microsoft
/
distributed_word_embedding
зеркало из https://github.com/microsoft/distributed_word_embedding.git
1
0
Форкнуть 0
Код Задачи Пакеты Проекты Релизы Вики Активность

Parameterloader optimization

This commit is contained in:
v-guoql 2015-11-21 17:18:08 +08:00
Родитель c146688429
Коммит 30a756e6fe
7 изменённых файлов: 88 добавлений и 64 удалений
Показать статистику Скачать Patch файл Скачать Diff файл Показать все файлы Свернуть все файлы

3
src/data_block.h
Просмотреть файл

@ -20,7 +20,8 @@ namespace multiverso
class DataBlock : public multiverso::DataBlockBase
{
public:
std::vector <int> input_nodes, output_nodes;
std::unordered_set <int> input_nodes, output_nodes;
std::unordered_set <int> negativesample_pools;
DataBlock(){}
~DataBlock();

10
src/distributed_wordembedding.cpp
Просмотреть файл

@ -280,6 +280,16 @@ namespace multiverso
}
multiverso::Multiverso::EndClock();
//Dump input-embedding weight
multiverso::Multiverso::BeginClock();
++data_block_count;
DataBlock *data_block = new (std::nothrow)DataBlock();
assert(data_block != nullptr);
data_block->SetType(DataBlockType::Test);
PushDataBlock(datablock_queue, data_block);
multiverso::Multiverso::EndClock();
}
//Dump input-embedding weight

28
src/parameter_loader.cpp
Просмотреть файл

@ -38,19 +38,16 @@ namespace multiverso
//input_nodes,output_nodes
multiverso::Log::Debug("Rank %d ParameterLoader parse begin %d\n",
multiverso::Multiverso::ProcessRank(), parse_and_request_count_);
WordEmbedding_->PrepareParameter(data, input_nodes, output_nodes);
WordEmbedding_->PrepareParameter(data);
multiverso::Log::Debug("Rank %d ParameterLoader parse end %d\n",
multiverso::Multiverso::ProcessRank(), parse_and_request_count_);
//Step 2, Request the parameter
multiverso::Log::Debug("Rank %d ParameterLoader request begin %d\n",
multiverso::Multiverso::ProcessRank(), parse_and_request_count_);
RequestParameter(data, input_nodes, output_nodes);
RequestParameter(data);
multiverso::Log::Debug("Rank %d ParameterLoader request end %d\n",
multiverso::Multiverso::ProcessRank(), parse_and_request_count_);
//Step 3, store the needed parameters in data_block
//it will be used to copy parameter from multiverso in trainer
data->input_nodes = std::move(input_nodes);
data->output_nodes = std::move(output_nodes);
multiverso::Log::Info("Rank %d ParameterLoader finish %d\n",
multiverso::Multiverso::ProcessRank(), parse_and_request_count_ - 1);
@ -58,9 +55,7 @@ namespace multiverso
fflush(log_file_);
}
void ParameterLoader::RequestParameter(DataBlock *data_block,
std::vector<int>& input_nodes,
std::vector<int>& output_nodes)
void ParameterLoader::RequestParameter(DataBlock *data_block)
{
//If the data_block is the last one, we need to dump
//the input-embedding weights
@ -68,16 +63,17 @@ namespace multiverso
RequestTable(kInputEmbeddingTableId);
RequestRow(kWordCountActualTableId, 0);
for (int i = 0; i < input_nodes.size(); ++i)
RequestRow(kInputEmbeddingTableId, input_nodes[i]);
for (int i = 0; i < output_nodes.size(); ++i)
RequestRow(kEmbeddingOutputTableId, output_nodes[i]);
for (auto node : data_block->input_nodes)
RequestRow(kInputEmbeddingTableId, node);
for (auto node : data_block->output_nodes)
RequestRow(kEmbeddingOutputTableId, node);
if (option_->use_adagrad)
{
for (int i = 0; i < input_nodes.size(); ++i)
RequestRow(kSumGradient2IETableId, input_nodes[i]);
for (int i = 0; i < output_nodes.size(); ++i)
RequestRow(kSumGradient2EOTableId, output_nodes[i]);
for (auto node : data_block->input_nodes)
RequestRow(kSumGradient2IETableId, node);
for (auto node : data_block->output_nodes)
RequestRow(kSumGradient2EOTableId, node);
}
}
}

4
src/parameter_loader.h
Просмотреть файл

@ -43,9 +43,7 @@ namespace multiverso
* \param input_nodes stores the input words'index
* \param output_nodes stores the output words'index
*/
void RequestParameter(DataBlock *data_block,
std::vector<int>& input_nodes,
std::vector<int>& output_nodes);
void RequestParameter(DataBlock *data_block);
//No copying allowed
ParameterLoader(const ParameterLoader&);
void operator=(const ParameterLoader&);

9
src/trainer.cpp
Просмотреть файл

@ -48,16 +48,16 @@ namespace multiverso
//Compute the total number of processes
if (process_count_ == -1)
process_count_ = multiverso::Multiverso::TotalProcessCount();
//Get the input_nodes and output_nodes from data_block
//The input_nodes and output_nodes are stored by ParameterLoader
DataBlock *data = reinterpret_cast<DataBlock*>(data_block);
std::vector<int>& input_nodes = data->input_nodes;
std::vector<int>& output_nodes = data->output_nodes;
std::vector<int> input_nodes(data->input_nodes.begin(), data->input_nodes.end());
std::vector<int> output_nodes(data->output_nodes.begin(), data->output_nodes.end());
//A trainer only copy or add apart of parameters
//This trainer should copy or add the parameters according to
//local_input_nodes and local_output_nodes
std::vector<int> local_input_nodes;
std::vector<int> local_output_nodes;
for (int i = trainer_id_; i < input_nodes.size(); i += option_->thread_cnt)
local_input_nodes.push_back(input_nodes[i]);
for (int i = trainer_id_; i < output_nodes.size(); i += option_->thread_cnt)
@ -121,6 +121,7 @@ namespace multiverso
{
SaveEmbedding(option_->output_file, option_->output_binary);
}
if (trainer_id_ == 0)
{
fprintf(log_file_, "%lf\n",

77
src/word_embedding.cpp
Просмотреть файл

@ -49,6 +49,8 @@ namespace multiverso
void WordEmbedding::Train(DataBlock *data_block, int index_start, int interval,
int64& word_count, real* hidden_act, real* hidden_err)
{
std::vector <int> negativesample(data_block->negativesample_pools.begin(),
data_block->negativesample_pools.end());
for (int i = index_start; i < data_block->Size(); i += interval)
{
int sentence_length;
@ -59,7 +61,7 @@ namespace multiverso
word_count_deta, next_random);
this->Train(sentence, sentence_length,
next_random, hidden_act, hidden_err);
next_random, hidden_act, hidden_err, negativesample);
word_count += word_count_deta;
}
@ -77,10 +79,10 @@ namespace multiverso
}
void WordEmbedding::Train(int* sentence, int sentence_length,
uint64 next_random, real* hidden_act, real* hidden_err)
uint64 next_random, real* hidden_act, real* hidden_err, std::vector <int> &negativesample_pools)
{
ParseSentence(sentence, sentence_length,
next_random, hidden_act, hidden_err, &WordEmbedding::TrainSample);
next_random, hidden_act, hidden_err, &WordEmbedding::TrainSample, negativesample_pools);
}
//Train with forward direction and get the input-hidden layer vector
void WordEmbedding::FeedForward(std::vector<int>& input_nodes, real* hidden_act)
@ -190,37 +192,52 @@ namespace multiverso
}
}
//Parapare the parameter for the datablock
void WordEmbedding::PrepareParameter(DataBlock* data_block,
std::vector<int>& input_nodes,
std::vector<int>& output_nodes)
void WordEmbedding::PrepareParameter(DataBlock* data_block)
{
input_nodes_.clear();
output_nodes_.clear();
int sentence_length;
int64 word_count_deta;
int64 word_count_delta;
int *sentence;
uint64 next_random;
if (option_->hs)
{
for (int i = 0; i < data_block->Size(); ++i)
{
data_block->GetSentence(i, sentence, sentence_length, word_count_deta,
next_random);
ParseSentence(sentence, sentence_length, next_random,
nullptr, nullptr, &WordEmbedding::DealPrepareParameter);
}
data_block->GetSentence(i, sentence, sentence_length, word_count_delta, next_random);
for (auto it = input_nodes_.begin(); it != input_nodes_.end(); it++)
for (int sentence_position = 0; sentence_position < sentence_length; ++sentence_position)
{
input_nodes.push_back(*it);
assert((*it) >= 0);
assert((*it) < dictionary_size_);
data_block->input_nodes.insert(sentence[sentence_position]);
}
for (auto it = output_nodes_.begin(); it != output_nodes_.end(); it++)
}
for (auto input_node : data_block->input_nodes)
{
output_nodes.push_back(*it);
assert((*it) >= 0);
assert((*it) < dictionary_size_);
auto info = huffmanEncoder_->GetLabelInfo(input_node);
for (int d = 0; d < info->codelen; d++)
data_block->output_nodes.insert(info->point[d]);
}
}
else
{
for (int i = 0; i < data_block->Size(); ++i)
{
data_block->GetSentence(i, sentence, sentence_length, word_count_delta, next_random);
for (int sentence_position = 0; sentence_position < sentence_length; ++sentence_position)
{
data_block->input_nodes.insert(sentence[sentence_position]);
}
}
for (auto input_node : data_block->input_nodes)
{
data_block->output_nodes.insert(input_node);
}
for (int d = 0; d < option_->negative_num * data_block->input_nodes.size(); d++)
{
next_random = sampler_->GetNextRandom(next_random);
int target = sampler_->NegativeSampling(next_random);
data_block->output_nodes.insert(target);
data_block->negativesample_pools.insert(target);
}
}
}
//Copy the input&ouput nodes
@ -236,7 +253,7 @@ namespace multiverso
//Parse the sentence and deepen into two branches
void WordEmbedding::ParseSentence(int* sentence, int sentence_length,
uint64 next_random, real* hidden_act, real* hidden_err,
FunctionType function)
FunctionType function, std::vector <int> &negativesample_pools)
{
if (sentence_length == 0)
return;
@ -263,7 +280,7 @@ namespace multiverso
input_nodes.clear();
output_nodes.clear();
Parse(feat + feat_size - 1, 1, sentence[sentence_position],
next_random, input_nodes, output_nodes);
next_random, input_nodes, output_nodes, negativesample_pools);
(this->*function)(input_nodes, output_nodes, hidden_act, hidden_err);
}
}
@ -273,7 +290,8 @@ namespace multiverso
input_nodes.clear();
output_nodes.clear();
Parse(feat, feat_size, sentence[sentence_position],
next_random, input_nodes, output_nodes);
next_random, input_nodes, output_nodes, negativesample_pools);
(this->*function)(input_nodes, output_nodes, hidden_act, hidden_err);
}
}
@ -281,7 +299,7 @@ namespace multiverso
//Parse the windows's input&output nodes
inline void WordEmbedding::Parse(int *feat, int feat_cnt, int word_idx,
uint64 &next_random, std::vector<int>& input_nodes,
std::vector<std::pair<int, int> >& output_nodes)
std::vector<std::pair<int, int> >& output_nodes, std::vector <int> &negativesample_pools)
{
for (int i = 0; i < feat_cnt; ++i)
{
@ -301,7 +319,8 @@ namespace multiverso
for (int d = 0; d < option_->negative_num; d++)
{
next_random = sampler_->GetNextRandom(next_random);
int target = sampler_->NegativeSampling(next_random);
int index = (next_random >> 8) % negativesample_pools.size();
int target = negativesample_pools[index];
if (target == word_idx) continue;
output_nodes.push_back(std::make_pair(target, 0));
}

9
src/word_embedding.h
Просмотреть файл

@ -48,8 +48,7 @@ namespace multiverso
* \param input_nodes input_nodes represent the parameter which input_layer includes
* \param output_nodes output_nodes represent the parameter which output_layer inclueds
*/
void PrepareParameter(DataBlock *data_block,
std::vector<int>& input_nodes, std::vector<int>& output_nodes);
void PrepareParameter(DataBlock *data_block);
/*!
* \brief Update the learning rate
*/
@ -92,7 +91,7 @@ namespace multiverso
*/
void Parse(int *feat, int feat_cnt, int word_idx, uint64 &next_random,
std::vector<int>& input_nodes,
std::vector<std::pair<int, int> >& output_nodes);
std::vector<std::pair<int, int> >& output_nodes, std::vector <int> &negativesample_pools);
/*!
* \brief Parse a sentence and deepen into two branchs
* \one for TrainNN,the other one is for Parameter_parse&request
@ -100,7 +99,7 @@ namespace multiverso
void ParseSentence(int* sentence, int sentence_length,
uint64 next_random,
real* hidden_act, real* hidden_err,
FunctionType function);
FunctionType function, std::vector <int> &negativesample_pools);
/*!
* \brief Get the hidden layer vector
* \param input_nodes represent the input nodes
@ -139,7 +138,7 @@ namespace multiverso
* \brief Train the sentence actually
*/
void Train(int* sentence, int sentence_length,
uint64 next_random, real* hidden_act, real* hidden_err);
uint64 next_random, real* hidden_act, real* hidden_err, std::vector <int> &negativesample_pools);
//No copying allowed
WordEmbedding(const WordEmbedding&);