#!/bin/bash
  
  # Copyright 2012-2014  Johns Hopkins University (Author: Daniel Povey).  Apache 2.0.
  
  # This script does MPE or MMI or state-level minimum bayes risk (sMBR) training.
  # This version (2) of the script uses a newer format for the discriminative-training
  # egs, as obtained by steps/nnet2/get_egs_discriminative2.sh.
  
  # Begin configuration section.
  cmd=run.pl
  num_epochs=4       # Number of epochs of training
  learning_rate=0.00002
  effective_lrate=    # If supplied, overrides the learning rate, which gets set to effective_lrate * num_jobs_nnet.
  acoustic_scale=0.1  # acoustic scale for MMI/MPFE/SMBR training.
  boost=0.0       # option relevant for MMI
  
  criterion=smbr
  drop_frames=false #  option relevant for MMI
  one_silence_class=true # option relevant for MPE/SMBR
  num_jobs_nnet=4    # Number of neural net jobs to run in parallel.  Note: this
                     # will interact with the learning rates (if you decrease
                     # this, you'll have to decrease the learning rate, and vice
                     # versa).
  
  modify_learning_rates=true
  last_layer_factor=1.0  # relates to modify-learning-rates
  first_layer_factor=1.0 # relates to modify-learning-rates
  shuffle_buffer_size=5000 # This "buffer_size" variable controls randomization of the samples
                  # on each iter.  You could set it to 0 or to a large value for complete
                  # randomization, but this would both consume memory and cause spikes in
                  # disk I/O.  Smaller is easier on disk and memory but less random.  It's
                  # not a huge deal though, as samples are anyway randomized right at the start.
  
  
  stage=-3
  
  adjust_priors=false
  num_threads=16  # this is the default but you may want to change it, e.g. to 1 if
                  # using GPUs.
  parallel_opts="--num-threads 16 --mem 1G"
    # by default we use 16 threads; this lets the queue know.
    # note: parallel_opts doesn't automatically get adjusted if you adjust num-threads.
  
  cleanup=true
  retroactive=false
  remove_egs=false
  src_model=  # will default to $degs_dir/final.mdl
  # End configuration section.
  
  
  echo "$0 $@"  # Print the command line for logging
  
  if [ -f path.sh ]; then . ./path.sh; fi
  . parse_options.sh || exit 1;
  
  
  if [ $# != 2 ]; then
    echo "Usage: $0 [opts] <degs-dir> <exp-dir>"
    echo " e.g.: $0 exp/tri4_mpe_degs exp/tri4_mpe"
    echo ""
    echo "You have to first call get_egs_discriminative2.sh to dump the egs."
    echo "Caution: the options 'drop-frames' and 'criterion' are taken here"
    echo "even though they were required also by get_egs_discriminative2.sh,"
    echo "and they should normally match."
    echo ""
    echo "Main options (for others, see top of script file)"
    echo "  --config <config-file>                           # config file containing options"
    echo "  --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
    echo "  --num-epochs <#epochs|4>                        # Number of epochs of training"
    echo "  --learning-rate <learning-rate|0.0002>           # Learning rate to use"
    echo "  --effective-lrate <effective-learning-rate>      # If supplied, learning rate will be set to"
    echo "                                                   # this value times num-jobs-nnet."
    echo "  --num-jobs-nnet <num-jobs|8>                     # Number of parallel jobs to use for main neural net"
    echo "                                                   # training (will affect results as well as speed; try 8, 16)"
    echo "                                                   # Note: if you increase this, you may want to also increase"
    echo "                                                   # the learning rate.  Also note: if there are fewer archives"
    echo "                                                   # of egs than this, it will get reduced automatically."
    echo "  --num-threads <num-threads|16>                   # Number of parallel threads per job (will affect results"
    echo "                                                   # as well as speed; may interact with batch size; if you increase"
    echo "                                                   # this, you may want to decrease the batch size.  With GPU, must be 1."
    echo "  --parallel-opts <opts|\"--num-threads 16 --mem 1G\">      # extra options to pass to e.g. queue.pl for processes that"
    echo "                                                   # use multiple threads... "
    echo "  --stage <stage|-3>                               # Used to run a partially-completed training process from somewhere in"
    echo "                                                   # the middle."
    echo "  --criterion <criterion|smbr>                     # Training criterion: may be smbr, mmi or mpfe"
    echo "  --boost <boost|0.0>                              # Boosting factor for MMI (e.g., 0.1)"
    echo "  --drop-frames <true,false|false>                 # Option that affects MMI training: if true, we exclude gradients from frames"
    echo "                                                   # where the numerator transition-id is not in the denominator lattice."
    echo "  --one-silence-class <true,false|false>           # Option that affects MPE/SMBR training (will tend to reduce insertions)"
    echo "  --modify-learning-rates <true,false|false>       # If true, modify learning rates to try to equalize relative"
    echo "                                                   # changes across layers."
    exit 1;
  fi
  
  degs_dir=$1
  dir=$2
  
  [ -z "$src_model" ] && src_model=$degs_dir/final.mdl
  
  # Check some files.
  for f in $degs_dir/degs.1.ark $degs_dir/info/{num_archives,silence.csl,frames_per_archive} $src_model; do
    [ ! -f $f ] && echo "$0: no such file $f" && exit 1;
  done
  
  mkdir -p $dir/log || exit 1;
  
  cp $degs_dir/phones.txt $dir 2>/dev/null
  # copy some things
  for f in splice_opts cmvn_opts tree final.mat; do
    if [ -f $degs_dir/$f ]; then
      cp $degs_dir/$f $dir/ || exit 1;
    fi
  done
  
  silphonelist=`cat $degs_dir/info/silence.csl` || exit 1;
  
  
  num_archives=$(cat $degs_dir/info/num_archives) || exit 1;
  
  if [ $num_jobs_nnet -gt $num_archives ]; then
    echo "$0: num-jobs-nnet $num_jobs_nnet exceeds number of archives $num_archives,"
    echo " ... setting it to $num_archives."
    num_jobs_nnet=$num_archives
  fi
  
  num_iters=$[($num_epochs*$num_archives)/$num_jobs_nnet]
  
  echo "$0: Will train for $num_epochs epochs = $num_iters iterations"
  
  for e in $(seq 1 $num_epochs); do
    x=$[($e*$num_archives)/$num_jobs_nnet] # gives the iteration number.
    iter_to_epoch[$x]=$e
  done
  
  if [ $stage -le -1 ]; then
    echo "$0: Copying initial model and modifying preconditioning setup"
  
    # Note, the baseline model probably had preconditioning, and we'll keep it;
    # but we want online preconditioning with a larger number of samples of
    # history, since in this setup the frames are only randomized at the segment
    # level so they are highly correlated.  It might make sense to tune this a
    # little, later on, although I doubt it matters once the --num-samples-history
    # is large enough.
  
    if [ ! -z "$effective_lrate" ]; then
      learning_rate=$(perl -e "print ($num_jobs_nnet*$effective_lrate);")
      echo "$0: setting learning rate to $learning_rate = --num-jobs-nnet * --effective-lrate."
    fi
  
    $cmd $dir/log/convert.log \
      nnet-am-copy --learning-rate=$learning_rate "$src_model" - \| \
      nnet-am-switch-preconditioning  --num-samples-history=50000 - $dir/0.mdl || exit 1;
  fi
  
  
  
  if [ $num_threads -eq 1 ]; then
   train_suffix="-simple" # this enables us to use GPU code if
                          # we have just one thread.
  else
    train_suffix="-parallel --num-threads=$num_threads"
  fi
  
  rm $dir/.error
  x=0   
  while [ $x -lt $num_iters ]; do
    if [ $stage -le $x ]; then
      
      echo "Training neural net (pass $x)"
  
      # The \$ below delays the evaluation of the expression until the script runs (and JOB
      # will be replaced by the job-id).  That expression in $[..] is responsible for
      # choosing the archive indexes to use for each job on each iteration... we cycle through
      # all archives.
  
      $cmd $parallel_opts JOB=1:$num_jobs_nnet $dir/log/train.$x.JOB.log \
        nnet-combine-egs-discriminative \
          "ark:$degs_dir/degs.\$[((JOB-1+($x*$num_jobs_nnet))%$num_archives)+1].ark" ark:- \| \
        nnet-train-discriminative$train_suffix --silence-phones=$silphonelist \
         --criterion=$criterion --drop-frames=$drop_frames \
         --one-silence-class=$one_silence_class \
         --boost=$boost --acoustic-scale=$acoustic_scale \
         $dir/$x.mdl ark:- $dir/$[$x+1].JOB.mdl || exit 1;
  
      nnets_list=$(for n in $(seq $num_jobs_nnet); do echo $dir/$[$x+1].$n.mdl; done)
  
      # below use run.pl instead of a generic $cmd for these very quick stages,
      # so that we don't run the risk of waiting for a possibly hard-to-get GPU.
      run.pl $dir/log/average.$x.log \
        nnet-am-average $nnets_list $dir/$[$x+1].mdl || exit 1;
  
      if $modify_learning_rates; then
        run.pl $dir/log/modify_learning_rates.$x.log \
          nnet-modify-learning-rates --retroactive=$retroactive \
          --last-layer-factor=$last_layer_factor \
          --first-layer-factor=$first_layer_factor \
          $dir/$x.mdl $dir/$[$x+1].mdl $dir/$[$x+1].mdl || exit 1;
      fi
      rm $nnets_list
    fi
    if $adjust_priors && [ ! -z "${iter_to_epoch[$x]}" ]; then
      if [ ! -f $degs_dir/priors_egs.1.ark ]; then
        echo "$0: Expecting $degs_dir/priors_egs.1.ark to exist since --adjust-priors was true."
        echo "$0: Run this script with --adjust-priors false to not adjust priors"
        exit 1
      fi
      (
      e=${iter_to_epoch[$x]}
      rm $dir/.error
      num_archives_priors=`cat $degs_dir/info/num_archives_priors` || { touch $dir/.error; echo "Could not find $degs_dir/info/num_archives_priors. Set --adjust-priors false to not adjust priors"; exit 1; }
  
      $cmd JOB=1:$num_archives_priors $dir/log/get_post.epoch$e.JOB.log \
        nnet-compute-from-egs "nnet-to-raw-nnet $dir/$x.mdl -|" \
        ark:$degs_dir/priors_egs.JOB.ark ark:- \| \
        matrix-sum-rows ark:- ark:- \| \
        vector-sum ark:- $dir/post.epoch$e.JOB.vec || \
        { touch $dir/.error; echo "Error in getting posteriors for adjusting priors. See $dir/log/get_post.epoch$e.*.log"; exit 1; }
  
      sleep 3;
  
      $cmd $dir/log/sum_post.epoch$e.log \
        vector-sum $dir/post.epoch$e.*.vec $dir/post.epoch$e.vec || \
        { touch $dir/.error; echo "Error in summing posteriors. See $dir/log/sum_post.epoch$e.log"; exit 1; }
  
      rm $dir/post.epoch$e.*.vec
  
      echo "Re-adjusting priors based on computed posteriors for iter $x"
      $cmd $dir/log/adjust_priors.epoch$e.log \
        nnet-adjust-priors $dir/$x.mdl $dir/post.epoch$e.vec $dir/$x.mdl \
        || { touch $dir/.error; echo "Error in adjusting priors. See $dir/log/adjust_priors.epoch$e.log"; exit 1; }
      ) &
    fi
  
    [ -f $dir/.error ] && exit 1
  
    x=$[$x+1]
  done
  
  rm $dir/final.mdl 2>/dev/null
  ln -s $x.mdl $dir/final.mdl
  
  echo Done
  
  epoch_final_iters=
  for e in $(seq 0 $num_epochs); do
    x=$[($e*$num_archives)/$num_jobs_nnet] # gives the iteration number.
    ln -sf $x.mdl $dir/epoch$e.mdl
    epoch_final_iters="$epoch_final_iters $x"
  done
  
  
  # function to remove egs that might be soft links.
  remove () { for x in $*; do [ -L $x ] && rm $(utils/make_absolute.sh $x); rm $x; done }
  
  if $cleanup && $remove_egs; then  # note: this is false by default.
    echo Removing training examples
    for n in $(seq $num_archives); do
      remove $degs_dir/degs.*
      remove $degs_dir/priors_egs.*
    done
  fi
  
  
  if $cleanup; then
    echo Removing most of the models
    for x in `seq 0 $num_iters`; do
      if ! echo $epoch_final_iters | grep -w $x >/dev/null; then 
        # if $x is not an epoch-final iteration..
        rm $dir/$x.mdl 2>/dev/null
      fi
    done
  fi