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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.nn.abstractnn.{Initializable, TensorModule}
import com.intel.analytics.bigdl.optim.Regularizer
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.utils.RandomGenerator._
import com.intel.analytics.bigdl.utils.{T, Table}
import scala.reflect.ClassTag
/**
* This layer has a weight tensor with given size. The weight will be multiplied element wise to
* the input tensor. If the element number of the weight tensor match the input tensor, a simply
* element wise multiply will be done. Or the bias will be expanded to the same size of the input.
* The expand means repeat on unmatched singleton dimension(if some unmatched dimension isn't
* singleton dimension, it will report an error). If the input is a batch, a singleton dimension
* will be add to the first dimension before the expand.
*
* @param size the size of the bias
* @param ev numeric operator
* @tparam T numeric type
*/
@SerialVersionUID(8888147326550637025L)
class CMul[@specialized(Float, Double) T: ClassTag](
val size: Array[Int],
var wRegularizer: Regularizer[T] = null)(
implicit ev: TensorNumeric[T]) extends TensorModule[T] with Initializable {
val weight: Tensor[T] = Tensor[T](size)
val gradWeight : Tensor[T] = Tensor[T](size)
private val _sum = Tensor[T]()
private val _repeat = Tensor[T]()
{
val stdv = 1 / math.sqrt(weight.nElement())
val wInit: InitializationMethod = RandomUniform(-stdv, stdv)
setInitMethod(weightInitMethod = wInit)
}
override def reset(): Unit = {
weightInitMethod.init(weight, VariableFormat.ONE_D)
zeroGradParameters()
}
override def updateOutput(input: Tensor[T]): Tensor[T] = {
output.resizeAs(input).copy(input)
if (input.nElement() == weight.nElement()) {
output.cmul(weight)
} else {
val expand = if (weight.dim() == input.dim()) {
weight.view(weight.size())
} else {
weight.view(Array(1) ++ weight.size())
}
val pivotDim = Utils.getOnlyDimGtOne(expand.size())
if (pivotDim > 0) {
mulOneDimWeight(pivotDim, expand, output)
} else {
expand.expandAs(output)
output.cmul(expand)
}
}
output
}
private def mulOneDimWeight(dim: Int, expand: Tensor[T], output: Tensor[T]): Unit = {
val (innerNum, outerNum) = Utils.getInnerOuterNum(dim, output)
val weightData = expand.storage().array()
val weightOffset = expand.storageOffset() - 1
var outer = 0
var offset = output.storageOffset() - 1
while (outer < outerNum) {
var k = 0
while (k < expand.nElement()) {
ev.scal(innerNum, weightData(k + weightOffset), output.storage().array(), offset, 1)
offset += innerNum
k += 1
}
outer += 1
}
}
override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = {
gradInput.resizeAs(input).zero()
if (weight.nElement() == gradOutput.nElement()) {
gradInput.addcmul(ev.fromType[Int](1), weight, gradOutput)
} else {
val expand = if (weight.dim() == gradOutput.dim()) {
weight.view(weight.size())
} else {
weight.view(Array(1) ++ weight.size())
}
val pivotDim = Utils.getOnlyDimGtOne(expand.size())
if (pivotDim > 0) {
gradInput.copy(gradOutput)
mulOneDimWeight(pivotDim, expand, gradInput)
} else {
expand.expandAs(gradOutput)
gradInput.cmul(expand, gradOutput)
}
}
gradInput
}
override def accGradParameters(input: Tensor[T], gradOutput: Tensor[T]): Unit = {
if (scaleW == 0) {
return
}
if (weight.nElement() == gradOutput.nElement()) {
gradWeight.addcmul(ev.fromType[Double](scaleW), input, gradOutput)
} else {
if (weight.dim() == input.dim()) {
_repeat.resizeAs(input).cmul(input, gradOutput)
var sumFrom = _repeat
var sumInto = _sum
var i = 1
while (i <= weight.dim()) {
if (weight.size(i) != input.size(i)) {
sumInto.sum(sumFrom, i)
sumInto = sumFrom
sumFrom = if (sumFrom == _repeat) _sum else _repeat
}
i += 1
}
gradWeight.add(ev.fromType[Double](scaleW), sumFrom)
} else {
_repeat.resizeAs(input).cmul(input, gradOutput)
_sum.sum(_repeat, 1)
gradWeight.add(ev.fromType[Double](scaleW), _sum)
}
}
if (null != wRegularizer && scaleW != 0) {
wRegularizer.accRegularization(weight, gradWeight, scaleW)
}
}
override def updateParameters(learningRate: T): Unit = {
weight.map(gradWeight, (a, b) => ev.minus(a, ev.times(learningRate, b)))
}
override def zeroGradParameters(): Unit = {
gradWeight.zero()
}
override def parameters(): (Array[Tensor[T]], Array[Tensor[T]]) = {
(Array(this.weight), Array(this.gradWeight))
}
override def getParametersTable(): Table = {
T(getName() -> T("weight" -> weight, "gradWeight" -> gradWeight))
}
override def clearState(): this.type = {
super.clearState()
_repeat.set()
_sum.set()
this
}
override def equals(obj: Any): Boolean = {
if (!super.equals(obj)) {
return false
}
if (!obj.isInstanceOf[CMul[T]]) {
return false
}
val other = obj.asInstanceOf[CMul[T]]
if (this.eq(other)) {
return true
}
size == other.size &&
gradWeight == other.gradWeight &&
weight == other.weight
}
override def hashCode() : Int = {
val seed = 37
var hash = super.hashCode()
hash = hash * seed + size.hashCode()
hash = hash * seed + gradWeight.hashCode()
hash = hash * seed + weight.hashCode()
hash
}
override def toString(): String = {
s"${getPrintName}(${java.util.Arrays.toString(size)})"
}
}
object CMul {
def apply[@specialized(Float, Double) T: ClassTag](
size: Array[Int], wRegularizer: Regularizer[T] = null)
(implicit ev: TensorNumeric[T]) : CMul[T] = {
new CMul[T](size, wRegularizer)
}
}
