transformer

.gitignore

@@ -1,5 +1,6 @@
 build*
 xcode*
+.vscode/
 .DS_Store
 .idea
 cmake-build-*

models/CMakeLists.txt

@@ -1,8 +1,16 @@
 cmake_minimum_required(VERSION 3.1.0 FATAL_ERROR)
 project(models)
 
-add_subdirectory(darknet)
-add_subdirectory(yolo)
+# Recurse into each model mlpack provides.
+set(DIRS
+  darknet
+  transformer
+  yolo
+)
+
+foreach(dir ${DIRS})
+    add_subdirectory(${dir})
+endforeach()
 
 # Add directory name to sources.
 set(DIR_SRCS ${CMAKE_CURRENT_SOURCE_DIR}/)

models/transformer/CMakeLists.txt

@@ -0,0 +1,20 @@
+cmake_minimum_required(VERSION 3.1.0 FATAL_ERROR)
+project(transformer)
+
+set(DIR_SRCS ${CMAKE_CURRENT_SOURCE_DIR}/)
+include_directories("${CMAKE_CURRENT_SOURCE_DIR}/../../")
+
+set(SOURCES
+  decoder.hpp
+  decoder_impl.hpp
+  encoder.hpp
+  encoder_impl.hpp
+  transformer.hpp
+  transformer_impl.hpp
+)
+
+foreach(file ${SOURCES})
+  set(DIR_SRCS ${DIR_SRCS} ${CMAKE_CURRENT_SOURCE_DIR}/${file})
+endforeach()
+
+set(DIRS ${DIRS} ${DIR_SRCS} PARENT_SCOPE)

models/transformer/decoder.hpp

@@ -0,0 +1,267 @@
+/**
+ * @file models/transformer/decoder.hpp
+ * @author Mikhail Lozhnikov
+ * @author Mrityunjay Tripathi
+ *
+ * Definition of the Transformer Decoder layer.
+ *
+ * mlpack is free software; you may redistribute it and/or modify it under the
+ * terms of the 3-clause BSD license.  You should have received a copy of the
+ * 3-clause BSD license along with mlpack.  If not, see
+ * http://www.opensource.org/licenses/BSD-3-Clause for more information.
+ */
+
+#ifndef MODELS_TRANSFORMER_DECODER_HPP
+#define MODELS_TRANSFORMER_DECODER_HPP
+
+#include <mlpack/prereqs.hpp>
+#include <mlpack/methods/ann/layer/layer_types.hpp>
+#include <mlpack/methods/ann/layer/base_layer.hpp>
+#include <mlpack/methods/ann/regularizer/no_regularizer.hpp>
+
+namespace mlpack {
+namespace ann /** Artificial Neural Network. */ {
+
+/**
+ * In addition to the two sub-layers in each encoder layer, the decoder inserts
+ * a third sub-layer, which performs multi-head attention over the output of the
+ * encoder stack. Similar to the encoder, we employ residual connections around
+ * each of the sub-layers, followed by layer normalization. We also modify the
+ * self-attention sub-layer in the decoder stack to prevent positions from
+ * attending to subsequent positions. This masking, combined with fact that the
+ * output embeddings are offset by one position, ensures that the predictions
+ * for position i can depend only on the known outputs at positions less than i.
+ *
+ * @tparam ActivationFunction The type of the activation function to be used in
+ *         the position-wise feed forward neural network.
+ * @tparam RegularizerType The type of regularizer to be applied to layer
+ *         parameters.
+ */
+template <
+  typename ActivationFunction = ReLULayer<>,
+  typename RegularizerType = NoRegularizer
+>
+class TransformerDecoder
+{
+ public:
+  TransformerDecoder();
+
+  /**
+   * Create the TransformerDecoder object using the specified parameters.
+   *
+   * @param numLayers The number of decoder blocks.
+   * @param tgtSeqLen Target Sequence Length.
+   * @param srcSeqLen Source Sequence Length.
+   * @param dModel The number of features in the input. Also, same as the
+   *        `embedDim` in `MultiheadAttention` layer.
+   * @param numHeads The number of attention heads.
+   * @param dimFFN The dimentionality of feedforward network.
+   * @param dropout The dropout rate.
+   * @param attentionMask The attention mask used to black-out future sequences.
+   * @param keyPaddingMask The padding mask used to black-out particular token.
+   * @param ownMemory Whether to delete the pointer-type decoder object.
+   */
+  TransformerDecoder(const size_t numLayers,
+                     const size_t tgtSeqLen,
+                     const size_t srcSeqLen,
+                     const size_t dModel = 512,
+                     const size_t numHeads = 8,
+                     const size_t dimFFN = 1024,
+                     const double dropout = 0.1,
+                     const arma::mat& attentionMask = arma::mat(),
+                     const arma::mat& keyPaddingMask = arma::mat(),
+                     const bool ownMemory = false);
+
+  /**
+   * Destructor.
+   */
+  ~TransformerDecoder()
+  {
+    if (ownMemory)
+      delete decoder;
+  }
+
+  /**
+   * Copy constructor.
+   */
+  TransformerDecoder(const TransformerDecoder& ) = delete;
+
+  /**
+   * Move constructor.
+   */
+  TransformerDecoder(TransformerDecoder&& ) = delete;
+
+  /**
+   * Copy assignment operator.
+   */
+  TransformerDecoder& operator = (const TransformerDecoder& ) = delete;
+
+  /**
+   * Move assignment operator.
+   */
+  TransformerDecoder& operator = (TransformerDecoder&& ) = delete;
+
+  /**
+   * Get the Transformer Decoder model.
+   */
+  Sequential<>* Model() { return decoder; }
+
+  /**
+   * Load the network from a local directory.
+   *
+   * @param filepath The location of the stored model.
+   */
+  void LoadModel(const std::string& filepath);
+
+  /**
+   * Save the network locally.
+   *
+   * @param filepath The location where the model is to be saved.
+   */
+  void SaveModel(const std::string& filepath);
+
+  //! Get the attention mask.
+  arma::mat const& AttentionMask() const { return attentionMask; }
+
+  //! Modify the attention mask.
+  arma::mat& AttentionMask() { return attentionMask; }
+
+  //! Get the key padding mask.
+  arma::mat const& KeyPaddingMask() const { return keyPaddingMask; }
+
+  //! Modify the key padding mask.
+  arma::mat& KeyPaddingMask() { return keyPaddingMask; }
+
+ private:
+  /**
+   * This method adds the attention block to the decoder.
+   */
+  Sequential<>* AttentionBlock()
+  {
+    Sequential<>* decoderBlockBottom = new Sequential<>();
+    decoderBlockBottom->Add<Subview<>>(1, 0, dModel * tgtSeqLen - 1, 0, -1);
+
+    // Broadcast the incoming input to decoder
+    // i.e. query into (query, key, value).
+    Concat<>* decoderInput = new Concat<>(true);
+    decoderInput->Add<IdentityLayer<>>();
+    decoderInput->Add<IdentityLayer<>>();
+    decoderInput->Add<IdentityLayer<>>();
+
+    // Masked Self attention layer.
+    Sequential<>* maskedSelfAttention = new Sequential<>();
+    maskedSelfAttention->Add(decoderInput);
+
+    MultiheadAttention<>* mha1 = new MultiheadAttention<>(tgtSeqLen,
+                                                          tgtSeqLen,
+                                                          dModel,
+                                                          numHeads);
+    mha1->AttentionMask() = attentionMask;
+
+    maskedSelfAttention->Add(mha1);
+
+    // Residual connection.
+    AddMerge<>* residualAdd1 = new AddMerge<>(true);
+    residualAdd1->Add(maskedSelfAttention);
+    residualAdd1->Add<IdentityLayer<>>();
+
+    decoderBlockBottom->Add(residualAdd1);
+
+    // Add the LayerNorm layer with required parameters.
+    decoderBlockBottom->Add<LayerNorm<>>(dModel * tgtSeqLen);
+
+    // This layer broadcasts the output of encoder i.e. key into (key, value).
+    Concat<>* broadcastEncoderOutput = new Concat<>(true);
+    broadcastEncoderOutput->Add<Subview<>>(1, dModel * tgtSeqLen, -1, 0, -1);
+    broadcastEncoderOutput->Add<Subview<>>(1, dModel * tgtSeqLen, -1, 0, -1);
+
+    // This layer concatenates the output of the bottom decoder block (query)
+    // and the output of the encoder (key, value).
+    Concat<>* encDecAttnInput = new Concat<>(true);
+    encDecAttnInput->Add<Subview<>>(1, 0, dModel * tgtSeqLen - 1, 0, -1);
+    encDecAttnInput->Add(broadcastEncoderOutput);
+
+    // Encoder-decoder attention.
+    Sequential<>* encoderDecoderAttention = new Sequential<>();
+    encoderDecoderAttention->Add(encDecAttnInput);
+
+    MultiheadAttention<>* mha2 = new MultiheadAttention<>(tgtSeqLen,
+                                                          srcSeqLen,
+                                                          dModel,
+                                                          numHeads);
+    mha2->KeyPaddingMask() = keyPaddingMask;
+    encoderDecoderAttention->Add(mha2);
+
+    // Residual connection.
+    AddMerge<>* residualAdd2 = new AddMerge<>(true);
+    residualAdd2->Add(encoderDecoderAttention);
+    residualAdd2->Add(decoderBlockBottom);
+
+    Sequential<>* decoderBlock = new Sequential<>();
+    decoderBlock->Add(residualAdd2);
+    decoderBlock->Add<LayerNorm<>>(dModel * tgtSeqLen);
+    return decoderBlock;
+  }
+
+  /**
+   * This method adds the position-wise feed forward network to the decoder.
+   */
+  Sequential<>* PositionWiseFFNBlock()
+  {
+    Sequential<>* positionWiseFFN = new Sequential<>();
+    positionWiseFFN->Add<Linear3D<>>(dModel, dimFFN);
+    positionWiseFFN->Add<ActivationFunction>();
+    positionWiseFFN->Add<Linear3D<>>(dimFFN, dModel);
+    positionWiseFFN->Add<Dropout<>>(dropout);
+
+    /* Residual connection. */
+    AddMerge<>* residualAdd = new AddMerge<>(true);
+    residualAdd->Add(positionWiseFFN);
+    residualAdd->Add<IdentityLayer<>>();
+
+    Sequential<>* decoderBlock = new Sequential<>();
+    decoderBlock->Add(residualAdd);
+    return decoderBlock;
+  }
+
+  //! Locally-stored number of decoder layers.
+  size_t numLayers;
+
+  //! Locally-stored target sequence length.
+  size_t tgtSeqLen;
+
+  //! Locally-stored source sequence length.
+  size_t srcSeqLen;
+
+  //! Locally-stored number of features in the input.
+  size_t dModel;
+
+  //! Locally-stored number of attention heads.
+  size_t numHeads;
+
+  //! Locally-stored dimensionality of position-wise feed forward network.
+  size_t dimFFN;
+
+  //! Locally-stored dropout rate.
+  double dropout;
+
+  //! Locally-stored attention mask.
+  arma::mat attentionMask;
+
+  //! Locally-stored key padding mask.
+  arma::mat keyPaddingMask;
+
+  //! Whether to delete pointer-type decoder object.
+  bool ownMemory;
+
+  //! Locally-stored complete decoder network.
+  Sequential<>* decoder;
+}; // class TransformerDecoder
+
+} // namespace ann
+} // namespace mlpack
+
+// Include implementation.
+#include "decoder_impl.hpp"
+
+#endif