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#include "LSTMNetwork.hpp"
LSTMNetworkImpl::LSTMNetworkImpl(int nbOutputs, int unknownValueThreshold, int leftBorder, int rightBorder, int nbStackElements, std::vector<std::string> columns, std::vector<int> focusedBufferIndexes, std::vector<int> focusedStackIndexes, std::vector<std::string> focusedColumns, std::vector<int> maxNbElements, int leftWindowRawInput, int rightWindowRawInput) : unknownValueThreshold(unknownValueThreshold), focusedBufferIndexes(focusedBufferIndexes), focusedStackIndexes(focusedStackIndexes), focusedColumns(focusedColumns), maxNbElements(maxNbElements), leftWindowRawInput(leftWindowRawInput), rightWindowRawInput(rightWindowRawInput)
{
constexpr int embeddingsSize = 64;
constexpr int hiddenSize = 1024;
constexpr int contextLSTMSize = 512;
constexpr int focusedLSTMSize = 64;
setLeftBorder(leftBorder);
setRightBorder(rightBorder);
setNbStackElements(nbStackElements);
setColumns(columns);
rawInputSize = leftWindowRawInput + rightWindowRawInput + 1;
if (leftWindowRawInput < 0 or rightWindowRawInput < 0)
rawInputSize = 0;
else
rawInputLSTM = register_module("rawInputLSTM", torch::nn::LSTM(torch::nn::LSTMOptions(embeddingsSize, focusedLSTMSize).batch_first(false).bidirectional(true)));
int rawInputLSTMOutputSize = rawInputSize == 0 ? 0 : (rawInputLSTM->options.hidden_size() * (rawInputLSTM->options.bidirectional() ? 4 : 1));
wordEmbeddings = register_module("word_embeddings", torch::nn::Embedding(torch::nn::EmbeddingOptions(maxNbEmbeddings, embeddingsSize)));
embeddingsDropout = register_module("embeddings_dropout", torch::nn::Dropout(0.3));
lstmDropout = register_module("lstm_dropout", torch::nn::Dropout(0.3));
hiddenDropout = register_module("hidden_dropout", torch::nn::Dropout(0.3));
contextLSTM = register_module("contextLSTM", torch::nn::LSTM(torch::nn::LSTMOptions(columns.size()*embeddingsSize, contextLSTMSize).batch_first(false).bidirectional(true)));
int totalLSTMOutputSize = contextLSTM->options.hidden_size() * (contextLSTM->options.bidirectional() ? 4 : 1) + rawInputLSTMOutputSize;
for (auto & col : focusedColumns)
{
lstms.emplace_back(register_module(fmt::format("LSTM_{}", col), torch::nn::LSTM(torch::nn::LSTMOptions(embeddingsSize, focusedLSTMSize).batch_first(false).bidirectional(true))));
totalLSTMOutputSize += lstms.back()->options.hidden_size() * (lstms.back()->options.bidirectional() ? 4 : 1) * (focusedBufferIndexes.size()+focusedStackIndexes.size());
}
linear1 = register_module("linear1", torch::nn::Linear(totalLSTMOutputSize, hiddenSize));
linear2 = register_module("linear2", torch::nn::Linear(hiddenSize, nbOutputs));
}
torch::Tensor LSTMNetworkImpl::forward(torch::Tensor input)
{
if (input.dim() == 1)
input = input.unsqueeze(0);
auto embeddings = embeddingsDropout(wordEmbeddings(input));
auto context = embeddings.narrow(1, rawInputSize, columns.size()*(1+leftBorder+rightBorder));
context = context.view({context.size(0), context.size(1)/(int)columns.size(), (int)columns.size()*(int)wordEmbeddings->options.embedding_dim()});
auto elementsEmbeddings = embeddings.narrow(1, rawInputSize+context.size(1), input.size(1)-(rawInputSize+context.size(1)));
context = context.permute({1,0,2});
std::vector<torch::Tensor> lstmOutputs;
if (rawInputSize != 0)
{
auto rawLetters = embeddings.narrow(1, 0, leftWindowRawInput+rightWindowRawInput+1).permute({1,0});
auto lstmOut = rawInputLSTM(rawLetters).output;
if (rawInputLSTM->options.bidirectional())
lstmOutputs.emplace_back(torch::cat({lstmOut[0],lstmOut[-1]}, 1));
else
lstmOutputs.emplace_back(lstmOut[-1]);
}
auto curIndex = 0;
for (unsigned int i = 0; i < focusedColumns.size(); i++)
{
long nbElements = maxNbElements[i];
for (unsigned int focused = 0; focused < focusedBufferIndexes.size()+focusedStackIndexes.size(); focused++)
{
auto lstmInput = elementsEmbeddings.narrow(1, curIndex, nbElements).permute({1,0,2});
curIndex += nbElements;
auto lstmOut = lstms[i](lstmInput).output;
if (lstms[i]->options.bidirectional())
lstmOutputs.emplace_back(torch::cat({lstmOut[0],lstmOut[-1]}, 1));
else
lstmOutputs.emplace_back(lstmOut[-1]);
}
}
auto lstmOut = contextLSTM(context).output;
if (contextLSTM->options.bidirectional())
lstmOutputs.emplace_back(torch::cat({lstmOut[0],lstmOut[-1]}, 1));
else
lstmOutputs.emplace_back(lstmOut[-1]);
auto totalInput = lstmDropout(torch::cat(lstmOutputs, 1));
return linear2(hiddenDropout(torch::relu(linear1(totalInput))));
}
std::vector<std::vector<long>> LSTMNetworkImpl::extractContext(Config & config, Dict & dict) const
{
if (dict.size() >= maxNbEmbeddings)
util::warning(fmt::format("dict.size()={} > maxNbEmbeddings={}", dict.size(), maxNbEmbeddings));
std::vector<long> contextIndexes = extractContextIndexes(config);
std::vector<std::vector<long>> context;
context.emplace_back();
if (rawInputSize > 0)
{
for (int i = 0; i < leftWindowRawInput; i++)
if (config.hasCharacter(config.getCharacterIndex()-leftWindowRawInput+i))
context.back().push_back(dict.getIndexOrInsert(fmt::format("{}", config.getLetter(config.getCharacterIndex()-leftWindowRawInput+i))));
else
context.back().push_back(dict.getIndexOrInsert(Dict::nullValueStr));
for (int i = 0; i <= rightWindowRawInput; i++)
if (config.hasCharacter(config.getCharacterIndex()+i))
context.back().push_back(dict.getIndexOrInsert(fmt::format("{}", config.getLetter(config.getCharacterIndex()+i))));
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else
context.back().push_back(dict.getIndexOrInsert(Dict::nullValueStr));
}
for (auto index : contextIndexes)
for (auto & col : columns)
if (index == -1)
for (auto & contextElement : context)
contextElement.push_back(dict.getIndexOrInsert(Dict::nullValueStr));
else
{
int dictIndex = dict.getIndexOrInsert(config.getAsFeature(col, index));
for (auto & contextElement : context)
contextElement.push_back(dictIndex);
if (is_training())
if (col == "FORM" || col == "LEMMA")
if (dict.getNbOccs(dictIndex) <= unknownValueThreshold)
{
context.emplace_back(context.back());
context.back().back() = dict.getIndexOrInsert(Dict::unknownValueStr);
}
}
for (auto & contextElement : context)
for (unsigned int colIndex = 0; colIndex < focusedColumns.size(); colIndex++)
{
auto & col = focusedColumns[colIndex];
std::vector<int> focusedIndexes;
for (auto relIndex : focusedBufferIndexes)
{
int index = relIndex + leftBorder;
if (index < 0 || index >= (int)contextIndexes.size())
focusedIndexes.push_back(-1);
else
focusedIndexes.push_back(contextIndexes[index]);
}
for (auto index : focusedStackIndexes)
{
if (!config.hasStack(index))
focusedIndexes.push_back(-1);
else if (!config.has(col, config.getStack(index), 0))
focusedIndexes.push_back(-1);
else
focusedIndexes.push_back(config.getStack(index));
}
for (auto index : focusedIndexes)
{
if (index == -1)
{
for (int i = 0; i < maxNbElements[colIndex]; i++)
contextElement.emplace_back(dict.getIndexOrInsert(Dict::nullValueStr));
continue;
}
std::vector<std::string> elements;
if (col == "FORM")
{
auto asUtf8 = util::splitAsUtf8(config.getAsFeature(col, index).get());
for (int i = 0; i < maxNbElements[colIndex]; i++)
if (i < (int)asUtf8.size())
elements.emplace_back(fmt::format("{}", asUtf8[i]));
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else
elements.emplace_back(Dict::nullValueStr);
}
else if (col == "FEATS")
{
auto splited = util::split(config.getAsFeature(col, index).get(), '|');
for (int i = 0; i < maxNbElements[colIndex]; i++)
if (i < (int)splited.size())
elements.emplace_back(fmt::format("FEATS({})", splited[i]));
else
elements.emplace_back(Dict::nullValueStr);
}
else if (col == "ID")
{
if (config.isTokenPredicted(index))
elements.emplace_back("ID(TOKEN)");
else if (config.isMultiwordPredicted(index))
elements.emplace_back("ID(MULTIWORD)");
else if (config.isEmptyNodePredicted(index))
elements.emplace_back("ID(EMPTYNODE)");
}
else
{
elements.emplace_back(config.getAsFeature(col, index));
}
if ((int)elements.size() != maxNbElements[colIndex])
util::myThrow(fmt::format("elements.size ({}) != maxNbElements[colIndex ({},{})]", elements.size(), maxNbElements[colIndex], col));
for (auto & element : elements)
contextElement.emplace_back(dict.getIndexOrInsert(element));
}
}
if (!is_training() && context.size() > 1)
util::myThrow(fmt::format("Not in training mode, yet context yields multiple variants (size={})", context.size()));
return context;
}