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Diffstat (limited to 'decoder/apply_models.cc')
| -rw-r--r-- | decoder/apply_models.cc | 344 |
1 files changed, 344 insertions, 0 deletions
diff --git a/decoder/apply_models.cc b/decoder/apply_models.cc new file mode 100644 index 00000000..b1d002f4 --- /dev/null +++ b/decoder/apply_models.cc @@ -0,0 +1,344 @@ +#include "apply_models.h" + +#include <vector> +#include <algorithm> +#include <tr1/unordered_map> +#include <tr1/unordered_set> + +#include <boost/functional/hash.hpp> + +#include "hg.h" +#include "ff.h" + +using namespace std; +using namespace std::tr1; + +struct Candidate; +typedef SmallVector JVector; +typedef vector<Candidate*> CandidateHeap; +typedef vector<Candidate*> CandidateList; + +// life cycle: candidates are created, placed on the heap +// and retrieved by their estimated cost, when they're +// retrieved, they're incorporated into the +LM hypergraph +// where they also know the head node index they are +// attached to. After they are added to the +LM hypergraph +// vit_prob_ and est_prob_ fields may be updated as better +// derivations are found (this happens since the successor's +// of derivation d may have a better score- they are +// explored lazily). However, the updates don't happen +// when a candidate is in the heap so maintaining the heap +// property is not an issue. +struct Candidate { + int node_index_; // -1 until incorporated + // into the +LM forest + const Hypergraph::Edge* in_edge_; // in -LM forest + Hypergraph::Edge out_edge_; + string state_; + const JVector j_; + prob_t vit_prob_; // these are fixed until the cand + // is popped, then they may be updated + prob_t est_prob_; + + Candidate(const Hypergraph::Edge& e, + const JVector& j, + const Hypergraph& out_hg, + const vector<CandidateList>& D, + const SentenceMetadata& smeta, + const ModelSet& models, + bool is_goal) : + node_index_(-1), + in_edge_(&e), + j_(j) { + InitializeCandidate(out_hg, smeta, D, models, is_goal); + } + + // used to query uniqueness + Candidate(const Hypergraph::Edge& e, + const JVector& j) : in_edge_(&e), j_(j) {} + + bool IsIncorporatedIntoHypergraph() const { + return node_index_ >= 0; + } + + void InitializeCandidate(const Hypergraph& out_hg, + const SentenceMetadata& smeta, + const vector<vector<Candidate*> >& D, + const ModelSet& models, + const bool is_goal) { + const Hypergraph::Edge& in_edge = *in_edge_; + out_edge_.rule_ = in_edge.rule_; + out_edge_.feature_values_ = in_edge.feature_values_; + out_edge_.i_ = in_edge.i_; + out_edge_.j_ = in_edge.j_; + out_edge_.prev_i_ = in_edge.prev_i_; + out_edge_.prev_j_ = in_edge.prev_j_; + Hypergraph::TailNodeVector& tail = out_edge_.tail_nodes_; + tail.resize(j_.size()); + prob_t p = prob_t::One(); + // cerr << "\nEstimating application of " << in_edge.rule_->AsString() << endl; + for (int i = 0; i < tail.size(); ++i) { + const Candidate& ant = *D[in_edge.tail_nodes_[i]][j_[i]]; + assert(ant.IsIncorporatedIntoHypergraph()); + tail[i] = ant.node_index_; + p *= ant.vit_prob_; + } + prob_t edge_estimate = prob_t::One(); + if (is_goal) { + assert(tail.size() == 1); + const string& ant_state = out_hg.nodes_[tail.front()].state_; + models.AddFinalFeatures(ant_state, &out_edge_); + } else { + models.AddFeaturesToEdge(smeta, out_hg, &out_edge_, &state_, &edge_estimate); + } + vit_prob_ = out_edge_.edge_prob_ * p; + est_prob_ = vit_prob_ * edge_estimate; + } +}; + +ostream& operator<<(ostream& os, const Candidate& cand) { + os << "CAND["; + if (!cand.IsIncorporatedIntoHypergraph()) { os << "PENDING "; } + else { os << "+LM_node=" << cand.node_index_; } + os << " edge=" << cand.in_edge_->id_; + os << " j=<"; + for (int i = 0; i < cand.j_.size(); ++i) + os << (i==0 ? "" : " ") << cand.j_[i]; + os << "> vit=" << log(cand.vit_prob_); + os << " est=" << log(cand.est_prob_); + return os << ']'; +} + +struct HeapCandCompare { + bool operator()(const Candidate* l, const Candidate* r) const { + return l->est_prob_ < r->est_prob_; + } +}; + +struct EstProbSorter { + bool operator()(const Candidate* l, const Candidate* r) const { + return l->est_prob_ > r->est_prob_; + } +}; + +// the same candidate <edge, j> can be added multiple times if +// j is multidimensional (if you're going NW in Manhattan, you +// can first go north, then west, or you can go west then north) +// this is a hash function on the relevant variables from +// Candidate to enforce this. +struct CandidateUniquenessHash { + size_t operator()(const Candidate* c) const { + size_t x = 5381; + x = ((x << 5) + x) ^ c->in_edge_->id_; + for (int i = 0; i < c->j_.size(); ++i) + x = ((x << 5) + x) ^ c->j_[i]; + return x; + } +}; + +struct CandidateUniquenessEquals { + bool operator()(const Candidate* a, const Candidate* b) const { + return (a->in_edge_ == b->in_edge_) && (a->j_ == b->j_); + } +}; + +typedef unordered_set<const Candidate*, CandidateUniquenessHash, CandidateUniquenessEquals> UniqueCandidateSet; +typedef unordered_map<string, Candidate*, boost::hash<string> > State2Node; + +class CubePruningRescorer { + +public: + CubePruningRescorer(const ModelSet& m, + const SentenceMetadata& sm, + const Hypergraph& i, + int pop_limit, + Hypergraph* o) : + models(m), + smeta(sm), + in(i), + out(*o), + D(in.nodes_.size()), + pop_limit_(pop_limit) { + cerr << " Applying feature functions (cube pruning, pop_limit = " << pop_limit_ << ')' << endl; + } + + void Apply() { + int num_nodes = in.nodes_.size(); + int goal_id = num_nodes - 1; + int pregoal = goal_id - 1; + int every = 1; + if (num_nodes > 100) every = 10; + assert(in.nodes_[pregoal].out_edges_.size() == 1); + cerr << " "; + for (int i = 0; i < in.nodes_.size(); ++i) { + if (i % every == 0) cerr << '.'; + KBest(i, i == goal_id); + } + cerr << endl; + cerr << " Best path: " << log(D[goal_id].front()->vit_prob_) + << "\t" << log(D[goal_id].front()->est_prob_) << endl; + out.PruneUnreachable(D[goal_id].front()->node_index_); + FreeAll(); + } + + private: + void FreeAll() { + for (int i = 0; i < D.size(); ++i) { + CandidateList& D_i = D[i]; + for (int j = 0; j < D_i.size(); ++j) + delete D_i[j]; + } + D.clear(); + } + + void IncorporateIntoPlusLMForest(Candidate* item, State2Node* s2n, CandidateList* freelist) { + Hypergraph::Edge* new_edge = out.AddEdge(item->out_edge_.rule_, item->out_edge_.tail_nodes_); + new_edge->feature_values_ = item->out_edge_.feature_values_; + new_edge->edge_prob_ = item->out_edge_.edge_prob_; + new_edge->i_ = item->out_edge_.i_; + new_edge->j_ = item->out_edge_.j_; + new_edge->prev_i_ = item->out_edge_.prev_i_; + new_edge->prev_j_ = item->out_edge_.prev_j_; + Candidate*& o_item = (*s2n)[item->state_]; + if (!o_item) o_item = item; + + int& node_id = o_item->node_index_; + if (node_id < 0) { + Hypergraph::Node* new_node = out.AddNode(in.nodes_[item->in_edge_->head_node_].cat_, item->state_); + node_id = new_node->id_; + } + Hypergraph::Node* node = &out.nodes_[node_id]; + out.ConnectEdgeToHeadNode(new_edge, node); + + // update candidate if we have a better derivation + // note: the difference between the vit score and the estimated + // score is the same for all items with a common residual DP + // state + if (item->vit_prob_ > o_item->vit_prob_) { + assert(o_item->state_ == item->state_); // sanity check! + o_item->est_prob_ = item->est_prob_; + o_item->vit_prob_ = item->vit_prob_; + } + if (item != o_item) freelist->push_back(item); + } + + void KBest(const int vert_index, const bool is_goal) { + // cerr << "KBest(" << vert_index << ")\n"; + CandidateList& D_v = D[vert_index]; + assert(D_v.empty()); + const Hypergraph::Node& v = in.nodes_[vert_index]; + // cerr << " has " << v.in_edges_.size() << " in-coming edges\n"; + const vector<int>& in_edges = v.in_edges_; + CandidateHeap cand; + CandidateList freelist; + cand.reserve(in_edges.size()); + UniqueCandidateSet unique_cands; + for (int i = 0; i < in_edges.size(); ++i) { + const Hypergraph::Edge& edge = in.edges_[in_edges[i]]; + const JVector j(edge.tail_nodes_.size(), 0); + cand.push_back(new Candidate(edge, j, out, D, smeta, models, is_goal)); + assert(unique_cands.insert(cand.back()).second); // these should all be unique! + } +// cerr << " making heap of " << cand.size() << " candidates\n"; + make_heap(cand.begin(), cand.end(), HeapCandCompare()); + State2Node state2node; // "buf" in Figure 2 + int pops = 0; + while(!cand.empty() && pops < pop_limit_) { + pop_heap(cand.begin(), cand.end(), HeapCandCompare()); + Candidate* item = cand.back(); + cand.pop_back(); + // cerr << "POPPED: " << *item << endl; + PushSucc(*item, is_goal, &cand, &unique_cands); + IncorporateIntoPlusLMForest(item, &state2node, &freelist); + ++pops; + } + D_v.resize(state2node.size()); + int c = 0; + for (State2Node::iterator i = state2node.begin(); i != state2node.end(); ++i) + D_v[c++] = i->second; + sort(D_v.begin(), D_v.end(), EstProbSorter()); + // cerr << " expanded to " << D_v.size() << " nodes\n"; + + for (int i = 0; i < cand.size(); ++i) + delete cand[i]; + // freelist is necessary since even after an item merged, it still stays in + // the unique set so it can't be deleted til now + for (int i = 0; i < freelist.size(); ++i) + delete freelist[i]; + } + + void PushSucc(const Candidate& item, const bool is_goal, CandidateHeap* pcand, UniqueCandidateSet* cs) { + CandidateHeap& cand = *pcand; + for (int i = 0; i < item.j_.size(); ++i) { + JVector j = item.j_; + ++j[i]; + if (j[i] < D[item.in_edge_->tail_nodes_[i]].size()) { + Candidate query_unique(*item.in_edge_, j); + if (cs->count(&query_unique) == 0) { + Candidate* new_cand = new Candidate(*item.in_edge_, j, out, D, smeta, models, is_goal); + cand.push_back(new_cand); + push_heap(cand.begin(), cand.end(), HeapCandCompare()); + assert(cs->insert(new_cand).second); // insert into uniqueness set, sanity check + } + } + } + } + + const ModelSet& models; + const SentenceMetadata& smeta; + const Hypergraph& in; + Hypergraph& out; + + vector<CandidateList> D; // maps nodes in in-HG to the + // equivalent nodes (many due to state + // splits) in the out-HG. + const int pop_limit_; +}; + +struct NoPruningRescorer { + NoPruningRescorer(const ModelSet& m, const Hypergraph& i, Hypergraph* o) : + models(m), + in(i), + out(*o) { + cerr << " Rescoring forest (full intersection)\n"; + } + + void RescoreNode(const int node_num, const bool is_goal) { + } + + void Apply() { + int num_nodes = in.nodes_.size(); + int goal_id = num_nodes - 1; + int pregoal = goal_id - 1; + int every = 1; + if (num_nodes > 100) every = 10; + assert(in.nodes_[pregoal].out_edges_.size() == 1); + cerr << " "; + for (int i = 0; i < in.nodes_.size(); ++i) { + if (i % every == 0) cerr << '.'; + RescoreNode(i, i == goal_id); + } + cerr << endl; + } + + private: + const ModelSet& models; + const Hypergraph& in; + Hypergraph& out; +}; + +// each node in the graph has one of these, it keeps track of +void ApplyModelSet(const Hypergraph& in, + const SentenceMetadata& smeta, + const ModelSet& models, + const PruningConfiguration& config, + Hypergraph* out) { + int pl = config.pop_limit; + if (pl > 100 && in.nodes_.size() > 80000) { + cerr << " Note: reducing pop_limit to " << pl << " for very large forest\n"; + pl = 30; + } + CubePruningRescorer ma(models, smeta, in, pl, out); + ma.Apply(); +} + |