSequenceLength_ + 1));
+}
+void RecurrentGradientMachine::createInFrameInfo_subseq(int inlinkId,
+                                                        const Argument& input,
+                                                        PassType passType) {
+  std::vector<int> allIds;
+
+  auto& seqInfo = seqInfos_[inlinkId];
+
+  Info* inlinkInfo = &info_[inlinkId];
+  inlinkInfo->idIndex.resize(1, 0);  // first idIndex = 0
   std::vector<int> sequenceStartPositions;
   const int* subSequenceStartPositions = nullptr;
 
-  if (hasSubseq) {  // for sequenceScatterAgentLayer
-    subSequenceStartPositions = input.subSequenceStartPositions->getData(false);
-    inlinkInfo->seqStartPosIndex.clear();
-    inlinkInfo->seqStartPosIndex.push_back(0);  // first seqStartPosIndex = 0
-  }
-  // maxSequenceLength_: max topLevelLength in allsamples
+  subSequenceStartPositions = input.subSequenceStartPositions->getData(false);
+  inlinkInfo->seqStartPosIndex.clear();
+  inlinkInfo->seqStartPosIndex.push_back(0);  // first seqStartPosIndex = 0
   for (int i = 0; i < maxSequenceLength_; ++i) {
-    if (hasSubseq) {
-      sequenceStartPositions.push_back(0);  // first element = 0
-    }
-    int numSeqs = 0;
-    for (size_t j = 0; j < numSequences; ++j) {
-      int seqLength = seqInfo[j].topLevelLength;
-      if (i >= seqLength) {
-        break;
-      }
-      ++numSeqs;
-      if (hasSubseq) {
-        int subSeqStart = subSequenceStartPositions[seqInfo[j].subSeqStart + i];
-        int subSeqEnd =
-            subSequenceStartPositions[seqInfo[j].subSeqStart + i + 1];
-        for (int k = subSeqStart; k < subSeqEnd; ++k) {
-          allIds.push_back(k);
-        }
-        sequenceStartPositions.push_back(sequenceStartPositions.back() +
-                                         subSeqEnd - subSeqStart);
-      } else {
-        int seqStart = seqInfo[j].seqStart;
-        allIds.push_back(reversed_ ? (seqStart + seqLength - 1 - i)
-                                   : (seqStart + i));
+    sequenceStartPositions.push_back(0);  // first element = 0
+    for (int j = 0; j < numSeqs_[i]; ++j) {
+      int subSeqStart = subSequenceStartPositions[seqInfo[j].subSeqStart + i];
+      int subSeqEnd = subSequenceStartPositions[seqInfo[j].subSeqStart + i + 1];
+      for (int k = subSeqStart; k < subSeqEnd; ++k) {
+        allIds.push_back(k);
       }
+      sequenceStartPositions.push_back(sequenceStartPositions.back() +
+                                       subSeqEnd - subSeqStart);
     }
     inlinkInfo->idIndex.push_back(allIds.size());
-    numSeqs_.push_back(numSeqs);
-    if (hasSubseq) {
-      inlinkInfo->seqStartPosIndex.push_back(sequenceStartPositions.size());
-    }
-  }
-  if (hasSubseq) {
-    // inFrameLine create sequenceStartPositions one time
-    CHECK_EQ(
-        sequenceStartPositions.size(),
-        static_cast<size_t>(maxSequenceLength_ + input.getNumSubSequences()));
-    CHECK_EQ(inlinkInfo->seqStartPosIndex.size(),
-             static_cast<size_t>(maxSequenceLength_ + 1));
-    createSeqPos(sequenceStartPositions, &inlinkInfo->sequenceStartPositions);
+    inlinkInfo->seqStartPosIndex.push_back(sequenceStartPositions.size());
   }
+  // inFrameLine create sequenceStartPositions one time
+  CHECK_EQ(
+      sequenceStartPositions.size(),
+      static_cast<size_t>(maxSequenceLength_ + input.getNumSubSequences()));
+  CHECK_EQ(inlinkInfo->seqStartPosIndex.size(),
+           static_cast<size_t>(maxSequenceLength_ + 1));
+  createSeqPos(sequenceStartPositions, &inlinkInfo->sequenceStartPositions);
 
   // copy and check scatterId
   copyScattedId(allIds, &inlinkInfo->allIds, input.getBatchSize());
@@ -717,11 +867,11 @@ void RecurrentGradientMachine::createMemoryFrameInfo(
   const Argument& input = (*memoryFrameLine).rootLayer->getOutput();
   size_t numSequences = input.getNumSequences();
   std::vector<int> allIds;
-  bool seqFlag = (*memoryFrameLine).is_sequence;
+  bool seqFlag = input.hasSeq();
+  CHECK(!input.hasSubseq())
+      << "Subsequence boot layer for memory is not supported";
 
   if (seqFlag) {  // for sequenceScatterAgentLayer
-    CHECK(input.sequenceStartPositions)
-        << "boot layer must be a sequence when is_sequence = true";
     std::vector<int> sequenceStartPositions;
     sequenceStartPositions.push_back(0);  // first element = 0
     const int* starts = input.sequenceStartPositions->getData(false);
@@ -804,8 +954,7 @@ size_t RecurrentGradientMachine::getGenBatchSize() {
   for (auto& memoryFrameLine : memoryFrameLines_) {
     if (!memoryFrameLine.rootLayer) continue;
     Argument& bootArg = memoryFrameLine.rootLayer->getOutput();
-    size_t batchSize = memoryFrameLine.is_sequence ? bootArg.getNumSequences()
-                                                   : bootArg.getBatchSize();
+    size_t batchSize = bootArg.getNumSequences();
     if (numSequences) {
       CHECK_EQ(numSequences, batchSize);
     } else {
@@ -845,12 +994,7 @@ void RecurrentGradientMachine::generateSequence() {
     if (memoryFrameLine.rootAgent) {
       auto scatterAgent =
           dynamic_cast<ScatterAgentLayer*>(memoryFrameLine.rootAgent.get());
-      bool seqFlag = memoryFrameLine.is_sequence;
-      scatterAgent->setRealLayer(memoryFrameLine.rootLayer, ids, seqFlag);
-      if (seqFlag) {
-        CHECK(memoryFrameLine.rootLayer->getOutput().sequenceStartPositions)
-            << "boot layer must be a sequence when is_sequence = true";
-      }
+      scatterAgent->setRealLayer(memoryFrameLine.rootLayer, ids);
     }
     NeuralNetwork::connect(
         memoryFrameLine.agents[0], memoryFrameLine.bootLayer, ids.size());
@@ -858,6 +1002,7 @@ void RecurrentGradientMachine::generateSequence() {
 
   // boot layer forward
   AsyncGpuBlock asyncGpuBlock;
+
   for (auto& memoryFrameLine : memoryFrameLines_) {
     memoryFrameLine.bootLayer->forward(PASS_TEST);
   }
@@ -930,8 +1075,7 @@ void RecurrentGradientMachine::oneWaySearch(size_t batchSize) {
         auto scatterAgent = dynamic_cast<ScatterAgentLayer*>(
             memoryFrameLine.scatterAgents[machineCur].get());
         scatterAgent->setRealLayer(memoryFrameLine.frames[machinePrev],
-                                   scatterIds,
-                                   memoryFrameLine.is_sequence);
+                                   scatterIds);
         scatterAgent->forward(PASS_TEST);
         NeuralNetwork::connect(memoryFrameLine.agents[machineCur],
                                memoryFrameLine.scatterAgents[machineCur]);
@@ -1003,8 +1147,7 @@ void RecurrentGradientMachine::connectPrevFrame(int stepId,
     auto scatterAgent = dynamic_cast<ScatterAgentLayer*>(
         memoryFrameLine.scatterAgents[machineCur].get());
     scatterAgent->setRealLayer(memoryFrameLine.frames[machinePrev],
-                               isOutIds ? topIds_ : machineIds_,
-                               memoryFrameLine.is_sequence);
+                               isOutIds ? topIds_ : machineIds_);
     scatterAgent->forward(PASS_TEST);
     NeuralNetwork::connect(memoryFrameLine.agents[machineCur],
                            memoryFrameLine.scatterAgents[machineCur]);

paddle/gserver/gradientmachines/RecurrentGradientMachine.h

@@ -284,6 +284,16 @@ public:
   }
 
 protected:
+  std::vector<Argument::SeqInfo> commonSeqInfo_;
+  ICpuGpuVectorPtr sequenceStartPositions_;
+  void calcSequenceStartPositions();
+  void checkInputConsistency(int inlinkId,
+                             const std::vector<Argument::SeqInfo>& seqInfo);
+  void reorganizeInput(PassType passType);
+  void reorganizeOutput(PassType passType);
+  void connectFrames(PassType passType);
+  void calcNumSequencesAtEachStep();
+
   void resizeOrCreateFrames(int numFrames);
   void resizeBootFrame(int numSequences);
 
@@ -295,8 +305,7 @@ protected:
     std::string linkName;
     LayerPtr inLayer;
     std::vector<LayerPtr> agents;  // Scatter Agents to reform batch input
-    bool hasSubseq;
-    Argument outArg;  // scatter output argument
+    Argument outArg;               // scatter output argument
   };
   std::vector<InFrameLine> inFrameLines_;
 
@@ -318,7 +327,6 @@ protected:
     std::vector<LayerPtr> agents;
     std::vector<LayerPtr> scatterAgents;  // scatter agent used by beam search
     Argument outArg;                      // scatter output argument
-    bool is_sequence;
     // Different memoryFrameLine have different element as follows
     IVectorPtr allIds;  // scattered id of realLayer
     ICpuGpuVectorPtr
@@ -330,22 +338,27 @@ protected:
   // and all outFrameLines(outlinks) share the info with one inFrameLine,
   // which is assigned by targetInfoInlinkId_.
   struct Info {
-    IVectorPtr allIds;         // scattered id of realLayer
-    std::vector<int> idIndex;  // index of allIds
+    // The original positions in the original batch
+    IVectorPtr allIds;  // scattered id of realLayer [batchSize]
+
+    // index of allIds for each step [maxSequenceLength_]
+    // idIndex[i] is the total length of the first i sequences
+    std::vector<int> idIndex;
+
     ICpuGpuVectorPtr
         sequenceStartPositions;         // scattered sequenceStartPositions
     std::vector<int> seqStartPosIndex;  // index of sequenceStartPositions
   };
-  std::vector<Info> info_;
+  std::vector<Info> info_;  // for input
 
   // numSeqs_[i] is the number sequences which is longer than i (for sequence
   // data) or has more than i subsequences (for subsequence data)
+  // Equivalently, numSeqs_[i] is the number of sequences at step i;
   std::vector<int> numSeqs_;
 
   std::vector<std::vector<Argument::SeqInfo>> seqInfos_;
 
-  // the id of inlink which share info with outlinks
-  int targetInfoInlinkId_;
+  void checkOutputConsistency(OutFrameLine& outFrameLine);
 
   /* create scattered id infomation for all realLayer of inFrameLines one time.
    *  If hasSubseq, will also create scattered sequenceStartPositions infomation
@@ -354,6 +367,28 @@ protected:
   void createInFrameInfo(int inlinks_id,
                          const Argument& input,
                          PassType passType);
+  void createInFrameInfo_nonseq(int inlinks_id,
+                                const Argument& input,
+                                PassType passType);
+  void createInFrameInfo_seq(int inlinks_id,
+                             const Argument& input,
+                             PassType passType);
+  void createInFrameInfo_subseq(int inlinks_id,
+                                const Argument& input,
+                                PassType passType);
+
+  void createOutFrameInfo(OutFrameLine& outFrameLine,
+                          Info& info,
+                          ICpuGpuVectorPtr& sequenceStartPositions,
+                          ICpuGpuVectorPtr& subSequenceStartPositions);
+  void createOutFrameInfo_seq(OutFrameLine& outFrameLine,
+                              Info& info,
+                              ICpuGpuVectorPtr& sequenceStartPositions,
+                              ICpuGpuVectorPtr& subSequenceStartPositions);
+  void createOutFrameInfo_subseq(OutFrameLine& outFrameLine,
+                                 Info& info,
+                                 ICpuGpuVectorPtr& sequenceStartPositions,
+                                 ICpuGpuVectorPtr& subSequenceStartPositions);
 
   void createMemoryFrameInfo(MemoryFrameLine* memoryFrameLine,
                              PassType passType);
@@ -386,9 +421,7 @@ protected:
   NeuralNetwork* rootNetwork_;
   bool reversed_;
 
-  // if hasSubseq: max number of sentences(subseq)in batchsize samples
-  // else: max number of tokens in batchsize samples(sentences)
-  int maxSequenceLength_;
+  int maxSequenceLength_;  // Max top-level length
   bool useGpu_;
   bool stopBeamSearch_;
 

paddle/gserver/layers/AgentLayer.cpp

@@ -36,14 +36,23 @@ void AgentLayer::forward(PassType passType) {
   Layer::forward(passType);
 
   Argument& realOutput = realLayer_->getOutput();
-  int realHeight = realOutput.getBatchSize();
-  CHECK_LE(numSamples_, realHeight);
+  int realNumSequences = realOutput.getNumSequences();
+  CHECK_LE(numSamples_, realNumSequences);
 
   // get Arguments from real layers
-  if (numSamples_ > 0 && numSamples_ < realHeight) {
-    if (realOutput.ids) {
-      output_.ids =
-          IVector::create(realOutput.ids->getData(), numSamples_, useGpu_);
+  if (numSamples_ > 0 && numSamples_ < realNumSequences) {
+    if (realOutput.hasSeq()) {
+      int numRows =
+          realOutput.sequenceStartPositions->getData(false)[numSamples_];
+      output_.subArgFrom(realOutput,
+                         /* offset */ 0,
+                         numRows,
+                         getSize(),
+                         useGpu_,
+                         /* trans */ false,
+                         /* seqFlag */ true,
+                         /* seqStart */ 0,
+                         /* seqSize */ numSamples_ + 1);
     } else {
       output_.subArgFrom(
           realOutput, /* offset */ 0, numSamples_, getSize(), useGpu_);
@@ -53,34 +62,6 @@ void AgentLayer::forward(PassType passType) {
   }
 }
 
-void SequenceAgentLayer::forward(PassType passType) {
-  Layer::forward(passType);
-
-  Argument& realOutput = realLayer_->getOutput();
-  int realNumSequences = realOutput.getNumSequences();
-  CHECK_LE(numSamples_, realNumSequences);
-
-  // get Arguments from real layers
-  if (numSamples_ > 0 && numSamples_ < realNumSequences) {
-    int numRows =
-        realOutput.sequenceStartPositions->getData(false)[numSamples_];
-    CHECK(!realOutput.ids) << "Not supported";
-    output_.subArgFrom(realOutput,
-                       /* offset */ 0,
-                       numRows,
-                       getSize(),
-                       useGpu_,
-                       /* trans */ false,
-                       /* seqFlag */ true,
-                       /* seqStart */ 0,
-                       /* seqSize */ numSamples_ + 1);
-  } else {
-    output_ = realOutput;
-  }
-}
-
-REGISTER_LAYER(sequence_agent, SequenceAgentLayer);
-
 bool GatherAgentLayer::init(const LayerMap& layerMap,
                             const ParameterMap& parameterMap) {
   CHECK_EQ(config_.inputs_size(), 0);
@@ -91,18 +72,26 @@ bool GatherAgentLayer::init(const LayerMap& layerMap,
   return true;
 }
 
-void GatherAgentLayer::copyIdAndSequenceInfo(const Argument& input,
-                                             const IVectorPtr& ids,
-                                             const std::vector<int>& idIndex) {
-  output_.sequenceStartPositions = input.sequenceStartPositions;
-  output_.subSequenceStartPositions = input.subSequenceStartPositions;
-  realLayers_.clear();
+void GatherAgentLayer::copyIdAndSequenceInfo(
+    ICpuGpuVectorPtr sequenceStartPositions,
+    ICpuGpuVectorPtr subSequenceStartPositions,
+    const IVectorPtr& ids,
+    const std::vector<int>& idIndex) {
+  output_.sequenceStartPositions = sequenceStartPositions;
+  output_.subSequenceStartPositions = subSequenceStartPositions;
   allIds_ = ids;
   idIndex_ = idIndex;
 }
 
 void GatherAgentLayer::forward(PassType passType) {
   Layer::forward(passType);
+  forwardIds(passType);
+  forwardValue(passType);
+}
+
+void GatherAgentLayer::forwardValue(PassType passType) {
+  MatrixPtr valueReal = realLayers_[0]->getOutputValue();
+  if (!valueReal) return;
 
   int height = allIds_->getSize();
   int width = this->getSize();
@@ -147,7 +136,9 @@ void ScatterAgentLayer::forward(PassType passType) {
   CHECK_EQ(realLayer_->getDeviceId(), this->getDeviceId());
 
   int width = this->getSize();
-  if (realOutArg_.value || realOutArg_.ids) {
+  if (realOutArg_.hasSeq()) {
+    forwardSequence(passType);
+  } else if (realOutArg_.value || realOutArg_.ids) {
     output_.subArgFrom(
         realOutArg_, /* offset */ idIndex_, idSize_, width, useGpu_);
   } else {  // used in generation
@@ -174,7 +165,7 @@ void ScatterAgentLayer::backward(const UpdateCallback& callback) {
   if (realGrad) {
     // for agent in inFrameLines and memoryFrameLines,
     // only first scatterAgentLayer should do addToRows in backward
-    if (idIndex_ == 0) {
+    if (handleBackward_) {
       outputGrad->addToRows(*realGrad, *ids_);
     }
   }
@@ -183,12 +174,14 @@ void ScatterAgentLayer::backward(const UpdateCallback& callback) {
 REGISTER_LAYER(gather_agent, GatherAgentLayer);
 REGISTER_LAYER(scatter_agent, ScatterAgentLayer);
 
-void SequenceGatherAgentLayer::forward(PassType passType) {
-  Layer::forward(passType);
+void GatherAgentLayer::forwardIds(PassType passType) {
   int height = 0;
-  int* starts = output_.subSequenceStartPositions->getMutableData(false);
   IVectorPtr idReal = realLayers_[0]->getOutputLabel();
-  if (idReal) {
+
+  if (!idReal) return;
+
+  if (output_.subSequenceStartPositions) {
+    int* starts = output_.subSequenceStartPositions->getMutableData(false);
     // Gather generator.idsVec
     // if is beam search generation result. Get first result.
     if (idReal->getData()[idReal->getSize() - 1] == -1) {
@@ -212,13 +205,11 @@ void SequenceGatherAgentLayer::forward(PassType passType) {
           ->copyFrom(*realLayers_[i]->getOutputLabel());
     }
   } else {
-    // Gather output.value, same as GatherAgentLayer
-    CHECK(output_.subSequenceStartPositions);
-    GatherAgentLayer::forward(passType);
+    LOG(FATAL) << "Not implemented";
   }
 }
 
-void SequenceScatterAgentLayer::forward(PassType passType) {
+void ScatterAgentLayer::forwardSequence(PassType passType) {
   Layer::forward(passType);
   CHECK_EQ(realLayer_->getDeviceId(), this->getDeviceId());
 
@@ -241,6 +232,7 @@ void SequenceScatterAgentLayer::forward(PassType passType) {
                        /* seqStart */ seqStartPosIndex_,
                        /* seqSize */ numSequences_);
   } else {
+    // Putting the generation logic here is really an ugly hack!
     // used in generation
     int height = 0;
     size_t numSequences = ids_->getSize();
@@ -284,7 +276,4 @@ void SequenceScatterAgentLayer::forward(PassType passType) {
   }
 }
 
-REGISTER_LAYER(sequence_gather_agent, SequenceGatherAgentLayer);
-REGISTER_LAYER(sequence_scatter_agent, SequenceScatterAgentLayer);
-
 }  // namespace paddle

paddle/gserver/layers/AgentLayer.h

@@ -49,18 +49,6 @@ public:
   void backward(const UpdateCallback& callback = nullptr) override {}
 };
 
-/**
- * like AgentLayer, but use first *numSamples* sequences
- */
-class SequenceAgentLayer : public AgentLayer {
-public:
-  explicit SequenceAgentLayer(const LayerConfig& config) : AgentLayer(config) {}
-  ~SequenceAgentLayer() {}
-
-  void forward(PassType passType) override;
-  void backward(const UpdateCallback& callback = nullptr) override {}
-};
-
 /**
  * Like AgentLayer, but it can gather many real layers. Each real
  * layer give a few rows of a sequence, after gather all real layers,
@@ -83,7 +71,10 @@ public:
             const ParameterMap& parameterMap) override;
 
   // call before addRealLayer
-  void copyIdAndSequenceInfo(const Argument& input,
+  void clearRealLayers() { realLayers_.clear(); }
+
+  void copyIdAndSequenceInfo(ICpuGpuVectorPtr sequenceStartPositions,
+                             ICpuGpuVectorPtr subSequenceStartPositions,
                              const IVectorPtr& allIds,
                              const std::vector<int>& idIndex);
 
@@ -92,24 +83,8 @@ public:
 
   void forward(PassType passType) override;
   void backward(const UpdateCallback& callback) override;
-};
-
-/**
- * Like GatherAgentLayer, but select a few sequence in real layer.
- * *ids* in addRealLayer() are the ids of selected sequence.
- * It's used to reorder sequence output.
- */
-class SequenceGatherAgentLayer : public GatherAgentLayer {
-public:
-  explicit SequenceGatherAgentLayer(const LayerConfig& config)
-      : GatherAgentLayer(config) {}
-  virtual ~SequenceGatherAgentLayer() {}
-
-  void forward(PassType passType);
-  void backward(const UpdateCallback& callback) {
-    // same as GatherAgentLayer
-    GatherAgentLayer::backward(callback);
-  }
+  void forwardValue(PassType passType);
+  void forwardIds(PassType passType);
 };
 
 /**
@@ -129,6 +104,11 @@ protected:
   int idSize_;
   int seqStartPosIndex_;
   int numSequences_;  // number of sequences in this scatterAgentLayer
+  bool handleBackward_;
+
+  // use to store expanded cpuStartPositions or subSequenceStartPositions
+  // of real layer.
+  ICpuGpuVectorPtr inputStartPos_;
 
 public:
   explicit ScatterAgentLayer(const LayerConfig& config) : Layer(config) {}
@@ -147,19 +127,15 @@ public:
    *                        false(default) in ScatterAgentLayer, and
    *                        true in SequenceScatterAgentLayer.
    */
-  void setRealLayer(LayerPtr layer,
-                    const std::vector<int>& ids,
-                    bool copyId = false) {
+  void setRealLayer(LayerPtr layer, const std::vector<int>& ids) {
     realLayer_ = layer;
     IVector::resizeOrCreate(ids_, ids.size(), useGpu_);
     ids_->copyFrom(ids.data(), ids.size());
-    if (copyId) {
-      if (useGpu_) {
-        IVector::resizeOrCreate(cpuIds_, ids.size(), false);
-        cpuIds_->copyFrom(ids.data(), ids.size());
-      } else {
-        cpuIds_ = ids_;
-      }
+    if (useGpu_) {
+      IVector::resizeOrCreate(cpuIds_, ids.size(), false);
+      cpuIds_->copyFrom(ids.data(), ids.size());
+    } else {
+      cpuIds_ = ids_;
     }
   }
 
@@ -169,12 +145,14 @@ public:
                              const Argument& outArg,
                              const IVectorPtr& ids,
                              int idIndex,
-                             int idSize) {
+                             int idSize,
+                             bool handleBackward) {
     realLayer_ = layer;
     realOutArg_ = outArg;
     ids_ = ids;
     idIndex_ = idIndex;
     idSize_ = idSize;
+    handleBackward_ = handleBackward;
   }
 
   void setSequenceStartPositions(const ICpuGpuVectorPtr& sequenceStartPositions,
@@ -187,28 +165,8 @@ public:
 
   void forward(PassType passType) override;
   void backward(const UpdateCallback& callback) override;
-};
 
-/**
- * Like ScatterAgentLayer, but select a few sequence in real layer.
- * *ids* in setRealLayer() or setRealLayerAndOutput() are the ids of
- * selected sequence. It's used to reorder sequence input.
- */
-class SequenceScatterAgentLayer : public ScatterAgentLayer {
-protected:
-  // use to store expanded cpuStartPositions or subSequenceStartPositions
-  // of real layer.
-  ICpuGpuVectorPtr inputStartPos_;
-
-public:
-  explicit SequenceScatterAgentLayer(const LayerConfig& config)
-      : ScatterAgentLayer(config) {}
-  virtual ~SequenceScatterAgentLayer() {}
-
-  void forward(PassType passType);
-  void backward(const UpdateCallback& callback) {
-    ScatterAgentLayer::backward(callback);
-  }
+  void forwardSequence(PassType passType);
 };
 
 }  // namespace paddle

paddle/gserver/layers/SequencePoolLayer.cpp

@@ -46,6 +46,9 @@ void SequencePoolLayer::forward(PassType passType) {
   Layer::forward(passType);
 
   const Argument& input = getInput(0);
+  CHECK(input.hasSeq() || input.hasSubseq())
+      << "Input should be a sequence or subsequence for layer " << getName();
+
   newBatchSize_ = type_ ? input.getNumSubSequences() : input.getNumSequences();
   size_t dim = getSize();
   // check

paddle/gserver/tests/rnn_data_provider.py

@@ -95,3 +95,22 @@ def process_unequalength_seq(settings, file_name):
         words1 = reduce(lambda x, y: x + y, d[0])
         words2 = reduce(lambda x, y: x + y, d[1])
         yield words1, words2, d[2]
+
+
+###########################################################
+data3 = [
+    [[[1, 2], [4, 5, 2]], [1, 2], 0],
+    [[[0, 2], [2, 5], [0, 1, 2]], [2, 3, 0], 1],
+]
+
+
+# Used for sequence_nest_mixed_inputs.conf
+@provider(
+    input_types=[
+        integer_value_sub_sequence(10), integer_value_sequence(10),
+        integer_value(2)
+    ],
+    should_shuffle=False)
+def process_mixed(settings, file_name):
+    for d in data3:
+        yield d

paddle/gserver/tests/sequence_nest_rnn_multi_input.conf

@@ -19,7 +19,7 @@ from paddle.trainer_config_helpers import *
 define_py_data_sources2(train_list='gserver/tests/Sequence/dummy.list',
                         test_list=None,
                         module='rnn_data_provider',
-                        obj='process_subseq2')
+                        obj='process_subseq')
 
 
 settings(batch_size=2, learning_rate=0.01)
@@ -57,7 +57,7 @@ def outer_step(wid, x):
     last = last_seq(input=inner_rnn_output, name="outer_rnn_state")
 
     # "return last" should also work. But currently RecurrentGradientMachine
-    # does not handle it, and will report error: In hierachical RNN, all out 
+    # does not handle it, and will report error: In hierachical RNN, all out
     # links should be from sequences now.
     return inner_rnn_output
 

paddle/gserver/tests/sequence_rnn_matched_inputs.py

+# 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.
+
+from paddle.trainer_config_helpers import *
+
+######################## data source ################################
+define_py_data_sources2(
+    train_list='gserver/tests/Sequence/dummy.list',
+    test_list=None,
+    module='rnn_data_provider',
+    obj='process_mixed')
+
+settings(batch_size=2, learning_rate=0.01)
+######################## network configure ################################
+dict_dim = 10
+word_dim = 2
+hidden_dim = 2
+label_dim = 2
+
+data1 = data_layer(name="word1", size=dict_dim)
+data2 = data_layer(name="word2", size=dict_dim)
+label = data_layer(name="label", size=label_dim)
+
+encoding = embedding_layer(input=data2, size=word_dim)
+
+subseq = embedding_layer(input=data1, size=word_dim)
+seq = embedding_layer(input=data2, size=word_dim)
+nonseq = embedding_layer(input=label, size=word_dim)
+
+
+# This hierarchical RNN is designed to be equivalent to the simple RNN in
+# sequence_rnn_multi_unequalength_inputs.conf
+def outer_step(subseq, seq, nonseq, encoding):
+    outer_mem = memory(name="outer_rnn_state", size=hidden_dim)
+
+    def inner_step(subseq, seq, nonseq):
+        inner_mem = memory(
+            name="inner_rnn_state", size=hidden_dim, boot_layer=outer_mem)
+
+        out = fc_layer(
+            input=[subseq, seq, nonseq, inner_mem],
+            size=hidden_dim,
+            act=TanhActivation(),
+            bias_attr=True,
+            name='inner_rnn_state')
+        return out
+
+    decoder = recurrent_group(
+        step=inner_step, name='inner', input=[subseq, seq, nonseq])
+    last = last_seq(name="outer_rnn_state", input=decoder)
+    context = simple_attention(
+        encoded_sequence=encoding, encoded_proj=encoding, decoder_state=last)
+    return context
+
+
+out = recurrent_group(
+    name="outer",
+    step=outer_step,
+    input=[
+        subseq, expand_layer(
+            seq, expand_as=subseq,
+            expand_level=ExpandLevel.FROM_SEQUENCE), expand_layer(
+                nonseq,
+                expand_as=subseq,
+                expand_level=ExpandLevel.FROM_NO_SEQUENCE),
+        StaticInput(encoding)
+    ])
+
+rep = last_seq(input=out)
+prob = fc_layer(
+    size=label_dim, input=rep, act=SoftmaxActivation(), bias_attr=True)
+
+outputs(classification_cost(input=prob, label=label))

paddle/gserver/tests/sequence_rnn_mixed_inputs.py

+# 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.
+
+from paddle.trainer_config_helpers import *
+
+######################## data source ################################
+define_py_data_sources2(
+    train_list='gserver/tests/Sequence/dummy.list',
+    test_list=None,
+    module='rnn_data_provider',
+    obj='process_mixed')
+
+settings(batch_size=2, learning_rate=0.01)
+######################## network configure ################################
+dict_dim = 10
+word_dim = 2
+hidden_dim = 2
+label_dim = 2
+
+data1 = data_layer(name="word1", size=dict_dim)
+data2 = data_layer(name="word2", size=dict_dim)
+label = data_layer(name="label", size=label_dim)
+
+encoding = embedding_layer(input=data2, size=word_dim)
+
+
+# This hierarchical RNN is designed to be equivalent to the simple RNN in
+# sequence_rnn_multi_unequalength_inputs.conf
+def outer_step(subseq, seq, nonseq, encoding):
+    outer_mem = memory(name="outer_rnn_state", size=hidden_dim)
+
+    def inner_step(data1, data2, label):
+        inner_mem = memory(
+            name="inner_rnn_state", size=hidden_dim, boot_layer=outer_mem)
+
+        subseq = embedding_layer(input=data1, size=word_dim)
+        seq = embedding_layer(input=data2, size=word_dim)
+        nonseq = embedding_layer(input=label, size=word_dim)
+
+        print_layer(input=[data1, seq, label, inner_mem])
+        out = fc_layer(
+            input=[subseq, seq, nonseq, inner_mem],
+            size=hidden_dim,
+            act=TanhActivation(),
+            bias_attr=True,
+            name='inner_rnn_state')
+        return out
+
+    decoder = recurrent_group(
+        step=inner_step, name='inner',
+        input=[subseq, StaticInput(seq), nonseq])
+    last = last_seq(name="outer_rnn_state", input=decoder)
+    context = simple_attention(
+        encoded_sequence=encoding, encoded_proj=encoding, decoder_state=last)
+    return context
+
+
+out = recurrent_group(
+    name="outer",
+    step=outer_step,
+    input=[data1, data2, StaticInput(label), StaticInput(encoding)])
+
+rep = last_seq(input=out)
+prob = fc_layer(
+    size=label_dim, input=rep, act=SoftmaxActivation(), bias_attr=True)
+
+outputs(classification_cost(input=prob, label=label))

paddle/gserver/tests/sequence_rnn_multi_input.conf

@@ -19,7 +19,7 @@ from paddle.trainer_config_helpers import *
 define_py_data_sources2(train_list='gserver/tests/Sequence/dummy.list',
                         test_list=None,
                         module='rnn_data_provider',
-                        obj='process_seq2')
+                        obj='process_seq')
 
 
 settings(batch_size=2, learning_rate=0.01)

paddle/gserver/tests/test_RecurrentGradientMachine.cpp

@@ -155,6 +155,15 @@ TEST(RecurrentGradientMachine, rnn_multi_unequalength_input) {
   }
 }
 
+TEST(RecurrentGradientMachine, rnn_mixed_input) {
+  for (bool useGpu : {false, true}) {
+    test("gserver/tests/sequence_rnn_mixed_inputs.py",
+         "gserver/tests/sequence_rnn_matched_inputs.py",
+         1e-6,
+         useGpu);
+  }
+}
+
 int main(int argc, char** argv) {
   testing::InitGoogleTest(&argc, argv);
 

paddle/math/Vector.cpp

@@ -908,12 +908,13 @@ const T* CpuGpuVectorT<T>::getData(bool useGpu) const {
 // Operation will change data and need to reset sync_ & syncFlag_.
 #define MUTABLE_VECTOR_OP(OP, useGpu, args...) \
   do {                                         \
-    setSync(useGpu);                           \
     if (useGpu) {                              \
       copyToGpu();                             \
+      setSync(useGpu);                         \
       return gpuVectorT_->OP(args);            \
     } else {                                   \
       copyToCpu();                             \
+      setSync(useGpu);                         \
       return cpuVectorT_->OP(args);            \
     }                                          \
   } while (0)
@@ -1030,7 +1031,7 @@ void CpuGpuVectorT<T>::copyToCpu() {
     case DATA_AT_GPU:
       CHECK(gpuVectorT_);
       this->resizeOrCreate(gpuVectorT_->getSize(), false);
-      cpuVectorT_->copyFrom(*gpuVectorT_, HPPL_STREAM_DEFAULT);
+      cpuVectorT_->copyFrom(*gpuVectorT_);
       setSync(SYNCED);
       break;
     case DATA_AT_CPU:
@@ -1049,7 +1050,7 @@ void CpuGpuVectorT<T>::copyToGpu() {
     case DATA_AT_CPU:
       CHECK(cpuVectorT_);
       this->resizeOrCreate(cpuVectorT_->getSize(), true);
-      gpuVectorT_->copyFrom(*cpuVectorT_, HPPL_STREAM_DEFAULT);
+      gpuVectorT_->copyFrom(*cpuVectorT_);
       setSync(SYNCED);
       break;
     case DATA_AT_GPU:

paddle/parameter/Argument.h

@@ -149,6 +149,7 @@ struct Argument {
                                      : getBatchSize();
   }
 
+  bool hasSeq() const { return sequenceStartPositions != nullptr; }
   bool hasSubseq() const { return subSequenceStartPositions != nullptr; }
 
   const int* getCpuStartPositions() const {

paddle/trainer/tests/test_recurrent_machine_generation.cpp

@@ -124,6 +124,8 @@ TEST(RecurrentGradientMachine, test_generation) {
                      bool beam_search) {
     FLAGS_config_args = beam_search ? "beam_search=1" : "beam_search=0";
     for (auto useGpu : useGpuConfs) {
+      LOG(INFO) << configFile << " useGpu=" << useGpu
+                << " beam_search=" << beam_search;
       testGeneration(configFile, useGpu, hasSubseq, expRetFile);
     }
   };

python/paddle/trainer/config_parser.py

@@ -328,53 +328,33 @@ def RecurrentLayerGroupWithoutOutLinksBegin(name,
     SubModelBegin(name)
     g_current_submodel.is_recurrent_layer_group = True
     g_current_submodel.reversed = seq_reversed
-    g_current_submodel.target_inlinkid = -1
     in_links_count = 0
     for linkid, link in enumerate(in_links):
         if isinstance(link, basestring):
             name = link
-            has_subseq = False
         else:
             name = link.link_name
-            has_subseq = link.has_subseq
-        # assign target_inlinkid according to target_inlinkname
-        if target_inlinkname == name:
-            g_current_submodel.target_inlinkid = linkid
 
-        if in_links_count == 0:
-            in_links_has_subseq = has_subseq
-        else:
-            config_assert(
-                in_links_has_subseq == has_subseq,
-                "The sequence type of in_links should be the same in RecurrentLayerGroup"
-            )
         in_links_count += 1
         layer_name = MakeLayerNameInParentSubmodel(name)
         layer = g_layer_map[layer_name]
-        if has_subseq:
-            SequenceScatterAgentLayer(name=name, size=layer.size)
-        else:
-            ScatterAgentLayer(name=name, size=layer.size)
+        ScatterAgentLayer(name=name, size=layer.size)
 
         pair = g_current_submodel.in_links.add()
         pair.layer_name = layer_name
         pair.link_name = MakeLayerNameInSubmodel(name)
-        pair.has_subseq = has_subseq
 
 
 @config_func
 def RecurrentLayerGroupSetOutLink(link):
     if isinstance(link, basestring):
         name = link
-        has_subseq = False
     else:
         name = link.link_name
-        has_subseq = link.has_subseq
     layer_name = MakeLayerNameInParentSubmodel(name)
     pair = g_current_submodel.out_links.add()
     pair.layer_name = MakeLayerNameInSubmodel(name)
     pair.link_name = layer_name
-    pair.has_subseq = has_subseq
 
 
 def RecurrentLayerGroupSetGenerator(generator=None):
@@ -389,8 +369,7 @@ def RecurrentLayerGroupBegin(name,
                              generator=None,
                              target_inlinkname="",
                              seq_reversed=False):
-    RecurrentLayerGroupWithoutOutLinksBegin(name, in_links, seq_reversed,
-                                            target_inlinkname)
+    RecurrentLayerGroupWithoutOutLinksBegin(name, in_links, seq_reversed)
     for link in out_links:
         RecurrentLayerGroupSetOutLink(link)
 
@@ -425,8 +404,6 @@ def RecurrentLayerGroupEnd(name):
         agent_name = GetLayerBaseName(pair.link_name)
         if prev_submodel.HasField("generator"):
             DataLayer(name=agent_name, size=layer.size)
-        elif pair.has_subseq:
-            SequenceGatherAgentLayer(name=agent_name, size=layer.size)
         else:
             GatherAgentLayer(name=agent_name, size=layer.size)
 
@@ -2253,13 +2230,6 @@ class AgentLayer(LayerBase):
             name, 'agent', size, inputs=[], device=device)
 
 
-@config_layer('sequence_agent')
-class SequenceAgentLayer(LayerBase):
-    def __init__(self, name, size, device=None):
-        super(SequenceAgentLayer, self).__init__(
-            name, 'sequence_agent', size, inputs=[], device=device)
-
-
 @config_layer('gather_agent')
 class GatherAgentLayer(LayerBase):
     def __init__(self, name, size, device=None):
@@ -2274,20 +2244,6 @@ class ScatterAgentLayer(LayerBase):
             name, 'scatter_agent', size, inputs=[], device=device)
 
 
-@config_layer('sequence_gather_agent')
-class SequenceGatherAgentLayer(LayerBase):
-    def __init__(self, name, size, device=None):
-        super(SequenceGatherAgentLayer, self).__init__(
-            name, 'sequence_gather_agent', size, inputs=[], device=device)
-
-
-@config_layer('sequence_scatter_agent')
-class SequenceScatterAgentLayer(LayerBase):
-    def __init__(self, name, size, device=None):
-        super(SequenceScatterAgentLayer, self).__init__(
-            name, 'sequence_scatter_agent', size, inputs=[], device=device)
-
-
 @config_layer('multiplex')
 class MultiplexLayer(LayerBase):
     def __init__(self, name, inputs, size, device=None):
@@ -2303,12 +2259,12 @@ class MultiplexLayer(LayerBase):
 
 
 @config_func
-def Link(
-        name,
-        has_subseq=False, ):
+def Link(name, has_subseq=False):
+    """
+    Still keeping has_subseq for backward compatibility
+    """
     link_config = LinkConfig()
     link_config.link_name = name
-    link_config.has_subseq = has_subseq
     return link_config
 
 
@@ -2341,20 +2297,13 @@ def Memory(name,
         config_assert(name is not None, "name needs cannot be None")
         memory_name = name + "+delay1"
     agent_name = memory_name
-    if is_sequence:
-        config_assert(
-            boot_layer is not None,
-            "there must be boot_layer in network when is_sequence = True")
-        agent_layer = SequenceAgentLayer(agent_name, size)
-    else:
-        agent_layer = AgentLayer(agent_name, size)
+    agent_layer = AgentLayer(agent_name, size)
     config_assert(g_current_submodel.is_recurrent_layer_group,
                   'Memory should be used in recurrent layer group only')
     memory = g_current_submodel.memories.add()
     if name is not None:
         memory.layer_name = MakeLayerNameInSubmodel(name)
     memory.link_name = MakeLayerNameInSubmodel(agent_name)
-    memory.is_sequence = is_sequence
     options = sum((boot_layer is not None, bool(boot_bias),
                    boot_with_const_id is not None))
     config_assert(

python/paddle/trainer_config_helpers/layers.py

@@ -311,18 +311,6 @@ class LayerOutput(object):
         self.outputs = outputs
         self.reverse = reverse
 
-    def __repr__(self):
-        """
-        Disable __repr__ for debug reason. Will be implemented when release
-        """
-        assert False, "this method should not be invoked"
-
-    def __str__(self):
-        """
-        Disable __str__ for debug reason. Will be implemented when release
-        """
-        assert False, "this method should not be invoked"
-
     def set_input(self, input):
         """
         Set the input for a memory layer. Can only be used for memory layer
@@ -2944,7 +2932,7 @@ def memory(name,
     :param memory_name: the name of the memory.
                         It is ignored when name is provided.
     :type memory_name: basestring
-    :param is_seq: is sequence for boot_layer
+    :param is_seq: DEPRECATED. is sequence for boot_layer
     :type is_seq: bool
     :param boot_layer: boot layer of memory.
     :type boot_layer: LayerOutput|None
@@ -2971,7 +2959,6 @@ def memory(name,
     memory_name = Memory(
         name,
         size,
-        is_sequence=is_seq,
         boot_layer=boot_layer.name if boot_layer is not None else None,
         boot_bias=boot_bias,
         boot_bias_active_type=boot_bias_active_type.name,
@@ -3318,19 +3305,21 @@ class StaticInput(object):
     """
     StaticInput is only used in recurrent_group which defines a read-only memory
     that can be a sequence or non-sequence.
+    :param size: DEPRECATED
+    :param is_seq: DEPRECATED
     """
 
     def __init__(self, input, is_seq=False, size=None):
         assert isinstance(input, LayerOutput)
         self.input = input
-        self.is_seq = is_seq
-        assert input.size is not None or size is not None
+        assert input.size is not None
         if size is not None:
-            input.size = size
+            assert input.size == size
 
 
-class SubsequenceInput(object):
+def SubsequenceInput(input):
     """
+    DEPRECATED.
     Input sequence has sub-sequence, used in recurrent_group.
 
     The example usage is:
@@ -3339,11 +3328,7 @@ class SubsequenceInput(object):
 
        input = SubsequenceInput(layer)
     """
-
-    def __init__(self, input):
-        assert isinstance(input, LayerOutput)
-        assert input.size is not None
-        self.input = input
+    return input
 
 
 @wrap_name_default("recurrent_group")
@@ -3407,7 +3392,8 @@ def recurrent_group(step,
                     input sequence in a reverse order.
     :type reverse: bool
 
-    :param targetInlink: the input layer which share info with layer group's output
+    :param targetInlink: DEPRECATED.
+                         The input layer which share info with layer group's output
 
                          Param input specifies multiple input layers. For
                          SubsequenceInput inputs, config should assign one input
@@ -3429,46 +3415,21 @@ def recurrent_group(step,
     model_type('recurrent_nn')
 
     def is_single_input(x):
-        return isinstance(x, LayerOutput) or isinstance(x, StaticInput) \
-               or isinstance(x, SubsequenceInput)
+        return isinstance(x, LayerOutput) or isinstance(x, StaticInput)
 
     if is_single_input(input):
         input = [input]
     assert isinstance(input, collections.Sequence)
 
     def is_in_links(x):
-        return isinstance(x, LayerOutput) or isinstance(x, SubsequenceInput)
+        return isinstance(x, LayerOutput)
 
     in_links = filter(is_in_links, input)
 
-    def targetInlink_in_inlinks():
-        for inlink in in_links:
-            if isinstance(inlink, SubsequenceInput):
-                if targetInlink == inlink.input:
-                    return True
-            elif targetInlink == inlink:
-                return True
-        return False
-
-    assert (targetInlink == None or targetInlink_in_inlinks())
-    targetInlinkName = None if targetInlink == None \
-        else targetInlink.name if isinstance(targetInlink, LayerOutput) \
-        else targetInlink.input.name
-
-    contains_sub_seq = [False]
-
-    def map_in_links(x):
-        if isinstance(x, SubsequenceInput):
-            contains_sub_seq[0] = True
-            return Link(name=x.input.name, has_subseq=True)
-        else:
-            return x.name
-
     RecurrentLayerGroupWithoutOutLinksBegin(
         name=name,
-        in_links=map(map_in_links, in_links),
-        seq_reversed=reverse,
-        target_inlinkname=targetInlinkName)
+        in_links=map(lambda x: x.name, in_links),
+        seq_reversed=reverse)
     in_args = []
     has_LayerOutput = False
     for each_input in input:
@@ -3476,21 +3437,13 @@ def recurrent_group(step,
         if isinstance(each_input, LayerOutput):
             in_args.append(each_input)
             has_LayerOutput = True
-        elif isinstance(each_input, SubsequenceInput):
-            in_args.append(each_input.input)
-            has_LayerOutput = True
-        else:
+        else:  # StaticInput
             mem_name = "__%s_memory__" % each_input.input.name
             mem = memory(
-                name=mem_name,
-                is_seq=each_input.is_seq,
+                name=None,
                 size=each_input.input.size,
                 boot_layer=each_input.input)
-            with mixed_layer(
-                    name=mem_name,
-                    size=each_input.input.size,
-                    act=IdentityActivation()) as mix:
-                mix += identity_projection(mem)
+            mem.set_input(mem)
             in_args.append(mem)
 
     assert (is_generating != has_LayerOutput)
@@ -3503,10 +3456,7 @@ def recurrent_group(step,
     for ot in layer_outs:
         assert isinstance(ot, LayerOutput)
         ot.reverse = reverse
-        if contains_sub_seq[0]:
-            RecurrentLayerGroupSetOutLink(Link(ot.name, has_subseq=True))
-        else:
-            RecurrentLayerGroupSetOutLink(ot.name)
+        RecurrentLayerGroupSetOutLink(ot.name)
 
     RecurrentLayerGroupEnd(name=name)
 
@@ -5608,13 +5558,13 @@ def row_conv_layer(input,
     to deploy in an online and low-latency setting. The lookahead convolution
     incorporates information from future subsequences in a computationally
     efficient manner to improve unidirectional recurrent neural networks.
- 
+
     The connection of row convolution is different form the 1D sequence
     convolution. Assumed that, the future context-length is k, that is to say,
     it can get the output at timestep t by using the the input feature from t-th
     timestep to (t+k+1)-th timestep. Assumed that the hidden dim of input
     activations are d, the activations r_t for the new layer at time-step t are:
- 
+
     .. math::
 
         r_{t,r} = \sum_{j=1}^{k + 1} {w_{i,j}h_{t+j-1, i}}

python/paddle/trainer_config_helpers/tests/configs/protostr/shared_gru.protostr

@@ -256,19 +256,15 @@ sub_models {
   memories {
     layer_name: "__simple_gru_0__@__simple_gru_0___recurrent_group"
     link_name: "__simple_gru_0__+delay1@__simple_gru_0___recurrent_group"
-    is_sequence: false
   }
   in_links {
     layer_name: "__simple_gru_0___transform"
     link_name: "__simple_gru_0___transform@__simple_gru_0___recurrent_group"
-    has_subseq: false
   }
   out_links {
     layer_name: "__simple_gru_0__@__simple_gru_0___recurrent_group"
     link_name: "__simple_gru_0__"
-    has_subseq: false
   }
-  target_inlinkid: -1
 }
 sub_models {
   name: "__simple_gru_1___recurrent_group"
@@ -280,18 +276,14 @@ sub_models {
   memories {
     layer_name: "__simple_gru_1__@__simple_gru_1___recurrent_group"
     link_name: "__simple_gru_1__+delay1@__simple_gru_1___recurrent_group"
-    is_sequence: false
   }
   in_links {
     layer_name: "__simple_gru_1___transform"
     link_name: "__simple_gru_1___transform@__simple_gru_1___recurrent_group"
-    has_subseq: false
   }
   out_links {
     layer_name: "__simple_gru_1__@__simple_gru_1___recurrent_group"
     link_name: "__simple_gru_1__"
-    has_subseq: false
   }
-  target_inlinkid: -1
 }
 

python/paddle/trainer_config_helpers/tests/configs/protostr/shared_lstm.protostr

@@ -341,24 +341,19 @@ sub_models {
   memories {
     layer_name: "__lstm_group_0__@__lstm_group_0___recurrent_group"
     link_name: "__lstm_group_0__+delay1@__lstm_group_0___recurrent_group"
-    is_sequence: false
   }
   memories {
     layer_name: "__lstm_group_0___state@__lstm_group_0___recurrent_group"
     link_name: "__lstm_group_0___state+delay1@__lstm_group_0___recurrent_group"
-    is_sequence: false
   }
   in_links {
     layer_name: "__mixed_0__"
     link_name: "__mixed_0__@__lstm_group_0___recurrent_group"
-    has_subseq: false
   }
   out_links {
     layer_name: "__lstm_group_0__@__lstm_group_0___recurrent_group"
     link_name: "__lstm_group_0__"
-    has_subseq: false
   }
-  target_inlinkid: -1
 }
 sub_models {
   name: "__lstm_group_1___recurrent_group"
@@ -373,23 +368,18 @@ sub_models {
   memories {
     layer_name: "__lstm_group_1__@__lstm_group_1___recurrent_group"
     link_name: "__lstm_group_1__+delay1@__lstm_group_1___recurrent_group"
-    is_sequence: false
   }
   memories {
     layer_name: "__lstm_group_1___state@__lstm_group_1___recurrent_group"
     link_name: "__lstm_group_1___state+delay1@__lstm_group_1___recurrent_group"
-    is_sequence: false
   }
   in_links {
     layer_name: "__mixed_1__"
     link_name: "__mixed_1__@__lstm_group_1___recurrent_group"
-    has_subseq: false
   }
   out_links {
     layer_name: "__lstm_group_1__@__lstm_group_1___recurrent_group"
     link_name: "__lstm_group_1__"
-    has_subseq: false
   }
-  target_inlinkid: -1
 }
 

python/paddle/trainer_config_helpers/tests/configs/protostr/test_rnn_group.protostr

@@ -155,7 +155,7 @@ layers {
 }
 layers {
   name: "sub_seq_input@__recurrent_group_2__"
-  type: "sequence_scatter_agent"
+  type: "scatter_agent"
   size: 100
   active_type: ""
 }
@@ -182,7 +182,7 @@ layers {
 }
 layers {
   name: "rnn_subseq_forward"
-  type: "sequence_gather_agent"
+  type: "gather_agent"
   size: 200
   active_type: ""
 }
@@ -618,19 +618,15 @@ sub_models {
   memories {
     layer_name: "rnn_forward@__recurrent_group_0__"
     link_name: "rnn_forward+delay1@__recurrent_group_0__"
-    is_sequence: false
   }
   in_links {
     layer_name: "seq_input"
     link_name: "seq_input@__recurrent_group_0__"
-    has_subseq: false
   }
   out_links {
     layer_name: "rnn_forward@__recurrent_group_0__"
     link_name: "rnn_forward"
-    has_subseq: false
   }
-  target_inlinkid: -1
 }
 sub_models {
   name: "__recurrent_group_1__"
@@ -642,19 +638,15 @@ sub_models {
   memories {
     layer_name: "rnn_back@__recurrent_group_1__"
     link_name: "rnn_back+delay1@__recurrent_group_1__"
-    is_sequence: false
   }
   in_links {
     layer_name: "seq_input"
     link_name: "seq_input@__recurrent_group_1__"
-    has_subseq: false
   }
   out_links {
     layer_name: "rnn_back@__recurrent_group_1__"
     link_name: "rnn_back"
-    has_subseq: false
   }
-  target_inlinkid: -1
 }
 sub_models {
   name: "__recurrent_group_2__"
@@ -666,19 +658,15 @@ sub_models {
   memories {
     layer_name: "rnn_subseq_forward@__recurrent_group_2__"
     link_name: "rnn_subseq_forward+delay1@__recurrent_group_2__"
-    is_sequence: false
   }
   in_links {
     layer_name: "sub_seq_input"
     link_name: "sub_seq_input@__recurrent_group_2__"
-    has_subseq: true
   }
   out_links {
     layer_name: "rnn_subseq_forward@__recurrent_group_2__"
     link_name: "rnn_subseq_forward"
-    has_subseq: true
   }
-  target_inlinkid: -1
 }
 sub_models {
   name: "__lstm_group_0___recurrent_group"
@@ -693,24 +681,19 @@ sub_models {
   memories {
     layer_name: "__lstm_group_0__@__lstm_group_0___recurrent_group"
     link_name: "__lstm_group_0__+delay1@__lstm_group_0___recurrent_group"
-    is_sequence: false
   }
   memories {
     layer_name: "__lstm_group_0___state@__lstm_group_0___recurrent_group"
     link_name: "__lstm_group_0___state+delay1@__lstm_group_0___recurrent_group"
-    is_sequence: false
   }
   in_links {
     layer_name: "__mixed_0__"
     link_name: "__mixed_0__@__lstm_group_0___recurrent_group"
-    has_subseq: false
   }
   out_links {
     layer_name: "__lstm_group_0__@__lstm_group_0___recurrent_group"
     link_name: "__lstm_group_0__"
-    has_subseq: false
   }
-  target_inlinkid: -1
 }
 sub_models {
   name: "__gru_group_0___recurrent_group"
@@ -722,19 +705,15 @@ sub_models {
   memories {
     layer_name: "__gru_group_0__@__gru_group_0___recurrent_group"
     link_name: "__gru_group_0__+delay1@__gru_group_0___recurrent_group"
-    is_sequence: false
   }
   in_links {
     layer_name: "__mixed_1__"
     link_name: "__mixed_1__@__gru_group_0___recurrent_group"
-    has_subseq: false
   }
   out_links {
     layer_name: "__gru_group_0__@__gru_group_0___recurrent_group"
     link_name: "__gru_group_0__"
-    has_subseq: false
   }
-  target_inlinkid: -1
 }
 sub_models {
   name: "__recurrent_group_3__"
@@ -746,18 +725,14 @@ sub_models {
   memories {
     layer_name: "__fc_layer_0__@__recurrent_group_3__"
     link_name: "__memory_6__@__recurrent_group_3__"
-    is_sequence: false
   }
   in_links {
     layer_name: "seq_input"
     link_name: "seq_input@__recurrent_group_3__"
-    has_subseq: false
   }
   out_links {
     layer_name: "__fc_layer_0__@__recurrent_group_3__"
     link_name: "__fc_layer_0__"
-    has_subseq: false
   }
-  target_inlinkid: -1
 }
 

python/paddle/v2/layer.py

@@ -260,7 +260,7 @@ def parse_network(output_layers, extra_layers=None):
     else:
         extra_layers = []
 
-    layer_names = __get_used_layers__(output_layers + extra_layers)
+    layer_names = __get_used_layers__(list(output_layers) + list(extra_layers))
     submodel_names = __get_used_submodels__(layer_names)
     submodel_names.add('root')
     evaluator_names = __get_used_evaluators__(layer_names)