#!/bin/bash
  
  # Copyright 2012-2014  Johns Hopkins University (Author: Daniel Povey)
  #           2014-2015  Vimal Manohar
  # Apache 2.0.
  
  set -o pipefail
  
  # This script does MPE or MMI or state-level minimum bayes risk (sMBR) training
  # using egs obtained by steps/nnet3/get_egs_discriminative.sh
  
  # Begin configuration section.
  cmd=run.pl
  num_epochs=4       # Number of epochs of training;
                     # the number of iterations is worked out from this.
                     # Be careful with this: we actually go over the data
                     # num-epochs * frame-subsampling-factor times, due to
                     # using different data-shifts.
  use_gpu=true
  apply_deriv_weights=true
  use_frame_shift=false
  run_diagnostics=true
  learning_rate=0.00002
  max_param_change=2.0
  scale_max_param_change=false # if this option is used, scale it by num-jobs.
  
  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).
  regularization_opts=
  minibatch_size=64  # This is the number of examples rather than the number of output frames.
  last_layer_factor=1.0  # relates to modify-learning-rates [deprecated]
  shuffle_buffer_size=1000 # 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
  
  num_threads=16  # this is the default but you may want to change it, e.g. to 1 if
                  # using GPUs.
  
  cleanup=true
  keep_model_iters=100
  remove_egs=false
  src_model=  # will default to $degs_dir/final.mdl
  
  num_jobs_compute_prior=10
  
  min_deriv_time=0
  max_deriv_time_relative=0
  # 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/nnet3/tdnn_sp_degs exp/nnet3/tdnn_sp_smbr"
    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. [deprecated]"
    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,frame_subsampling_factor} $src_model; do
    [ ! -f $f ] && echo "$0: no such file $f" && exit 1;
  done
  
  mkdir -p $dir/log || exit 1;
  
  
  model_left_context=$(nnet3-am-info $src_model | grep "^left-context:" | awk '{print $2}')
  model_right_context=$(nnet3-am-info $src_model | grep "^right-context:" | awk '{print $2}')
  
  # Copy the ivector information
  if [ -f $degs_dir/info/final.ie.id ]; then
    cp $degs_dir/info/final.ie.id $dir/ 2>/dev/null || true
  fi
  
  # 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 $degs_dir/info/num_archives) || exit 1;
  frame_subsampling_factor=$(cat $degs_dir/info/frame_subsampling_factor)
  
  echo $frame_subsampling_factor > $dir/frame_subsampling_factor
  
  if $use_frame_shift; then
    num_archives_expanded=$[$num_archives*$frame_subsampling_factor]
  else
    num_archives_expanded=$num_archives
  fi
  
  if [ $num_jobs_nnet -gt $num_archives_expanded ]; then
    echo "$0: num-jobs-nnet $num_jobs_nnet exceeds number of archives $num_archives_expanded,"
    echo " ... setting it to $num_archives."
    num_jobs_nnet=$num_archives_expanded
  fi
  
  num_archives_to_process=$[$num_epochs*$num_archives_expanded]
  num_archives_processed=0
  num_iters=$[$num_archives_to_process/$num_jobs_nnet]
  
  echo "$0: Will train for $num_epochs epochs = $num_iters iterations"
  
  if $use_gpu; then
    parallel_suffix=""
    train_queue_opt="--gpu 1"
    parallel_train_opts=
    if ! cuda-compiled; then
      echo "$0: WARNING: you are running with one thread but you have not compiled"
      echo "   for CUDA.  You may be running a setup optimized for GPUs.  If you have"
      echo "   GPUs and have nvcc installed, go to src/ and do ./configure; make"
      exit 1
    fi
  else
    echo "$0: without using a GPU this will be very slow.  nnet3 does not yet support multiple threads."
    parallel_train_opts="--use-gpu=no"
  fi
  
  if $use_frame_shift; then
    num_epochs_expanded=$[num_epochs*frame_subsampling_factor]
  else
    num_epochs_expanded=$num_epochs
  fi
  
  for e in $(seq 1 $num_epochs_expanded); do
    x=$[($e*$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
  
  
    # set the learning rate to $learning_rate, and
    # set the output-layer's learning rate to
    # $learning_rate times $last_layer_factor.
    edits_str="set-learning-rate learning-rate=$learning_rate"
    if [ "$last_layer_factor" != "1.0" ]; then
      last_layer_lrate=$(perl -e "print ($learning_rate*$last_layer_factor);") || exit 1
      edits_str="$edits_str; set-learning-rate name=output.affine learning-rate=$last_layer_lrate"
    fi
  
    $cmd $dir/log/convert.log \
      nnet3-am-copy --edits="$edits_str" "$src_model" $dir/0.mdl || exit 1;
  
    ln -sf 0.mdl $dir/epoch0.mdl
  fi
  
  
  rm $dir/.error 2>/dev/null
  
  x=0
  
  while [ $x -lt $num_iters ]; do
    if [ $stage -le $x ]; then
      if $run_diagnostics; then
        # Set off jobs doing some diagnostics, in the background.  # Use the egs dir from the previous iteration for the diagnostics
        $cmd $dir/log/compute_objf_valid.$x.log \
          nnet3-discriminative-compute-objf  $regularization_opts \
          --silence-phones=$silphonelist \
          --criterion=$criterion --drop-frames=$drop_frames \
          --one-silence-class=$one_silence_class \
          --boost=$boost --acoustic-scale=$acoustic_scale \
          $dir/$x.mdl \
          "ark,bg:nnet3-discriminative-copy-egs ark:$degs_dir/valid_diagnostic.degs ark:- | nnet3-discriminative-merge-egs --minibatch-size=1:64 ark:- ark:- |" &
        $cmd $dir/log/compute_objf_train.$x.log \
          nnet3-discriminative-compute-objf  $regularization_opts \
          --silence-phones=$silphonelist \
          --criterion=$criterion --drop-frames=$drop_frames \
          --one-silence-class=$one_silence_class \
          --boost=$boost --acoustic-scale=$acoustic_scale \
          $dir/$x.mdl \
          "ark,bg:nnet3-discriminative-copy-egs ark:$degs_dir/train_diagnostic.degs ark:- | nnet3-discriminative-merge-egs --minibatch-size=1:64 ark:- ark:- |" &
      fi
  
      if [ $x -gt 0 ]; then
        $cmd $dir/log/progress.$x.log \
          nnet3-show-progress --use-gpu=no "nnet3-am-copy --raw=true $dir/$[$x-1].mdl - |" "nnet3-am-copy --raw=true $dir/$x.mdl - |" \
          '&&' \
          nnet3-info "nnet3-am-copy --raw=true $dir/$x.mdl - |" &
      fi
  
  
      echo "Training neural net (pass $x)"
  
      cache_read_opt="--read-cache=$dir/cache.$x"
  
      ( # this sub-shell is so that when we "wait" below,
        # we only wait for the training jobs that we just spawned,
        # not the diagnostic jobs that we spawned above.
  
        # We can't easily use a single parallel SGE job to do the main training,
        # because the computation of which archive and which --frame option
        # to use for each job is a little complex, so we spawn each one separately.
        for n in `seq $num_jobs_nnet`; do
          k=$[$num_archives_processed + $n - 1]; # k is a zero-based index that we'll derive
                                                 # the other indexes from.
          archive=$[($k%$num_archives)+1]; # work out the 1-based archive index.
  
          if [ $n -eq 1 ]; then
            # an option for writing cache (storing pairs of nnet-computations and
            # computation-requests) during training.
            cache_write_opt=" --write-cache=$dir/cache.$[$x+1]"
          else
            cache_write_opt=""
          fi
  
          if $use_frame_shift; then
            frame_shift=$[(k%num_archives + k/num_archives) % frame_subsampling_factor]
          else
            frame_shift=0
          fi
  
          #archive=$[(($n+($x*$num_jobs_nnet))%$num_archives)+1]
          if $scale_max_param_change; then
            this_max_param_change=$(perl -e "print ($max_param_change * $num_jobs_nnet);")
          else
            this_max_param_change=$max_param_change
          fi
  
          $cmd $train_queue_opt $dir/log/train.$x.$n.log \
            nnet3-discriminative-train $cache_read_opt $cache_write_opt \
            --apply-deriv-weights=$apply_deriv_weights \
            --optimization.min-deriv-time=-$model_left_context \
            --optimization.max-deriv-time-relative=$model_right_context \
              $parallel_train_opts \
            --max-param-change=$this_max_param_change \
            --silence-phones=$silphonelist \
            --criterion=$criterion --drop-frames=$drop_frames \
            --one-silence-class=$one_silence_class \
            --boost=$boost --acoustic-scale=$acoustic_scale $regularization_opts \
            $dir/$x.mdl \
            "ark,bg:nnet3-discriminative-copy-egs --frame-shift=$frame_shift ark:$degs_dir/degs.$archive.ark ark:- | nnet3-discriminative-shuffle-egs --buffer-size=$shuffle_buffer_size --srand=$x ark:- ark:- | nnet3-discriminative-merge-egs --minibatch-size=$minibatch_size ark:- ark:- |" \
            $dir/$[$x+1].$n.raw || touch $dir/.error &
        done
        wait
        [ -f $dir/.error ] && exit 1
      )
      [ -f $dir/.error ] && { echo "Found $dir/.error. See $dir/log/train.$x.*.log"; exit 1; }
  
      nnets_list=$(for n in $(seq $num_jobs_nnet); do echo $dir/$[$x+1].$n.raw; 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 \
        nnet3-average $nnets_list - \| \
        nnet3-am-copy --set-raw-nnet=- $dir/$x.mdl $dir/$[$x+1].mdl || exit 1;
  
      rm $nnets_list
      [ ! -f $dir/$[$x+1].mdl ] && echo "$0: Did not create $dir/$[$x+1].mdl" && exit 1;
      if [ -f $dir/$[$x-1].mdl ] && $cleanup && \
         [ $[($x-1)%$keep_model_iters] -ne 0  ] && \
         [ -z "${iter_to_epoch[$[$x-1]]}" ]; then
        rm $dir/$[$x-1].mdl
      fi
  
      [ -f $dir/.error ] && { echo "Found $dir/.error. Error on iteration $x"; exit 1; }
    fi
  
    rm $dir/cache.$x 2>/dev/null || true
    x=$[$x+1]
    num_archives_processed=$[num_archives_processed+num_jobs_nnet]
  
    if [ $stage -le $x ] && [ ! -z "${iter_to_epoch[$x]}" ]; then
      e=${iter_to_epoch[$x]}
      ln -sf $x.mdl $dir/epoch$e.mdl
  
      (
        rm $dir/.error 2> /dev/null
  
        steps/nnet3/adjust_priors.sh --egs-type degs \
          --num-jobs-compute-prior $num_jobs_compute_prior \
          --cmd "$cmd" --use-gpu false \
          --minibatch-size $minibatch_size \
          --use-raw-nnet false --iter epoch$e $dir $degs_dir \
          || { touch $dir/.error; echo "Error in adjusting priors. See errors above."; exit 1; }
      ) &
    fi
  
  done
  
  rm $dir/final.mdl 2>/dev/null
  cp $dir/$x.mdl $dir/final.mdl
  
  # 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
    remove $degs_dir/degs.*
    remove $degs_dir/priors_egs.*
  fi
  
  
  if $cleanup; then
    echo Removing most of the models
    for x in `seq 1 $keep_model_iters $num_iters`; do
      if [ -z "${iter_to_epoch[$x]}" ]; then
        # if $x is not an epoch-final iteration..
        rm $dir/$x.mdl 2>/dev/null
      fi
    done
  fi
  
  wait
  [ -f $dir/.error ] && { echo "Found $dir/.error."; exit 1; }
  
  echo Done && exit 0