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
|
#include "lm/model.hh"
#include "util/file_piece.hh"
#include <cstdlib>
#include <exception>
#include <iostream>
#include <iomanip>
#include <math.h>
#include <stdlib.h>
#include <unistd.h>
namespace lm {
namespace ngram {
namespace {
void Usage(const char *name) {
std::cerr << "Usage: " << name << " [-u log10_unknown_probability] [-s] [-n] [-p probing_multiplier] [-t trie_temporary] [-m trie_building_megabytes] [type] input.arpa output.mmap\n\n"
"-u sets the default log10 probability for <unk> if the ARPA file does not have\n"
"one.\n"
"-s allows models to be built even if they do not have <s> and </s>.\n"
"-i allows buggy models from IRSTLM by mapping positive log probability to 0.\n"
"type is one of probing, trie, or sorted:\n\n"
"probing uses a probing hash table. It is the fastest but uses the most memory.\n"
"-p sets the space multiplier and must be >1.0. The default is 1.5.\n\n"
"trie is a straightforward trie with bit-level packing. It uses the least\n"
"memory and is still faster than SRI or IRST. Building the trie format uses an\n"
"on-disk sort to save memory.\n"
"-t is the temporary directory prefix. Default is the output file name.\n"
"-m limits memory use for sorting. Measured in MB. Default is 1024MB.\n\n"
/*"sorted is like probing but uses a sorted uniform map instead of a hash table.\n"
"It uses more memory than trie and is also slower, so there's no real reason to\n"
"use it.\n\n"*/
"See http://kheafield.com/code/kenlm/benchmark/ for data structure benchmarks.\n"
"Passing only an input file will print memory usage of each data structure.\n"
"If the ARPA file does not have <unk>, -u sets <unk>'s probability; default 0.0.\n";
exit(1);
}
// I could really use boost::lexical_cast right about now.
float ParseFloat(const char *from) {
char *end;
float ret = strtod(from, &end);
if (*end) throw util::ParseNumberException(from);
return ret;
}
unsigned long int ParseUInt(const char *from) {
char *end;
unsigned long int ret = strtoul(from, &end, 10);
if (*end) throw util::ParseNumberException(from);
return ret;
}
void ShowSizes(const char *file, const lm::ngram::Config &config) {
std::vector<uint64_t> counts;
util::FilePiece f(file);
lm::ReadARPACounts(f, counts);
std::size_t probing_size = ProbingModel::Size(counts, config);
// probing is always largest so use it to determine number of columns.
long int length = std::max<long int>(5, lrint(ceil(log10(probing_size))));
std::cout << "Memory estimate:\ntype ";
// right align bytes.
for (long int i = 0; i < length - 5; ++i) std::cout << ' ';
std::cout << "bytes\n"
"probing " << std::setw(length) << probing_size << " assuming -p " << config.probing_multiplier << "\n"
"trie " << std::setw(length) << TrieModel::Size(counts, config) << "\n";
}
} // namespace ngram
} // namespace lm
} // namespace
int main(int argc, char *argv[]) {
using namespace lm::ngram;
try {
lm::ngram::Config config;
int opt;
while ((opt = getopt(argc, argv, "siu:p:t:m:")) != -1) {
switch(opt) {
case 'u':
config.unknown_missing_logprob = ParseFloat(optarg);
break;
case 'p':
config.probing_multiplier = ParseFloat(optarg);
break;
case 't':
config.temporary_directory_prefix = optarg;
break;
case 'm':
config.building_memory = ParseUInt(optarg) * 1048576;
break;
case 's':
config.sentence_marker_missing = lm::SILENT;
break;
case 'i':
config.positive_log_probability = lm::SILENT;
break;
default:
Usage(argv[0]);
}
}
if (optind + 1 == argc) {
ShowSizes(argv[optind], config);
} else if (optind + 2 == argc) {
config.write_mmap = argv[optind + 1];
ProbingModel(argv[optind], config);
} else if (optind + 3 == argc) {
const char *model_type = argv[optind];
const char *from_file = argv[optind + 1];
config.write_mmap = argv[optind + 2];
if (!strcmp(model_type, "probing")) {
ProbingModel(from_file, config);
} else if (!strcmp(model_type, "trie")) {
TrieModel(from_file, config);
} else {
Usage(argv[0]);
}
} else {
Usage(argv[0]);
}
}
catch (std::exception &e) {
std::cerr << e.what() << std::endl;
abort();
}
return 0;
}
|
