adding dropout-by row

egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1i_dp.sh

@@ -29,6 +29,7 @@ ihm_gmm=tri3  # the gmm for the IHM system (if --use-ihm-ali true).
 num_threads_ubm=32
 nnet3_affix=_cleaned  # cleanup affix for nnet3 and chain dirs, e.g. _cleaned
 dropout_schedule='0,0@0.20,0.5@0.50,0@0.50,0'
+dropout_per_frame=false
 chunk_width=150
 chunk_left_context=40
 chunk_right_context=0
@@ -193,15 +194,15 @@ if [ $stage -le 15 ]; then
   relu-renorm-layer name=tdnn3 input=Append(-1,0,1) dim=1024
 
   # check steps/libs/nnet3/xconfig/lstm.py for the other options and defaults
-  lstmp-layer name=lstm1 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0
+  lstmp-layer name=lstm1 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0 dropout-per-frame=false
   relu-renorm-layer name=tdnn4 input=Append(-3,0,3) dim=1024
   relu-renorm-layer name=tdnn5 input=Append(-3,0,3) dim=1024
   relu-renorm-layer name=tdnn6 input=Append(-3,0,3) dim=1024
-  lstmp-layer name=lstm2 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0
+  lstmp-layer name=lstm2 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0 dropout-per-frame=false
   relu-renorm-layer name=tdnn7 input=Append(-3,0,3) dim=1024
   relu-renorm-layer name=tdnn8 input=Append(-3,0,3) dim=1024
   relu-renorm-layer name=tdnn9 input=Append(-3,0,3) dim=1024
-  lstmp-layer name=lstm3 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0
+  lstmp-layer name=lstm3 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0 dropout-per-frame=false
 
   ## adding the layers for chain branch
   output-layer name=output input=lstm3 output-delay=$label_delay include-log-softmax=false dim=$num_targets max-change=1.5
@@ -243,6 +244,7 @@ if [ $stage -le 16 ]; then
     --egs.chunk-left-context $chunk_left_context \
     --egs.chunk-right-context $chunk_right_context \
     --trainer.dropout-schedule $dropout_schedule \
+    --trainer.dropout-per-frame $dropout_per_frame \
     --trainer.num-chunk-per-minibatch 64 \
     --trainer.frames-per-iter 1500000 \
     --trainer.num-epochs 4 \

egs/wsj/s5/steps/libs/nnet3/train/chain_objf/acoustic_model.py

@@ -225,6 +225,7 @@ def train_one_iteration(dir, iter, srand, egs_dir,
                         frame_subsampling_factor, truncate_deriv_weights,
                         run_opts,
                         dropout_proportions=None,
+                        dropout_per_frame=None,
                         background_process_handler=None):
     """ Called from steps/nnet3/chain/train.py for one iteration for
     neural network training with LF-MMI objective
@@ -307,7 +308,7 @@ def train_one_iteration(dir, iter, srand, egs_dir,
     dropout_info_str = ''
     if dropout_proportions is not None:
         raw_model_string, dropout_info = common_train_lib.apply_dropout(
-            dropout_proportions, raw_model_string)
+            dropout_proportions, dropout_per_frame, raw_model_string)
         dropout_info_str = ', {0}'.format(", ".join(dropout_info))
 
     shrink_info_str = ''

egs/wsj/s5/steps/libs/nnet3/train/common.py

@@ -511,7 +511,7 @@ def _get_component_dropout(dropout_schedule, num_archives_processed):
             + initial_dropout)
 
 
-def apply_dropout(dropout_proportions, raw_model_string):
+def apply_dropout(dropout_proportions, dropout_per_frame, raw_model_string):
     """Adds an nnet3-copy --edits line to modify raw_model_string to
     set dropout proportions according to dropout_proportions.
 
@@ -523,10 +523,10 @@ def apply_dropout(dropout_proportions, raw_model_string):
 
     for component_name, dropout_proportion in dropout_proportions:
         edit_config_lines.append(
-            "set-dropout-proportion name={0} proportion={1}".format(
-                component_name, dropout_proportion))
-        dropout_info.append("pattern/dropout-proportion={0}/{1}".format(
-            component_name, dropout_proportion))
+            "set-dropout-proportion name={0} proportion={1} dropout-per-frame={2}".format(
+                component_name, dropout_proportion, dropout_per_frame))
+        dropout_info.append("pattern/dropout-proportion={0}/{1} dropout-per-frame={2}".format(
+            component_name, dropout_proportion, dropout_per_frame))
 
     return ("""{raw_model_string} nnet3-copy --edits='{edits}' \
             - - |""".format(raw_model_string=raw_model_string,
@@ -771,6 +771,11 @@ def __init__(self):
                                  lstm*=0,0.2,0'.  More general should precede
                                  less general patterns, as they are applied
                                  sequentially.""")
+        self.parser.add_argument("--trainer.dropout-per-frame", type=str,
+                                 action=common_lib.NullstrToNoneAction,
+                                 dest='dropout_per_frame', default=None,
+                                 help="""this option is used to control whether
+                                 using dropout by frame level or by vector level""")
 
         # General options
         self.parser.add_argument("--stage", type=int, default=-4,

egs/wsj/s5/steps/libs/nnet3/xconfig/lstm.py

@@ -251,6 +251,7 @@ def set_default_configs(self):
                         'zeroing-interval' : 20,
                         'zeroing-threshold' : 15.0,
                         'dropout-proportion' : -1.0 # -1.0 stands for no dropout will be added
+                        'dropout-per-frame' : 'false'
                        }
 
     def set_derived_configs(self):
@@ -285,6 +286,10 @@ def check_configs(self):
              self.config['dropout-proportion'] < 0.0) and
              self.config['dropout-proportion'] != -1.0 ):
              raise xparser_error("dropout-proportion has invalid value {0}.".format(self.config['dropout-proportion']))
+
+        if (self.config['dropout-per-frame'] != 'false' or
+            self.config['dropout-per-frame'] != 'true'):
+            raise xparser_error("dropout-per-frame has invalid value {0}.".format(self.config['dropout-per-frame']))
 
     def auxiliary_outputs(self):
         return ['c_t']
@@ -347,7 +352,8 @@ def generate_lstm_config(self):
         pes_str = self.config['ng-per-element-scale-options']
         lstm_dropout_value = self.config['dropout-proportion']
         lstm_dropout_str = 'dropout-proportion='+str(self.config['dropout-proportion'])
-
+        lstm_dropout_per_frame_value = self.config['dropout-per-frame']
+        lstm_dropout_per_frame_str = 'dropout-per-frame='+str(self.config['dropout-per-frame'])
         # Natural gradient per element scale parameters
         # TODO: decide if we want to keep exposing these options
         if re.search('param-mean', pes_str) is None and \

egs/wsj/s5/steps/nnet3/chain/train.py

@@ -202,7 +202,10 @@ def process_args(args):
             "value={0}. We recommend using the option "
             "--trainer.deriv-truncate-margin.".format(
                 args.deriv_truncate_margin))
-
+    if ( args.dropout_schedule is None ) 
+            and (args.dropout_per_frame is not None) :
+        raise Exception("The dropout schedule is null, but dropout_per_frame"
+                        "option is not null")
     if (not os.path.exists(args.dir)
             or not os.path.exists(args.dir+"/configs")):
         raise Exception("This scripts expects {0} to exist and have a configs "
@@ -441,6 +444,9 @@ def learning_rate(iter, current_num_jobs, num_archives_processed):
                     None if args.dropout_schedule is None
                     else common_train_lib.get_dropout_proportions(
                         dropout_schedule, num_archives_processed)),
+                dropout_per_frame=(
+                    None if args.dropout_schedule is None
+                    else args.dropout_per_frame),
                 shrinkage_value=shrinkage_value,
                 num_chunk_per_minibatch=args.num_chunk_per_minibatch,
                 num_hidden_layers=num_hidden_layers,

src/nnet3/nnet-chain-combine.cc

@@ -38,7 +38,7 @@ NnetChainCombiner::NnetChainCombiner(const NnetCombineConfig &combine_config,
     nnet_params_(std::min(num_nnets, combine_config_.max_effective_inputs),
                  NumParameters(first_nnet)),
     tot_input_weighting_(nnet_params_.NumRows()) {
-  SetDropoutProportion(0, &nnet_);
+  SetDropoutProportion(0, false, &nnet_);
   SubVector<BaseFloat> first_params(nnet_params_, 0);
   VectorizeNnet(nnet_, &first_params);
   tot_input_weighting_(0) += 1.0;

src/nnet3/nnet-combine.cc

@@ -34,7 +34,7 @@ NnetCombiner::NnetCombiner(const NnetCombineConfig &config,
     nnet_params_(std::min(num_nnets, config_.max_effective_inputs),
                  NumParameters(first_nnet)),
     tot_input_weighting_(nnet_params_.NumRows()) {
-  SetDropoutProportion(0, &nnet_);
+  SetDropoutProportion(0, false, &nnet_);
   SubVector<BaseFloat> first_params(nnet_params_, 0);
   VectorizeNnet(nnet_, &first_params);
   tot_input_weighting_(0) += 1.0;

src/nnet3/nnet-simple-component.cc

@@ -87,27 +87,37 @@ void PnormComponent::Write(std::ostream &os, bool binary) const {
 }
 
 
-void DropoutComponent::Init(int32 dim, BaseFloat dropout_proportion) {
+void DropoutComponent::Init(int32 dim, BaseFloat dropout_proportion, bool dropout_per_frame) {
   dropout_proportion_ = dropout_proportion;
+  dropout_per_frame_ = dropout_per_frame;
   dim_ = dim;
 }
 
 void DropoutComponent::InitFromConfig(ConfigLine *cfl) {
   int32 dim = 0;
   BaseFloat dropout_proportion = 0.0;
+  bool dropout_per_frame = false;
   bool ok = cfl->GetValue("dim", &dim) &&
     cfl->GetValue("dropout-proportion", &dropout_proportion);
+  bool ok2 = cfl->GetValue("dropout-per-frame", &dropout_per_frame);
   if (!ok || cfl->HasUnusedValues() || dim <= 0 ||
       dropout_proportion < 0.0 || dropout_proportion > 1.0)
     KALDI_ERR << "Invalid initializer for layer of type "
               << Type() << ": \"" << cfl->WholeLine() << "\"";
-  Init(dim, dropout_proportion);
+  if( ! ok2 )
+  {
+      dropout_per_frame = false;
+      Init(dim, dropout_proportion, dropout_per_frame);
+  } else {
+      Init(dim, dropout_proportion, dropout_per_frame);
+  }
 }
 
 std::string DropoutComponent::Info() const {
   std::ostringstream stream;
   stream << Type() << ", dim=" << dim_
-         << ", dropout-proportion=" << dropout_proportion_;
+         << ", dropout-proportion=" << dropout_proportion_
+         << ", dropout-per-frame=" << dropout_per_frame_;
   return stream.str();
 }
 
@@ -119,16 +129,34 @@ void DropoutComponent::Propagate(const ComponentPrecomputedIndexes *indexes,
 
   BaseFloat dropout = dropout_proportion_;
   KALDI_ASSERT(dropout >= 0.0 && dropout <= 1.0);
+  if(dropout_per_frame_)
+  {
+    // This const_cast is only safe assuming you don't attempt
+    // to use multi-threaded code with the GPU.
+    const_cast<CuRand<BaseFloat>&>(random_generator_).RandUniform(out);
 
-  // This const_cast is only safe assuming you don't attempt
-  // to use multi-threaded code with the GPU.
-  const_cast<CuRand<BaseFloat>&>(random_generator_).RandUniform(out);
+    out->Add(-dropout); // now, a proportion "dropout" will be <0.0
+    out->ApplyHeaviside(); // apply the function (x>0?1:0).  Now, a proportion "dropout" will
+                          // be zero and (1 - dropout) will be 1.0.
 
-  out->Add(-dropout); // now, a proportion "dropout" will be <0.0
-  out->ApplyHeaviside(); // apply the function (x>0?1:0).  Now, a proportion "dropout" will
-                         // be zero and (1 - dropout) will be 1.0.
+    out->MulElements(in);
+  } else {
 
-  out->MulElements(in);
+    // This const_cast is only safe assuming you don't attempt
+    // to use multi-threaded code with the GPU.
+    const_cast<CuRand<BaseFloat>&>(random_generator_).RandUniform(out);
+    out->Add(-dropout); // now, a proportion "dropout" will be <0.0
+    out->ApplyHeaviside(); // apply the function (x>0?1:0).  Now, a proportion "dropout" will
+                           // be zero and (1 - dropout) will be 1.0.
+    CuVector<BaseFloat> *random_drop_vector = new CuVector<BaseFloat>(in.NumRows(), kSetZero);
+    MatrixIndexT i = 0;
+    random_drop_vector->CopyColFromMat(*out, i);
+    for (MatrixIndexT i = 0; i < in.NumCols(); i++)
+    {
+       out->CopyColFromVec(*random_drop_vector, i);
+    }
+    out->MulElements(in);
+  }
 }
 
 
@@ -154,6 +182,8 @@ void DropoutComponent::Read(std::istream &is, bool binary) {
   ReadBasicType(is, binary, &dim_);
   ExpectToken(is, binary, "<DropoutProportion>");
   ReadBasicType(is, binary, &dropout_proportion_);
+  ExpectToken(is, binary, "<DropoutPerFrame>");
+  ReadBasicType(is, binary, &dropout_per_frame_);
   ExpectToken(is, binary, "</DropoutComponent>");
 }
 
@@ -163,6 +193,8 @@ void DropoutComponent::Write(std::ostream &os, bool binary) const {
   WriteBasicType(os, binary, dim_);
   WriteToken(os, binary, "<DropoutProportion>");
   WriteBasicType(os, binary, dropout_proportion_);
+  WriteToken(os, binary, "<DropoutPerFrame>");
+  WriteBasicType(os, binary, dropout_per_frame_);
   WriteToken(os, binary, "</DropoutComponent>");
 }
 

src/nnet3/nnet-simple-component.h

@@ -87,11 +87,11 @@ class PnormComponent: public Component {
 // "Dropout: A Simple Way to Prevent Neural Networks from Overfitting".
 class DropoutComponent : public RandomComponent {
  public:
-  void Init(int32 dim, BaseFloat dropout_proportion = 0.0);
+  void Init(int32 dim, BaseFloat dropout_proportion = 0.0, bool dropout_per_frame = false);
 
-  DropoutComponent(int32 dim, BaseFloat dropout = 0.0) { Init(dim, dropout); }
+  DropoutComponent(int32 dim, BaseFloat dropout = 0.0, bool dropout_per_frame = false) { Init(dim, dropout, dropout_per_frame); }
 
-  DropoutComponent(): dim_(0), dropout_proportion_(0.0) { }
+  DropoutComponent(): dim_(0), dropout_proportion_(0.0), dropout_per_frame_(false) { }
 
   virtual int32 Properties() const {
     return kLinearInInput|kBackpropInPlace|kSimpleComponent|kBackpropNeedsInput|kBackpropNeedsOutput;
@@ -120,17 +120,21 @@ class DropoutComponent : public RandomComponent {
                         Component *to_update,
                         CuMatrixBase<BaseFloat> *in_deriv) const;
   virtual Component* Copy() const { return new DropoutComponent(dim_,
-                                                                dropout_proportion_); }
+                                                                dropout_proportion_,
+                                                                dropout_per_frame_); }
   virtual std::string Info() const;
 
-  void SetDropoutProportion(BaseFloat dropout_proportion) { dropout_proportion_ = dropout_proportion; }
+  void SetDropoutProportion(BaseFloat dropout_proportion, bool dropout_per_frame) {
+     dropout_proportion_ = dropout_proportion;
+     dropout_per_frame_ = dropout_per_frame;
+      }
 
  private:
   int32 dim_;
   /// dropout-proportion is the proportion that is dropped out,
   /// e.g. if 0.1, we set 10% to zero value.
   BaseFloat dropout_proportion_;
-
+  bool dropout_per_frame_;
 };
 
 class ElementwiseProductComponent: public Component {

src/nnet3/nnet-utils.cc

@@ -524,12 +524,14 @@ std::string NnetInfo(const Nnet &nnet) {
 }
 
 void SetDropoutProportion(BaseFloat dropout_proportion,
+                          bool dropout_per_frame,
                           Nnet *nnet) {
+  dropout_per_frame = false;
   for (int32 c = 0; c < nnet->NumComponents(); c++) {
     Component *comp = nnet->GetComponent(c);
     DropoutComponent *dc = dynamic_cast<DropoutComponent*>(comp);
     if (dc != NULL)
-      dc->SetDropoutProportion(dropout_proportion);
+      dc->SetDropoutProportion(dropout_proportion, dropout_per_frame);
   }
 }
 
@@ -694,18 +696,22 @@ void ReadEditConfig(std::istream &edit_config_is, Nnet *nnet) {
       // matches names of components, not nodes.
       config_line.GetValue("name", &name_pattern);
       BaseFloat proportion = -1;
+      bool dropout_per_frame = false;
       if (!config_line.GetValue("proportion", &proportion)) {
         KALDI_ERR << "In edits-config, expected proportion to be set in line: "
                   << config_line.WholeLine();
       }
+      if (!config_line.GetValue("dropout-per-frame", &dropout_per_frame)) {
+        dropout_per_frame = false;
+      }
       DropoutComponent *component = NULL;
       int32 num_dropout_proportions_set = 0;
       for (int32 c = 0; c < nnet->NumComponents(); c++) {
         if (NameMatchesPattern(nnet->GetComponentName(c).c_str(),
                                name_pattern.c_str()) &&
             (component =
              dynamic_cast<DropoutComponent*>(nnet->GetComponent(c)))) {
-          component->SetDropoutProportion(proportion);
+          component->SetDropoutProportion(proportion, dropout_per_frame);
           num_dropout_proportions_set++;
         }
       }

src/nnet3/nnet-utils.h

@@ -182,7 +182,7 @@ std::string NnetInfo(const Nnet &nnet);
 
 /// This function sets the dropout proportion in all dropout component to 
 /// dropout_proportion value.
-void SetDropoutProportion(BaseFloat dropout_proportion, Nnet *nnet);
+void SetDropoutProportion(BaseFloat dropout_proportion, bool dropout_per_frame, Nnet *nnet);
 
 /// This function finds a list of components that are never used, and outputs
 /// the integer comopnent indexes (you can use these to index
@@ -233,7 +233,7 @@ void FindOrphanNodes(const Nnet &nnet, std::vector<int32> *nodes);
        remove internal nodes directly; instead you should use the command
        'remove-orphans'.
 
-    set-dropout-proportion [name=<name-pattern>] proportion=<dropout-proportion>
+    set-dropout-proportion [name=<name-pattern>] proportion=<dropout-proportion> dropout-per-frame=<dropout-per-frame>
        Sets the dropout rates for any components of type DropoutComponent whose
        names match the given <name-pattern> (e.g. lstm*).  <name-pattern> defaults to "*".
    \endverbatim

src/nnet3bin/nnet3-combine.cc

@@ -104,7 +104,7 @@ int main(int argc, char *argv[]) {
     } else {
       KALDI_LOG << "Copying the single input model directly to the output, "
                 << "without any combination.";
-      SetDropoutProportion(0, &nnet);
+      SetDropoutProportion(0, false, &nnet);
       WriteKaldiObject(nnet, nnet_wxfilename, binary_write);
     } 
     KALDI_LOG << "Finished combining neural nets, wrote model to "