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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.TensorModule
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.utils.{Engine, Shape}
import scala.concurrent.Future
import scala.reflect.ClassTag
/**
* Applies the SoftMax function to an n-dimensional input Tensor, rescaling them so that the
* elements of the n-dimensional output Tensor lie in the range (0, 1) and sum to 1.
* Softmax is defined as: f_i(x) = exp(x_i - shift) / sum_j exp(x_j - shift)
* where shift = max_i(x_i).
*/
@SerialVersionUID(- 7842335603491194236L)
class SoftMax[T: ClassTag](var pos: Int = 1)(implicit ev: TensorNumeric[T])
extends TensorModule[T] {
@transient
private var results: Array[Future[Unit]] = null
private def getPositiveDimension(input: Tensor[T]): Int = {
val inputDim = input.nDimension() // data batch dim
pos = if (pos <= 0) {
inputDim + pos
}
else pos
require(1 <= pos && pos <= input.nDimension(),
s"Invalid position: $pos ." + s"input dimension ${input.nDimension()}")
pos
}
override def updateOutput(input: Tensor[T]): Tensor[T] = {
require(1 <= input.nDimension() && input.nDimension() <= 4,
"1D, 2D, 3D or 4D tensor expected" +
s"input dimension ${input.nDimension()}")
pos = getPositiveDimension(input)
// get nFrame and stride value based on the input
val (nFrame, stride) = input.nDimension() - pos match {
case 0 => (1, 1)
case 1 => (input.size(pos), 1)
case 2 => (1, input.size(pos + 1) * input.size(pos + 2))
case _ => (input.size(pos), input.size(pos + 2) * input.size(pos + 3))
}
if (results == null || results.length != nFrame * stride) {
results = new Array[Future[Unit]](nFrame * stride)
}
output.resizeAs(input)
SoftMax.updateOutput[T](input, output, results, pos)
output
}
override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = {
gradInput.resizeAs(output)
SoftMax.updateGradInput[T](input, gradOutput, gradInput, output, results, pos)
gradInput
}
override def computeOutputShape(inputShape: Shape): Shape = {
inputShape
}
}
object SoftMax{
def apply[@specialized(Float, Double) T: ClassTag](pos: Int = 1)
(implicit ev: TensorNumeric[T]) : SoftMax[T] = {
new SoftMax[T](pos)
}
def apply[@specialized(Float, Double) T: ClassTag]
(implicit ev: TensorNumeric[T]) : SoftMax[T] = {
new SoftMax[T](1)
}
// Notice: SoftMin will call this function
private[nn] def updateOutput[T: ClassTag](input: Tensor[T], output: Tensor[T],
results: Array[Future[Unit]], pos: Int = 1) (implicit ev: TensorNumeric[T]): Tensor[T] = {
// get nFrame, dim and stride value based on the input tensor and pos
val (nFrame, dim, stride) = input.nDimension() - pos match {
case 0 => (1, input.size(pos), 1)
case 1 => (input.size(pos), input.size(pos + 1), 1)
case 2 => (1, input.size(pos), input.size(pos + 1) * input.size(pos + 2))
case _ => (input.size(pos), input.size(pos + 1), input.size(pos + 2) * input.size(pos + 3))
}
val outputArray = output.storage().array()
val inputArray = if (input.isContiguous()) {
input.storage().array()
} else {
input.contiguous().storage().array()
}
val storageOffset = input.storageOffset() - 1
// calculate softmax
var t = 0
while (t < stride * nFrame) {
val _t = t
results(_t) = Engine.model.invoke(() => {
val inputOffset = (_t / stride) * dim * stride + _t % stride + storageOffset
val outputOffset = (_t / stride) * dim * stride + _t % stride
var inputMax = ev.fromType[Float](Float.MinValue)
var d = 0
while (d < dim) {
if (ev.isGreater(inputArray(d * stride + inputOffset), inputMax)) {
inputMax = inputArray(d * stride + inputOffset)
}
d += 1
}
var sum = ev.fromType[Int](0)
d = 0
while (d < dim) {
val z = ev.exp(ev.minus(inputArray(d * stride + inputOffset), inputMax))
outputArray(d * stride + outputOffset) = z
sum = ev.plus(sum, z)
d += 1
}
d = 0
while (d < dim) {
outputArray(d * stride + outputOffset) =
ev.times(outputArray(d * stride + outputOffset), ev.divide(ev.fromType[Int](1), sum))
d += 1
}
})
t += 1
}
Engine.model.sync(results)
output
}
private[nn] def updateGradInput[T: ClassTag](input: Tensor[T], gradOutput: Tensor[T],
gradInput: Tensor[T], output: Tensor[T],
results: Array[Future[Unit]], pos: Int = 1
)(implicit ev: TensorNumeric[T]): Tensor[T] = {
require(input.size().deep == gradOutput.size().deep,
"input should have the same size with gradOutput" +
s"inputsize ${input.size().deep} gradOutput ${gradOutput.size().deep}")
// get nFrame, dim and stride value based on the output tensor and pos
val (nFrame, dim, stride) = output.nDimension() - pos match {
case 0 => (1, output.size(pos), 1)
case 1 => (output.size(pos), output.size(pos + 1), 1)
case 2 => (1, output.size(pos), output.size(pos + 1) * output.size(pos + 2))
case _ =>
(output.size(pos), output.size(pos + 1), output.size(pos + 2) * output.size(pos + 3))
}
val gradInputArray = gradInput.storage().array()
val outputArray = if (output.isContiguous()) {
output.storage().array()
} else {
output.contiguous().storage().array()
}
val gradOutputArray = if (gradOutput.isContiguous()) {
gradOutput.storage().array()
} else {
gradOutput.contiguous().storage().array()
}
// calculate softmax
var t = 0
while (t < stride * nFrame) {
val _t = t
results(_t) = Engine.model.invoke(() => {
val gradInputOffset = (_t / stride) * dim * stride + _t % stride
val outputOffset = (_t / stride) * dim * stride + _t % stride
val gradOutputOffset = (_t / stride) * dim * stride + _t % stride
var sum = ev.fromType[Int](0)
var d = 0
while (d < dim) {
sum = ev.plus(sum, ev.times(gradOutputArray(d * stride + gradOutputOffset),
outputArray(d * stride + outputOffset)))
d += 1
}
d = 0
while (d < dim) {
gradInputArray(d * stride + gradInputOffset) =
ev.times(outputArray(d * stride + outputOffset),
ev.minus(gradOutputArray(d * stride + gradOutputOffset), sum))
d += 1
}
})
t += 1
}
Engine.model.sync(results)
gradInput
}
}
