path: root/decoder/ff_fsa.h

#ifndef FF_FSA_H
#define FF_FSA_H

/*
  features whose score is just some PFSA over target string.  however, PFSA can use edge and smeta info (e.g. spans on edge) - not usually useful.

//SEE ALSO: ff_fsa_dynamic.h, ff_from_fsa.h

  state is some fixed width byte array.  could actually be a void *, WordID sequence, whatever.

  TODO: fsa ff scores phrases not just words
  TODO: fsa feature aggregator that presents itself as a single fsa; benefit: when wrapped in ff_from_fsa, only one set of left words is stored.  downside: compared to separate ff, the inside portion of lower-order models is incorporated later.  however, the full heuristic is already available and exact for those words.  so don't sweat it.

  TODO: state (+ possibly span-specific) custom heuristic, e.g. in "longer than previous word" model, you can expect a higher outside if your state is a word of 2 letters.  this is on top of the nice heuristic for the unscored words, of course.  in ngrams, the avg prob will be about the same, but if the words possible for a source span are summarized, maybe it's possible to predict.  probably not worth the effort.
*/


//TODO: decide whether to use init_features / add_value vs. summing elsewhere + set_value once (or inefficient for from_fsa: sum distinct feature_vectors.  but L->R if we only scan 1 word at a time, that's fine

#define FSA_DEBUG 0

#if USE_INFO_EDGE
#define FSA_DEBUG_CERR 0
#else
#define FSA_DEBUG_CERR 1
#endif

#define FSA_DEBUG_DEBUG 0
# define FSADBGif(i,e,x) do { if (i) { if (FSA_DEBUG_CERR){std::cerr<<x;}  INFO_EDGE(e,x); if (FSA_DEBUG_DEBUG){std::cerr<<"FSADBGif edge.info "<<&e<<" = "<<e.info()<<std::endl;}} } while(0)
# define FSADBGif_nl(i,e) do { if (i) { if (FSA_DEBUG_CERR) std::cerr<<std::endl; INFO_EDGE(e,"; "); } } while(0)
#if FSA_DEBUG
# include <iostream>
# define FSADBG(e,x) FSADBGif(d().debug(),e,x)
# define FSADBGnl(e) FSADBGif_nl(d().debug(),e,x)
#else
# define FSADBG(e,x)
# define FSADBGnl(e)
#endif

#include <boost/lexical_cast.hpp>
#include <sstream>
#include <stdint.h> //C99
#include <string>
#include "ff.h"
#include "sparse_vector.h"
#include "value_array.h" // used to hold state
#include "tdict.h"
#include "hg.h"
#include "sentences.h"

typedef ValueArray<uint8_t> Bytes;

/*
usage:
struct SameFirstLetter : public FsaFeatureFunctionBase<SameFirstLetter> {
SameFirstLetter(string const& param) : FsaFeatureFunctionBase<SameFirstLetter>(1,singleton_sentence("END")) {  start[0]='a';h_start[0]=0; } // 1 byte of state, scan final (single) symbol "END" to get final state cost
  int markov_order() const { return 1; }
  Featval Scan1(WordID w,void const* old_state,void *new_state) const {
    char cw=TD::Convert(w)[0];
    char co=*(char const*)old_state;
    *(char *)new_state = cw;
    return cw==co?1:0;
  }
  void print_state(std::ostream &o,void const* st) const {
    o<<*(char const*)st;
  }
  static std::string usage(bool param,bool verbose) {
    return FeatureFunction::usage_helper("SameFirstLetter","[no args]","1 each time 2 consecutive words start with the same letter",param,verbose);
  }
};

// then, to decode, see ff_from_fsa.h
 */

template <class Impl>
struct FsaFeatureFunctionBase {
  Impl const& d() const { return static_cast<Impl const&>(*this); }
  Impl & d()  { return static_cast<Impl &>(*this); }
protected:
  int state_bytes_; // don't forget to set this. default 0 (it may depend on params of course)
  Bytes start,h_start; // start state and estimated-features (heuristic) start state.  set these.  default empty.
  Sentence end_phrase_; // words appended for final traversal (final state cost is assessed using Scan) e.g. "</s>" for lm.
  void set_state_bytes(int sb=0) {
    if (start.size()!=sb) start.resize(sb);
    if (h_start.size()!=sb) h_start.resize(sb);
    state_bytes_=sb;
  }
  void set_end_phrase(WordID single) {
    end_phrase_=singleton_sentence(single);
  }

  int fid_; // you can have more than 1 feature of course.
  void Init() { // CALL THIS MANUALLY (because feature name(s) may depend on param
    fid_=FD::Convert(d().name());
  }

  inline void static to_state(void *state,char const* begin,char const* end) {
    std::memcpy(state,begin,end-begin);
  }
  inline void static to_state(void *state,char const* begin,int n) {
    std::memcpy(state,begin,n);
  }
  template <class T>
  inline void static to_state(void *state,T const* begin,int n=1) {
    to_state(state,(char const*)begin,n*sizeof(T));
  }
  template <class T>
  inline void static to_state(void *state,T const* begin,T const* end) {
    to_state(state,(char const*)begin,(char const*)end);
  }

  inline static char hexdigit(int i) {
    int j=i-10;
    return j>=0?'a'+j:'0'+i;
  }
  inline static void print_hex_byte(std::ostream &o,unsigned c) {
      o<<hexdigit(c>>4);
      o<<hexdigit(c&0x0f);
  }

public:
  void state_copy(void *to,void const*from) const {
    std::memcpy(to,from,state_bytes_);
  }
  void state_zero(void *st) const { // you should call this if you don't know the state yet and want it to be hashed/compared properly
    std::memset(st,0,state_bytes_);
  }

  // can override to different return type, e.g. just return feats:
  Featval describe_features(FeatureVector const& feats) const {
    return feats.get(fid_);
  }

  bool debug() const { return true; }
  int fid() const { return fid_; } // return the one most important feature (for debugging)
  std::string name() const {
    return Impl::usage(false,false);
  }

  void print_state(std::ostream &o,void const*state) const {
    char const* i=(char const*)state;
    char const* e=i+state_bytes_;
    for (;i!=e;++i)
      print_hex_byte(o,*i);
  }

  std::string describe_state(void const* state) const {
    std::ostringstream o;
    d().print_state(o,state);
    return o.str();
  }

  //edges may have old features on them.  override if you have more than 1 fid.  we need to call this explicitly because edges may have old feature values already, and I chose to use add_value (+=) to simplify scanning a phrase, rather than set_value (=) for fsa ffs.  could revisit this and use set_value and therefore sum
  void init_features(FeatureVector *fv) const {
    fv->set_value(fid_,0);
    //erase(fid_)
  }
  // return m: all strings x with the same final m+1 letters must end in this state
  /* markov chain of order m: P(xn|xn-1...x1)=P(xn|xn-1...xn-m) */
  int markov_order() const { return 0; } // override if you use state.  order 0 implies state_bytes()==0 as well, as far as scoring/splitting is concerned (you can still track state, though)
  //TODO: if we wanted, we could mark certain states as maximal-context, but this would lose our fixed amount of left context in ff_from_fsa, and lose also our vector operations (have to scan left words 1 at a time, checking always to see where you change from h to inside - BUT, could detect equivalent LM states, which would be nice).

  Features features() const { // override this if >1 fid
    return FeatureFunction::single_feature(fid_);
  }

  // override this (static)
  static std::string usage(bool param,bool verbose) {
    return FeatureFunction::usage_helper("unnamed_fsa_feature","","",param,verbose);
  }
  int state_bytes() const { return state_bytes_; } // or override this
  void const* start_state() const {
    return start.begin();
  }
  void const * heuristic_start_state() const {
    return h_start.begin();
  }
  Sentence const& end_phrase() const { return end_phrase_; }
  // move from state to next_state after seeing word x, while emitting features->add_value(fid,val) possibly with duplicates.  state and next_state will never be the same memory.
  //TODO: decide if we want to require you to support dest same as src, since that's how we use it most often in ff_from_fsa bottom-up wrapper (in l->r scoring, however, distinct copies will be the rule), and it probably wouldn't be too hard for most people to support.  however, it's good to hide the complexity here, once (see overly clever FsaScan loop that swaps src/dest addresses repeatedly to scan a sequence by effectively swapping)

  // different name because of inheritance method hiding; simple/common case; 1 fid
  Featval Scan1(WordID w,void const* state,void *next_state) const {
    return 0;
  }

  // NOTE: if you want to e.g. track statistics, cache, whatever, cast const away or use mutable members
  inline void Scan(SentenceMetadata const& smeta,const Hypergraph::Edge& edge,WordID w,void const* state,void *next_state,FeatureVector *features) const {
    maybe_add_feat(features,d().Scan1(w,state,next_state));
  }

  inline void maybe_add_feat(FeatureVector *features,Featval v) const {
    features->maybe_add(fid_,v);
  }
  inline void add_feat(FeatureVector *features,Featval v) const {
    features->add_value(fid_,v);
  }

  // don't set state-bytes etc. in ctor because it may depend on parsing param string
  FsaFeatureFunctionBase(int statesz=0,Sentence const& end_sentence_phrase=Sentence()) : state_bytes_(statesz),start(statesz),h_start(statesz),end_phrase_(end_sentence_phrase) {}

};

// init state is in cs; overwrite cs, ns repeatedly (alternatively).  return resulting state
template <class FsaFF>
void *FsaScan(FsaFF const& ff,SentenceMetadata const& smeta,const Hypergraph::Edge& edge,WordID const* i, WordID const* end,FeatureVector *features, void *cs,void *ns) {
  // extra code - IT'S FOR EFFICIENCY, MAN!  IT'S OK!  definitely no bugs here.
  void *os,*es;
  if ((end-i)&1) { // odd # of words
    os=cs;
    es=ns;
    goto odd;
  } else {
    i+=1;
    es=cs;
    os=ns;
  }
  for (;i<end;i+=2) {
    ff.Scan(smeta,edge,i[-1],es,os,features); // e->o
  odd:
    ff.Scan(smeta,edge,i[0],os,es,features); // o->e
  }
  return es;
}

// if you have a more efficient implementation for scanning a phrase than one word at a time (e.g. LM context using sliding window buffer rather than rotating through a fixed state size), you can override this
template <class FsaFF>
void Scan(FsaFF const& ff,SentenceMetadata const& smeta,const Hypergraph::Edge& edge,WordID const* i,WordID const* e,void const* state,void *next_state,FeatureVector *features) {
  int ssz=ff.state_bytes();
  if (!ssz) {
    for (;i<e;++i)
      ff.Scan(smeta,edge,*i,0,0,features);
    return;
  }
  Bytes tstate(ssz);
  void *tst=tstate.begin();
  bool odd=(e-i)&1;
  void *cs,*ns;
  if (odd) {
    cs=tst;
    ns=next_state;
  } else {
    cs=next_state;
    ns=tst;
  }
  std::memcpy(cs,state,ssz);
  void *est=FsaScan(ff,smeta,edge,i,end,features,cs,ns);
  assert(est==next_state);
}


// like above Scan, but don't bother storing final state (for FinalTraversalFeatures)
template <class FF>
void AccumFeatures(FF const& ff,SentenceMetadata const& smeta,const Hypergraph::Edge& edge,WordID const* i, WordID const* end,FeatureVector *h_features,void const* start_state) {
  int ssz=ff.state_bytes();
  if (ssz) {
    Bytes state(ssz),state2(ssz);
    void *cs=state.begin(),*ns=state2.begin();
    memcpy(cs,start_state,ff.state_bytes());
    FsaScan(ff,smeta,edge,i,end,h_features,cs,ns);
  } else
    for (;i<end;++i)
      ff.Scan(smeta,edge,*i,0,0,h_features);
}

// if State is pod.  sets state size and allocs start, h_start
// usage:
// struct ShorterThanPrev : public FsaTypedBase<int,ShorterThanPrev>
// i.e. Impl is a CRTP
template <class St,class Impl>
struct FsaTypedBase : public FsaFeatureFunctionBase<Impl> {
  Impl const& d() const { return static_cast<Impl const&>(*this); }
  Impl & d()  { return static_cast<Impl &>(*this); }
protected:
  typedef FsaFeatureFunctionBase<Impl> Base;
  typedef St State;
  static inline State & state(void *state) {
    return *(State*)state;
  }
  static inline State const& state(void const* state) {
    return *(State const*)state;
  }
  void set_starts(State const& s,State const& heuristic_s) {
    if (0) { // already in ctor
      Base::start.resize(sizeof(State));
      Base::h_start.resize(sizeof(State));
    }
    assert(Base::start.size()==sizeof(State));
    assert(Base::h_start.size()==sizeof(State));
    state(Base::start.begin())=s;
    state(Base::h_start.begin())=heuristic_s;
  }
  FsaTypedBase(St const& start_st=St()
               ,St const& h_start_st=St()
               ,Sentence const& end_sentence_phrase=Sentence())
    : Base(sizeof(State),end_sentence_phrase) {
    set_starts(start_st,h_start_st);
  }
public:
  void print_state(std::ostream &o,void const*st) const {
    o<<state(st);
  }
  int markov_order() const { return 1; }
  Featval ScanT1(WordID w,St const&,St &) const { return 0; }
  inline void ScanT(SentenceMetadata const& smeta,const Hypergraph::Edge& edge,WordID w,St const& prev_st,St &new_st,FeatureVector *features) const {
    features->maybe_add(d().fid_,d().ScanT1(w,prev_st,new_st));
  }
  inline void Scan(SentenceMetadata const& smeta,const Hypergraph::Edge& edge,WordID w,void const* st,void *next_state,FeatureVector *features) const {
    Impl const& im=d();
    FSADBG(edge,"Scan "<<FD::Convert(im.fid_)<<" = "<<im.describe_features(*features)<<" "<<im.state(st)<<"->"<<TD::Convert(w)<<" ");
    im.ScanT(smeta,edge,w,state(st),state(next_state),features);
    FSADBG(edge,state(next_state)<<" = "<<im.describe_features(*features));
    FSADBGnl(edge);
  }

};


const bool optimize_FsaScanner_zerostate=false;

// do not use if state size is 0.  should crash (maybe won't if you set optimize_FsaScanner_zerostate true)
template <class FF>
struct FsaScanner {
//  enum {ALIGN=8};
  static const int ALIGN=8;
  FF const& ff;
  SentenceMetadata const& smeta;
  int ssz;
  Bytes states; // first is at begin, second is at (char*)begin+stride
  void *st0; // states
  void *st1; // states+stride
  void *cs; // initially st0, alternates between st0 and st1
  inline void *nexts() const {
    return (cs==st0)?st1:st0;
  }
  const Hypergraph::Edge& edge;
  FsaScanner(FF const& ff,SentenceMetadata const& smeta,const Hypergraph::Edge& edge) : ff(ff),smeta(smeta),edge(edge)
  {
    ssz=ff.state_bytes();
    int stride=((ssz+ALIGN-1)/ALIGN)*ALIGN; // round up to multiple of ALIGN
    states.resize(stride+ssz);
    st0=states.begin();
    st1=(char*)st0+stride;
//    for (int i=0;i<2;++i) st[i]=cs+(i*stride);
  }
  void reset(void const* state) {
    cs=st0;
    std::memcpy(st0,state,ssz);
  }
  void scan(WordID w,FeatureVector *feat) {
    if (optimize_FsaScanner_zerostate && !ssz) {
      ff.Scan(smeta,edge,w,0,0,feat);
      return;
    }
    void *ns=nexts();
    ff.Scan(smeta,edge,w,cs,ns,feat);
    cs=ns;
  }

  void scan(WordID const* i,WordID const* end,FeatureVector *feat) {
#if 1
    // faster.
    if (optimize_FsaScanner_zerostate && !ssz)
      for (;i<end;++i)
        ff.Scan(smeta,edge,*i,0,0,feat);
    else
      cs=FsaScan(ff,smeta,edge,i,end,feat,cs,nexts());
#else
    for (;i<end;++i)
      scan(*i,feat);
#endif
  }
};


//TODO: combine 2 FsaFeatures typelist style (can recurse for more)




#endif