Merge pull request #76 from emailweixu/fix_RecurrentGradientMachine

Further fix for memory of RecurrentGradientMachine

paddle/cuda/src/hl_cuda_matrix.cu

@@ -19,6 +19,7 @@ limitations under the License. */
 #include "hl_matrix_apply.cuh"
 #include "hl_sequence.h"
 #include "paddle/utils/Logging.h"
+#include "hl_device_functions.cuh"
 
 DEFINE_MATRIX_UNARY_OP(Zero, a = 0);
 DEFINE_MATRIX_TERNARY_PARAMETER_OP(_add, TWO_PARAMETER, c = p1*a + p2*b);

paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp

@@ -434,23 +434,25 @@ void RecurrentGradientMachine::forward(const std::vector<Argument>& inArgs,
     }
   }
 
-  seqLengthAndStart_.clear();
   info_.clear();
   info_.resize(inFrameLines_.size());
-  seqLengthAndStart_.resize(inFrameLines_.size());
+
+  seqInfos_.clear();
+  seqInfos_.resize(inFrameLines_.size());
 
   {
     AsyncGpuBlock asyncGpuBlock;
     // if shareInlinkInfo, only calculate info of the first inlink
     // else, calculate info for each inlink
     if (shareInlinkInfo) {
-      input.getSeqLengthAndStart(&seqLengthAndStart_[0], &maxSequenceLength_);
+      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.getSeqLengthAndStart(&seqLengthAndStart_[i],
-                                    &maxSequenceLength_);
+        input1.getSeqInfo(&seqInfos_[i]);
+        maxSequenceLength_ = seqInfos_[i][0].topLevelLength;
         createInFrameInfo(i, input1, passType);
       }
     }
@@ -614,7 +616,7 @@ void RecurrentGradientMachine::removeBeamSearchStatisticsCallbacks() {
  * for all realLayer of inFrameLines one time.
 */
 
-void RecurrentGradientMachine::createInFrameInfo(int inlinks_id,
+void RecurrentGradientMachine::createInFrameInfo(int inlinkId,
                                                  const Argument& input,
                                                  PassType passType) {
   bool hasSubseq = input.hasSubseq();
@@ -622,66 +624,67 @@ void RecurrentGradientMachine::createInFrameInfo(int inlinks_id,
   size_t numSequences = input.getNumSequences();
   std::vector<int> allIds;
 
+  auto& seqInfo = seqInfos_[inlinkId];
+
   numSeqs_.clear();
-  Info* inlink_info = &info_[inlinks_id];
-  inlink_info->idIndex.clear();
-  inlink_info->idIndex.push_back(0);  // first idIndex = 0
+  Info* inlinkInfo = &info_[inlinkId];
+  inlinkInfo->idIndex.clear();
+  inlinkInfo->idIndex.push_back(0);  // first idIndex = 0
+
+  std::vector<int> sequenceStartPositions;
+  const int* subSequenceStartPositions = nullptr;
+
   if (hasSubseq) {                    // for sequenceScatterAgentLayer
-    // numSubSequences : all sentences within all samples(batch)
-    size_t numSubSequences = input.getNumSubSequences();
-    std::vector<int> sequenceStartPositions;
-    inlink_info->seqStartPosIndex.clear();
-    inlink_info->seqStartPosIndex.push_back(0);  // first seqStartPosIndex = 0
-    // maxSequenceLength_: max number of sentences(subseq) in allsamples
-    for (int i = 0; i < maxSequenceLength_; ++i) {
+    subSequenceStartPositions =
+        input.subSequenceStartPositions->getData(false);
+    inlinkInfo->seqStartPosIndex.clear();
+    inlinkInfo->seqStartPosIndex.push_back(0);  // first seqStartPosIndex = 0
+  }
+  // maxSequenceLength_: max topLevelLength in allsamples
+  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 < numSubSequences; ++j) {  // for each sentence
-        // seqLengthAndStart_[inlinks_id][j]:
-        // a 4-tuple including <subseqlen, subseqstart, seqid, subseqid>
-        if (std::get<3>(seqLengthAndStart_[inlinks_id][j]) == i) {
-          ++numSeqs;
-          // subseqstart: the cpuSubSequenceStartPositions of this subseq
-          int subSeqStart = std::get<1>(seqLengthAndStart_[inlinks_id][j]);
-          int subSeqLength = std::get<0>(seqLengthAndStart_[inlinks_id][j]);
-          for (int k = subSeqStart; k < subSeqStart + subSeqLength; ++k) {
-            allIds.push_back(k);
-          }
-          sequenceStartPositions.push_back(sequenceStartPositions.back() +
-                                           subSeqLength);
-        }
-      }
-      inlink_info->idIndex.push_back(allIds.size());
-      inlink_info->seqStartPosIndex.push_back(sequenceStartPositions.size());
-      numSeqs_.push_back(numSeqs);
     }
-    // inFrameLine create sequenceStartPositions one time
-    CHECK_EQ(sequenceStartPositions.size(),
-             maxSequenceLength_ + numSubSequences);
-    CHECK_EQ(inlink_info->seqStartPosIndex.size(),
-             static_cast<size_t>(maxSequenceLength_ + 1));
-    createSeqPos(sequenceStartPositions, &inlink_info->sequenceStartPositions);
-  } else {  // for scatterAgentLayer
-    for (int i = 0; i < maxSequenceLength_; ++i) {
-      int numSeqs = 0;
-      for (size_t j = 0; j < numSequences; ++j) {
-        int seqLength = std::get<0>(seqLengthAndStart_[inlinks_id][j]);
-        if (i >= seqLength) {
-          break;
+    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);
         }
-        ++numSeqs;
-        int seqStart = std::get<1>(seqLengthAndStart_[inlinks_id][j]);
+        sequenceStartPositions.push_back(sequenceStartPositions.back() +
+                                         subSeqEnd - subSeqStart);
+      } else {
+        int seqStart = seqInfo[j].seqStart;
         allIds.push_back(reversed_ ? (seqStart + seqLength - 1 - i)
                                    : (seqStart + i));
       }
-      inlink_info->idIndex.push_back(allIds.size());
-      numSeqs_.push_back(numSeqs);
     }
+    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(),
+             maxSequenceLength_ + input.getNumSubSequences());
+    CHECK_EQ(inlinkInfo->seqStartPosIndex.size(),
+             static_cast<size_t>(maxSequenceLength_ + 1));
+    createSeqPos(sequenceStartPositions, &inlinkInfo->sequenceStartPositions);
   }
 
   // copy and check scatterId
-  copyScattedId(allIds, &inlink_info->allIds, input.getBatchSize());
-  CHECK_EQ(inlink_info->idIndex.size(),
+  copyScattedId(allIds, &inlinkInfo->allIds, input.getBatchSize());
+  CHECK_EQ(inlinkInfo->idIndex.size(),
            static_cast<size_t>(maxSequenceLength_ + 1));
 }
 
@@ -701,7 +704,7 @@ void RecurrentGradientMachine::createMemoryFrameInfo(
     const int* starts = input.sequenceStartPositions->getData(false);
     for (size_t i = 0; i < numSequences; ++i) {
       // memory info adopt info of inlinks[0]
-      int seqId = std::get<2>(seqLengthAndStart_[0][i]);
+      int seqId = seqInfos_[0][i].seqId;
       for (int k = starts[seqId]; k < starts[seqId + 1]; ++k) {
         allIds.push_back(k);
       }
@@ -713,7 +716,7 @@ void RecurrentGradientMachine::createMemoryFrameInfo(
 
   } else {  // for scatterAgentLayer
     for (size_t i = 0; i < numSequences; ++i) {
-      allIds.push_back(std::get<2>(seqLengthAndStart_[0][i]));
+      allIds.push_back(seqInfos_[0][i].seqId);
     }
   }
   // copy and check scatterId

paddle/gserver/gradientmachines/RecurrentGradientMachine.h

@@ -337,11 +337,7 @@ protected:
   // data) or has more than i subsequences (for subsequence data)
   std::vector<int> numSeqs_;
 
-  // each inlinks has a "std::vector<std::tuple<int, int, int, int>>" denotes
-  // its sequence info:
-  //  if hasSubSeq, tuple of (subSeqLength, subSeqStart, seqIndex, subSeqIndex)
-  //  else, tuple of (seqLength, seqStart, seqIndex, seqIndex)
-  std::vector<std::vector<std::tuple<int, int, int, int>>> seqLengthAndStart_;
+  std::vector<std::vector<Argument::SeqInfo>> seqInfos_;
 
   // the id of inlink which share info with outlinks
   int targetInfoInlinkId_;

paddle/gserver/layers/PrintLayer.cpp

+/* Copyright (c) 2016 Baidu, Inc. 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"
+
+namespace paddle {
+
+class PrintLayer : public Layer {
+public:
+  explicit PrintLayer(const LayerConfig& config)
+      : Layer(config) {}
+  void forward(PassType passType);
+  void backward(const UpdateCallback& callback) {}
+};
+
+void PrintLayer::forward(PassType passType) {
+  Layer::forward(passType);
+  for (size_t i = 0; i != inputLayers_.size(); ++i) {
+    const auto& argu = getInput(i);
+    const std::string& name = inputLayers_[i]->getName();
+    if (argu.value) {
+      std::ostringstream os;
+      argu.value->print(os);
+      LOG(INFO) << "layer=" << name << " value matrix:\n" << os.str();
+    }
+    if (argu.ids) {
+      std::ostringstream os;
+      argu.ids->print(os, argu.ids->getSize());
+      LOG(INFO) << "layer=" << name << " ids vector:\n" << os.str();
+    }
+    if (auto startPos = argu.sequenceStartPositions) {
+      std::ostringstream os;
+      startPos->getVector(false)->print(os, startPos->getSize());
+      LOG(INFO) << "layer=" << name << " sequence pos vector:\n" << os.str();
+    }
+    if (auto subStartPos = argu.subSequenceStartPositions) {
+      std::ostringstream os;
+      subStartPos->getVector(false)->print(os, subStartPos->getSize());
+      LOG(INFO) << "layer=" << name << " sub-sequence pos vector:\n"
+                << os.str();
+    }
+  }
+}
+
+REGISTER_LAYER(print, PrintLayer);
+
+}  // namespace paddle

paddle/gserver/tests/sequence_nest_rnn.conf

@@ -42,14 +42,16 @@ def outer_step(x):
         inner_mem = memory(name="inner_rnn_state",
                            size=hidden_dim,
                            boot_layer=outer_mem)
-        return fc_layer(input=[y, inner_mem],
+        out = fc_layer(input=[y, inner_mem],
                         size=hidden_dim,
                         act=TanhActivation(),
                         bias_attr=True,
                         name="inner_rnn_state")
+        return out
 
     inner_rnn_output = recurrent_group(
         step=inner_step,
+        name="inner",
         input=x)
     last = last_seq(input=inner_rnn_output, name="outer_rnn_state")
 
@@ -60,11 +62,10 @@ def outer_step(x):
     return inner_rnn_output
 
 out = recurrent_group(
+    name="outer",
     step=outer_step,
     input=SubsequenceInput(emb))
 
-value_printer_evaluator(input=out)
-
 rep = last_seq(input=out)
 prob = fc_layer(size=label_dim,
                 input=rep,

paddle/gserver/tests/sequence_rnn.conf

@@ -35,18 +35,18 @@ emb = embedding_layer(input=data, size=word_dim)
 
 def step(y):
     mem = memory(name="rnn_state", size=hidden_dim)
-    return fc_layer(input=[y, mem],
+    out = fc_layer(input=[y, mem],
                     size=hidden_dim,
                     act=TanhActivation(),
                     bias_attr=True,
                     name="rnn_state")
+    return out
 
 out = recurrent_group(
+    name="rnn",
     step=step,
     input=emb)
 
-value_printer_evaluator(input=out)
-
 rep = last_seq(input=out)
 prob = fc_layer(size=label_dim,
                 input=rep,

paddle/gserver/tests/test_RecurrentGradientMachine.cpp

@@ -92,7 +92,7 @@ void CalCost(const string& conf, const string& dir, real* cost,
   rmDir(dir.c_str());
 }
 
-void test(const string& conf1, const string& conf2) {
+void test(const string& conf1, const string& conf2, double eps) {
   int num_passes = 5;
   real* cost1 = new real[num_passes];
   const string dir1 = "gserver/tests/t1";
@@ -104,8 +104,9 @@ void test(const string& conf1, const string& conf2) {
 
   for (int i = 0; i < num_passes; i++) {
     LOG(INFO) << "num_passes: " << i << ", cost1=" << cost1[i]
-              << ", cost2=" << cost2[i];
-    ASSERT_NEAR(cost1[i], cost2[i], 1e-3);
+              << ", cost2=" << cost2[i]
+              << ", diff=" << std::abs(cost1[i] - cost2[i]);
+    ASSERT_NEAR(cost1[i], cost2[i], eps);
   }
   delete[] cost1;
   delete[] cost2;
@@ -113,12 +114,14 @@ void test(const string& conf1, const string& conf2) {
 
 TEST(RecurrentGradientMachine, HasSubSequence) {
   test("gserver/tests/sequence_layer_group.conf",
-       "gserver/tests/sequence_nest_layer_group.conf");
+       "gserver/tests/sequence_nest_layer_group.conf",
+       1e-5);
 }
 
 TEST(RecurrentGradientMachine, rnn) {
   test("gserver/tests/sequence_rnn.conf",
-       "gserver/tests/sequence_nest_rnn.conf");
+       "gserver/tests/sequence_nest_rnn.conf",
+       0);
 }
 
 

paddle/parameter/Argument.cpp

@@ -477,51 +477,34 @@ void Argument::splitByDataId(const std::vector<Argument>& argus,
   }
 }
 
-void Argument::getSeqLengthAndStart(
-    std::vector<std::tuple<int, int, int, int>>* seqLengthAndStart,
-    int* maxSequenceLength) const {
+void Argument::getSeqInfo(std::vector<SeqInfo>* seqInfo) const {
   const int* starts = sequenceStartPositions->getData(false);
-  if (hasSubseq()) {
-    size_t numSubSequences = getNumSubSequences();
-    (*seqLengthAndStart).reserve(numSubSequences);
-    const int* subStarts = subSequenceStartPositions->getData(false);
-    int seqIndex = 0;
-    int subSeqIndex = 0;
-    *maxSequenceLength = 0;
-    for (size_t i = 0; i < numSubSequences; ++i) {
-      if (subStarts[i] == starts[seqIndex]) {
-        subSeqIndex = 0;
-        (*seqLengthAndStart)
-            .push_back(std::make_tuple<int, int, int, int>(
-                subStarts[i + 1] - subStarts[i], (int)subStarts[i],
-                (int)seqIndex, (int)subSeqIndex));
-        ++subSeqIndex;
-        ++seqIndex;
-      } else if (subStarts[i] < starts[seqIndex]) {
-        (*seqLengthAndStart)
-            .push_back(std::make_tuple<int, int, int, int>(
-                subStarts[i + 1] - subStarts[i], (int)subStarts[i],
-                (int)seqIndex - 1, (int)subSeqIndex));
-        ++subSeqIndex;
+  const int* subStarts = hasSubseq()
+      ? subSequenceStartPositions->getData(false) : nullptr;
+  size_t numSequences = getNumSequences();
+  seqInfo->reserve(numSequences);
+  int subSeqEnd = 0;
+  for (size_t i = 0; i < numSequences; ++i) {
+    SeqInfo info;
+    info.seqStart = starts[i];
+    info.subLevelLength = starts[i + 1] - starts[i];
+    info.seqId = i;
+    if (hasSubseq()) {
+      info.subSeqStart = subSeqEnd;
+      while (subStarts[subSeqEnd] < starts[i + 1]) {
+        ++subSeqEnd;
       }
-      // maxSequenceLength_ = 1 + max(subSeqIndex) in each Seq.
-      if (*maxSequenceLength < std::get<3>((*seqLengthAndStart)[i]))
-        *maxSequenceLength = std::get<3>((*seqLengthAndStart)[i]);
-    }
-    *maxSequenceLength += 1;
-  } else {
-    size_t numSequences = getNumSequences();
-    (*seqLengthAndStart).reserve(numSequences);
-    for (size_t i = 0; i < numSequences; ++i) {
-      (*seqLengthAndStart)
-          .push_back(std::make_tuple<int, int, int, int>(
-              starts[i + 1] - starts[i], (int)starts[i], (int)i, (int)i));
+      info.topLevelLength = subSeqEnd - info.subSeqStart;
+    } else {
+      info.topLevelLength = info.subLevelLength;
+      info.subSeqStart = 0;  // not used
     }
-    std::sort((*seqLengthAndStart).begin(), (*seqLengthAndStart).end(),
-              std::greater<std::tuple<int, int, int, int>>());
-
-    *maxSequenceLength = std::get<0>((*seqLengthAndStart)[0]);
+    seqInfo->push_back(info);
   }
+  std::sort(seqInfo->begin(), seqInfo->end(),
+            [](const SeqInfo& a, const SeqInfo& b) {
+              return a.topLevelLength > b.topLevelLength;
+            });
 }
 
 void Argument::checkSubset() const {

paddle/parameter/Argument.h

@@ -253,21 +253,29 @@ struct Argument {
   static void splitByDataId(const std::vector<Argument>& argus,
                             std::vector<std::vector<Argument>>* arguGroups);
 
+  struct SeqInfo {
+    // Equal to sequence length for sequence data
+    // Equal to number of subsequences for subsequence data
+    int topLevelLength;
+
+    int seqStart;
+    int seqId;
+
+    // Equal to topLevelLength for sequence data
+    // Equal to sum of the length of subsequences for subsequence data
+    int subLevelLength;
+
+    // Only used for subsequence data, start position of this sequence
+    // is subSequenceStartPositions, i.e.
+    // subSequenceStartPositions[subSeqStart] == seqStart
+    int subSeqStart;
+  };
   /*
-   Get Sequence Length, startPositions and max Length according to input
-   1. For sequence data:
-      Each tuple is (seq_length, seq_start, seq_id, seq_id)
-      The tuples are sorted according to seq_length or subseq_length
-      *maxSequenceLength is the maximal sequence length
-
-   2. For subsequence data:
-      Each tuple is (subseq_length, subseq_start, seq_id, subseq_id)
-      The tuples are not sorted. They are in the original order.
-      *maxSequenceLenth is the maximal number of subsequences in each sequence.
-   */
-  void getSeqLengthAndStart(
-      std::vector<std::tuple<int, int, int, int>>* seqLengthAndStart,
-      int* maxSequenceLength) const;
+    Get SeqInfo for each sequence of this argument
+    Elements in *seqInfo are sorted by topLevelLength in descending order
+  */
+  void getSeqInfo(std::vector<SeqInfo>* segInfo) const;
+
   /*
    Check Whether sequenceStartPositions is subset of
    subSequenceStartPositions.

python/paddle/trainer/config_parser.py

@@ -1408,6 +1408,14 @@ class SelectiveFCLayer(LayerBase):
                 input_index, psize, dims, sparse, format)
         self.create_bias_parameter(bias, self.config.size)
 
+@config_layer('print')
+class PrintLayer(LayerBase):
+    def __init__(
+            self,
+            name,
+            inputs):
+        super(PrintLayer, self).__init__(name, 'print', 0, inputs)
+
 @config_layer('data')
 class DataLayer(LayerBase):
     def __init__(

python/paddle/trainer_config_helpers/layers.py

@@ -21,7 +21,6 @@ from .evaluators import *
 from .poolings import MaxPooling, AvgPooling, BasePoolingType
 from .attrs import *
 from .default_decorators import *
-
 try:
     import cPickle as pickle
 except ImportError:
@@ -52,7 +51,7 @@ __all__ = ["full_matrix_projection", "AggregateLevel", "ExpandLevel",
            'cross_entropy_with_selfnorm', 'cross_entropy',
            'multi_binary_label_cross_entropy',
            'rank_cost', 'lambda_cost', 'huber_cost',
-           'block_expand_layer', 'out_prod_layer',
+           'block_expand_layer', 'out_prod_layer', 'print_layer'
            ]
 
 
@@ -108,6 +107,8 @@ class LayerType(object):
     LINEAR_COMBINATION_LAYER = "convex_comb"
     BLOCK_EXPAND = "blockexpand"
 
+    PRINT_LAYER = "print"
+
     CTC_LAYER = "ctc"
     CRF_LAYER = "crf"
     CRF_DECODING_LAYER = "crf_decoding"
@@ -202,6 +203,25 @@ ERROR_CLIPPING = 'error_clipping_threshold'
 DROPOUT = 'drop_rate'
 
 
+def check_input(input):
+    """
+    Check input is a LayerOutput or list of LayerOutput or tuple of LayerOutput
+    if is a LayerOutput,
+
+    :param input: The input layer. Could be a list/tuple of input layer.
+    :type input: LayerOutput|list|tuple
+    :return: list of LayerOutput
+    :rtype: list of LayerOutput
+    """
+
+    if isinstance(input, LayerOutput):
+        return [LayerOutput]
+    assert isinstance(input, list)
+    for inp in input:
+        assert isinstance(inp, LayerOutput)
+    return list(input)
+
+
 def layer_support(*attrs):
     def decorator(method):
         @functools.wraps(method)
@@ -730,6 +750,27 @@ def fc_layer(input, size, act=None, name=None,
                        size=size)
 
 
+@wrap_name_default("print")
+def print_layer(input, name=None):
+    """
+    Print the output value of input layers. This layer is useful for debugging.
+
+    :param name: The Layer Name.
+    :type name: basestring
+    :param input: The input layer. Could be a list/tuple of input layer.
+    :type input: LayerOutput|list|tuple
+    :return: No return
+    """
+    check_input(input)
+
+    Layer(
+        name=name,
+        type=LayerType.PRINT_LAYER,
+        inputs=[l.name for l in input],
+    )
+    LayerOutput(name, LayerType.PRINT_LAYER, input)
+
+
 @wrap_name_default("seq_pooling")
 @wrap_bias_attr_default(has_bias=False)
 @wrap_param_default(['pooling_type'], default_factory=lambda _: MaxPooling())

python/paddle/trainer_config_helpers/tests/layers_test_config.py

@@ -34,6 +34,8 @@ out = fc_layer(input=[cos1, cos3, linear_comb, z],
                size=num_classes,
                act=SoftmaxActivation())
 
+print_layer(input=[out])
+
 outputs(classification_cost(out, data_layer(name="label", size=num_classes)))
 
 # for ctc