From 8adb34622a1cd429004d55b514321b9adf57de04 Mon Sep 17 00:00:00 2001
From: Franck Dary <franck.dary@lis-lab.fr>
Date: Wed, 8 Apr 2020 00:50:45 +0200
Subject: [PATCH] code refactoring
---
reading_machine/src/Classifier.cpp | 50 -----
torch_modules/include/CNN.hpp | 2 +-
torch_modules/include/CNNNetwork.hpp | 35 ----
torch_modules/include/ConcatWordsNetwork.hpp | 21 --
torch_modules/include/ContextLSTM.hpp | 30 +++
.../include/DepthLayerTreeEmbedding.hpp | 25 +++
torch_modules/include/FocusedColumnLSTM.hpp | 28 +++
torch_modules/include/LSTM.hpp | 6 +-
torch_modules/include/LSTMNetwork.hpp | 23 +--
torch_modules/include/MLP.hpp | 14 --
torch_modules/include/NeuralNetwork.hpp | 23 +--
torch_modules/include/RLTNetwork.hpp | 31 ---
torch_modules/include/RandomNetwork.hpp | 1 +
torch_modules/include/RawInputLSTM.hpp | 26 +++
torch_modules/include/SplitTransLSTM.hpp | 26 +++
torch_modules/include/Submodule.hpp | 22 ++
torch_modules/src/CNN.cpp | 2 +-
torch_modules/src/CNNNetwork.cpp | 187 -----------------
torch_modules/src/ConcatWordsNetwork.cpp | 25 ---
torch_modules/src/ContextLSTM.cpp | 62 ++++++
torch_modules/src/DepthLayerTreeEmbedding.cpp | 17 ++
torch_modules/src/FocusedColumnLSTM.cpp | 94 +++++++++
torch_modules/src/LSTM.cpp | 2 +-
torch_modules/src/LSTMNetwork.cpp | 104 ++++------
torch_modules/src/MLP.cpp | 8 -
torch_modules/src/NeuralNetwork.cpp | 191 ------------------
torch_modules/src/RLTNetwork.cpp | 190 -----------------
torch_modules/src/RandomNetwork.cpp | 10 +-
torch_modules/src/RawInputLSTM.cpp | 40 ++++
torch_modules/src/SplitTransLSTM.cpp | 33 +++
torch_modules/src/Submodule.cpp | 7 +
31 files changed, 479 insertions(+), 856 deletions(-)
delete mode 100644 torch_modules/include/CNNNetwork.hpp
delete mode 100644 torch_modules/include/ConcatWordsNetwork.hpp
create mode 100644 torch_modules/include/ContextLSTM.hpp
create mode 100644 torch_modules/include/DepthLayerTreeEmbedding.hpp
create mode 100644 torch_modules/include/FocusedColumnLSTM.hpp
delete mode 100644 torch_modules/include/MLP.hpp
delete mode 100644 torch_modules/include/RLTNetwork.hpp
create mode 100644 torch_modules/include/RawInputLSTM.hpp
create mode 100644 torch_modules/include/SplitTransLSTM.hpp
create mode 100644 torch_modules/include/Submodule.hpp
delete mode 100644 torch_modules/src/CNNNetwork.cpp
delete mode 100644 torch_modules/src/ConcatWordsNetwork.cpp
create mode 100644 torch_modules/src/ContextLSTM.cpp
create mode 100644 torch_modules/src/DepthLayerTreeEmbedding.cpp
create mode 100644 torch_modules/src/FocusedColumnLSTM.cpp
delete mode 100644 torch_modules/src/MLP.cpp
delete mode 100644 torch_modules/src/RLTNetwork.cpp
create mode 100644 torch_modules/src/RawInputLSTM.cpp
create mode 100644 torch_modules/src/SplitTransLSTM.cpp
create mode 100644 torch_modules/src/Submodule.cpp
diff --git a/reading_machine/src/Classifier.cpp b/reading_machine/src/Classifier.cpp
index 54dccff..20a5056 100644
--- a/reading_machine/src/Classifier.cpp
+++ b/reading_machine/src/Classifier.cpp
@@ -1,8 +1,5 @@
#include "Classifier.hpp"
#include "util.hpp"
-#include "ConcatWordsNetwork.hpp"
-#include "RLTNetwork.hpp"
-#include "CNNNetwork.hpp"
#include "LSTMNetwork.hpp"
#include "RandomNetwork.hpp"
@@ -40,45 +37,6 @@ void Classifier::initNeuralNetwork(const std::string & topology)
this->nn.reset(new RandomNetworkImpl(this->transitionSet->size()));
}
},
- {
- std::regex("ConcatWords\\(\\{(.*)\\},\\{(.*)\\}\\)"),
- "ConcatWords({bufferContext},{stackContext}) : Concatenate embeddings of words in context.",
- [this,topology](auto sm)
- {
- std::vector<int> bufferContext, stackContext;
- for (auto s : util::split(sm.str(1), ','))
- bufferContext.emplace_back(std::stoi(s));
- for (auto s : util::split(sm.str(2), ','))
- stackContext.emplace_back(std::stoi(s));
- this->nn.reset(new ConcatWordsNetworkImpl(this->transitionSet->size(), bufferContext, stackContext));
- }
- },
- {
- std::regex("CNN\\(([+\\-]?\\d+),\\{(.*)\\},\\{(.*)\\},\\{(.*)\\},\\{(.*)\\},\\{(.*)\\},\\{(.*)\\},\\{(.*)\\}\\,([+\\-]?\\d+)\\,([+\\-]?\\d+)\\)"),
- "CNN(unknownValueThreshold,{bufferContext},{stackContext},{columns},{focusedBuffer},{focusedStack},{focusedColumns},{maxNbElements},leftBorderRawInput, rightBorderRawInput) : CNN to capture context.",
- [this,topology](auto sm)
- {
- std::vector<int> focusedBuffer, focusedStack, maxNbElements, bufferContext, stackContext;
- std::vector<std::string> focusedColumns, columns;
- for (auto s : util::split(sm.str(2), ','))
- bufferContext.emplace_back(std::stoi(s));
- for (auto s : util::split(sm.str(3), ','))
- stackContext.emplace_back(std::stoi(s));
- for (auto s : util::split(sm.str(4), ','))
- columns.emplace_back(s);
- for (auto s : util::split(sm.str(5), ','))
- focusedBuffer.push_back(std::stoi(s));
- for (auto s : util::split(sm.str(6), ','))
- focusedStack.push_back(std::stoi(s));
- for (auto s : util::split(sm.str(7), ','))
- focusedColumns.emplace_back(s);
- for (auto s : util::split(sm.str(8), ','))
- maxNbElements.push_back(std::stoi(s));
- if (focusedColumns.size() != maxNbElements.size())
- util::myThrow("focusedColumns.size() != maxNbElements.size()");
- this->nn.reset(new CNNNetworkImpl(this->transitionSet->size(), std::stoi(sm.str(1)), bufferContext, stackContext, columns, focusedBuffer, focusedStack, focusedColumns, maxNbElements, std::stoi(sm.str(9)), std::stoi(sm.str(10))));
- }
- },
{
std::regex("LSTM\\(([+\\-]?\\d+),\\{(.*)\\},\\{(.*)\\},\\{(.*)\\},\\{(.*)\\},\\{(.*)\\},\\{(.*)\\},\\{(.*)\\}\\,([+\\-]?\\d+)\\,([+\\-]?\\d+)\\)"),
"LSTM(unknownValueThreshold,{bufferContext},{stackContext},{columns},{focusedBuffer},{focusedStack},{focusedColumns},{maxNbElements},leftBorderRawInput, rightBorderRawInput) : CNN to capture context.",
@@ -105,14 +63,6 @@ void Classifier::initNeuralNetwork(const std::string & topology)
this->nn.reset(new LSTMNetworkImpl(this->transitionSet->size(), std::stoi(sm.str(1)), bufferContext, stackContext, columns, focusedBuffer, focusedStack, focusedColumns, maxNbElements, std::stoi(sm.str(9)), std::stoi(sm.str(10))));
}
},
- {
- std::regex("RLT\\(([+\\-]?\\d+),([+\\-]?\\d+),([+\\-]?\\d+)\\)"),
- "RLT(leftBorder,rightBorder,nbStack) : Recursive tree LSTM.",
- [this,topology](auto sm)
- {
- this->nn.reset(new RLTNetworkImpl(this->transitionSet->size(), std::stoi(sm.str(1)), std::stoi(sm.str(2)), std::stoi(sm.str(3))));
- }
- },
};
std::string errorMessage = fmt::format("Unknown neural network '{}', available networks :\n", topology);
diff --git a/torch_modules/include/CNN.hpp b/torch_modules/include/CNN.hpp
index 509be85..66c405c 100644
--- a/torch_modules/include/CNN.hpp
+++ b/torch_modules/include/CNN.hpp
@@ -17,7 +17,7 @@ class CNNImpl : public torch::nn::Module
CNNImpl(std::vector<int> windowSizes, int nbFilters, int elementSize);
torch::Tensor forward(torch::Tensor input);
- int getOutputSize();
+ std::size_t getOutputSize();
};
TORCH_MODULE(CNN);
diff --git a/torch_modules/include/CNNNetwork.hpp b/torch_modules/include/CNNNetwork.hpp
deleted file mode 100644
index 6fb985a..0000000
--- a/torch_modules/include/CNNNetwork.hpp
+++ /dev/null
@@ -1,35 +0,0 @@
-#ifndef CNNNETWORK__H
-#define CNNNETWORK__H
-
-#include "NeuralNetwork.hpp"
-#include "CNN.hpp"
-
-class CNNNetworkImpl : public NeuralNetworkImpl
-{
- private :
-
- int unknownValueThreshold;
- std::vector<std::string> focusedColumns;
- std::vector<int> maxNbElements;
- int leftWindowRawInput;
- int rightWindowRawInput;
- int rawInputSize;
-
- torch::nn::Embedding wordEmbeddings{nullptr};
- torch::nn::Dropout embeddingsDropout{nullptr};
- torch::nn::Dropout cnnDropout{nullptr};
- torch::nn::Dropout hiddenDropout{nullptr};
- torch::nn::Linear linear1{nullptr};
- torch::nn::Linear linear2{nullptr};
- CNN contextCNN{nullptr};
- CNN rawInputCNN{nullptr};
- std::vector<CNN> cnns;
-
- public :
-
- CNNNetworkImpl(int nbOutputs, int unknownValueThreshold, std::vector<int> bufferContext, std::vector<int> stackContext, 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);
- torch::Tensor forward(torch::Tensor input) override;
- std::vector<std::vector<long>> extractContext(Config & config, Dict & dict) const override;
-};
-
-#endif
diff --git a/torch_modules/include/ConcatWordsNetwork.hpp b/torch_modules/include/ConcatWordsNetwork.hpp
deleted file mode 100644
index b2c9691..0000000
--- a/torch_modules/include/ConcatWordsNetwork.hpp
+++ /dev/null
@@ -1,21 +0,0 @@
-#ifndef CONCATWORDSNETWORK__H
-#define CONCATWORDSNETWORK__H
-
-#include "NeuralNetwork.hpp"
-
-class ConcatWordsNetworkImpl : public NeuralNetworkImpl
-{
- private :
-
- torch::nn::Embedding wordEmbeddings{nullptr};
- torch::nn::Linear linear1{nullptr};
- torch::nn::Linear linear2{nullptr};
- torch::nn::Dropout dropout{nullptr};
-
- public :
-
- ConcatWordsNetworkImpl(int nbOutputs, const std::vector<int> & bufferContext, const std::vector<int> & stackContext);
- torch::Tensor forward(torch::Tensor input) override;
-};
-
-#endif
diff --git a/torch_modules/include/ContextLSTM.hpp b/torch_modules/include/ContextLSTM.hpp
new file mode 100644
index 0000000..136029c
--- /dev/null
+++ b/torch_modules/include/ContextLSTM.hpp
@@ -0,0 +1,30 @@
+#ifndef CONTEXTLSTM__H
+#define CONTEXTLSTM__H
+
+#include <torch/torch.h>
+#include "Submodule.hpp"
+#include "LSTM.hpp"
+
+class ContextLSTMImpl : public torch::nn::Module, public Submodule
+{
+ private :
+
+ LSTM lstm{nullptr};
+ std::vector<std::string> columns;
+ std::vector<int> bufferContext;
+ std::vector<int> stackContext;
+ int unknownValueThreshold;
+ std::vector<std::string> unknownValueColumns{"FORM", "LEMMA"};
+
+ public :
+
+ ContextLSTMImpl(std::vector<std::string> columns, int embeddingsSize, int outEmbeddingsSize, std::vector<int> bufferContext, std::vector<int> stackContext, LSTMImpl::LSTMOptions options, int unknownValueThreshold);
+ torch::Tensor forward(torch::Tensor input);
+ std::size_t getOutputSize() override;
+ std::size_t getInputSize() override;
+ void addToContext(std::vector<std::vector<long>> & context, Dict & dict, const Config & config) const override;
+};
+TORCH_MODULE(ContextLSTM);
+
+#endif
+
diff --git a/torch_modules/include/DepthLayerTreeEmbedding.hpp b/torch_modules/include/DepthLayerTreeEmbedding.hpp
new file mode 100644
index 0000000..d471e6b
--- /dev/null
+++ b/torch_modules/include/DepthLayerTreeEmbedding.hpp
@@ -0,0 +1,25 @@
+#ifndef DEPTHLAYERTREEEMBEDDING__H
+#define DEPTHLAYERTREEEMBEDDING__H
+
+#include <torch/torch.h>
+#include "fmt/core.h"
+#include "LSTM.hpp"
+
+class DepthLayerTreeEmbeddingImpl : public torch::nn::Module
+{
+ private :
+
+ std::vector<LSTM> depthLstm;
+ int maxDepth;
+ int maxElemPerDepth;
+
+ public :
+
+ DepthLayerTreeEmbeddingImpl(int maxDepth, int maxElemPerDepth);
+ torch::Tensor forward(torch::Tensor input);
+ int getOutputSize();
+};
+TORCH_MODULE(DepthLayerTreeEmbedding);
+
+#endif
+
diff --git a/torch_modules/include/FocusedColumnLSTM.hpp b/torch_modules/include/FocusedColumnLSTM.hpp
new file mode 100644
index 0000000..6ea836a
--- /dev/null
+++ b/torch_modules/include/FocusedColumnLSTM.hpp
@@ -0,0 +1,28 @@
+#ifndef FOCUSEDCOLUMNLSTM__H
+#define FOCUSEDCOLUMNLSTM__H
+
+#include <torch/torch.h>
+#include "Submodule.hpp"
+#include "LSTM.hpp"
+
+class FocusedColumnLSTMImpl : public torch::nn::Module, public Submodule
+{
+ private :
+
+ LSTM lstm{nullptr};
+ std::vector<int> focusedBuffer, focusedStack;
+ std::string column;
+ int maxNbElements;
+
+ public :
+
+ FocusedColumnLSTMImpl(std::vector<int> focusedBuffer, std::vector<int> focusedStack, std::string column, int maxNbElements, int embeddingsSize, int outEmbeddingsSize, LSTMImpl::LSTMOptions options);
+ torch::Tensor forward(torch::Tensor input);
+ std::size_t getOutputSize() override;
+ std::size_t getInputSize() override;
+ void addToContext(std::vector<std::vector<long>> & context, Dict & dict, const Config & config) const override;
+};
+TORCH_MODULE(FocusedColumnLSTM);
+
+#endif
+
diff --git a/torch_modules/include/LSTM.hpp b/torch_modules/include/LSTM.hpp
index eb06c45..c45cb9f 100644
--- a/torch_modules/include/LSTM.hpp
+++ b/torch_modules/include/LSTM.hpp
@@ -6,6 +6,10 @@
class LSTMImpl : public torch::nn::Module
{
+ public :
+
+ using LSTMOptions = std::tuple<bool,bool,int,float,bool>;
+
private :
torch::nn::LSTM lstm{nullptr};
@@ -13,7 +17,7 @@ class LSTMImpl : public torch::nn::Module
public :
- LSTMImpl(int inputSize, int outputSize, std::tuple<bool,bool,int,float,bool> options);
+ LSTMImpl(int inputSize, int outputSize, LSTMOptions options);
torch::Tensor forward(torch::Tensor input);
int getOutputSize(int sequenceLength);
};
diff --git a/torch_modules/include/LSTMNetwork.hpp b/torch_modules/include/LSTMNetwork.hpp
index 860f407..5762ad1 100644
--- a/torch_modules/include/LSTMNetwork.hpp
+++ b/torch_modules/include/LSTMNetwork.hpp
@@ -2,29 +2,28 @@
#define LSTMNETWORK__H
#include "NeuralNetwork.hpp"
-#include "LSTM.hpp"
+#include "ContextLSTM.hpp"
+#include "RawInputLSTM.hpp"
+#include "SplitTransLSTM.hpp"
+#include "FocusedColumnLSTM.hpp"
class LSTMNetworkImpl : public NeuralNetworkImpl
{
private :
- int unknownValueThreshold;
- std::vector<std::string> focusedColumns;
- std::vector<int> maxNbElements;
- int leftWindowRawInput;
- int rightWindowRawInput;
- int rawInputSize;
-
torch::nn::Embedding wordEmbeddings{nullptr};
torch::nn::Dropout embeddingsDropout{nullptr};
torch::nn::Dropout lstmDropout{nullptr};
torch::nn::Dropout hiddenDropout{nullptr};
torch::nn::Linear linear1{nullptr};
torch::nn::Linear linear2{nullptr};
- LSTM contextLSTM{nullptr};
- LSTM rawInputLSTM{nullptr};
- LSTM splitTransLSTM{nullptr};
- std::vector<LSTM> lstms;
+
+ ContextLSTM contextLSTM{nullptr};
+ RawInputLSTM rawInputLSTM{nullptr};
+ SplitTransLSTM splitTransLSTM{nullptr};
+ std::vector<FocusedColumnLSTM> focusedLstms;
+
+ bool hasRawInputLSTM{false};
public :
diff --git a/torch_modules/include/MLP.hpp b/torch_modules/include/MLP.hpp
deleted file mode 100644
index 90bde50..0000000
--- a/torch_modules/include/MLP.hpp
+++ /dev/null
@@ -1,14 +0,0 @@
-#ifndef MLP__H
-#define MLP__H
-
-#include <torch/torch.h>
-
-class MLPImpl : torch::nn::Module
-{
- public :
-
- MLPImpl(const std::string & topology);
-};
-TORCH_MODULE(MLP);
-
-#endif
diff --git a/torch_modules/include/NeuralNetwork.hpp b/torch_modules/include/NeuralNetwork.hpp
index ffc0052..be25c87 100644
--- a/torch_modules/include/NeuralNetwork.hpp
+++ b/torch_modules/include/NeuralNetwork.hpp
@@ -15,31 +15,10 @@ class NeuralNetworkImpl : public torch::nn::Module
static constexpr int maxNbEmbeddings = 150000;
- std::vector<std::string> columns{"FORM"};
- std::vector<int> bufferContext{-3,-2,-1,0,1};
- std::vector<int> stackContext{};
- std::vector<int> bufferFocused{};
- std::vector<int> stackFocused{};
-
- protected :
-
- void setBufferContext(const std::vector<int> & bufferContext);
- void setStackContext(const std::vector<int> & stackContext);
- void setBufferFocused(const std::vector<int> & bufferFocused);
- void setStackFocused(const std::vector<int> & stackFocused);
-
public :
virtual torch::Tensor forward(torch::Tensor input) = 0;
- virtual std::vector<std::vector<long>> extractContext(Config & config, Dict & dict) const;
- std::vector<long> extractContextIndexes(const Config & config) const;
- std::vector<long> extractFocusedIndexes(const Config & config) const;
- int getContextSize() const;
- void setColumns(const std::vector<std::string> & columns);
- void addAppliableSplitTransitions(std::vector<std::vector<long>> & context, Dict & dict, const Config & config) const;
- void addRawInput(std::vector<std::vector<long>> & context, Dict & dict, const Config & config, int leftWindowRawInput, int rightWindowRawInput) const;
- void addContext(std::vector<std::vector<long>> & context, Dict & dict, const Config & config, const std::vector<long> & contextIndexes, int unknownValueThreshold, const std::vector<std::string> & unknownValueColumns) const;
- void addFocused(std::vector<std::vector<long>> & context, Dict & dict, const Config & config, const std::vector<long> & focusedIndexes, const std::vector<std::string> & focusedColumns, const std::vector<int> & maxNbElements) const;
+ virtual std::vector<std::vector<long>> extractContext(Config & config, Dict & dict) const = 0;
};
TORCH_MODULE(NeuralNetwork);
diff --git a/torch_modules/include/RLTNetwork.hpp b/torch_modules/include/RLTNetwork.hpp
deleted file mode 100644
index 71b8aa5..0000000
--- a/torch_modules/include/RLTNetwork.hpp
+++ /dev/null
@@ -1,31 +0,0 @@
-#ifndef RLTNETWORK__H
-#define RLTNETWORK__H
-
-#include "NeuralNetwork.hpp"
-
-class RLTNetworkImpl : public NeuralNetworkImpl
-{
- private :
-
- static constexpr long maxNbChilds{8};
- static inline std::vector<long> focusedBufferIndexes{0,1,2};
- static inline std::vector<long> focusedStackIndexes{0,1};
-
- int leftBorder, rightBorder;
-
- torch::nn::Embedding wordEmbeddings{nullptr};
- torch::nn::Linear linear1{nullptr};
- torch::nn::Linear linear2{nullptr};
- torch::nn::LSTM vectorBiLSTM{nullptr};
- torch::nn::LSTM treeLSTM{nullptr};
- torch::Tensor S;
- torch::Tensor nullTree;
-
- public :
-
- RLTNetworkImpl(int nbOutputs, int leftBorder, int rightBorder, int nbStackElements);
- torch::Tensor forward(torch::Tensor input) override;
- std::vector<std::vector<long>> extractContext(Config & config, Dict & dict) const override;
-};
-
-#endif
diff --git a/torch_modules/include/RandomNetwork.hpp b/torch_modules/include/RandomNetwork.hpp
index e715cc4..8f58d7b 100644
--- a/torch_modules/include/RandomNetwork.hpp
+++ b/torch_modules/include/RandomNetwork.hpp
@@ -13,6 +13,7 @@ class RandomNetworkImpl : public NeuralNetworkImpl
RandomNetworkImpl(long outputSize);
torch::Tensor forward(torch::Tensor input) override;
+ std::vector<std::vector<long>> extractContext(Config &, Dict &) const override;
};
#endif
diff --git a/torch_modules/include/RawInputLSTM.hpp b/torch_modules/include/RawInputLSTM.hpp
new file mode 100644
index 0000000..db17d6f
--- /dev/null
+++ b/torch_modules/include/RawInputLSTM.hpp
@@ -0,0 +1,26 @@
+#ifndef RAWINPUTLSTM__H
+#define RAWINPUTLSTM__H
+
+#include <torch/torch.h>
+#include "Submodule.hpp"
+#include "LSTM.hpp"
+
+class RawInputLSTMImpl : public torch::nn::Module, public Submodule
+{
+ private :
+
+ LSTM lstm{nullptr};
+ int leftWindow, rightWindow;
+
+ public :
+
+ RawInputLSTMImpl(int leftWindow, int rightWindow, int embeddingsSize, int outEmbeddingsSize, LSTMImpl::LSTMOptions options);
+ torch::Tensor forward(torch::Tensor input);
+ std::size_t getOutputSize() override;
+ std::size_t getInputSize() override;
+ void addToContext(std::vector<std::vector<long>> & context, Dict & dict, const Config & config) const override;
+};
+TORCH_MODULE(RawInputLSTM);
+
+#endif
+
diff --git a/torch_modules/include/SplitTransLSTM.hpp b/torch_modules/include/SplitTransLSTM.hpp
new file mode 100644
index 0000000..f90c0ed
--- /dev/null
+++ b/torch_modules/include/SplitTransLSTM.hpp
@@ -0,0 +1,26 @@
+#ifndef SPLITTRANSLSTM__H
+#define SPLITTRANSLSTM__H
+
+#include <torch/torch.h>
+#include "Submodule.hpp"
+#include "LSTM.hpp"
+
+class SplitTransLSTMImpl : public torch::nn::Module, public Submodule
+{
+ private :
+
+ LSTM lstm{nullptr};
+ int maxNbTrans;
+
+ public :
+
+ SplitTransLSTMImpl(int maxNbTrans, int embeddingsSize, int outEmbeddingsSize, LSTMImpl::LSTMOptions options);
+ torch::Tensor forward(torch::Tensor input);
+ std::size_t getOutputSize() override;
+ std::size_t getInputSize() override;
+ void addToContext(std::vector<std::vector<long>> & context, Dict & dict, const Config & config) const override;
+};
+TORCH_MODULE(SplitTransLSTM);
+
+#endif
+
diff --git a/torch_modules/include/Submodule.hpp b/torch_modules/include/Submodule.hpp
new file mode 100644
index 0000000..437bbfa
--- /dev/null
+++ b/torch_modules/include/Submodule.hpp
@@ -0,0 +1,22 @@
+#ifndef SUBMODULE__H
+#define SUBMODULE__H
+
+#include "Dict.hpp"
+#include "Config.hpp"
+
+class Submodule
+{
+ protected :
+
+ std::size_t firstInputIndex{0};
+
+ public :
+
+ void setFirstInputIndex(std::size_t firstInputIndex);
+ virtual std::size_t getOutputSize() = 0;
+ virtual std::size_t getInputSize() = 0;
+ virtual void addToContext(std::vector<std::vector<long>> & context, Dict & dict, const Config & config) const = 0;
+};
+
+#endif
+
diff --git a/torch_modules/src/CNN.cpp b/torch_modules/src/CNN.cpp
index cc67d60..dbc3797 100644
--- a/torch_modules/src/CNN.cpp
+++ b/torch_modules/src/CNN.cpp
@@ -26,7 +26,7 @@ torch::Tensor CNNImpl::forward(torch::Tensor input)
return cnnOut;
}
-int CNNImpl::getOutputSize()
+std::size_t CNNImpl::getOutputSize()
{
return windowSizes.size()*nbFilters;
}
diff --git a/torch_modules/src/CNNNetwork.cpp b/torch_modules/src/CNNNetwork.cpp
deleted file mode 100644
index c03b347..0000000
--- a/torch_modules/src/CNNNetwork.cpp
+++ /dev/null
@@ -1,187 +0,0 @@
-#include "CNNNetwork.hpp"
-
-CNNNetworkImpl::CNNNetworkImpl(int nbOutputs, int unknownValueThreshold, std::vector<int> bufferContext, std::vector<int> stackContext, 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), focusedColumns(focusedColumns), maxNbElements(maxNbElements), leftWindowRawInput(leftWindowRawInput), rightWindowRawInput(rightWindowRawInput)
-{
- constexpr int embeddingsSize = 64;
- constexpr int hiddenSize = 1024;
- constexpr int nbFiltersContext = 512;
- constexpr int nbFiltersFocused = 64;
-
- setBufferContext(bufferContext);
- setStackContext(stackContext);
- setColumns(columns);
- setBufferFocused(focusedBufferIndexes);
- setStackFocused(focusedStackIndexes);
-
- rawInputSize = leftWindowRawInput + rightWindowRawInput + 1;
- if (leftWindowRawInput < 0 or rightWindowRawInput < 0)
- rawInputSize = 0;
- else
- rawInputCNN = register_module("rawInputCNN", CNN(std::vector<int>{2,3,4}, nbFiltersFocused, embeddingsSize));
- int rawInputCNNOutputSize = rawInputSize == 0 ? 0 : rawInputCNN->getOutputSize();
-
- wordEmbeddings = register_module("word_embeddings", torch::nn::Embedding(torch::nn::EmbeddingOptions(maxNbEmbeddings, embeddingsSize)));
- embeddingsDropout = register_module("embeddings_dropout", torch::nn::Dropout(0.3));
- cnnDropout = register_module("cnn_dropout", torch::nn::Dropout(0.3));
- hiddenDropout = register_module("hidden_dropout", torch::nn::Dropout(0.3));
- contextCNN = register_module("contextCNN", CNN(std::vector<int>{2,3,4}, nbFiltersContext, columns.size()*embeddingsSize));
- int totalCnnOutputSize = contextCNN->getOutputSize()+rawInputCNNOutputSize;
- for (auto & col : focusedColumns)
- {
- std::vector<int> windows{2,3,4};
- cnns.emplace_back(register_module(fmt::format("CNN_{}", col), CNN(windows, nbFiltersFocused, embeddingsSize)));
- totalCnnOutputSize += cnns.back()->getOutputSize() * (focusedBufferIndexes.size()+focusedStackIndexes.size());
- }
- linear1 = register_module("linear1", torch::nn::Linear(totalCnnOutputSize, hiddenSize));
- linear2 = register_module("linear2", torch::nn::Linear(hiddenSize, nbOutputs));
-}
-
-torch::Tensor CNNNetworkImpl::forward(torch::Tensor input)
-{
- if (input.dim() == 1)
- input = input.unsqueeze(0);
-
- auto embeddings = embeddingsDropout(wordEmbeddings(input));
-
- auto context = embeddings.narrow(1, rawInputSize, getContextSize());
- 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)));
-
- std::vector<torch::Tensor> cnnOutputs;
-
- if (rawInputSize != 0)
- {
- auto rawLetters = embeddings.narrow(1, 0, leftWindowRawInput+rightWindowRawInput+1);
- cnnOutputs.emplace_back(rawInputCNN(rawLetters.unsqueeze(1)));
- }
-
- auto curIndex = 0;
- for (unsigned int i = 0; i < focusedColumns.size(); i++)
- {
- long nbElements = maxNbElements[i];
- for (unsigned int focused = 0; focused < bufferFocused.size()+stackFocused.size(); focused++)
- {
- auto cnnInput = elementsEmbeddings.narrow(1, curIndex, nbElements).unsqueeze(1);
- curIndex += nbElements;
- cnnOutputs.emplace_back(cnns[i](cnnInput));
- }
- }
-
- cnnOutputs.emplace_back(contextCNN(context.unsqueeze(1)));
-
- auto totalInput = cnnDropout(torch::cat(cnnOutputs, 1));
-
- return linear2(hiddenDropout(torch::relu(linear1(totalInput))));
-}
-
-std::vector<std::vector<long>> CNNNetworkImpl::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))));
- 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);
- }
- }
-
- std::vector<long> focusedIndexes = extractFocusedIndexes(config);
-
- for (auto & contextElement : context)
- for (unsigned int colIndex = 0; colIndex < focusedColumns.size(); colIndex++)
- {
- auto & col = focusedColumns[colIndex];
-
- 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]));
- 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;
-}
-
diff --git a/torch_modules/src/ConcatWordsNetwork.cpp b/torch_modules/src/ConcatWordsNetwork.cpp
deleted file mode 100644
index 2331d59..0000000
--- a/torch_modules/src/ConcatWordsNetwork.cpp
+++ /dev/null
@@ -1,25 +0,0 @@
-#include "ConcatWordsNetwork.hpp"
-
-ConcatWordsNetworkImpl::ConcatWordsNetworkImpl(int nbOutputs, const std::vector<int> & bufferContext, const std::vector<int> & stackContext)
-{
- constexpr int embeddingsSize = 64;
- constexpr int hiddenSize = 500;
-
- setBufferContext(bufferContext);
- setStackContext(stackContext);
- setColumns({"FORM", "UPOS"});
-
- wordEmbeddings = register_module("word_embeddings", torch::nn::Embedding(torch::nn::EmbeddingOptions(maxNbEmbeddings, embeddingsSize)));
- linear1 = register_module("linear1", torch::nn::Linear(getContextSize()*embeddingsSize, hiddenSize));
- linear2 = register_module("linear2", torch::nn::Linear(hiddenSize, nbOutputs));
- dropout = register_module("dropout", torch::nn::Dropout(0.3));
-}
-
-torch::Tensor ConcatWordsNetworkImpl::forward(torch::Tensor input)
-{
- if (input.dim() == 1)
- input = input.unsqueeze(0);
- auto wordsAsEmb = dropout(wordEmbeddings(input).view({input.size(0), -1}));
- return linear2(torch::relu(linear1(wordsAsEmb)));
-}
-
diff --git a/torch_modules/src/ContextLSTM.cpp b/torch_modules/src/ContextLSTM.cpp
new file mode 100644
index 0000000..95daa69
--- /dev/null
+++ b/torch_modules/src/ContextLSTM.cpp
@@ -0,0 +1,62 @@
+#include "ContextLSTM.hpp"
+
+ContextLSTMImpl::ContextLSTMImpl(std::vector<std::string> columns, int embeddingsSize, int outEmbeddingsSize, std::vector<int> bufferContext, std::vector<int> stackContext, LSTMImpl::LSTMOptions options, int unknownValueThreshold) : columns(columns), bufferContext(bufferContext), stackContext(stackContext), unknownValueThreshold(unknownValueThreshold)
+{
+ lstm = register_module("lstm", LSTM(columns.size()*embeddingsSize, outEmbeddingsSize, options));
+}
+
+std::size_t ContextLSTMImpl::getOutputSize()
+{
+ return lstm->getOutputSize(bufferContext.size()+stackContext.size());
+}
+
+std::size_t ContextLSTMImpl::getInputSize()
+{
+ return columns.size()*(bufferContext.size()+stackContext.size());
+}
+
+void ContextLSTMImpl::addToContext(std::vector<std::vector<long>> & context, Dict & dict, const Config & config) const
+{
+ std::vector<long> contextIndexes;
+
+ for (int index : bufferContext)
+ contextIndexes.emplace_back(config.getRelativeWordIndex(index));
+
+ for (int index : stackContext)
+ if (config.hasStack(index))
+ contextIndexes.emplace_back(config.getStack(index));
+ else
+ contextIndexes.emplace_back(-1);
+
+ 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())
+ for (auto & targetCol : unknownValueColumns)
+ if (col == targetCol)
+ if (dict.getNbOccs(dictIndex) <= unknownValueThreshold)
+ {
+ context.emplace_back(context.back());
+ context.back().back() = dict.getIndexOrInsert(Dict::unknownValueStr);
+ }
+ }
+}
+
+torch::Tensor ContextLSTMImpl::forward(torch::Tensor input)
+{
+ auto context = input.narrow(1, firstInputIndex, getInputSize());
+
+ context = context.view({context.size(0), context.size(1)/(int)columns.size(), (int)columns.size()*context.size(2)});
+
+ return lstm(context);
+}
+
diff --git a/torch_modules/src/DepthLayerTreeEmbedding.cpp b/torch_modules/src/DepthLayerTreeEmbedding.cpp
new file mode 100644
index 0000000..d53a04a
--- /dev/null
+++ b/torch_modules/src/DepthLayerTreeEmbedding.cpp
@@ -0,0 +1,17 @@
+#include "DepthLayerTreeEmbedding.hpp"
+
+DepthLayerTreeEmbeddingImpl::DepthLayerTreeEmbeddingImpl(int maxDepth, int maxElemPerDepth) : maxDepth(maxDepth), maxElemPerDepth(maxElemPerDepth)
+{
+
+}
+
+torch::Tensor DepthLayerTreeEmbeddingImpl::forward(torch::Tensor input)
+{
+
+}
+
+int DepthLayerTreeEmbeddingImpl::getOutputSize()
+{
+
+}
+
diff --git a/torch_modules/src/FocusedColumnLSTM.cpp b/torch_modules/src/FocusedColumnLSTM.cpp
new file mode 100644
index 0000000..9b5f52f
--- /dev/null
+++ b/torch_modules/src/FocusedColumnLSTM.cpp
@@ -0,0 +1,94 @@
+#include "FocusedColumnLSTM.hpp"
+
+FocusedColumnLSTMImpl::FocusedColumnLSTMImpl(std::vector<int> focusedBuffer, std::vector<int> focusedStack, std::string column, int maxNbElements, int embeddingsSize, int outEmbeddingsSize, LSTMImpl::LSTMOptions options) : focusedBuffer(focusedBuffer), focusedStack(focusedStack), column(column), maxNbElements(maxNbElements)
+{
+ lstm = register_module("lstm", LSTM(embeddingsSize, outEmbeddingsSize, options));
+}
+
+torch::Tensor FocusedColumnLSTMImpl::forward(torch::Tensor input)
+{
+ std::vector<torch::Tensor> outputs;
+ for (unsigned int i = 0; i < focusedBuffer.size()+focusedStack.size(); i++)
+ outputs.emplace_back(lstm(input.narrow(1, firstInputIndex+i*maxNbElements, maxNbElements)));
+
+ return torch::cat(outputs, 1);
+}
+
+std::size_t FocusedColumnLSTMImpl::getOutputSize()
+{
+ return (focusedBuffer.size()+focusedStack.size())*lstm->getOutputSize(maxNbElements);
+}
+
+std::size_t FocusedColumnLSTMImpl::getInputSize()
+{
+ return (focusedBuffer.size()+focusedStack.size()) * maxNbElements;
+}
+
+void FocusedColumnLSTMImpl::addToContext(std::vector<std::vector<long>> & context, Dict & dict, const Config & config) const
+{
+ std::vector<long> focusedIndexes;
+
+ for (int index : focusedBuffer)
+ focusedIndexes.emplace_back(config.getRelativeWordIndex(index));
+
+ for (int index : focusedStack)
+ if (config.hasStack(index))
+ focusedIndexes.emplace_back(config.getStack(index));
+ else
+ focusedIndexes.emplace_back(-1);
+
+ for (auto & contextElement : context)
+ {
+ for (auto index : focusedIndexes)
+ {
+ if (index == -1)
+ {
+ for (int i = 0; i < maxNbElements; i++)
+ contextElement.emplace_back(dict.getIndexOrInsert(Dict::nullValueStr));
+ continue;
+ }
+
+ std::vector<std::string> elements;
+ if (column == "FORM")
+ {
+ auto asUtf8 = util::splitAsUtf8(config.getAsFeature(column, index).get());
+
+ for (int i = 0; i < maxNbElements; i++)
+ if (i < (int)asUtf8.size())
+ elements.emplace_back(fmt::format("{}", asUtf8[i]));
+ else
+ elements.emplace_back(Dict::nullValueStr);
+ }
+ else if (column == "FEATS")
+ {
+ auto splited = util::split(config.getAsFeature(column, index).get(), '|');
+
+ for (int i = 0; i < maxNbElements; i++)
+ if (i < (int)splited.size())
+ elements.emplace_back(fmt::format("FEATS({})", splited[i]));
+ else
+ elements.emplace_back(Dict::nullValueStr);
+ }
+ else if (column == "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(column, index));
+ }
+
+ if ((int)elements.size() != maxNbElements)
+ util::myThrow(fmt::format("elements.size ({}) != maxNbElements ({})", elements.size(), maxNbElements));
+
+ for (auto & element : elements)
+ contextElement.emplace_back(dict.getIndexOrInsert(element));
+ }
+ }
+}
+
diff --git a/torch_modules/src/LSTM.cpp b/torch_modules/src/LSTM.cpp
index 58b102a..b8f8e7f 100644
--- a/torch_modules/src/LSTM.cpp
+++ b/torch_modules/src/LSTM.cpp
@@ -1,6 +1,6 @@
#include "LSTM.hpp"
-LSTMImpl::LSTMImpl(int inputSize, int outputSize, std::tuple<bool,bool,int,float,bool> options) : outputAll(std::get<4>(options))
+LSTMImpl::LSTMImpl(int inputSize, int outputSize, LSTMOptions options) : outputAll(std::get<4>(options))
{
auto lstmOptions = torch::nn::LSTMOptions(inputSize, outputSize)
.batch_first(std::get<0>(options))
diff --git a/torch_modules/src/LSTMNetwork.cpp b/torch_modules/src/LSTMNetwork.cpp
index 268b504..430b226 100644
--- a/torch_modules/src/LSTMNetwork.cpp
+++ b/torch_modules/src/LSTMNetwork.cpp
@@ -1,6 +1,6 @@
#include "LSTMNetwork.hpp"
-LSTMNetworkImpl::LSTMNetworkImpl(int nbOutputs, int unknownValueThreshold, std::vector<int> bufferContext, std::vector<int> stackContext, 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), focusedColumns(focusedColumns), maxNbElements(maxNbElements), leftWindowRawInput(leftWindowRawInput), rightWindowRawInput(rightWindowRawInput)
+LSTMNetworkImpl::LSTMNetworkImpl(int nbOutputs, int unknownValueThreshold, std::vector<int> bufferContext, std::vector<int> stackContext, 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)
{
constexpr int embeddingsSize = 256;
constexpr int hiddenSize = 8192;
@@ -8,41 +8,45 @@ LSTMNetworkImpl::LSTMNetworkImpl(int nbOutputs, int unknownValueThreshold, std::
constexpr int focusedLSTMSize = 256;
constexpr int rawInputLSTMSize = 32;
- std::tuple<bool,bool,int,float,bool> lstmOptions{true,true,2,0.3,false};
+ LSTMImpl::LSTMOptions lstmOptions{true,true,2,0.3,false};
auto lstmOptionsAll = lstmOptions;
std::get<4>(lstmOptionsAll) = true;
- setBufferContext(bufferContext);
- setStackContext(stackContext);
- setColumns(columns);
- setBufferFocused(focusedBufferIndexes);
- setStackFocused(focusedStackIndexes);
-
- rawInputSize = leftWindowRawInput + rightWindowRawInput + 1;
- int rawInputLSTMOutSize = 0;
- if (leftWindowRawInput < 0 or rightWindowRawInput < 0)
- rawInputSize = 0;
- else
+ int currentOutputSize = embeddingsSize;
+ int currentInputSize = 1;
+
+ contextLSTM = register_module("contextLSTM", ContextLSTM(columns, embeddingsSize, contextLSTMSize, bufferContext, stackContext, lstmOptions, unknownValueThreshold));
+ contextLSTM->setFirstInputIndex(currentInputSize);
+ currentOutputSize += contextLSTM->getOutputSize();
+ currentInputSize += contextLSTM->getInputSize();
+
+ if (leftWindowRawInput >= 0 and rightWindowRawInput >= 0)
{
- rawInputLSTM = register_module("rawInputLSTM", LSTM(embeddingsSize, rawInputLSTMSize, lstmOptionsAll));
- rawInputLSTMOutSize = rawInputLSTM->getOutputSize(rawInputSize);
+ hasRawInputLSTM = true;
+ rawInputLSTM = register_module("rawInputLSTM", RawInputLSTM(leftWindowRawInput, rightWindowRawInput, embeddingsSize, rawInputLSTMSize, lstmOptionsAll));
+ rawInputLSTM->setFirstInputIndex(currentInputSize);
+ currentOutputSize += rawInputLSTM->getOutputSize();
+ currentInputSize += rawInputLSTM->getInputSize();
}
- wordEmbeddings = register_module("word_embeddings", torch::nn::Embedding(torch::nn::EmbeddingOptions(maxNbEmbeddings, embeddingsSize)));
- embeddingsDropout = register_module("embeddings_dropout", torch::nn::Dropout(0.3));
- hiddenDropout = register_module("hidden_dropout", torch::nn::Dropout(0.3));
- contextLSTM = register_module("contextLSTM", LSTM(columns.size()*embeddingsSize, contextLSTMSize, lstmOptions));
- splitTransLSTM = register_module("splitTransLSTM", LSTM(embeddingsSize, embeddingsSize, lstmOptionsAll));
-
- int totalLSTMOutputSize = rawInputLSTMOutSize + contextLSTM->getOutputSize(getContextSize()) + splitTransLSTM->getOutputSize(Config::maxNbAppliableSplitTransitions);
+ splitTransLSTM = register_module("splitTransLSTM", SplitTransLSTM(Config::maxNbAppliableSplitTransitions, embeddingsSize, embeddingsSize, lstmOptionsAll));
+ splitTransLSTM->setFirstInputIndex(currentInputSize);
+ currentOutputSize += splitTransLSTM->getOutputSize();
+ currentInputSize += splitTransLSTM->getInputSize();
for (unsigned int i = 0; i < focusedColumns.size(); i++)
{
- lstms.emplace_back(register_module(fmt::format("LSTM_{}", focusedColumns[i]), LSTM(embeddingsSize, focusedLSTMSize, lstmOptions)));
- totalLSTMOutputSize += (bufferFocused.size()+stackFocused.size())*lstms.back()->getOutputSize(maxNbElements[i]);
+ focusedLstms.emplace_back(register_module(fmt::format("LSTM_{}", focusedColumns[i]), FocusedColumnLSTM(focusedBufferIndexes, focusedStackIndexes, focusedColumns[i], maxNbElements[i], embeddingsSize, focusedLSTMSize, lstmOptions)));
+ focusedLstms.back()->setFirstInputIndex(currentInputSize);
+ currentOutputSize += focusedLstms.back()->getOutputSize();
+ currentInputSize += focusedLstms.back()->getInputSize();
}
- linear1 = register_module("linear1", torch::nn::Linear(embeddingsSize+totalLSTMOutputSize, hiddenSize));
+ wordEmbeddings = register_module("word_embeddings", torch::nn::Embedding(torch::nn::EmbeddingOptions(maxNbEmbeddings, embeddingsSize)));
+ embeddingsDropout = register_module("embeddings_dropout", torch::nn::Dropout(0.3));
+ hiddenDropout = register_module("hidden_dropout", torch::nn::Dropout(0.3));
+
+ linear1 = register_module("linear1", torch::nn::Linear(currentOutputSize, hiddenSize));
linear2 = register_module("linear2", torch::nn::Linear(hiddenSize, nbOutputs));
}
@@ -53,40 +57,19 @@ torch::Tensor LSTMNetworkImpl::forward(torch::Tensor input)
auto embeddings = embeddingsDropout(wordEmbeddings(input));
- auto state = embeddings.narrow(1, 0, 1).squeeze(1);
-
- auto splitTrans = embeddings.narrow(1, 1, Config::maxNbAppliableSplitTransitions);
+ std::vector<torch::Tensor> outputs{embeddings.narrow(1,0,1).squeeze(1)};
- auto context = embeddings.narrow(1, 1+splitTrans.size(1)+rawInputSize, getContextSize());
+ outputs.emplace_back(contextLSTM(embeddings));
- context = context.view({context.size(0), context.size(1)/(int)columns.size(), (int)columns.size()*(int)wordEmbeddings->options.embedding_dim()});
+ if (hasRawInputLSTM)
+ outputs.emplace_back(rawInputLSTM(embeddings));
- auto elementsEmbeddings = embeddings.narrow(1, 1+splitTrans.size(1)+rawInputSize+context.size(1), input.size(1)-(1+splitTrans.size(1)+rawInputSize+context.size(1)));
+ outputs.emplace_back(splitTransLSTM(embeddings));
- std::vector<torch::Tensor> lstmOutputs;
+ for (auto & lstm : focusedLstms)
+ outputs.emplace_back(lstm(embeddings));
- lstmOutputs.emplace_back(state);
-
- if (rawInputSize != 0)
- {
- auto rawLetters = embeddings.narrow(1, splitTrans.size(1), rawInputSize);
- lstmOutputs.emplace_back(rawInputLSTM(rawLetters));
- }
-
- lstmOutputs.emplace_back(splitTransLSTM(splitTrans));
-
- auto curIndex = 0;
- for (unsigned int i = 0; i < focusedColumns.size(); i++)
- for (unsigned int focused = 0; focused < bufferFocused.size()+stackFocused.size(); focused++)
- {
- auto lstmInput = elementsEmbeddings.narrow(1, curIndex, maxNbElements[i]);
- curIndex += maxNbElements[i];
- lstmOutputs.emplace_back(lstms[i](lstmInput));
- }
-
- lstmOutputs.emplace_back(contextLSTM(context));
-
- auto totalInput = torch::cat(lstmOutputs, 1);
+ auto totalInput = torch::cat(outputs, 1);
return linear2(hiddenDropout(torch::relu(linear1(totalInput))));
}
@@ -101,13 +84,12 @@ std::vector<std::vector<long>> LSTMNetworkImpl::extractContext(Config & config,
context.back().emplace_back(dict.getIndexOrInsert(config.getState()));
- addAppliableSplitTransitions(context, dict, config);
-
- addRawInput(context, dict, config, leftWindowRawInput, rightWindowRawInput);
-
- addContext(context, dict, config, extractContextIndexes(config), unknownValueThreshold, {"FORM","LEMMA"});
-
- addFocused(context, dict, config, extractFocusedIndexes(config), focusedColumns, maxNbElements);
+ contextLSTM->addToContext(context, dict, config);
+ if (hasRawInputLSTM)
+ rawInputLSTM->addToContext(context, dict, config);
+ splitTransLSTM->addToContext(context, dict, config);
+ for (auto & lstm : focusedLstms)
+ lstm->addToContext(context, dict, config);
if (!is_training() && context.size() > 1)
util::myThrow(fmt::format("Not in training mode, yet context yields multiple variants (size={})", context.size()));
diff --git a/torch_modules/src/MLP.cpp b/torch_modules/src/MLP.cpp
deleted file mode 100644
index 182e880..0000000
--- a/torch_modules/src/MLP.cpp
+++ /dev/null
@@ -1,8 +0,0 @@
-#include "MLP.hpp"
-#include <regex>
-
-MLPImpl::MLPImpl(const std::string & topology)
-{
-
-}
-
diff --git a/torch_modules/src/NeuralNetwork.cpp b/torch_modules/src/NeuralNetwork.cpp
index 4a123b2..02e8a19 100644
--- a/torch_modules/src/NeuralNetwork.cpp
+++ b/torch_modules/src/NeuralNetwork.cpp
@@ -1,195 +1,4 @@
#include "NeuralNetwork.hpp"
-#include "Transition.hpp"
torch::Device NeuralNetworkImpl::device(torch::cuda::is_available() ? torch::kCUDA : torch::kCPU);
-std::vector<long> NeuralNetworkImpl::extractContextIndexes(const Config & config) const
-{
- std::vector<long> context;
-
- for (int index : bufferContext)
- context.emplace_back(config.getRelativeWordIndex(index));
-
- for (int index : stackContext)
- if (config.hasStack(index))
- context.emplace_back(config.getStack(index));
- else
- context.emplace_back(-1);
-
- return context;
-}
-
-std::vector<long> NeuralNetworkImpl::extractFocusedIndexes(const Config & config) const
-{
- std::vector<long> context;
-
- for (int index : bufferFocused)
- context.emplace_back(config.getRelativeWordIndex(index));
-
- for (int index : stackFocused)
- if (config.hasStack(index))
- context.emplace_back(config.getStack(index));
- else
- context.emplace_back(-1);
-
- return context;
-}
-
-std::vector<std::vector<long>> NeuralNetworkImpl::extractContext(Config & config, Dict & dict) const
-{
- std::vector<long> indexes = extractContextIndexes(config);
- std::vector<long> context;
-
- for (auto & col : columns)
- for (auto index : indexes)
- if (index == -1)
- context.push_back(dict.getIndexOrInsert(Dict::nullValueStr));
- else
- context.push_back(dict.getIndexOrInsert(config.getAsFeature(col, index)));
-
- return {context};
-}
-
-int NeuralNetworkImpl::getContextSize() const
-{
- return columns.size()*(bufferContext.size()+stackContext.size());
-}
-
-void NeuralNetworkImpl::setBufferContext(const std::vector<int> & bufferContext)
-{
- this->bufferContext = bufferContext;
-}
-
-void NeuralNetworkImpl::setStackContext(const std::vector<int> & stackContext)
-{
- this->stackContext = stackContext;
-}
-
-void NeuralNetworkImpl::setBufferFocused(const std::vector<int> & bufferFocused)
-{
- this->bufferFocused = bufferFocused;
-}
-
-void NeuralNetworkImpl::setStackFocused(const std::vector<int> & stackFocused)
-{
- this->stackFocused = stackFocused;
-}
-
-void NeuralNetworkImpl::setColumns(const std::vector<std::string> & columns)
-{
- this->columns = columns;
-}
-
-void NeuralNetworkImpl::addAppliableSplitTransitions(std::vector<std::vector<long>> & context, Dict & dict, const Config & config) const
-{
- auto & splitTransitions = config.getAppliableSplitTransitions();
- for (int i = 0; i < Config::maxNbAppliableSplitTransitions; i++)
- if (i < (int)splitTransitions.size())
- context.back().emplace_back(dict.getIndexOrInsert(splitTransitions[i]->getName()));
- else
- context.back().emplace_back(dict.getIndexOrInsert(Dict::nullValueStr));
-}
-
-void NeuralNetworkImpl::addRawInput(std::vector<std::vector<long>> & context, Dict & dict, const Config & config, int leftWindowRawInput, int rightWindowRawInput) const
-{
- if (leftWindowRawInput < 0 or rightWindowRawInput < 0)
- return;
-
- 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))));
- else
- context.back().push_back(dict.getIndexOrInsert(Dict::nullValueStr));
-}
-
-void NeuralNetworkImpl::addContext(std::vector<std::vector<long>> & context, Dict & dict, const Config & config, const std::vector<long> & contextIndexes, int unknownValueThreshold, const std::vector<std::string> & unknownValueColumns) const
-{
- 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())
- for (auto & targetCol : unknownValueColumns)
- if (col == targetCol)
- if (dict.getNbOccs(dictIndex) <= unknownValueThreshold)
- {
- context.emplace_back(context.back());
- context.back().back() = dict.getIndexOrInsert(Dict::unknownValueStr);
- }
- }
-}
-
-void NeuralNetworkImpl::addFocused(std::vector<std::vector<long>> & context, Dict & dict, const Config & config, const std::vector<long> & focusedIndexes, const std::vector<std::string> & focusedColumns, const std::vector<int> & maxNbElements) const
-{
- for (auto & contextElement : context)
- for (unsigned int colIndex = 0; colIndex < focusedColumns.size(); colIndex++)
- {
- auto & col = focusedColumns[colIndex];
-
- 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]));
- 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));
- }
- }
-}
-
diff --git a/torch_modules/src/RLTNetwork.cpp b/torch_modules/src/RLTNetwork.cpp
deleted file mode 100644
index e4f3fc2..0000000
--- a/torch_modules/src/RLTNetwork.cpp
+++ /dev/null
@@ -1,190 +0,0 @@
-#include "RLTNetwork.hpp"
-
-RLTNetworkImpl::RLTNetworkImpl(int nbOutputs, int leftBorder, int rightBorder, int nbStackElements)
-{
- constexpr int embeddingsSize = 30;
- constexpr int lstmOutputSize = 128;
- constexpr int treeEmbeddingsSize = 256;
- constexpr int hiddenSize = 500;
-
- //TODO gerer ces context
- this->leftBorder = leftBorder;
- this->rightBorder = rightBorder;
- setBufferContext({});
- setStackContext({});
- setColumns({"FORM", "UPOS"});
-
- wordEmbeddings = register_module("word_embeddings", torch::nn::Embedding(torch::nn::EmbeddingOptions(maxNbEmbeddings, embeddingsSize)));
- linear1 = register_module("linear1", torch::nn::Linear(treeEmbeddingsSize*(focusedBufferIndexes.size()+focusedStackIndexes.size()), hiddenSize));
- linear2 = register_module("linear2", torch::nn::Linear(hiddenSize, nbOutputs));
- vectorBiLSTM = register_module("vector_lstm", torch::nn::LSTM(torch::nn::LSTMOptions(embeddingsSize*columns.size(), lstmOutputSize).batch_first(true).bidirectional(true)));
- treeLSTM = register_module("tree_lstm", torch::nn::LSTM(torch::nn::LSTMOptions(treeEmbeddingsSize+2*lstmOutputSize, treeEmbeddingsSize).batch_first(true).bidirectional(false)));
- S = register_parameter("S", torch::randn(treeEmbeddingsSize));
- nullTree = register_parameter("null_tree", torch::randn(treeEmbeddingsSize));
-}
-
-torch::Tensor RLTNetworkImpl::forward(torch::Tensor input)
-{
- if (input.dim() == 1)
- input = input.unsqueeze(0);
-
- auto focusedIndexes = input.narrow(1, 0, focusedBufferIndexes.size()+focusedStackIndexes.size());
- auto computeOrder = input.narrow(1, focusedIndexes.size(1), getContextSize()/columns.size());
- auto childsFlat = input.narrow(1, focusedIndexes.size(1)+computeOrder.size(1), maxNbChilds*(getContextSize()/columns.size()));
- auto childs = torch::reshape(childsFlat, {childsFlat.size(0), computeOrder.size(1), maxNbChilds});
- auto wordIndexes = input.narrow(1, focusedIndexes.size(1)+computeOrder.size(1)+childsFlat.size(1), getContextSize());
- auto baseEmbeddings = wordEmbeddings(wordIndexes);
- auto concatBaseEmbeddings = torch::reshape(baseEmbeddings, {baseEmbeddings.size(0), (int)baseEmbeddings.size(1)/(int)columns.size(), (int)baseEmbeddings.size(2)*(int)columns.size()});
- auto vectorRepresentations = vectorBiLSTM(concatBaseEmbeddings).output;
-
- std::vector<std::map<int, torch::Tensor>> treeRepresentations;
- for (unsigned int batch = 0; batch < computeOrder.size(0); batch++)
- {
- treeRepresentations.emplace_back();
- for (unsigned int i = 0; i < computeOrder[batch].size(0); i++)
- {
- int index = computeOrder[batch][i].item<int>();
- if (index == -1)
- break;
- std::vector<torch::Tensor> inputVector;
- inputVector.emplace_back(torch::cat({vectorRepresentations[batch][index], S}, 0));
- for (unsigned int childIndex = 0; childIndex < maxNbChilds; childIndex++)
- {
- int child = childs[batch][index][childIndex].item<int>();
- if (child == -1)
- break;
- inputVector.emplace_back(torch::cat({vectorRepresentations[batch][index], treeRepresentations[batch].count(child) ? treeRepresentations[batch][child] : nullTree}, 0));
- }
- auto lstmInput = torch::stack(inputVector, 0).unsqueeze(0);
- auto lstmOut = treeLSTM(lstmInput).output.permute({1,0,2})[-1].squeeze();
- treeRepresentations[batch][index] = lstmOut;
- }
- }
-
- std::vector<torch::Tensor> focusedTrees;
- std::vector<torch::Tensor> representations;
- for (unsigned int batch = 0; batch < focusedIndexes.size(0); batch++)
- {
- focusedTrees.clear();
- for (unsigned int i = 0; i < focusedIndexes[batch].size(0); i++)
- {
- int index = focusedIndexes[batch][i].item<int>();
- if (index == -1)
- focusedTrees.emplace_back(nullTree);
- else
- focusedTrees.emplace_back(treeRepresentations[batch].count(index) ? treeRepresentations[batch][index] : nullTree);
- }
- representations.emplace_back(torch::cat(focusedTrees, 0).unsqueeze(0));
- }
-
- auto representation = torch::cat(representations, 0);
- return linear2(torch::relu(linear1(representation)));
-}
-
-std::vector<std::vector<long>> RLTNetworkImpl::extractContext(Config & config, Dict & dict) const
-{
- std::vector<long> contextIndexes;
- std::stack<int> leftContext;
- for (int index = config.getWordIndex()-1; config.has(0,index,0) && (int)leftContext.size() < leftBorder; --index)
- if (config.isToken(index))
- leftContext.push(index);
-
- while ((int)contextIndexes.size() < leftBorder-(int)leftContext.size())
- contextIndexes.emplace_back(-1);
- while (!leftContext.empty())
- {
- contextIndexes.emplace_back(leftContext.top());
- leftContext.pop();
- }
-
- for (int index = config.getWordIndex(); config.has(0,index,0) && (int)contextIndexes.size() < leftBorder+rightBorder+1; ++index)
- if (config.isToken(index))
- contextIndexes.emplace_back(index);
-
- while ((int)contextIndexes.size() < leftBorder+rightBorder+1)
- contextIndexes.emplace_back(-1);
-
- std::map<long, long> indexInContext;
- for (auto & l : contextIndexes)
- indexInContext.emplace(std::make_pair(l, indexInContext.size()));
-
- std::vector<long> headOf;
- for (auto & l : contextIndexes)
- {
- if (l == -1)
- headOf.push_back(-1);
- else
- {
- auto & head = config.getAsFeature(Config::headColName, l);
- if (util::isEmpty(head) or head == "_")
- headOf.push_back(-1);
- else if (indexInContext.count(std::stoi(head)))
- headOf.push_back(std::stoi(head));
- else
- headOf.push_back(-1);
- }
- }
-
- std::vector<std::vector<long>> childs(headOf.size());
- for (unsigned int i = 0; i < headOf.size(); i++)
- if (headOf[i] != -1)
- childs[indexInContext[headOf[i]]].push_back(contextIndexes[i]);
-
- std::vector<long> treeComputationOrder;
- std::vector<bool> treeIsComputed(contextIndexes.size(), false);
-
- std::function<void(long)> depthFirst;
- depthFirst = [&config, &depthFirst, &indexInContext, &treeComputationOrder, &treeIsComputed, &childs](long root)
- {
- if (!indexInContext.count(root))
- return;
-
- if (treeIsComputed[indexInContext[root]])
- return;
-
- for (auto child : childs[indexInContext[root]])
- depthFirst(child);
-
- treeIsComputed[indexInContext[root]] = true;
- treeComputationOrder.push_back(indexInContext[root]);
- };
-
- for (auto & l : focusedBufferIndexes)
- if (contextIndexes[leftBorder+l] != -1)
- depthFirst(contextIndexes[leftBorder+l]);
-
- for (auto & l : focusedStackIndexes)
- if (config.hasStack(l))
- depthFirst(config.getStack(l));
-
- std::vector<long> context;
-
- for (auto & c : focusedBufferIndexes)
- context.push_back(leftBorder+c);
- for (auto & c : focusedStackIndexes)
- if (config.hasStack(c) && indexInContext.count(config.getStack(c)))
- context.push_back(indexInContext[config.getStack(c)]);
- else
- context.push_back(-1);
- for (auto & c : treeComputationOrder)
- context.push_back(c);
- while (context.size() < contextIndexes.size()+focusedBufferIndexes.size()+focusedStackIndexes.size())
- context.push_back(-1);
- for (auto & c : childs)
- {
- for (unsigned int i = 0; i < maxNbChilds; i++)
- if (i < c.size())
- context.push_back(indexInContext[c[i]]);
- else
- context.push_back(-1);
- }
- for (auto & l : contextIndexes)
- for (auto & col : columns)
- if (l == -1)
- context.push_back(dict.getIndexOrInsert(Dict::nullValueStr));
- else
- context.push_back(dict.getIndexOrInsert(config.getAsFeature(col, l)));
-
- return {context};
-}
-
diff --git a/torch_modules/src/RandomNetwork.cpp b/torch_modules/src/RandomNetwork.cpp
index 8e973e4..9730dd2 100644
--- a/torch_modules/src/RandomNetwork.cpp
+++ b/torch_modules/src/RandomNetwork.cpp
@@ -2,11 +2,6 @@
RandomNetworkImpl::RandomNetworkImpl(long outputSize) : outputSize(outputSize)
{
- setBufferContext({0});
- setStackContext({});
- setBufferFocused({});
- setStackFocused({});
- setColumns({"FORM"});
}
torch::Tensor RandomNetworkImpl::forward(torch::Tensor input)
@@ -17,3 +12,8 @@ torch::Tensor RandomNetworkImpl::forward(torch::Tensor input)
return torch::randn({input.size(0), outputSize}, torch::TensorOptions().device(device).requires_grad(true));
}
+std::vector<std::vector<long>> RandomNetworkImpl::extractContext(Config &, Dict &) const
+{
+ return std::vector<std::vector<long>>();
+}
+
diff --git a/torch_modules/src/RawInputLSTM.cpp b/torch_modules/src/RawInputLSTM.cpp
new file mode 100644
index 0000000..ebcfbfd
--- /dev/null
+++ b/torch_modules/src/RawInputLSTM.cpp
@@ -0,0 +1,40 @@
+#include "RawInputLSTM.hpp"
+
+RawInputLSTMImpl::RawInputLSTMImpl(int leftWindow, int rightWindow, int embeddingsSize, int outEmbeddingsSize, LSTMImpl::LSTMOptions options) : leftWindow(leftWindow), rightWindow(rightWindow)
+{
+ lstm = register_module("lstm", LSTM(embeddingsSize, outEmbeddingsSize, options));
+}
+
+torch::Tensor RawInputLSTMImpl::forward(torch::Tensor input)
+{
+ return lstm(input.narrow(1, firstInputIndex, getInputSize()));
+}
+
+std::size_t RawInputLSTMImpl::getOutputSize()
+{
+ return lstm->getOutputSize(leftWindow + rightWindow + 1);
+}
+
+std::size_t RawInputLSTMImpl::getInputSize()
+{
+ return leftWindow + rightWindow + 1;
+}
+
+void RawInputLSTMImpl::addToContext(std::vector<std::vector<long>> & context, Dict & dict, const Config & config) const
+{
+ if (leftWindow < 0 or rightWindow < 0)
+ return;
+
+ for (int i = 0; i < leftWindow; i++)
+ if (config.hasCharacter(config.getCharacterIndex()-leftWindow+i))
+ context.back().push_back(dict.getIndexOrInsert(fmt::format("{}", config.getLetter(config.getCharacterIndex()-leftWindow+i))));
+ else
+ context.back().push_back(dict.getIndexOrInsert(Dict::nullValueStr));
+
+ for (int i = 0; i <= rightWindow; i++)
+ if (config.hasCharacter(config.getCharacterIndex()+i))
+ context.back().push_back(dict.getIndexOrInsert(fmt::format("{}", config.getLetter(config.getCharacterIndex()+i))));
+ else
+ context.back().push_back(dict.getIndexOrInsert(Dict::nullValueStr));
+}
+
diff --git a/torch_modules/src/SplitTransLSTM.cpp b/torch_modules/src/SplitTransLSTM.cpp
new file mode 100644
index 0000000..283358c
--- /dev/null
+++ b/torch_modules/src/SplitTransLSTM.cpp
@@ -0,0 +1,33 @@
+#include "SplitTransLSTM.hpp"
+#include "Transition.hpp"
+
+SplitTransLSTMImpl::SplitTransLSTMImpl(int maxNbTrans, int embeddingsSize, int outEmbeddingsSize, LSTMImpl::LSTMOptions options) : maxNbTrans(maxNbTrans)
+{
+ lstm = register_module("lstm", LSTM(embeddingsSize, outEmbeddingsSize, options));
+}
+
+torch::Tensor SplitTransLSTMImpl::forward(torch::Tensor input)
+{
+ return lstm(input.narrow(1, firstInputIndex, getInputSize()));
+}
+
+std::size_t SplitTransLSTMImpl::getOutputSize()
+{
+ return lstm->getOutputSize(maxNbTrans);
+}
+
+std::size_t SplitTransLSTMImpl::getInputSize()
+{
+ return maxNbTrans;
+}
+
+void SplitTransLSTMImpl::addToContext(std::vector<std::vector<long>> & context, Dict & dict, const Config & config) const
+{
+ auto & splitTransitions = config.getAppliableSplitTransitions();
+ for (int i = 0; i < maxNbTrans; i++)
+ if (i < (int)splitTransitions.size())
+ context.back().emplace_back(dict.getIndexOrInsert(splitTransitions[i]->getName()));
+ else
+ context.back().emplace_back(dict.getIndexOrInsert(Dict::nullValueStr));
+}
+
diff --git a/torch_modules/src/Submodule.cpp b/torch_modules/src/Submodule.cpp
new file mode 100644
index 0000000..2af75a3
--- /dev/null
+++ b/torch_modules/src/Submodule.cpp
@@ -0,0 +1,7 @@
+#include "Submodule.hpp"
+
+void Submodule::setFirstInputIndex(std::size_t firstInputIndex)
+{
+ this->firstInputIndex = firstInputIndex;
+}
+
--
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