| // Copyright (c) 2011 The Chromium Authors. All rights reserved. |
| // Use of this source code is governed by a BSD-style license that can be |
| // found in the LICENSE file. |
| |
| #include "courgette/adjustment_method.h" |
| |
| #include <stddef.h> |
| #include <stdint.h> |
| |
| #include <algorithm> |
| #include <limits> |
| #include <list> |
| #include <map> |
| #include <set> |
| #include <string> |
| #include <vector> |
| |
| #include "base/format_macros.h" |
| #include "base/logging.h" |
| #include "base/macros.h" |
| #include "base/strings/stringprintf.h" |
| #include "base/time/time.h" |
| #include "courgette/assembly_program.h" |
| #include "courgette/courgette.h" |
| #include "courgette/encoded_program.h" |
| |
| /* |
| |
| Shingle weighting matching. |
| |
| We have a sequence S1 of symbols from alphabet A1={A,B,C,...} called the 'model' |
| and a second sequence of S2 of symbols from alphabet A2={U,V,W,....} called the |
| 'program'. Each symbol in A1 has a unique numerical name or index. We can |
| transcribe the sequence S1 to a sequence T1 of indexes of the symbols. We wish |
| to assign indexes to the symbols in A2 so that when we transcribe S2 into T2, T2 |
| has long subsequences that occur in T1. This will ensure that the sequence |
| T1;T2 compresses to be only slightly larger than the compressed T1. |
| |
| The algorithm for matching members of S2 with members of S1 is eager - it makes |
| matches without backtracking, until no more matches can be made. Each variable |
| (symbol) U,V,... in A2 has a set of candidates from A1, each candidate with a |
| weight summarizing the evidence for the match. We keep a VariableQueue of |
| U,V,... sorted by how much the evidence for the best choice outweighs the |
| evidence for the second choice, i.e. prioritized by how 'clear cut' the best |
| assignment is. We pick the variable with the most clear-cut candidate, make the |
| assignment, adjust the evidence and repeat. |
| |
| What has not been described so far is how the evidence is gathered and |
| maintained. We are working under the assumption that S1 and S2 are largely |
| similar. (A different assumption might be that S1 and S2 are dissimilar except |
| for many long subsequences.) |
| |
| A naive algorithm would consider all pairs (A,U) and for each pair assess the |
| benefit, or score, the assignment U:=A. The score might count the number of |
| occurrences of U in S2 which appear in similar contexts to A in S1. |
| |
| To distinguish contexts we view S1 and S2 as a sequence of overlapping k-length |
| substrings or 'shingles'. Two shingles are compatible if the symbols in one |
| shingle could be matched with the symbols in the other symbol. For example, ABC |
| is *not* compatible with UVU because it would require conflicting matches A=U |
| and C=U. ABC is compatible with UVW, UWV, WUV, VUW etc. We can't tell which |
| until we make an assignment - the compatible shingles form an equivalence class. |
| After assigning U:=A then only UVW and UWV (equivalently AVW, AWV) are |
| compatible. As we make assignments the number of equivalence classes of |
| shingles increases and the number of members of each equivalence class |
| decreases. The compatibility test becomes more restrictive. |
| |
| We gather evidence for the potential assignment U:=A by counting how many |
| shingles containing U are compatible with shingles containing A. Thus symbols |
| occurring a large number of times in compatible contexts will be assigned first. |
| |
| Finding the 'most clear-cut' assignment by considering all pairs symbols and for |
| each pair comparing the contexts of each pair of occurrences of the symbols is |
| computationally infeasible. We get the job done in a reasonable time by |
| approaching it 'backwards' and making incremental changes as we make |
| assignments. |
| |
| First the shingles are partitioned according to compatibility. In S1=ABCDD and |
| S2=UVWXX we have a total of 6 shingles, each occuring once. (ABC:1 BCD:1 CDD:1; |
| UVW:1 VWX: WXX:1) all fit the pattern <V0 V1 V2> or the pattern <V0 V1 V1>. The |
| first pattern indicates that each position matches a different symbol, the |
| second pattern indicates that the second symbol is repeated. |
| |
| pattern S1 members S2 members |
| <V0 V1 V2>: {ABC:1, BCD:1}; {UVW:1, VWX:1} |
| <V0 V1 V1>: {CDD:1} {WXX:1} |
| |
| The second pattern appears to have a unique assignment but we don't make the |
| assignment on such scant evidence. If S1 and S2 do not match exactly, there |
| will be numerous spurious low-score matches like this. Instead we must see what |
| assignments are indicated by considering all of the evidence. |
| |
| First pattern has 2 x 2 = 4 shingle pairs. For each pair we count the number |
| of symbol assignments. For ABC:a * UVW:b accumulate min(a,b) to each of |
| {U:=A, V:=B, W:=C}. |
| After accumulating over all 2 x 2 pairs: |
| U: {A:1 B:1} |
| V: {A:1 B:2 C:1} |
| W: {B:1 C:2 D:1 } |
| X: {C:1 D:1} |
| The second pattern contributes: |
| W: {C:1} |
| X: {D:2} |
| Sum: |
| U: {A:1 B:1} |
| V: {A:1 B:2 C:1} |
| W: {B:1 C:3 D:1} |
| X: {C:1 D:3} |
| |
| From this we decide to assign X:=D (because this assignment has both the largest |
| difference above the next candidate (X:=C) and this is also the largest |
| proportionately over the sum of alternatives). |
| |
| Lets assume D has numerical 'name' 77. The assignment X:=D sets X to 77 too. |
| Next we repartition all the shingles containing X or D: |
| |
| pattern S1 members S2 members |
| <V0 V1 V2>: {ABC:1}; {UVW:1} |
| <V0 V1 77>: {BCD:1}; {VWX:1} |
| <V0 77 77>: {CDD:1} {WXX:1} |
| As we repartition, we recalculate the contributions to the scores: |
| U: {A:1} |
| V: {B:2} |
| W: {C:3} |
| All the remaining assignments are now fixed. |
| |
| There is one step in the incremental algorithm that is still infeasibly |
| expensive: the contributions due to the cross product of large equivalence |
| classes. We settle for making an approximation by computing the contribution of |
| the cross product of only the most common shingles. The hope is that the noise |
| from the long tail of uncounted shingles is well below the scores being used to |
| pick assignments. The second hope is that as assignment are made, the large |
| equivalence class will be partitioned into smaller equivalence classes, reducing |
| the noise over time. |
| |
| In the code below the shingles are bigger (Shingle::kWidth = 5). |
| Class ShinglePattern holds the data for one pattern. |
| |
| There is an optimization for this case: |
| <V0 V1 V1>: {CDD:1} {WXX:1} |
| |
| Above we said that we don't make an assignment on this "scant evidence". There |
| is an exception: if there is only one variable unassigned (more like the <V0 77 |
| 77> pattern) AND there are no occurrences of C and W other than those counted in |
| this pattern, then there is no competing evidence and we go ahead with the |
| assignment immediately. This produces slightly better results because these |
| cases tend to be low-scoring and susceptible to small mistakes made in |
| low-scoring assignments in the approximation for large equivalence classes. |
| |
| */ |
| |
| namespace courgette { |
| namespace adjustment_method_2 { |
| |
| //////////////////////////////////////////////////////////////////////////////// |
| |
| class AssignmentCandidates; |
| class LabelInfoMaker; |
| class Shingle; |
| class ShinglePattern; |
| |
| // The purpose of adjustment is to assign indexes to Labels of a program 'p' to |
| // make the sequence of indexes similar to a 'model' program 'm'. Labels |
| // themselves don't have enough information to do this job, so we work with a |
| // LabelInfo surrogate for each label. |
| // |
| class LabelInfo { |
| public: |
| // Just a no-argument constructor and copy constructor. Actual LabelInfo |
| // objects are allocated in std::pair structs in a std::map. |
| LabelInfo() |
| : label_(nullptr), |
| is_model_(false), |
| debug_index_(0), |
| refs_(0), |
| assignment_(nullptr), |
| candidates_(nullptr) {} |
| |
| ~LabelInfo(); |
| |
| AssignmentCandidates* candidates(); |
| |
| Label* label_; // The label that this info a surrogate for. |
| |
| uint32_t is_model_ : 1; // Is the label in the model? |
| uint32_t debug_index_ : 31; // A small number for naming the label in debug |
| // output. The pair (is_model_, debug_index_) is |
| // unique. |
| |
| int refs_; // Number of times this Label is referenced. |
| |
| LabelInfo* assignment_; // Label from other program corresponding to this. |
| |
| std::vector<uint32_t> positions_; // Offsets into the trace of references. |
| |
| private: |
| AssignmentCandidates* candidates_; |
| |
| void operator=(const LabelInfo*); // Disallow assignment only. |
| // Public compiler generated copy constructor is needed to constuct |
| // std::pair<Label*, LabelInfo> so that fresh LabelInfos can be allocated |
| // inside a std::map. |
| }; |
| |
| typedef std::vector<LabelInfo*> Trace; |
| |
| std::string ToString(const LabelInfo* info) { |
| std::string s; |
| base::StringAppendF(&s, "%c%d", "pm"[info->is_model_], info->debug_index_); |
| if (info->label_->index_ != Label::kNoIndex) |
| base::StringAppendF(&s, " (%d)", info->label_->index_); |
| |
| base::StringAppendF(&s, " #%u", info->refs_); |
| return s; |
| } |
| |
| // LabelInfoMaker maps labels to their surrogate LabelInfo objects. |
| class LabelInfoMaker { |
| public: |
| LabelInfoMaker() : debug_label_index_gen_(0) {} |
| |
| LabelInfo* MakeLabelInfo(Label* label, bool is_model, uint32_t position) { |
| LabelInfo& slot = label_infos_[label]; |
| if (slot.label_ == nullptr) { |
| slot.label_ = label; |
| slot.is_model_ = is_model; |
| slot.debug_index_ = ++debug_label_index_gen_; |
| } |
| slot.positions_.push_back(position); |
| ++slot.refs_; |
| return &slot; |
| } |
| |
| void ResetDebugLabel() { debug_label_index_gen_ = 0; } |
| |
| private: |
| int debug_label_index_gen_; |
| |
| // Note LabelInfo is allocated 'flat' inside map::value_type, so the LabelInfo |
| // lifetimes are managed by the map. |
| std::map<Label*, LabelInfo> label_infos_; |
| |
| DISALLOW_COPY_AND_ASSIGN(LabelInfoMaker); |
| }; |
| |
| struct OrderLabelInfo { |
| bool operator()(const LabelInfo* a, const LabelInfo* b) const { |
| if (a->label_->rva_ < b->label_->rva_) return true; |
| if (a->label_->rva_ > b->label_->rva_) return false; |
| if (a == b) return false; |
| return a->positions_ < b->positions_; // Lexicographic ordering of vector. |
| } |
| }; |
| |
| // AssignmentCandidates is a priority queue of candidate assignments to |
| // a single program LabelInfo, |program_info_|. |
| class AssignmentCandidates { |
| public: |
| explicit AssignmentCandidates(LabelInfo* program_info) |
| : program_info_(program_info) {} |
| |
| LabelInfo* program_info() const { return program_info_; } |
| |
| bool empty() const { return label_to_score_.empty(); } |
| |
| LabelInfo* top_candidate() const { return queue_.begin()->second; } |
| |
| void Update(LabelInfo* model_info, int delta_score) { |
| LOG_ASSERT(delta_score != 0); |
| int old_score = 0; |
| int new_score = 0; |
| LabelToScore::iterator p = label_to_score_.find(model_info); |
| if (p != label_to_score_.end()) { |
| old_score = p->second; |
| new_score = old_score + delta_score; |
| queue_.erase(ScoreAndLabel(old_score, p->first)); |
| if (new_score == 0) { |
| label_to_score_.erase(p); |
| } else { |
| p->second = new_score; |
| queue_.insert(ScoreAndLabel(new_score, model_info)); |
| } |
| } else { |
| new_score = delta_score; |
| label_to_score_.insert(std::make_pair(model_info, new_score)); |
| queue_.insert(ScoreAndLabel(new_score, model_info)); |
| } |
| LOG_ASSERT(queue_.size() == label_to_score_.size()); |
| } |
| |
| int TopScore() const { |
| int first_value = 0; |
| int second_value = 0; |
| Queue::const_iterator p = queue_.begin(); |
| if (p != queue_.end()) { |
| first_value = p->first; |
| ++p; |
| if (p != queue_.end()) { |
| second_value = p->first; |
| } |
| } |
| return first_value - second_value; |
| } |
| |
| bool HasPendingUpdates() { return !pending_updates_.empty(); } |
| |
| void AddPendingUpdate(LabelInfo* model_info, int delta_score) { |
| LOG_ASSERT(delta_score != 0); |
| pending_updates_[model_info] += delta_score; |
| } |
| |
| void ApplyPendingUpdates() { |
| // TODO(sra): try to walk |pending_updates_| and |label_to_score_| in |
| // lockstep. Try to batch updates to |queue_|. |
| size_t zeroes = 0; |
| for (LabelToScore::iterator p = pending_updates_.begin(); |
| p != pending_updates_.end(); |
| ++p) { |
| if (p->second != 0) |
| Update(p->first, p->second); |
| else |
| ++zeroes; |
| } |
| pending_updates_.clear(); |
| } |
| |
| void Print(int max) { |
| VLOG(2) << "score " << TopScore() << " " << ToString(program_info_) |
| << " := ?"; |
| if (!pending_updates_.empty()) |
| VLOG(2) << pending_updates_.size() << " pending"; |
| int count = 0; |
| for (Queue::iterator q = queue_.begin(); q != queue_.end(); ++q) { |
| if (++count > max) break; |
| VLOG(2) << " " << q->first << " " << ToString(q->second); |
| } |
| } |
| |
| private: |
| typedef std::map<LabelInfo*, int, OrderLabelInfo> LabelToScore; |
| typedef std::pair<int, LabelInfo*> ScoreAndLabel; |
| struct OrderScoreAndLabelByScoreDecreasing { |
| OrderLabelInfo tie_breaker; |
| bool operator()(const ScoreAndLabel& a, const ScoreAndLabel& b) const { |
| if (a.first > b.first) return true; |
| if (a.first < b.first) return false; |
| return tie_breaker(a.second, b.second); |
| } |
| }; |
| typedef std::set<ScoreAndLabel, OrderScoreAndLabelByScoreDecreasing> Queue; |
| |
| LabelInfo* program_info_; |
| LabelToScore label_to_score_; |
| LabelToScore pending_updates_; |
| Queue queue_; |
| }; |
| |
| AssignmentCandidates* LabelInfo::candidates() { |
| if (candidates_ == nullptr) |
| candidates_ = new AssignmentCandidates(this); |
| return candidates_; |
| } |
| |
| LabelInfo::~LabelInfo() { |
| delete candidates_; |
| } |
| |
| // A Shingle is a short fixed-length string of LabelInfos that actually occurs |
| // in a Trace. A Shingle may occur many times. We repesent the Shingle by the |
| // position of one of the occurrences in the Trace. |
| class Shingle { |
| public: |
| static const uint8_t kWidth = 5; |
| |
| struct InterningLess { |
| bool operator()(const Shingle& a, const Shingle& b) const; |
| }; |
| |
| typedef std::set<Shingle, InterningLess> OwningSet; |
| |
| static Shingle* Find(const Trace& trace, size_t position, |
| OwningSet* owning_set) { |
| std::pair<OwningSet::iterator, bool> pair = |
| owning_set->insert(Shingle(trace, position)); |
| // pair.first iterator 'points' to the newly inserted Shingle or the |
| // previouly inserted one that looks the same according to the comparator. |
| |
| // const_cast required because key is const. We modify the Shingle |
| // extensively but not in a way that affects InterningLess. |
| Shingle* shingle = const_cast<Shingle*>(&*pair.first); |
| shingle->add_position(position); |
| return shingle; |
| } |
| |
| LabelInfo* at(size_t i) const { return trace_[exemplar_position_ + i]; } |
| void add_position(size_t position) { |
| positions_.push_back(static_cast<uint32_t>(position)); |
| } |
| int position_count() const { return static_cast<int>(positions_.size()); } |
| |
| bool InModel() const { return at(0)->is_model_; } |
| |
| ShinglePattern* pattern() const { return pattern_; } |
| void set_pattern(ShinglePattern* pattern) { pattern_ = pattern; } |
| |
| struct PointerLess { |
| bool operator()(const Shingle* a, const Shingle* b) const { |
| // Arbitrary but repeatable (memory-address) independent ordering: |
| return a->exemplar_position_ < b->exemplar_position_; |
| // return InterningLess()(*a, *b); |
| } |
| }; |
| |
| private: |
| Shingle(const Trace& trace, size_t exemplar_position) |
| : trace_(trace), |
| exemplar_position_(exemplar_position), |
| pattern_(nullptr) {} |
| |
| const Trace& trace_; // The shingle lives inside trace_. |
| size_t exemplar_position_; // At this position (and other positions). |
| std::vector<uint32_t> positions_; // Includes exemplar_position_. |
| |
| ShinglePattern* pattern_; // Pattern changes as LabelInfos are assigned. |
| |
| friend std::string ToString(const Shingle* instance); |
| |
| // We can't disallow the copy constructor because we use std::set<Shingle> and |
| // VS2005's implementation of std::set<T>::set() requires T to have a copy |
| // constructor. |
| // DISALLOW_COPY_AND_ASSIGN(Shingle); |
| void operator=(const Shingle&) = delete; // Disallow assignment only. |
| }; |
| |
| std::string ToString(const Shingle* instance) { |
| std::string s; |
| const char* sep = "<"; |
| for (uint8_t i = 0; i < Shingle::kWidth; ++i) { |
| // base::StringAppendF(&s, "%s%x ", sep, instance.at(i)->label_->rva_); |
| s += sep; |
| s += ToString(instance->at(i)); |
| sep = ", "; |
| } |
| base::StringAppendF(&s, ">(%" PRIuS ")@{%d}", |
| instance->exemplar_position_, |
| instance->position_count()); |
| return s; |
| } |
| |
| |
| bool Shingle::InterningLess::operator()( |
| const Shingle& a, |
| const Shingle& b) const { |
| for (uint8_t i = 0; i < kWidth; ++i) { |
| LabelInfo* info_a = a.at(i); |
| LabelInfo* info_b = b.at(i); |
| if (info_a->label_->rva_ < info_b->label_->rva_) |
| return true; |
| if (info_a->label_->rva_ > info_b->label_->rva_) |
| return false; |
| if (info_a->is_model_ < info_b->is_model_) |
| return true; |
| if (info_a->is_model_ > info_b->is_model_) |
| return false; |
| if (info_a != info_b) { |
| NOTREACHED(); |
| } |
| } |
| return false; |
| } |
| |
| class ShinglePattern { |
| public: |
| enum { kOffsetMask = 7, // Offset lives in low bits. |
| kFixed = 0, // kind & kVariable == 0 => fixed. |
| kVariable = 8 // kind & kVariable == 1 => variable. |
| }; |
| // sequence[position + (kinds_[i] & kOffsetMask)] gives LabelInfo for position |
| // i of shingle. Below, second 'A' is duplicate of position 1, second '102' |
| // is duplicate of position 0. |
| // |
| // <102, A, 103, A , 102> |
| // --> <kFixed+0, kVariable+1, kFixed+2, kVariable+1, kFixed+0> |
| struct Index { |
| explicit Index(const Shingle* instance); |
| uint8_t kinds_[Shingle::kWidth]; |
| uint8_t variables_; |
| uint8_t unique_variables_; |
| uint8_t first_variable_index_; |
| uint32_t hash_; |
| int assigned_indexes_[Shingle::kWidth]; |
| }; |
| |
| // ShinglePattern keeps histograms of member Shingle instances, ordered by |
| // decreasing number of occurrences. We don't have a pair (occurrence count, |
| // Shingle instance), so we use a FreqView adapter to make the instance |
| // pointer look like the pair. |
| class FreqView { |
| public: |
| explicit FreqView(const Shingle* instance) : instance_(instance) {} |
| int count() const { return instance_->position_count(); } |
| const Shingle* instance() const { return instance_; } |
| struct Greater { |
| bool operator()(const FreqView& a, const FreqView& b) const { |
| if (a.count() > b.count()) return true; |
| if (a.count() < b.count()) return false; |
| return resolve_ties(a.instance(), b.instance()); |
| } |
| private: |
| Shingle::PointerLess resolve_ties; |
| }; |
| private: |
| const Shingle* instance_; |
| }; |
| |
| typedef std::set<FreqView, FreqView::Greater> Histogram; |
| |
| ShinglePattern() |
| : index_(nullptr), model_coverage_(0), program_coverage_(0) {} |
| |
| const Index* index_; // Points to the key in the owning map value_type. |
| Histogram model_histogram_; |
| Histogram program_histogram_; |
| int model_coverage_; |
| int program_coverage_; |
| }; |
| |
| std::string ToString(const ShinglePattern::Index* index) { |
| std::string s; |
| if (index == nullptr) { |
| s = "<null>"; |
| } else { |
| base::StringAppendF(&s, "<%d: ", index->variables_); |
| const char* sep = ""; |
| for (uint8_t i = 0; i < Shingle::kWidth; ++i) { |
| s += sep; |
| sep = ", "; |
| uint32_t kind = index->kinds_[i]; |
| int offset = kind & ShinglePattern::kOffsetMask; |
| if (kind & ShinglePattern::kVariable) |
| base::StringAppendF(&s, "V%d", offset); |
| else |
| base::StringAppendF(&s, "%d", index->assigned_indexes_[offset]); |
| } |
| base::StringAppendF(&s, " %x", index->hash_); |
| s += ">"; |
| } |
| return s; |
| } |
| |
| std::string HistogramToString(const ShinglePattern::Histogram& histogram, |
| size_t snippet_max) { |
| std::string s; |
| size_t histogram_size = histogram.size(); |
| size_t snippet_size = 0; |
| for (ShinglePattern::Histogram::const_iterator p = histogram.begin(); |
| p != histogram.end(); |
| ++p) { |
| if (++snippet_size > snippet_max && snippet_size != histogram_size) { |
| s += " ..."; |
| break; |
| } |
| base::StringAppendF(&s, " %d", p->count()); |
| } |
| return s; |
| } |
| |
| std::string HistogramToStringFull(const ShinglePattern::Histogram& histogram, |
| const char* indent, |
| size_t snippet_max) { |
| std::string s; |
| |
| size_t histogram_size = histogram.size(); |
| size_t snippet_size = 0; |
| for (ShinglePattern::Histogram::const_iterator p = histogram.begin(); |
| p != histogram.end(); |
| ++p) { |
| s += indent; |
| if (++snippet_size > snippet_max && snippet_size != histogram_size) { |
| s += "...\n"; |
| break; |
| } |
| base::StringAppendF(&s, "(%d) ", p->count()); |
| s += ToString(&(*p->instance())); |
| s += "\n"; |
| } |
| return s; |
| } |
| |
| std::string ToString(const ShinglePattern* pattern, size_t snippet_max = 3) { |
| std::string s; |
| if (pattern == nullptr) { |
| s = "<null>"; |
| } else { |
| s = "{"; |
| s += ToString(pattern->index_); |
| base::StringAppendF(&s, "; %d(%d):", |
| static_cast<int>(pattern->model_histogram_.size()), |
| pattern->model_coverage_); |
| |
| s += HistogramToString(pattern->model_histogram_, snippet_max); |
| base::StringAppendF(&s, "; %d(%d):", |
| static_cast<int>(pattern->program_histogram_.size()), |
| pattern->program_coverage_); |
| s += HistogramToString(pattern->program_histogram_, snippet_max); |
| s += "}"; |
| } |
| return s; |
| } |
| |
| std::string ShinglePatternToStringFull(const ShinglePattern* pattern, |
| size_t max) { |
| std::string s; |
| s += ToString(pattern->index_); |
| s += "\n"; |
| size_t model_size = pattern->model_histogram_.size(); |
| size_t program_size = pattern->program_histogram_.size(); |
| base::StringAppendF(&s, " model shingles %" PRIuS "\n", model_size); |
| s += HistogramToStringFull(pattern->model_histogram_, " ", max); |
| base::StringAppendF(&s, " program shingles %" PRIuS "\n", program_size); |
| s += HistogramToStringFull(pattern->program_histogram_, " ", max); |
| return s; |
| } |
| |
| struct ShinglePatternIndexLess { |
| bool operator()(const ShinglePattern::Index& a, |
| const ShinglePattern::Index& b) const { |
| if (a.hash_ < b.hash_) return true; |
| if (a.hash_ > b.hash_) return false; |
| |
| for (uint8_t i = 0; i < Shingle::kWidth; ++i) { |
| if (a.kinds_[i] < b.kinds_[i]) return true; |
| if (a.kinds_[i] > b.kinds_[i]) return false; |
| if ((a.kinds_[i] & ShinglePattern::kVariable) == 0) { |
| if (a.assigned_indexes_[i] < b.assigned_indexes_[i]) |
| return true; |
| if (a.assigned_indexes_[i] > b.assigned_indexes_[i]) |
| return false; |
| } |
| } |
| return false; |
| } |
| }; |
| |
| static uint32_t hash_combine(uint32_t h, uint32_t v) { |
| h += v; |
| return (h * (37 + 0x0000d100)) ^ (h >> 13); |
| } |
| |
| ShinglePattern::Index::Index(const Shingle* instance) { |
| uint32_t hash = 0; |
| variables_ = 0; |
| unique_variables_ = 0; |
| first_variable_index_ = 255; |
| |
| for (uint8_t i = 0; i < Shingle::kWidth; ++i) { |
| LabelInfo* info = instance->at(i); |
| uint8_t kind = 0; |
| int code = -1; |
| uint8_t j = 0; |
| for ( ; j < i; ++j) { |
| if (info == instance->at(j)) { // Duplicate LabelInfo |
| kind = kinds_[j]; |
| break; |
| } |
| } |
| if (j == i) { // Not found above. |
| if (info->assignment_) { |
| code = info->label_->index_; |
| assigned_indexes_[i] = code; |
| kind = kFixed + i; |
| } else { |
| kind = kVariable + i; |
| ++unique_variables_; |
| if (i < first_variable_index_) |
| first_variable_index_ = i; |
| } |
| } |
| if (kind & kVariable) ++variables_; |
| hash = hash_combine(hash, code); |
| hash = hash_combine(hash, kind); |
| kinds_[i] = kind; |
| assigned_indexes_[i] = code; |
| } |
| hash_ = hash; |
| } |
| |
| struct ShinglePatternLess { |
| bool operator()(const ShinglePattern& a, const ShinglePattern& b) const { |
| return index_less(*a.index_, *b.index_); |
| } |
| ShinglePatternIndexLess index_less; |
| }; |
| |
| struct ShinglePatternPointerLess { |
| bool operator()(const ShinglePattern* a, const ShinglePattern* b) const { |
| return pattern_less(*a, *b); |
| } |
| ShinglePatternLess pattern_less; |
| }; |
| |
| template<int (*Scorer)(const ShinglePattern*)> |
| struct OrderShinglePatternByScoreDescending { |
| bool operator()(const ShinglePattern* a, const ShinglePattern* b) const { |
| int score_a = Scorer(a); |
| int score_b = Scorer(b); |
| if (score_a > score_b) return true; |
| if (score_a < score_b) return false; |
| return break_ties(a, b); |
| } |
| ShinglePatternPointerLess break_ties; |
| }; |
| |
| // Returns a score for a 'Single Use' rule. Returns -1 if the rule is not |
| // applicable. |
| int SingleUseScore(const ShinglePattern* pattern) { |
| if (pattern->index_->variables_ != 1) |
| return -1; |
| |
| if (pattern->model_histogram_.size() != 1 || |
| pattern->program_histogram_.size() != 1) |
| return -1; |
| |
| // Does this pattern account for all uses of the variable? |
| const ShinglePattern::FreqView& program_freq = |
| *pattern->program_histogram_.begin(); |
| const ShinglePattern::FreqView& model_freq = |
| *pattern->model_histogram_.begin(); |
| int p1 = program_freq.count(); |
| int m1 = model_freq.count(); |
| if (p1 == m1) { |
| const Shingle* program_instance = program_freq.instance(); |
| const Shingle* model_instance = model_freq.instance(); |
| size_t variable_index = pattern->index_->first_variable_index_; |
| LabelInfo* program_info = program_instance->at(variable_index); |
| LabelInfo* model_info = model_instance->at(variable_index); |
| if (!program_info->assignment_) { |
| if (program_info->refs_ == p1 && model_info->refs_ == m1) { |
| return p1; |
| } |
| } |
| } |
| return -1; |
| } |
| |
| // The VariableQueue is a priority queue of unassigned LabelInfos from |
| // the 'program' (the 'variables') and their AssignmentCandidates. |
| class VariableQueue { |
| public: |
| typedef std::pair<int, LabelInfo*> ScoreAndLabel; |
| |
| VariableQueue() = default; |
| |
| bool empty() const { return queue_.empty(); } |
| |
| const ScoreAndLabel& first() const { return *queue_.begin(); } |
| |
| // For debugging only. |
| void Print() const { |
| for (Queue::const_iterator p = queue_.begin(); p != queue_.end(); ++p) { |
| AssignmentCandidates* candidates = p->second->candidates(); |
| candidates->Print(std::numeric_limits<int>::max()); |
| } |
| } |
| |
| void AddPendingUpdate(LabelInfo* program_info, LabelInfo* model_info, |
| int delta_score) { |
| AssignmentCandidates* candidates = program_info->candidates(); |
| if (!candidates->HasPendingUpdates()) { |
| pending_update_candidates_.push_back(candidates); |
| } |
| candidates->AddPendingUpdate(model_info, delta_score); |
| } |
| |
| void ApplyPendingUpdates() { |
| for (size_t i = 0; i < pending_update_candidates_.size(); ++i) { |
| AssignmentCandidates* candidates = pending_update_candidates_[i]; |
| int old_score = candidates->TopScore(); |
| queue_.erase(ScoreAndLabel(old_score, candidates->program_info())); |
| candidates->ApplyPendingUpdates(); |
| if (!candidates->empty()) { |
| int new_score = candidates->TopScore(); |
| queue_.insert(ScoreAndLabel(new_score, candidates->program_info())); |
| } |
| } |
| pending_update_candidates_.clear(); |
| } |
| |
| private: |
| struct OrderScoreAndLabelByScoreDecreasing { |
| bool operator()(const ScoreAndLabel& a, const ScoreAndLabel& b) const { |
| if (a.first > b.first) return true; |
| if (a.first < b.first) return false; |
| return OrderLabelInfo()(a.second, b.second); |
| } |
| }; |
| typedef std::set<ScoreAndLabel, OrderScoreAndLabelByScoreDecreasing> Queue; |
| |
| Queue queue_; |
| std::vector<AssignmentCandidates*> pending_update_candidates_; |
| |
| DISALLOW_COPY_AND_ASSIGN(VariableQueue); |
| }; |
| |
| |
| class AssignmentProblem { |
| public: |
| AssignmentProblem(const Trace& trace, size_t model_end) |
| : trace_(trace), |
| model_end_(model_end) { |
| VLOG(2) << "AssignmentProblem::AssignmentProblem " << model_end << ", " |
| << trace.size(); |
| } |
| |
| bool Solve() { |
| if (model_end_ < Shingle::kWidth || |
| trace_.size() - model_end_ < Shingle::kWidth) { |
| // Nothing much we can do with such a short problem. |
| return true; |
| } |
| instances_.resize(trace_.size() - Shingle::kWidth + 1, nullptr); |
| AddShingles(0, model_end_); |
| AddShingles(model_end_, trace_.size()); |
| InitialClassify(); |
| AddPatternsNeedingUpdatesToQueues(); |
| |
| patterns_needing_updates_.clear(); |
| while (FindAndAssignBestLeader()) |
| patterns_needing_updates_.clear(); |
| PrintActivePatterns(); |
| |
| return true; |
| } |
| |
| private: |
| typedef std::set<Shingle*, Shingle::PointerLess> ShingleSet; |
| |
| typedef std::set<const ShinglePattern*, ShinglePatternPointerLess> |
| ShinglePatternSet; |
| |
| // Patterns are partitioned into the following sets: |
| |
| // * Retired patterns (not stored). No shingles exist for this pattern (they |
| // all now match more specialized patterns). |
| // * Useless patterns (not stored). There are no 'program' shingles for this |
| // pattern (they all now match more specialized patterns). |
| // * Single-use patterns - single_use_pattern_queue_. |
| // * Other patterns - active_non_single_use_patterns_ / variable_queue_. |
| |
| typedef std::set<const ShinglePattern*, |
| OrderShinglePatternByScoreDescending<&SingleUseScore> > |
| SingleUsePatternQueue; |
| |
| void PrintPatternsHeader() const { |
| VLOG(2) << shingle_instances_.size() << " instances " |
| << trace_.size() << " trace length " |
| << patterns_.size() << " shingle indexes " |
| << single_use_pattern_queue_.size() << " single use patterns " |
| << active_non_single_use_patterns_.size() << " active patterns"; |
| } |
| |
| void PrintActivePatterns() const { |
| for (ShinglePatternSet::const_iterator p = |
| active_non_single_use_patterns_.begin(); |
| p != active_non_single_use_patterns_.end(); |
| ++p) { |
| const ShinglePattern* pattern = *p; |
| VLOG(2) << ToString(pattern, 10); |
| } |
| } |
| |
| void PrintPatterns() const { |
| PrintAllPatterns(); |
| PrintActivePatterns(); |
| PrintAllShingles(); |
| } |
| |
| void PrintAllPatterns() const { |
| for (IndexToPattern::const_iterator p = patterns_.begin(); |
| p != patterns_.end(); |
| ++p) { |
| const ShinglePattern& pattern = p->second; |
| VLOG(2) << ToString(&pattern, 10); |
| } |
| } |
| |
| void PrintAllShingles() const { |
| for (Shingle::OwningSet::const_iterator p = shingle_instances_.begin(); |
| p != shingle_instances_.end(); |
| ++p) { |
| const Shingle& instance = *p; |
| VLOG(2) << ToString(&instance) << " " << ToString(instance.pattern()); |
| } |
| } |
| |
| |
| void AddShingles(size_t begin, size_t end) { |
| for (size_t i = begin; i + Shingle::kWidth - 1 < end; ++i) { |
| instances_[i] = Shingle::Find(trace_, i, &shingle_instances_); |
| } |
| } |
| |
| void Declassify(Shingle* shingle) { |
| ShinglePattern* pattern = shingle->pattern(); |
| if (shingle->InModel()) { |
| pattern->model_histogram_.erase(ShinglePattern::FreqView(shingle)); |
| pattern->model_coverage_ -= shingle->position_count(); |
| } else { |
| pattern->program_histogram_.erase(ShinglePattern::FreqView(shingle)); |
| pattern->program_coverage_ -= shingle->position_count(); |
| } |
| shingle->set_pattern(nullptr); |
| } |
| |
| void Reclassify(Shingle* shingle) { |
| ShinglePattern* pattern = shingle->pattern(); |
| LOG_ASSERT(pattern == nullptr); |
| |
| ShinglePattern::Index index(shingle); |
| if (index.variables_ == 0) |
| return; |
| |
| std::pair<IndexToPattern::iterator, bool> inserted = |
| patterns_.insert(std::make_pair(index, ShinglePattern())); |
| |
| pattern = &inserted.first->second; |
| pattern->index_ = &inserted.first->first; |
| shingle->set_pattern(pattern); |
| patterns_needing_updates_.insert(pattern); |
| |
| if (shingle->InModel()) { |
| pattern->model_histogram_.insert(ShinglePattern::FreqView(shingle)); |
| pattern->model_coverage_ += shingle->position_count(); |
| } else { |
| pattern->program_histogram_.insert(ShinglePattern::FreqView(shingle)); |
| pattern->program_coverage_ += shingle->position_count(); |
| } |
| } |
| |
| void InitialClassify() { |
| for (Shingle::OwningSet::iterator p = shingle_instances_.begin(); |
| p != shingle_instances_.end(); |
| ++p) { |
| // GCC's set<T>::iterator::operator *() returns a const object. |
| Reclassify(const_cast<Shingle*>(&*p)); |
| } |
| } |
| |
| // For the positions in |info|, find the shingles that overlap that position. |
| void AddAffectedPositions(LabelInfo* info, ShingleSet* affected_shingles) { |
| const uint8_t kWidth = Shingle::kWidth; |
| for (size_t i = 0; i < info->positions_.size(); ++i) { |
| size_t position = info->positions_[i]; |
| // Find bounds to the subrange of |trace_| we are in. |
| size_t start = position < model_end_ ? 0 : model_end_; |
| size_t end = position < model_end_ ? model_end_ : trace_.size(); |
| |
| // Clip [position-kWidth+1, position+1) |
| size_t low = |
| position > start + kWidth - 1 ? position - kWidth + 1 : start; |
| size_t high = position + kWidth < end ? position + 1 : end - kWidth + 1; |
| |
| for (size_t shingle_position = low; |
| shingle_position < high; |
| ++shingle_position) { |
| Shingle* overlapping_shingle = instances_.at(shingle_position); |
| affected_shingles->insert(overlapping_shingle); |
| } |
| } |
| } |
| |
| void RemovePatternsNeedingUpdatesFromQueues() { |
| for (ShinglePatternSet::iterator p = patterns_needing_updates_.begin(); |
| p != patterns_needing_updates_.end(); |
| ++p) { |
| RemovePatternFromQueues(*p); |
| } |
| } |
| |
| void AddPatternsNeedingUpdatesToQueues() { |
| for (ShinglePatternSet::iterator p = patterns_needing_updates_.begin(); |
| p != patterns_needing_updates_.end(); |
| ++p) { |
| AddPatternToQueues(*p); |
| } |
| variable_queue_.ApplyPendingUpdates(); |
| } |
| |
| void RemovePatternFromQueues(const ShinglePattern* pattern) { |
| int single_use_score = SingleUseScore(pattern); |
| if (single_use_score > 0) { |
| size_t n = single_use_pattern_queue_.erase(pattern); |
| LOG_ASSERT(n == 1); |
| } else if (pattern->program_histogram_.empty() && |
| pattern->model_histogram_.empty()) { |
| NOTREACHED(); // Should not come back to life. |
| } else if (pattern->program_histogram_.empty()) { |
| // Useless pattern. |
| } else { |
| active_non_single_use_patterns_.erase(pattern); |
| AddPatternToLabelQueue(pattern, -1); |
| } |
| } |
| |
| void AddPatternToQueues(const ShinglePattern* pattern) { |
| int single_use_score = SingleUseScore(pattern); |
| if (single_use_score > 0) { |
| single_use_pattern_queue_.insert(pattern); |
| } else if (pattern->program_histogram_.empty() && |
| pattern->model_histogram_.empty()) { |
| } else if (pattern->program_histogram_.empty()) { |
| // Useless pattern. |
| } else { |
| active_non_single_use_patterns_.insert(pattern); |
| AddPatternToLabelQueue(pattern, +1); |
| } |
| } |
| |
| void AddPatternToLabelQueue(const ShinglePattern* pattern, int sign) { |
| // For each possible assignment in this pattern, update the potential |
| // contributions to the LabelInfo queues. |
| |
| // We want to find for each symbol (LabelInfo) the maximum contribution that |
| // could be achieved by making shingle-wise assignments between shingles in |
| // the model and shingles in the program. |
| // |
| // If the shingles in the histograms are independent (no two shingles have a |
| // symbol in common) then any permutation of the assignments is possible, |
| // and the maximum contribution can be found by taking the maximum over all |
| // the pairs. |
| // |
| // If the shingles are dependent two things happen. The maximum |
| // contribution to any given symbol is a sum because the symbol has |
| // contributions from all the shingles containing it. Second, some |
| // assignments are blocked by previous incompatible assignments. We want to |
| // avoid a combinatorial search, so we ignore the blocking. |
| |
| const size_t kUnwieldy = 5; |
| |
| typedef std::map<LabelInfo*, int> LabelToScore; |
| typedef std::map<LabelInfo*, LabelToScore > ScoreSet; |
| ScoreSet maxima; |
| |
| size_t n_model_samples = 0; |
| for (ShinglePattern::Histogram::const_iterator model_iter = |
| pattern->model_histogram_.begin(); |
| model_iter != pattern->model_histogram_.end(); |
| ++model_iter) { |
| if (++n_model_samples > kUnwieldy) break; |
| const ShinglePattern::FreqView& model_freq = *model_iter; |
| int m1 = model_freq.count(); |
| const Shingle* model_instance = model_freq.instance(); |
| |
| ScoreSet sums; |
| size_t n_program_samples = 0; |
| for (ShinglePattern::Histogram::const_iterator program_iter = |
| pattern->program_histogram_.begin(); |
| program_iter != pattern->program_histogram_.end(); |
| ++program_iter) { |
| if (++n_program_samples > kUnwieldy) break; |
| const ShinglePattern::FreqView& program_freq = *program_iter; |
| int p1 = program_freq.count(); |
| const Shingle* program_instance = program_freq.instance(); |
| |
| // int score = p1; // ? weigh all equally?? |
| int score = std::min(p1, m1); |
| |
| for (uint8_t i = 0; i < Shingle::kWidth; ++i) { |
| LabelInfo* program_info = program_instance->at(i); |
| LabelInfo* model_info = model_instance->at(i); |
| if ((model_info->assignment_ == nullptr) != |
| (program_info->assignment_ == nullptr)) { |
| VLOG(2) << "ERROR " << i |
| << "\n\t" << ToString(pattern, 10) |
| << "\n\t" << ToString(program_instance) |
| << "\n\t" << ToString(model_instance); |
| } |
| if (!program_info->assignment_ && !model_info->assignment_) { |
| sums[program_info][model_info] += score; |
| } |
| } |
| } |
| |
| for (ScoreSet::iterator assignee_iterator = sums.begin(); |
| assignee_iterator != sums.end(); |
| ++assignee_iterator) { |
| LabelInfo* program_info = assignee_iterator->first; |
| for (LabelToScore::iterator p = assignee_iterator->second.begin(); |
| p != assignee_iterator->second.end(); |
| ++p) { |
| LabelInfo* model_info = p->first; |
| int score = p->second; |
| int* slot = &maxima[program_info][model_info]; |
| *slot = std::max(*slot, score); |
| } |
| } |
| } |
| |
| for (ScoreSet::iterator assignee_iterator = maxima.begin(); |
| assignee_iterator != maxima.end(); |
| ++assignee_iterator) { |
| LabelInfo* program_info = assignee_iterator->first; |
| for (LabelToScore::iterator p = assignee_iterator->second.begin(); |
| p != assignee_iterator->second.end(); |
| ++p) { |
| LabelInfo* model_info = p->first; |
| int score = sign * p->second; |
| variable_queue_.AddPendingUpdate(program_info, model_info, score); |
| } |
| } |
| } |
| |
| void AssignOne(LabelInfo* model_info, LabelInfo* program_info) { |
| LOG_ASSERT(!model_info->assignment_); |
| LOG_ASSERT(!program_info->assignment_); |
| LOG_ASSERT(model_info->is_model_); |
| LOG_ASSERT(!program_info->is_model_); |
| |
| VLOG(3) << "Assign " << ToString(program_info) |
| << " := " << ToString(model_info); |
| |
| ShingleSet affected_shingles; |
| AddAffectedPositions(model_info, &affected_shingles); |
| AddAffectedPositions(program_info, &affected_shingles); |
| |
| for (ShingleSet::iterator p = affected_shingles.begin(); |
| p != affected_shingles.end(); |
| ++p) { |
| patterns_needing_updates_.insert((*p)->pattern()); |
| } |
| |
| RemovePatternsNeedingUpdatesFromQueues(); |
| |
| for (ShingleSet::iterator p = affected_shingles.begin(); |
| p != affected_shingles.end(); |
| ++p) { |
| Declassify(*p); |
| } |
| |
| program_info->label_->index_ = model_info->label_->index_; |
| // Mark as assigned |
| model_info->assignment_ = program_info; |
| program_info->assignment_ = model_info; |
| |
| for (ShingleSet::iterator p = affected_shingles.begin(); |
| p != affected_shingles.end(); |
| ++p) { |
| Reclassify(*p); |
| } |
| |
| AddPatternsNeedingUpdatesToQueues(); |
| } |
| |
| bool AssignFirstVariableOfHistogramHead(const ShinglePattern& pattern) { |
| const ShinglePattern::FreqView& program_1 = |
| *pattern.program_histogram_.begin(); |
| const ShinglePattern::FreqView& model_1 = *pattern.model_histogram_.begin(); |
| const Shingle* program_instance = program_1.instance(); |
| const Shingle* model_instance = model_1.instance(); |
| size_t variable_index = pattern.index_->first_variable_index_; |
| LabelInfo* program_info = program_instance->at(variable_index); |
| LabelInfo* model_info = model_instance->at(variable_index); |
| AssignOne(model_info, program_info); |
| return true; |
| } |
| |
| bool FindAndAssignBestLeader() { |
| LOG_ASSERT(patterns_needing_updates_.empty()); |
| |
| if (!single_use_pattern_queue_.empty()) { |
| const ShinglePattern& pattern = **single_use_pattern_queue_.begin(); |
| return AssignFirstVariableOfHistogramHead(pattern); |
| } |
| |
| if (variable_queue_.empty()) |
| return false; |
| |
| const VariableQueue::ScoreAndLabel best = variable_queue_.first(); |
| int score = best.first; |
| LabelInfo* assignee = best.second; |
| |
| // TODO(sra): score (best.first) can be zero. A zero score means we are |
| // blindly picking between two (or more) alternatives which look the same. |
| // If we exit on the first zero-score we sometimes get 3-4% better total |
| // compression. This indicates that 'infill' is doing a better job than |
| // picking blindly. Perhaps we can use an extended region around the |
| // undistinguished competing alternatives to break the tie. |
| if (score == 0) { |
| variable_queue_.Print(); |
| return false; |
| } |
| |
| AssignmentCandidates* candidates = assignee->candidates(); |
| if (candidates->empty()) |
| return false; // Should not happen. |
| |
| AssignOne(candidates->top_candidate(), assignee); |
| return true; |
| } |
| |
| private: |
| // The trace vector contains the model sequence [0, model_end_) followed by |
| // the program sequence [model_end_, trace.end()) |
| const Trace& trace_; |
| size_t model_end_; |
| |
| // |shingle_instances_| is the set of 'interned' shingles. |
| Shingle::OwningSet shingle_instances_; |
| |
| // |instances_| maps from position in |trace_| to Shingle at that position. |
| std::vector<Shingle*> instances_; |
| |
| SingleUsePatternQueue single_use_pattern_queue_; |
| ShinglePatternSet active_non_single_use_patterns_; |
| VariableQueue variable_queue_; |
| |
| // Transient information: when we make an assignment, we need to recompute |
| // priority queue information derived from these ShinglePatterns. |
| ShinglePatternSet patterns_needing_updates_; |
| |
| typedef std::map<ShinglePattern::Index, |
| ShinglePattern, ShinglePatternIndexLess> IndexToPattern; |
| IndexToPattern patterns_; |
| |
| DISALLOW_COPY_AND_ASSIGN(AssignmentProblem); |
| }; |
| |
| class Adjuster : public AdjustmentMethod { |
| public: |
| Adjuster() : prog_(nullptr), model_(nullptr) {} |
| ~Adjuster() = default; |
| |
| bool Adjust(const AssemblyProgram& model, AssemblyProgram* program) { |
| VLOG(1) << "Adjuster::Adjust"; |
| prog_ = program; |
| model_ = &model; |
| return Finish(); |
| } |
| |
| bool Finish() { |
| prog_->UnassignIndexes(); |
| Trace abs32_trace_; |
| Trace rel32_trace_; |
| CollectTraces(model_, &abs32_trace_, &rel32_trace_, true); |
| size_t abs32_model_end = abs32_trace_.size(); |
| size_t rel32_model_end = rel32_trace_.size(); |
| CollectTraces(prog_, &abs32_trace_, &rel32_trace_, false); |
| Solve(abs32_trace_, abs32_model_end); |
| Solve(rel32_trace_, rel32_model_end); |
| prog_->AssignRemainingIndexes(); |
| return true; |
| } |
| |
| private: |
| void CollectTraces(const AssemblyProgram* program, Trace* abs32, Trace* rel32, |
| bool is_model) { |
| label_info_maker_.ResetDebugLabel(); |
| |
| for (Label* label : program->abs32_label_annotations()) |
| ReferenceLabel(abs32, is_model, label); |
| for (Label* label : program->rel32_label_annotations()) |
| ReferenceLabel(rel32, is_model, label); |
| |
| // TODO(sra): we could simply append all the labels in index order to |
| // incorporate some costing for entropy (bigger deltas) that will be |
| // introduced into the label address table by non-monotonic ordering. This |
| // would have some knock-on effects to parts of the algorithm that work on |
| // single-occurrence labels. |
| } |
| |
| void Solve(const Trace& model, size_t model_end) { |
| base::Time start_time = base::Time::Now(); |
| AssignmentProblem a(model, model_end); |
| a.Solve(); |
| VLOG(1) << " Adjuster::Solve " |
| << (base::Time::Now() - start_time).InSecondsF(); |
| } |
| |
| void ReferenceLabel(Trace* trace, bool is_model, Label* label) { |
| trace->push_back(label_info_maker_.MakeLabelInfo( |
| label, is_model, static_cast<uint32_t>(trace->size()))); |
| } |
| |
| AssemblyProgram* prog_; // Program to be adjusted, owned by caller. |
| const AssemblyProgram* model_; // Program to be mimicked, owned by caller. |
| |
| LabelInfoMaker label_info_maker_; |
| |
| private: |
| DISALLOW_COPY_AND_ASSIGN(Adjuster); |
| }; |
| |
| //////////////////////////////////////////////////////////////////////////////// |
| |
| } // namespace adjustment_method_2 |
| |
| AdjustmentMethod* AdjustmentMethod::MakeShingleAdjustmentMethod() { |
| return new adjustment_method_2::Adjuster(); |
| } |
| |
| } // namespace courgette |