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/*
* Copyright 2016 The BigDL Authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.intel.analytics.bigdl.nn
import com.intel.analytics.bigdl._
import com.intel.analytics.bigdl.nn.Graph.ModuleNode
import com.intel.analytics.bigdl.nn.Input
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.utils.{T, Table}
import scala.collection.mutable.ArrayBuffer
import scala.reflect.ClassTag
import scala.util.control.Breaks._
/**
* This class is an implementation of Binary TreeLSTM (Constituency Tree LSTM).
* @param inputSize input units size
* @param hiddenSize hidden units size
* @param gateOutput whether gate output
* @param withGraph whether create lstms with [[Graph]], the default value is true.
*/
class BinaryTreeLSTM[T: ClassTag](
inputSize: Int,
hiddenSize: Int,
gateOutput: Boolean = true,
withGraph: Boolean = true
)(implicit ev: TensorNumeric[T])
extends TreeLSTM[T](inputSize, hiddenSize) {
val composer: Module[T] = createComposer()
val leafModule: Module[T] = createLeafModule()
val composers: ArrayBuffer[Module[T]] = ArrayBuffer[Module[T]](composer)
val leafModules: ArrayBuffer[Module[T]] = ArrayBuffer[Module[T]](leafModule)
val cells: ArrayBuffer[ArrayBuffer[Module[T]]] = ArrayBuffer[ArrayBuffer[Module[T]]]()
def createLeafModule(): Module[T] = {
if (withGraph) createLeafModuleWithGraph()
else createLeafModuleWithSequential()
}
def createComposer(): Module[T] = {
if (withGraph) createComposerWithGraph()
else createComposerWithSequential()
}
def createLeafModuleWithGraph(): Module[T] = {
val input = Input()
val c = Linear(inputSize, hiddenSize).inputs(input)
val h: ModuleNode[T] = if (gateOutput) {
val o = Sigmoid().inputs(Linear(inputSize, hiddenSize).inputs(input))
CMulTable().inputs(o, Tanh().inputs(c))
} else {
Tanh().inputs(c)
}
val leafModule = Graph(Array(input), Array(c, h))
if (this.leafModule != null) {
shareParams(leafModule, this.leafModule)
}
leafModule
}
def createComposerWithGraph(): Module[T] = {
val (lc, lh) = (Input(), Input())
val (rc, rh) = (Input(), Input())
def newGate(): ModuleNode[T] = CAddTable().inputs(
Linear(hiddenSize, hiddenSize).inputs(lh),
Linear(hiddenSize, hiddenSize).inputs(rh)
)
val i = Sigmoid().inputs(newGate())
val lf = Sigmoid().inputs(newGate())
val rf = Sigmoid().inputs(newGate())
val update = Tanh().inputs(newGate())
val c = CAddTable().inputs(
CMulTable().inputs(i, update),
CMulTable().inputs(lf, lc),
CMulTable().inputs(rf, rc)
)
val h = if (this.gateOutput) {
val o = Sigmoid().inputs(newGate())
CMulTable().inputs(o, Tanh().inputs(c))
} else {
Tanh().inputs(c)
}
val composer = Graph(Array(lc, lh, rc, rh), Array(c, h))
if (this.composer != null) {
shareParams(composer, this.composer)
}
composer
}
def createLeafModuleWithSequential(): Module[T] = {
val gate = ConcatTable()
.add(Sequential()
.add(Linear(inputSize, hiddenSize))
.add(ConcatTable()
.add(Identity())
.add(Tanh())))
.add(Sequential()
.add(Linear(inputSize, hiddenSize))
.add(Sigmoid()))
val leafModule = Sequential()
.add(gate)
.add(FlattenTable())
.add(ConcatTable()
.add(SelectTable(1))
.add(Sequential()
.add(NarrowTable(2, 2))
.add(CMulTable())))
if (this.leafModule != null) {
shareParams(leafModule, this.leafModule)
}
leafModule
}
def createComposerWithSequential(): Module[T] = {
def newGate(): Module[T] =
Sequential()
.add(ParallelTable()
.add(Linear(hiddenSize, hiddenSize))
.add(Linear(hiddenSize, hiddenSize)))
.add(CAddTable())
val gates = Sequential()
.add(ConcatTable()
.add(SelectTable(2))
.add(SelectTable(4)))
.add(ConcatTable()
.add(Sequential()
.add(newGate())
.add(Sigmoid())) // i
.add(Sequential()
.add(newGate())
.add(Sigmoid())) // lf
.add(Sequential()
.add(newGate())
.add(Sigmoid())) // rf
.add(Sequential()
.add(newGate())
.add(Tanh())) // update
.add(Sequential()
.add(newGate())
.add(Sigmoid()))) // o
val i2c = Sequential()
.add(ConcatTable()
.add(Sequential()
.add(ConcatTable()
.add(SelectTable(3)) // i
.add(SelectTable(6))) // update
.add(CMulTable()))
.add(Sequential()
.add(ConcatTable()
.add(SelectTable(4)) // lf
.add(SelectTable(1))) // lc
.add(CMulTable()))
.add(Sequential()
.add(ConcatTable()
.add(SelectTable(5)) // rf
.add(SelectTable(2))) // rc
.add(CMulTable())))
.add(CAddTable())
val composer = Sequential()
.add(ConcatTable()
.add(SelectTable(1)) // lc
.add(SelectTable(3)) // rc
.add(gates))
.add(FlattenTable())
.add(ConcatTable()
.add(i2c)
.add(SelectTable(7))) // o
.add(ConcatTable()
.add(SelectTable(1)) // c
.add(Sequential()
.add(ParallelTable()
.add(Tanh()) // Tanh(c)
.add(Identity()))// o
.add(CMulTable())))// h
if (this.composer != null) {
shareParams(composer, this.composer)
}
composer
}
override def updateOutput(input: Table): Tensor[T] = {
cells.clear()
val inputs = input[Tensor[T]](1)
val trees = input[Tensor[T]](2)
val batchSize = inputs.size(1)
val nodeSize = trees.size(2)
output.resize(batchSize, nodeSize, hiddenSize)
output.zero()
for (b <- 1 to batchSize) {
cells.append(ArrayBuffer[Module[T]]())
}
var leafIndex = 0
var composerIndex = 0
for (b <- 1 to batchSize) {
val tensorTree = new TensorTree[T](trees(b))
for (i <- 1 to tensorTree.nodeNumber) {
if (tensorTree.noChild(i)) {
if (leafIndex > leafModules.length - 1) {
val leafModule = createLeafModule()
cells(b - 1).append(leafModule)
leafModules.append(leafModule)
} else {
cells(b - 1).append(leafModules(leafIndex))
}
leafIndex += 1
} else if (tensorTree.hasChild(i)) {
if (composerIndex > composers.length - 1) {
val composer = createComposer()
cells(b - 1).append(composer)
composers.append(composer)
} else {
cells(b - 1).append(composers(composerIndex))
}
composerIndex += 1
}
}
recursiveForward(b, inputs.select(1, b), tensorTree, tensorTree.getRoot)
for (i <- 1 to cells(b - 1).size) {
output(b)(i).copy(unpackState(cells(b - 1)(i - 1).output.toTable)._2)
}
}
output
}
def recursiveForward(
batch: Int,
input: Tensor[T],
tree: TensorTree[T],
nodeIndex: Int): Table = {
val out = if (tree.noChild(nodeIndex)) {
cells(batch - 1)(nodeIndex - 1)
.forward(input.select(1, tree.leafIndex(nodeIndex))).toTable
} else {
val leftOut = recursiveForward(batch, input, tree, tree.children(nodeIndex)(0))
val rightOut = recursiveForward(batch, input, tree, tree.children(nodeIndex)(1))
val (lc, lh) = unpackState(leftOut)
val (rc, rh) = unpackState(rightOut)
val cell = cells(batch - 1)(nodeIndex - 1)
cell.forward(T(lc, lh, rc, rh)).toTable
}
out
}
override def updateGradInput(input: Table, gradOutput: Tensor[T]): Table = {
if (!(gradInput.contains(1) || gradInput.contains(2))) {
gradInput = T(Tensor(), Tensor())
}
val inputs = input[Tensor[T]](1)
val trees = input[Tensor[T]](2)
gradInput[Tensor[T]](1).resizeAs(inputs)
gradInput[Tensor[T]](2).resizeAs(trees)
val batchSize = inputs.size(1)
for (b <- 1 to batchSize) {
val tensorTree = new TensorTree[T](trees(b))
recursiveBackward(
b,
inputs(b),
tensorTree,
gradOutput(b),
T(memZero, memZero),
tensorTree.getRoot)
}
gradInput
}
def recursiveBackward(
batch: Int,
inputs: Tensor[T],
tree: TensorTree[T],
outputGrads: Tensor[T],
gradOutput: Table,
nodeIndex: Int
): Unit = {
val outputGrad = outputGrads(nodeIndex)
if (tree.noChild(nodeIndex)) {
gradInput[Tensor[T]](1)(batch)(tree.leafIndex(nodeIndex))
.copy(
cells(batch - 1)(nodeIndex - 1)
.backward(
inputs.select(1, tree.leafIndex(nodeIndex)),
T(gradOutput(1), gradOutput[Tensor[T]](2) + outputGrad)
).toTensor)
} else {
val children = tree.children(nodeIndex)
val (lc, lh) = unpackState(cells(batch - 1)(children(0) - 1).output.toTable)
val (rc, rh) = unpackState(cells(batch - 1)(children(1) - 1).output.toTable)
val composerGrad = cells(batch - 1)(nodeIndex - 1)
.backward(T(lc, lh, rc, rh), T(gradOutput(1), gradOutput[Tensor[T]](2) + outputGrad))
.toTable
recursiveBackward(
batch,
inputs,
tree,
outputGrads,
T(composerGrad[Tensor[T]](1),
composerGrad[Tensor[T]](2)),
children(0))
recursiveBackward(
batch,
inputs,
tree,
outputGrads,
T(composerGrad[Tensor[T]](3),
composerGrad[Tensor[T]](4)),
children(1))
}
}
def unpackState(state: Table): (Tensor[T], Tensor[T]) = {
if (state.length() == 0) {
(memZero, memZero)
} else {
(state(1), state(2))
}
}
override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = {
val (cp, cg) = composer.parameters()
val (lp, lg) = leafModule.parameters()
(cp ++ lp, cg ++ lg)
}
override def updateParameters(learningRate: T): Unit = {
composer.updateParameters(learningRate)
leafModule.updateParameters(learningRate)
}
override def getParametersTable(): Table = {
val pt = T()
val t1 = composer.getParametersTable()
val t2 = leafModule.getParametersTable()
t1.keySet.foreach(key => pt(key) = t1(key))
t2.keySet.foreach(key => pt(key) = t2(key))
pt
}
override def zeroGradParameters(): Unit = {
composer.zeroGradParameters()
leafModule.zeroGradParameters()
}
override def reset(): Unit = {
composer.reset()
leafModule.reset()
}
override def hashCode(): Int = {
def getHashCode(a: Any): Int = if (a == null) 0 else a.hashCode()
val state = Seq(super.hashCode(), composer, leafModule)
state.map(getHashCode).foldLeft(0)((a, b) => 31 * a + b)
}
override def canEqual(other: Any): Boolean = other.isInstanceOf[BinaryTreeLSTM[T]]
override def equals(other: Any): Boolean = other match {
case that: BinaryTreeLSTM[T] =>
super.equals(that) &&
(that canEqual this) &&
composer == that.composer &&
leafModule == that.leafModule
case _ => false
}
}
object BinaryTreeLSTM {
def apply[@specialized(Float, Double) T: ClassTag](
inputSize: Int,
hiddenSize: Int,
gateOutput: Boolean = true,
withGraph: Boolean = true
)(implicit ev: TensorNumeric[T]): BinaryTreeLSTM[T] =
new BinaryTreeLSTM[T](inputSize, hiddenSize, gateOutput, withGraph)
}
/**
* [[TensorTree]] class is used to decode a tensor to a tree structure.
* The given input `content` is a tensor which encodes a constituency parse tree.
* The tensor should have the following structure:
*
* Each row of the tensor represents a tree node and the row number is node number
* For each row, except the last column, all other columns represent the children
* node number of this node. Assume the value of a certain column of the row is not zero,
* the value `p` means this node has a child whose node number is `p` (lies in the `p`-th)
* row. Each leaf has a leaf number, in the tensor, the last column represents the leaf number.
* Each leaf does not have any children, so all the columns of a leaf except the last should
* be zero. If a node is the root, the last column should equal to `-1`.
*
* Note: if any row for padding, the padding rows should be placed at the last rows with all
* elements equal to `-1`.
*
* eg. a tensor represents a binary tree:
*
* [11, 10, -1;
* 0, 0, 1;
* 0, 0, 2;
* 0, 0, 3;
* 0, 0, 4;
* 0, 0, 5;
* 0, 0, 6;
* 4, 5, 0;
* 6, 7, 0;
* 8, 9, 0;
* 2, 3, 0;
* -1, -1, -1;
* -1, -1, -1]
*
* @param content the tensor to be encoded
* @param ev implicit tensor numeric
* @tparam T Numeric type [[Float]] or [[Double]]
*/
class TensorTree[T: ClassTag](val content: Tensor[T])
(implicit ev: TensorNumeric[T]) extends Serializable {
require(content.dim() == 2, "The content of TensorTree should be a two-dimensional tensor")
def size: Array[Int] = content.size()
def nodeNumber: Int = size(0)
def children(index: Int): Array[Int] =
content.select(1, index).toBreezeVector().toArray.map(ev.toType[Int])
def addChild(parent: Int, child: T): Unit = {
breakable {
for (i <- 1 until size(1)) {
if (content(Array(parent, i)) == ev.zero) {
content.setValue(parent, i, child)
break()
}
}
}
}
def markAsRoot(index: Int): Unit = {
content.setValue(index, size(1), ev.negative(ev.one))
}
def getRoot: Int = {
for (i <- 1 to size(0)) {
if (ev.toType[Int](content(Array(i, size(1)))) == -1) {
return i
}
}
throw new RuntimeException("There is no root in the tensor tree")
}
def markAsLeaf(index: Int, leafIndex: Int): Unit = {
content.setValue(index, size(1), ev.fromType(leafIndex))
}
def leafIndex(index: Int): Int = {
ev.toType[Int](content(Array(index, size(1))))
}
def hasChild(index: Int): Boolean = {
ev.toType[Int](content(Array(index, 1))) > 0
}
def noChild(index: Int): Boolean = {
ev.toType[Int](content(Array(index, 1))) == 0
}
def exists(index: Int): Boolean = {
index >= 1 && index <= size(0)
}
def isPadding(index: Int): Boolean = {
ev.toType[Int](content(Array(index, 1))) == -1
}
}
