// nnet3bin/nnet3-chain-acc-lda-stats.cc
  
  // Copyright 2015  Johns Hopkins University (author: Daniel Povey)
  
  // See ../../COPYING for clarification regarding multiple authors
  //
  // 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
  //
  // THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  // KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
  // WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
  // MERCHANTABLITY OR NON-INFRINGEMENT.
  // See the Apache 2 License for the specific language governing permissions and
  // limitations under the License.
  
  #include "base/kaldi-common.h"
  #include "util/common-utils.h"
  #include "hmm/transition-model.h"
  #include "lat/lattice-functions.h"
  #include "nnet3/nnet-nnet.h"
  #include "nnet3/nnet-chain-example.h"
  #include "nnet3/nnet-compute.h"
  #include "nnet3/nnet-optimize.h"
  #include "transform/lda-estimate.h"
  
  
  namespace kaldi {
  namespace nnet3 {
  
  class NnetChainLdaStatsAccumulator {
   public:
    NnetChainLdaStatsAccumulator(BaseFloat rand_prune,
                                 const Nnet &nnet):
        rand_prune_(rand_prune), nnet_(nnet), compiler_(nnet) { }
  
  
    void AccStats(const NnetChainExample &eg) {
      ComputationRequest request;
      bool need_backprop = false, store_stats = false,
          need_xent = false, need_xent_deriv = false;
  
      GetChainComputationRequest(nnet_, eg, need_backprop, store_stats,
                                 need_xent, need_xent_deriv, &request);
  
      const NnetComputation &computation = *(compiler_.Compile(request));
  
      NnetComputeOptions options;
      if (GetVerboseLevel() >= 3)
        options.debug = true;
      NnetComputer computer(options, computation, nnet_, NULL);
  
      computer.AcceptInputs(nnet_, eg.inputs);
      computer.Run();
      const CuMatrixBase<BaseFloat> &nnet_output = computer.GetOutput("output");
      if (eg.outputs[0].supervision.fst.NumStates() > 0) {
        AccStatsFst(eg, nnet_output);
      } else {
        AccStatsAlignment(eg, nnet_output);
      }
    }
  
    void WriteStats(const std::string &stats_wxfilename, bool binary) {
      if (lda_stats_.TotCount() == 0) {
        KALDI_ERR << "Accumulated no stats.";
      } else {
        WriteKaldiObject(lda_stats_, stats_wxfilename, binary);
        KALDI_LOG << "Accumulated stats, soft frame count = "
                  << lda_stats_.TotCount() << ".  Wrote to "
                  << stats_wxfilename;
      }
    }
   private:
    void AccStatsFst(const NnetChainExample &eg,
                     const CuMatrixBase<BaseFloat> &nnet_output) {
      BaseFloat rand_prune = rand_prune_;
  
      if (eg.outputs.size() != 1 || eg.outputs[0].name != "output")
        KALDI_ERR << "Expecting the example to have one output named 'output'.";
  
  
      const chain::Supervision &supervision = eg.outputs[0].supervision;
      // handling the one-sequence-per-eg case is easier so we just do that.
      KALDI_ASSERT(supervision.num_sequences == 1 &&
                   "This program expects one sequence per eg.");
      int32 num_frames = supervision.frames_per_sequence,
          num_pdfs = supervision.label_dim;
      KALDI_ASSERT(num_frames == nnet_output.NumRows());
  
      const fst::StdVectorFst &fst = supervision.fst;
  
      Lattice lat;
      // convert the FST to a lattice, putting all the weight on
      // the graph weight.  This is to save us having to implement the
      // forward-backward on FSTs.
      ConvertFstToLattice(fst, &lat);
      Posterior post;
      LatticeForwardBackward(lat, &post);
      KALDI_ASSERT(post.size() == static_cast<size_t>(num_frames));
  
      // Subtract one, to convert the (pdf-id + 1) which appears in the
      // supervision FST, to a pdf-id.
      for (size_t i = 0; i < post.size(); i++)
        for (size_t j = 0; j < post[i].size(); j++)
          post[i][j].first--;
  
      if (lda_stats_.Dim() == 0)
        lda_stats_.Init(num_pdfs,
                        nnet_output.NumCols());
  
      for (int32 t = 0; t < num_frames; t++) {
        // the following, transferring row by row to CPU, would be wasteful if we
        // actually were using a GPU, but we don't anticipate using a GPU in this
        // program.
        CuSubVector<BaseFloat> cu_row(nnet_output, t);
        // "row" is actually just a redudant copy, since we're likely on CPU,
        // but we're about to do an outer product, so this doesn't dominate.
        Vector<BaseFloat> row(cu_row);
  
        std::vector<std::pair<int32,BaseFloat> >::const_iterator
            iter = post[t].begin(), end = post[t].end();
  
        for (; iter != end; ++iter) {
          int32 pdf = iter->first;
          BaseFloat weight = iter->second;
          BaseFloat pruned_weight = RandPrune(weight, rand_prune);
          if (pruned_weight != 0.0)
            lda_stats_.Accumulate(row, pdf, pruned_weight);
        }
      }
    }
  
  
    void AccStatsAlignment(const NnetChainExample &eg,
                            const CuMatrixBase<BaseFloat> &nnet_output) {
      BaseFloat rand_prune = rand_prune_;
  
      if (eg.outputs.size() != 1 || eg.outputs[0].name != "output")
        KALDI_ERR << "Expecting the example to have one output named 'output'.";
  
      const chain::Supervision &supervision = eg.outputs[0].supervision;
      // handling the one-sequence-per-eg case is easier so we just do that.
      KALDI_ASSERT(supervision.num_sequences == 1 &&
                   "This program expects one sequence per eg.");
  
      int32 num_frames = supervision.frames_per_sequence,
          num_pdfs = supervision.label_dim;
      KALDI_ASSERT(num_frames == nnet_output.NumRows());
  
      if (supervision.alignment_pdfs.size() !=
          static_cast<size_t>(num_frames))
        KALDI_ERR << "Alignment pdfs not present or wrong length.  Using e2e egs?";
  
      if (lda_stats_.Dim() == 0)
        lda_stats_.Init(num_pdfs,
                        nnet_output.NumCols());
  
      for (int32 t = 0; t < num_frames; t++) {
        // the following, transferring row by row to CPU, would be wasteful if we
        // actually were using a GPU, but we don't anticipate using a GPU in this
        // program.
        CuSubVector<BaseFloat> cu_row(nnet_output, t);
        // "row" is actually just a redudant copy, since we're likely on CPU,
        // but we're about to do an outer product, so this doesn't dominate.
        Vector<BaseFloat> row(cu_row);
  
        int32 pdf = supervision.alignment_pdfs[t];
        BaseFloat weight = 1.0;
        BaseFloat pruned_weight = RandPrune(weight, rand_prune);
        if (pruned_weight != 0.0)
          lda_stats_.Accumulate(row, pdf, pruned_weight);
      }
    }
  
    BaseFloat rand_prune_;
    const Nnet &nnet_;
    CachingOptimizingCompiler compiler_;
    LdaEstimate lda_stats_;
  };
  
  }
  }
  
  int main(int argc, char *argv[]) {
    try {
      using namespace kaldi;
      using namespace kaldi::nnet3;
      typedef kaldi::int32 int32;
      typedef kaldi::int64 int64;
  
      const char *usage =
          "Accumulate statistics in the same format as acc-lda (i.e. stats for
  "
          "estimation of LDA and similar types of transform), starting from nnet+chain
  "
          "training examples.  This program puts the features through the network,
  "
          "and the network output will be the features; the supervision in the
  "
          "training examples is used for the class labels.  Used in obtaining
  "
          "feature transforms that help nnet training work better.
  "
          "Note: the time boundaries it gets from the chain supervision will be
  "
          "a little fuzzy (which is not ideal), but it should not matter much in
  "
          "this situation
  "
          "
  "
          "Usage:  nnet3-chain-acc-lda-stats [options] <raw-nnet-in> <training-examples-in> <lda-stats-out>
  "
          "e.g.:
  "
          "nnet3-chain-acc-lda-stats 0.raw ark:1.cegs 1.acc
  "
          "See also: nnet-get-feature-transform
  ";
  
      bool binary_write = true;
      BaseFloat rand_prune = 0.0;
  
      ParseOptions po(usage);
      po.Register("binary", &binary_write, "Write output in binary mode");
      po.Register("rand-prune", &rand_prune,
                  "Randomized pruning threshold for posteriors");
  
      po.Read(argc, argv);
  
      if (po.NumArgs() != 3) {
        po.PrintUsage();
        exit(1);
      }
  
      std::string nnet_rxfilename = po.GetArg(1),
          examples_rspecifier = po.GetArg(2),
          lda_accs_wxfilename = po.GetArg(3);
  
      // Note: this neural net is probably just splicing the features at this
      // point.
      Nnet nnet;
      ReadKaldiObject(nnet_rxfilename, &nnet);
  
      NnetChainLdaStatsAccumulator accumulator(rand_prune, nnet);
  
      int64 num_egs = 0;
  
      SequentialNnetChainExampleReader example_reader(examples_rspecifier);
      for (; !example_reader.Done(); example_reader.Next(), num_egs++)
        accumulator.AccStats(example_reader.Value());
  
      KALDI_LOG << "Processed " << num_egs << " examples.";
      // the next command will die if we accumulated no stats.
      accumulator.WriteStats(lda_accs_wxfilename, binary_write);
  
      return 0;
    } catch(const std::exception &e) {
      std::cerr << e.what() << '
  ';
      return -1;
    }
  }