1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
|
#pragma once
#include <vector>
#include <boost/iterator/permutation_iterator.hpp>
#include "common/scorer.h"
#include "common/god.h"
#include "common/exception.h"
#include "cpu/mblas/matrix.h"
#include "cpu/decoder/encoder_decoder.h"
namespace amunmt {
namespace CPU {
struct ProbCompare {
ProbCompare(const float* data) : data_(data) {}
bool operator()(const unsigned a, const unsigned b) {
return data_[a] > data_[b];
}
const float* data_;
};
class BestHyps : public BestHypsBase
{
public:
BestHyps(const God &god)
: BestHypsBase(god,
!god.Get<bool>("allow-unk"),
god.Get<std::vector<std::string>>("softmax-filter").size(),
god.Get<bool>("return-alignment") || god.Get<bool>("return-soft-alignment") || god.Get<bool>("return-nematus-alignment"),
god.GetScorerWeights())
{}
void CalcBeam(
const Beam& prevHyps,
const std::vector<ScorerPtr>& scorers,
const Words& filterIndices,
std::vector<Beam>& beams,
std::vector<uint>& beamSizes)
{
using namespace mblas;
mblas::ArrayMatrix& Probs = static_cast<mblas::ArrayMatrix&>(scorers[0]->GetProbs());
mblas::ArrayMatrix Costs(Probs.rows(), 1);
for (size_t i = 0; i < prevHyps.size(); ++i) {
Costs.data()[i] = prevHyps[i]->GetCost();
}
Probs *= weights_.at(scorers[0]->GetName());
AddBiasVector<byColumn>(Probs, Costs);
for (size_t i = 1; i < scorers.size(); ++i) {
mblas::ArrayMatrix &currProb = static_cast<mblas::ArrayMatrix&>(scorers[i]->GetProbs());
Probs += weights_.at(scorers[i]->GetName()) * currProb;
}
size_t size = Probs.rows() * Probs.columns(); // Probs.size();
std::vector<size_t> keys(size);
for (size_t i = 0; i < keys.size(); ++i) {
keys[i] = i;
}
size_t beamSize = beamSizes[0];
std::vector<size_t> bestKeys(beamSize);
std::vector<float> bestCosts(beamSize);
if (forbidUNK_) {
blaze::column(Probs, UNK_ID) = std::numeric_limits<float>::lowest();
}
std::nth_element(keys.begin(), keys.begin() + beamSize, keys.end(),
ProbCompare(Probs.data()));
for (size_t i = 0; i < beamSize; ++i) {
bestKeys[i] = keys[i];
bestCosts[i] = Probs.data()[keys[i]];
}
std::vector<std::vector<float>> breakDowns;
if (god_.ReturnNBestList()) {
breakDowns.push_back(bestCosts);
for (auto& scorer : scorers) {
std::vector<float> modelCosts(beamSize);
mblas::ArrayMatrix &currProb = static_cast<mblas::ArrayMatrix&>(scorer->GetProbs());
auto it = boost::make_permutation_iterator(currProb.begin(), keys.begin());
std::copy(it, it + beamSize, modelCosts.begin());
breakDowns.push_back(modelCosts);
}
}
for (size_t i = 0; i < beamSize; i++) {
size_t wordIndex = bestKeys[i] % Probs.columns();
if (isInputFiltered_) {
wordIndex = filterIndices[wordIndex];
}
size_t hypIndex = bestKeys[i] / Probs.columns();
float cost = bestCosts[i];
HypothesisPtr hyp;
if (returnAttentionWeights_) {
std::vector<SoftAlignmentPtr> alignments;
for (auto& scorer : scorers) {
if (CPU::CPUEncoderDecoderBase* encdec = dynamic_cast<CPU::CPUEncoderDecoderBase*>(scorer.get())) {
auto& attention = encdec->GetAttention();
alignments.emplace_back(new SoftAlignment(attention.begin(hypIndex),
attention.end(hypIndex)));
} else {
amunmt_UTIL_THROW2("Return Alignment is allowed only with Nematus scorer.");
}
}
hyp.reset(new Hypothesis(prevHyps[hypIndex], wordIndex, hypIndex, cost, alignments));
} else {
hyp.reset(new Hypothesis(prevHyps[hypIndex], wordIndex, hypIndex, cost));
}
if (god_.ReturnNBestList()) {
hyp->GetCostBreakdown().resize(scorers.size());
float sum = 0;
for(size_t j = 0; j < scorers.size(); ++j) {
if (j == 0) {
hyp->GetCostBreakdown()[0] = breakDowns[0][i];
} else {
float cost = 0;
if (j < scorers.size()) {
if (prevHyps[hypIndex]->GetCostBreakdown().size() < scorers.size())
const_cast<HypothesisPtr&>(prevHyps[hypIndex])->GetCostBreakdown().resize(scorers.size(), 0.0);
cost = breakDowns[j][i] + const_cast<HypothesisPtr&>(prevHyps[hypIndex])->GetCostBreakdown()[j];
}
sum += weights_.at(scorers[j]->GetName()) * cost;
hyp->GetCostBreakdown()[j] = cost;
}
}
hyp->GetCostBreakdown()[0] -= sum;
hyp->GetCostBreakdown()[0] /= weights_.at(scorers[0]->GetName());
}
beams[0].push_back(hyp);
}
}
};
} // namespace CPU
} // namespace amunmt
|
