[src] Add batched xvector computation

src/nnet3/nnet-general-component.cc

@@ -573,7 +573,7 @@ void StatisticsPoolingComponent::InitFromConfig(ConfigLine *cfl) {
 StatisticsPoolingComponent::StatisticsPoolingComponent():
     input_dim_(-1), input_period_(1), left_context_(-1), right_context_(-1),
     num_log_count_features_(0), output_stddevs_(false),
-    variance_floor_(1.0e-10) { }
+    variance_floor_(1.0e-10), require_direct_input_(false) { }
 
 
 StatisticsPoolingComponent::StatisticsPoolingComponent(
@@ -582,7 +582,8 @@ StatisticsPoolingComponent::StatisticsPoolingComponent(
     left_context_(other.left_context_), right_context_(other.right_context_),
     num_log_count_features_(other.num_log_count_features_),
     output_stddevs_(other.output_stddevs_),
-    variance_floor_(1.0e-10) {
+    variance_floor_(other.variance_floor_),
+    require_direct_input_(other.require_direct_input_) {
   Check();
 }
 
@@ -614,6 +615,9 @@ void StatisticsPoolingComponent::Read(std::istream &is, bool binary) {
   ExpectToken(is, binary, "<VarianceFloor>");
   ReadBasicType(is, binary, &variance_floor_);
   ExpectToken(is, binary, "</StatisticsPoolingComponent>");
+  require_direct_input_ = false;  // This is not written to disk, it's only used
+                                  // temporarily, in memory (see
+                                  // nnet3-xvector-compute-batched.cc).
   Check();
 }
 

src/nnet3/nnet-general-component.h

@@ -331,7 +331,8 @@ class StatisticsExtractionComponentPrecomputedIndexes:
  or whatever, instead of just component-name, because its output is only defined at multiples
  of its input-period.
 
- The output of StatisticsPoolingComponent will only be defined if at least one input was defined.
+ The output of StatisticsPoolingComponent will only be defined if at least one
+ input was defined.
  */
 class StatisticsPoolingComponent: public Component {
  public:
@@ -396,6 +397,11 @@ class StatisticsPoolingComponent: public Component {
       const std::vector<Index> &output_indexes,
       bool need_backprop) const;
 
+  // Used in computing the 'real' context of networks involving this component;
+  // with the default value of false, the left/right context will always appear
+  // to be 0.
+  void SetRequireDirectInput(bool b) { require_direct_input_ = b; }
+
  private:
   // Checks that the parameters are valid.
   void Check() const;
@@ -411,6 +417,13 @@ class StatisticsPoolingComponent: public Component {
   int32 num_log_count_features_;
   bool output_stddevs_;
   BaseFloat variance_floor_;
+  // If require_direct_input_ is set to true, in order for a particular 't'
+  // value to be available at the output of this component, it will require that
+  // 't' value to be computable at the input.  This is used in computing the
+  // "real" left/right context of the network, but this member isn't currently
+  // written to disk and will default to false when read.
+  bool require_direct_input_;
+
 };
 
 class StatisticsPoolingComponentPrecomputedIndexes:

src/nnet3/nnet-utils.cc

@@ -300,6 +300,15 @@ void SetNnetAsGradient(Nnet *nnet) {
   }
 }
 
+void SetRequireDirectInput(bool b, Nnet *nnet) {
+  for (int32 c = 0; c < nnet->NumComponents(); c++) {
+    Component *comp = nnet->GetComponent(c);
+    if (dynamic_cast<StatisticsPoolingComponent*>(comp) != NULL)
+      dynamic_cast<StatisticsPoolingComponent*>(comp)->SetRequireDirectInput(b);
+  }
+}
+
+
 void ScaleNnet(BaseFloat scale, Nnet *nnet) {
   if (scale == 1.0) return;
   else {
@@ -724,7 +733,7 @@ class SvdApplier {
               << " components to FixedAffineComponent.";
   }
 
-  // This function finds the minimum index of 
+  // This function finds the minimum index of
   // the Descending order sorted [input_vector],
   // over a range of indices from [lower] to [upper] index,
   // for which the sum of elements upto the found min. index is greater
@@ -743,7 +752,7 @@ class SvdApplier {
     }
     return (i+1);
   }
- 
+
 // Here we perform SVD based refactorig of an input Affine component.
 // After applying SVD , we sort the Singularity values in descending order,
 // and take the subset of values which contribute to energy_threshold times
@@ -777,7 +786,7 @@ class SvdApplier {
     if (energy_threshold_ > 0) {
       BaseFloat min_singular_sum = energy_threshold_ * s2_sum_orig;
       bottleneck_dim_ = GetReducedDimension(s2, 0, s2.Dim()-1, min_singular_sum);
-    } 
+    }
     SubVector<BaseFloat> this_part(s2, 0, bottleneck_dim_);
     BaseFloat s2_sum_reduced = this_part.Sum();
     BaseFloat shrinkage_ratio =

src/nnet3/nnet-utils.h

@@ -118,6 +118,14 @@ void ScaleNnet(BaseFloat scale, Nnet *nnet);
 /// learning_rate_ to 1 for each UpdatableComponent in nnet
 void SetNnetAsGradient(Nnet *nnet);
 
+
+/// Calls the corresponding function in any component of type
+/// StatisticsPoolingComponent; used as a way to compute the 'real' left-right
+/// context of networks including SatisticsPoolingComponent, which will give you
+/// the minimum chunk size they can consume.
+void SetRequireDirectInput(bool b, Nnet *nnet);
+
+
 /// Does *dest += alpha * src (affects nnet parameters and
 /// stored stats).
 void AddNnet(const Nnet &src, BaseFloat alpha, Nnet *dest);

src/nnet3bin/Makefile

@@ -19,6 +19,7 @@ BINFILES = nnet3-init nnet3-info nnet3-get-egs nnet3-copy-egs nnet3-subset-egs \
    nnet3-discriminative-subset-egs nnet3-get-egs-simple \
    nnet3-discriminative-compute-from-egs nnet3-latgen-faster-looped \
    nnet3-egs-augment-image nnet3-xvector-get-egs nnet3-xvector-compute \
+   nnet3-xvector-compute-batched \
    nnet3-latgen-grammar nnet3-compute-batch nnet3-latgen-faster-batch \
    cuda-gpu-available cuda-compiled
 
@@ -36,4 +37,5 @@ ADDLIBS = ../nnet3/kaldi-nnet3.a ../chain/kaldi-chain.a \
           ../tree/kaldi-tree.a ../util/kaldi-util.a ../matrix/kaldi-matrix.a \
           ../base/kaldi-base.a
 
+
 include ../makefiles/default_rules.mk