Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance.
Project price only 1 $
You can buy this project and download/modify it how often you want.
com.intel.analytics.bigdl.tensor.DenseTensor.scala Maven / Gradle / Ivy
/*
* 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.tensor
import breeze.linalg.{DenseMatrix => BrzDenseMatrix, DenseVector => BrzDenseVector}
import com.intel.analytics.bigdl.mkl.MKL
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.utils.RandomGenerator._
import com.intel.analytics.bigdl.utils.{File, Table}
import org.apache.spark.mllib.linalg.{DenseMatrix, DenseVector, Matrix, Vector}
import scala.collection.mutable.ArrayBuffer
import scala.reflect.ClassTag
@SerialVersionUID(5876322619614900645L)
private[tensor] class DenseTensor[@specialized T: ClassTag](
private[tensor] var _storage: ArrayStorage[T],
private[tensor] var _storageOffset: Int,
private[tensor] var _size: Array[Int],
private[tensor] var _stride: Array[Int],
var nDimension: Int)(implicit ev: TensorNumeric[T])
extends Tensor[T] {
override def isEmpty: Boolean = this.storage() == null || this.storage().length() == 0
override def isScalar: Boolean = !this.isEmpty && this.nDimension == 0
override def storage(): Storage[T] = _storage
override def storageOffset(): Int = _storageOffset + 1
override def dim(): Int = nDimension
override def nElement(): Int = {
if (this.isEmpty) {
0
} else {
var n = 1
var d = 0
while (d < this.nDimension) {
n = n * this._size(d)
d += 1
}
n
}
}
override def squeeze(): Tensor[T] = DenseTensor.squeeze(this)
override def squeeze(dim: Int): Tensor[T] = DenseTensor.squeeze(this, dim - 1)
override def squeezeNewTensor(): Tensor[T] = {
val result = new DenseTensor(this._storage, this.storageOffset(), this._size, this._stride)
result.squeeze()
}
override def size(): Array[Int] = {
if (_size == null) null else _size.slice(0, this.nDimension)
}
override def size(dim: Int): Int = {
require(dim > 0 && dim <= this.nDimension,
s"dimension ${dim} out of range of ${this.nDimension}D tensor")
_size(dim - 1)
}
override def stride(): Array[Int] = {
if (_stride == null) null else _stride.slice(0, this.nDimension)
}
override def stride(dim: Int): Int = {
require(dim > 0 && dim <= this.nDimension,
s"dimension ${dim} out of range of ${this.nDimension}D tensor")
_stride(dim - 1)
}
override def resizeAs(src: Tensor[_]): Tensor[T] = {
DenseTensor.resizeAs(this, src)
this
}
override def cast[@specialized(Long, Int, Short, Double, Float) D: ClassTag]
(castTensor: Tensor[D])
(implicit ev1: TensorNumeric[D]): Tensor[D] = {
castTensor.getType() match {
case FloatType =>
castTensor.applyFun[T](this.asInstanceOf[Tensor[T]],
x => ev.toType[Float](x).asInstanceOf[D])
case DoubleType =>
castTensor.applyFun[T](this.asInstanceOf[Tensor[T]],
x => ev.toType[Double](x).asInstanceOf[D])
case LongType =>
castTensor.applyFun[T](this.asInstanceOf[Tensor[T]],
x => ev.toType[Long](x).asInstanceOf[D])
case IntType =>
castTensor.applyFun[T](this.asInstanceOf[Tensor[T]],
x => ev.toType[Int](x).asInstanceOf[D])
case ShortType =>
castTensor.applyFun[T](this.asInstanceOf[Tensor[T]],
x => ev.toType[Short](x).asInstanceOf[D])
case _ =>
throw new RuntimeException("Unspported type")
}
castTensor
}
override def resize(sizes: Array[Int], strides: Array[Int]): Tensor[T] = {
DenseTensor.resize(this, sizes, strides)
this
}
override def resize(size1: Int): Tensor[T] = {
if (this.nDimension != 1 || this.size(1) != size1) {
DenseTensor.resize(this, Array(size1))
} else {
this
}
}
override def resize(size1: Int, size2: Int): Tensor[T] = {
if (this.nDimension != 2 || this.size(1) != size1 || this.size(2) != size2) {
DenseTensor.resize(this, Array(size1, size2))
} else {
this
}
}
override def resize(size1: Int, size2: Int, size3: Int): Tensor[T] = {
if (this.nDimension != 3 || this.size(1) != size1 || this.size(2) != size2 ||
this.size(3) != size3) {
DenseTensor.resize(this, Array(size1, size2, size3))
} else {
this
}
}
override def resize(size1: Int, size2: Int, size3: Int, size4: Int): Tensor[T] = {
if (this.nDimension != 4 || this.size(1) != size1 || this.size(2) != size2 ||
this.size(3) != size3 ||
this.size(4) != size4) {
DenseTensor.resize(this, Array(size1, size2, size3, size4))
} else {
this
}
}
override def resize(size1: Int, size2: Int, size3: Int, size4: Int, size5: Int): Tensor[T] = {
if (this.nDimension != 5 || this.size(1) != size1 || this.size(2) != size2 ||
this.size(3) != size3 || this.size(4) != size4 || this.size(5) != size5) {
DenseTensor.resize(this, Array(size1, size2, size3, size4, size5))
} else {
this
}
}
override def view(sizes: Array[Int]): Tensor[T] = {
require(this.isContiguous(), "current tensor is not contiguous")
require(sizes.product == this.nElement(), "invalid size eElement")
new DenseTensor(this._storage, this.storageOffset(), sizes.clone())
}
override def unfold(dim: Int, size: Int, step: Int): Tensor[T] = {
require(this.nDimension > 0, "cannot unfold an empty tensor")
require(dim > 0 && dim <= this.nDimension, "out of range")
require(size <= this.size(dim), "out of range")
require(step > 0, "invalid step")
val newTensor = this
val newSize = new Array[Int](this.nDimension + 1)
val newStride = new Array[Int](this.nDimension + 1)
newSize(this.nDimension) = size
newStride(this.nDimension) = this.stride(dim)
var d = 0
while (d < this.nDimension) {
if (d + 1 == dim) {
newSize(d) = (this.size(d + 1) - size) / step + 1
newStride(d) = step * this.stride(d + 1)
} else {
newSize(d) = this.size(d + 1)
newStride(d) = this.stride(d + 1)
}
d = d + 1
}
new DenseTensor(this._storage, this._storageOffset, newSize, newStride, this.dim() + 1)
}
private[tensor] def this(d1: Int)(implicit ev: TensorNumeric[T]) =
this(new ArrayStorage[T](new Array[T](d1)), 0, Array(d1),
Array(1), 1)
private[tensor] def this(d1: Int, d2: Int)(implicit ev: TensorNumeric[T]) =
this(new ArrayStorage[T](new Array[T](d1 * d2)), 0, Array(d1, d2),
Array(d2, 1), 2)
private[tensor] def this(d1: Int, d2: Int, d3: Int)(implicit ev: TensorNumeric[T]) =
this(new ArrayStorage[T](new Array[T](d1 * d2 * d3)), 0, Array(d1, d2, d3),
Array(d3 * d2, d3, 1), 3)
private[tensor] def this(d1: Int, d2: Int, d3: Int, d4: Int)(implicit ev: TensorNumeric[T]) =
this(new ArrayStorage[T](new Array[T](d1 * d2 * d3 * d4)), 0, Array(d1, d2, d3, d4),
Array(d4 * d3 * d2, d4 * d3, d4, 1), 4)
private[tensor] def this(d1: Int, d2: Int, d3: Int, d4: Int, d5: Int)(
implicit ev: TensorNumeric[T]) =
this(new ArrayStorage[T](new Array[T](d1 * d2 * d3 * d4 * d5)), 0, Array(d1, d2, d3, d4, d5),
Array(d5 * d4 * d3 * d2, d5 * d4 * d3, d5 * d4, d5, 1), 5)
private[tensor] def this(dims: Int*)(implicit ev: TensorNumeric[T]) =
this(new ArrayStorage[T](new Array[T](dims.product)), 0, dims.toArray,
DenseTensor.size2Stride(dims.toArray), dims.length)
private[tensor] def this(storage: ArrayStorage[T])(implicit ev: TensorNumeric[T]) = {
this(null, 0, null, null, 0)
val _storageOffset = 0
val _size = Array(storage.length)
val _stride = Array(1)
DenseTensor.newWithStorage(this, storage, _storageOffset, _size, _stride, ev)
}
private[tensor] def this(storage: ArrayStorage[T], storageOffset: Int, size: Array[Int] = null,
stride: Array[Int] = null)(implicit ev: TensorNumeric[T]) = {
this(null, 0, null, null, 0)
if (storage != null) {
val _storageOffset = storageOffset - 1
val _size = if (size == null) Array(storage.length) else size
val _stride = if (size == null) null else stride
DenseTensor.newWithStorage(this, storage, _storageOffset, _size, _stride, ev)
}
}
private[tensor] def this(other: Tensor[T])(implicit ev: TensorNumeric[T]) = {
this(null, 0, null, null, 0)
require(other.isInstanceOf[DenseTensor[_]], "Only support dense tensor in this operation")
val _storage = other.storage().asInstanceOf[ArrayStorage[T]]
val _storageOffset = other.storageOffset() - 1
val _size = other.size()
val _stride = other.stride()
DenseTensor.newWithStorage(this, _storage, _storageOffset, _size, _stride, ev)
}
private[tensor] def this()(implicit ev: TensorNumeric[T]) = this(null, 0, null, null, 0)
override def fill(v: T): Tensor[T] = {
if (this.storage() == null) return this
if (this.isContiguous()) {
this.storage().fill(v, this.storageOffset(), this.nElement())
} else {
val func = new TensorFunc2[T] {
override def apply(data: Array[T], index: Int): Unit = {
data(index) = v
}
}
DenseTensorApply.apply1[T](this, func)
}
this
}
override def forceFill(v: Any): Tensor[T] = {
this.fill(v.asInstanceOf[T])
}
override def zero(): Tensor[T] = {
this.fill(ev.zero)
}
override def randn(): Tensor[T] = {
randn(0, 1)
}
override def randn(mean: Double, stdv: Double): Tensor[T] = {
if (this.isContiguous()) {
var i = 0
val total = this.nElement()
val data = this.storage().array()
val offset = this.storageOffset() - 1
while (i < total) {
data(offset + i) = ev.fromType(RNG.normal(mean, stdv))
i += 1
}
} else {
val func = new TensorFunc2[T] {
override def apply(data: Array[T], index: Int): Unit = {
data(index) = ev.fromType(RNG.normal(mean, stdv))
}
}
DenseTensorApply.apply1[T](this, func)
}
this
}
override def bernoulli(p: Double): Tensor[T] = {
if (this.isContiguous()) {
var i = 0
val total = this.nElement()
val data = this.storage().array()
val offset = this.storageOffset() - 1
while (i < total) {
data(offset + i) = if (RNG.bernoulli(p)) {
ev.fromType[Int](1)
} else {
ev.fromType[Int](0)
}
i += 1
}
} else {
val func = new TensorFunc2[T] {
override def apply(data: Array[T], index: Int): Unit = {
data(index) =
if (RNG.bernoulli(p)) {
ev.fromType[Int](1)
} else {
ev.fromType[Int](0)
}
}
}
DenseTensorApply.apply1[T](this, func)
}
this
}
override def rand(): Tensor[T] = rand(0.0, 1.0)
override def rand(lowerBound: Double, upperBound: Double): Tensor[T] = {
if (this.isContiguous()) {
var i = 0
val total = this.nElement()
val data = this.storage().array()
val offset = this.storageOffset() - 1
while (i < total) {
data(offset + i) = ev.fromType(RNG.uniform(lowerBound, upperBound))
i += 1
}
} else {
val func = new TensorFunc2[T] {
override def apply(data: Array[T], index: Int): Unit = {
data(index) = ev.fromType(RNG.uniform(lowerBound, upperBound))
}
}
DenseTensorApply.apply1[T](this, func)
}
this
}
override def set(other: Tensor[T]): Tensor[T] = {
require(other.isInstanceOf[DenseTensor[_]], "Only support dense tensor in this operation")
DenseTensor.rawSet(this, other.storage().asInstanceOf[ArrayStorage[T]],
other.storageOffset() - 1, other.nDimension(), other.size(), other.stride())
}
override def set(storage: Storage[T], storageOffset: Int = 1, sizes: Array[Int] = null,
strides: Array[Int] = null): Tensor[T] = {
if (sizes != null && strides != null) {
require(sizes.length == strides.length)
}
require(storage.isInstanceOf[ArrayStorage[_]], "Only support array storage in this operation")
DenseTensor.rawSet(this, storage.asInstanceOf[ArrayStorage[T]], storageOffset - 1,
if (sizes == null) 0 else sizes.length,
sizes, strides)
}
override def set(): Tensor[T] = {
if (this._storage != null) {
this._storage.resize(0)
}
this.nDimension = 0
this._size = Array[Int]()
this
}
override def transpose(dim1: Int, dim2: Int): Tensor[T] = {
val result = DenseTensor.newWithTensor(this)
DenseTensor.transpose(result, null, dim1 - 1, dim2 - 1)
result
}
override def t(): Tensor[T] = {
require(this.nDimension == 2, "t() is only for 2D tensor")
transpose(1, 2)
}
override def select(dim: Int, index: Int): Tensor[T] = {
val _dimension = dim - 1
val _sliceIndex = index - 1
require(this.nDimension > 0, "empty or scalar tensor cannot be selected")
val result = DenseTensor.newWithTensor(this)
DenseTensor.select(result, null, _dimension, _sliceIndex)
result
}
override def clone(): Tensor[T] = {
DenseTensor.newClone(this)
}
override def shallowClone(): Tensor[T] = {
Tensor(Storage(this.storage().array()), storageOffset(), size(), stride())
}
override def emptyInstance(): Tensor[T] = {
Tensor[T]()
}
override def copy(other: Tensor[T]): Tensor[T] = {
other match {
case t: DnnTensor[_] =>
require(this.nElement() == other.nElement(), "tensor size must match")
this.storage().copy(other.storage(), this.storageOffset() - 1, 0, other.nElement())
case t: DenseTensor[_] =>
DenseTensor.copy(this, other)
case _ => throw new UnsupportedOperationException(
"only support copy from dense tensor or dnn tensor")
}
this
}
override def narrow(dim: Int, index: Int, size: Int): Tensor[T] = {
val result = DenseTensor.newWithTensor(this)
DenseTensor.narrow(result, null, dim - 1, index - 1, size)
result
}
def applyFun[A: ClassTag](
t: Tensor[A],
func: (A) => T): Tensor[T] = {
val func2 = new TensorDiffTypeFunc4[A, T] {
override def apply(
data1: Array[A], index1: Int,
data2: Array[T], index2: Int): Unit = {
data2(index2) = func(data1(index1))
}
}
DenseTensorApply.apply1[A, T](t, this, func2)
this
}
def zipWith[A: ClassTag, B: ClassTag](
t1: Tensor[A],
t2: Tensor[B],
func: (A, B) => T): Tensor[T] = {
val func2 = new TensorDiffTypeFunc6[A, B, T] {
override def apply(
data1: Array[A], index1: Int,
data2: Array[B], index2: Int,
data3: Array[T], index3: Int): Unit = {
data3(index3) = func(data1(index1), data2(index2))
}
}
DenseTensorApply.apply2(t1, t2, this, func2)
this
}
override def apply1(func: T => T): Tensor[T] = {
val func2 = new TensorFunc2[T] {
override def apply(data: Array[T], index: Int): Unit = {
data(index) = func(data(index))
}
}
DenseTensorApply.apply1[T](this, func2)
this
}
override def map(other: Tensor[T], func: (T, T) => T): Tensor[T] = {
val func2 = new TensorFunc4[T] {
override def apply(data1: Array[T], index1: Int, data2: Array[T], index2: Int): Unit = {
data1(index1) = func(data1(index1), data2(index2))
}
}
DenseTensorApply.apply2[T](this, other, func2)
this
}
override def apply(index: Int): Tensor[T] = {
require(this.nDimension > 0, "empty or scalar tensor")
var _index = index - 1
if (_index < 0) _index = this._size(0) + _index + 1
require(_index >= 0 && _index < this._size(0),
s"out of range, ${_index}: 0 to ${this._size(0)}")
val result = DenseTensor.newWithTensor(this)
DenseTensor.select(result, null, 0, _index)
result
}
override def apply(table: Table): Tensor[T] = {
val (tensor, offset) = subset(table)
offset match {
case Some(i) =>
val result = new DenseTensor[T](1)
result.setValue(1, tensor.storage()(i))
result
case None => tensor
}
}
override def update(table: Table, value: T): Unit = {
val (tensor, offset) = subset(table)
offset match {
case Some(i) => tensor.storage()(i) = value
case None => tensor.fill(value)
}
}
override def update(table: Table, src: Tensor[T]): Unit = {
val (tensor, offset) = subset(table)
tensor.copy(src)
}
override def update(index: Int, src: Tensor[T]): Unit = {
require(this.nDimension > 0, "empty or scalar tensor")
var _index = index - 1
if (_index < 0) _index = this._size(0) + _index + 1
require(_index >= 0 && _index < this._size(0), "out of range")
val tensor = DenseTensor.newWithTensor(this)
DenseTensor.narrow(tensor, null, 0, _index, 1)
tensor.copy(src)
}
private def subset(table: Table): (Tensor[T], Option[Int]) = {
var cdim = 0
require(table.length <= this.nDimension, "too many indices provided")
val tensor = DenseTensor.newWithTensor(this)
var d = 1
while (d <= table.length) {
table[Any](d) match {
case index: Int =>
var z = index - 1
if (z < 0) z = tensor._size(cdim) + z + 1
require(z >= 0 && z < tensor._size(cdim), "index out of bound")
if (tensor.nDimension == 1) {
return (tensor, Some(tensor._storageOffset + z * tensor._stride(0)))
} else {
DenseTensor.select(tensor, null, cdim, z)
}
case range: Table =>
var start = 0
var end = tensor._size(cdim) - 1
if (range.length >= 1) {
range[Any](1) match {
case left: Int =>
start = left - 1
}
end = start
}
if (start < 0) start = tensor._size(cdim) + start + 1
require(start >= 0 && start < tensor._size(cdim), "start index out of bound")
if (range.length >= 2) {
range[Any](2) match {
case right: Int =>
end = right - 1
}
}
if (end < 0) end = tensor._size(cdim) + end + 1
require(end >= 0 && end < tensor._size(cdim), "end index out of bound")
require(end >= start, "end index must be greater or equal to start index")
DenseTensor.narrow(tensor, null, cdim, start, end - start + 1)
cdim = cdim + 1
}
d += 1
}
(tensor, None)
}
override def apply(indexes: Array[Int]): T = {
require(indexes.length == this.nDimension, "invalid size")
var offset = this._storageOffset
var d = 0
while (d < indexes.length) {
offset += getOffset(indexes(d) - 1, d + 1)
d += 1
}
this._storage(offset)
}
override def value(): T = {
require(1 == this.nElement(), s"invalid size: 1 == ${this.nElement()}")
var offset = this._storageOffset
this._storage(offset)
}
override def valueAt(d1: Int): T = {
require(1 == this.nDimension, s"invalid size: 1 == ${this.nDimension}")
var offset = this._storageOffset
offset += getOffset(d1 - 1, 1)
this._storage(offset)
}
override def valueAt(d1: Int, d2: Int): T = {
require(2 == this.nDimension, "invalid size")
var offset = this._storageOffset
offset += getOffset(d1 - 1, 1)
offset += getOffset(d2 - 1, 2)
this._storage(offset)
}
override def valueAt(d1: Int, d2: Int, d3: Int): T = {
require(3 == this.nDimension, "invalid size")
var offset = this._storageOffset
offset += getOffset(d1 - 1, 1)
offset += getOffset(d2 - 1, 2)
offset += getOffset(d3 - 1, 3)
this._storage(offset)
}
override def valueAt(d1: Int, d2: Int, d3: Int, d4: Int): T = {
require(4 == this.nDimension, "invalid size")
var offset = this._storageOffset
offset += getOffset(d1 - 1, 1)
offset += getOffset(d2 - 1, 2)
offset += getOffset(d3 - 1, 3)
offset += getOffset(d4 - 1, 4)
this._storage(offset)
}
override def valueAt(d1: Int, d2: Int, d3: Int, d4: Int, d5: Int): T = {
require(5 == this.nDimension, "invalid size")
var offset = this._storageOffset
offset += getOffset(d1 - 1, 1)
offset += getOffset(d2 - 1, 2)
offset += getOffset(d3 - 1, 3)
offset += getOffset(d4 - 1, 4)
offset += getOffset(d5 - 1, 5)
this._storage(offset)
}
private def getOffset(z: Int, dim: Int): Int = {
var _z = z
if (_z < 0) {
_z = this.size(dim) + _z + 1
}
require(_z >= 0 && _z < this.size(dim), "index out of bound")
_z * this.stride(dim)
}
override def update(index: Int, value: T): Unit = {
require(this.nDimension > 0, "empty tensor")
var _index = index - 1
if (_index < 0) _index = this._size(0) + _index + 1
require(_index >= 0 && _index < this._size(0), "out of range")
if (this.nDimension == 1) {
_storage(this._storageOffset + _index * this._stride(0)) = value
} else {
val tensor = DenseTensor.newWithTensor(this)
DenseTensor.narrow(tensor, null, 0, _index, 1)
tensor.fill(value)
}
}
override def update(indexes: Array[Int], value: T): Unit = {
require(indexes.length == this.nDimension, "invalid size")
var offset = this._storageOffset
var d = 0
while (d < indexes.length) {
offset += getOffset(indexes(d) - 1, d + 1)
d += 1
}
this._storage(offset) = value
}
override def setValue(d1: Int, d2: Int, d3: Int, d4: Int, value: T): this.type = {
require(4 == this.nDimension, "invalid size")
var offset = this._storageOffset
offset += getOffset(d1 - 1, 1)
offset += getOffset(d2 - 1, 2)
offset += getOffset(d3 - 1, 3)
offset += getOffset(d4 - 1, 4)
this._storage(offset) = value
this
}
override def setValue(d1: Int, d2: Int, d3: Int, d4: Int, d5: Int, value: T): this.type = {
require(5 == this.nDimension, "invalid size")
var offset = this._storageOffset
offset += getOffset(d1 - 1, 1)
offset += getOffset(d2 - 1, 2)
offset += getOffset(d3 - 1, 3)
offset += getOffset(d4 - 1, 4)
offset += getOffset(d5 - 1, 5)
this._storage(offset) = value
this
}
override def setValue(d1: Int, d2: Int, d3: Int, value: T): this.type = {
require(3 == this.nDimension, "invalid size")
var offset = this._storageOffset
offset += getOffset(d1 - 1, 1)
offset += getOffset(d2 - 1, 2)
offset += getOffset(d3 - 1, 3)
this._storage(offset) = value
this
}
override def setValue(d1: Int, d2: Int, value: T): this.type = {
require(2 == this.nDimension, "invalid size")
var offset = this._storageOffset
offset += getOffset(d1 - 1, 1)
offset += getOffset(d2 - 1, 2)
this._storage(offset) = value
this
}
override def setValue(d1: Int, value: T): this.type = {
require(1 == this.nDimension, "invalid size")
var offset = this._storageOffset
offset += getOffset(d1 - 1, 1)
this._storage(offset) = value
this
}
override def setValue(value: T): this.type = {
require(0 == this.nDimension, "invalid size, you can only call this on a scalar")
var offset = this._storageOffset
this._storage(offset) = value
this
}
override def update(func: T => Boolean, value: T): Unit = {
val func2 = new TensorFunc2[T] {
override def apply(data: Array[T], index: Int): Unit = {
data(index) = if (func(data(index))) value else data(index)
}
}
DenseTensorApply.apply1[T](this, func2)
}
override def isContiguous(): Boolean = {
DenseTensor.isContiguous(this)
}
override def contiguous(): Tensor[T] = {
DenseTensor.newContiguous(this)
}
override def isSameSizeAs(other: Tensor[_]): Boolean = {
DenseTensor.isSameSizeAs(this, other)
}
override def split(size: Int, dim: Int): Array[Tensor[T]] = {
val result = new ArrayBuffer[Tensor[T]]()
val dimLength = this.size(dim)
var start = 1
while (start <= dimLength) {
val curSize = math.min(size, dimLength - start + 1)
result.append(this.narrow(dim, start, curSize))
start += curSize
}
result.toArray
}
override def split(dim: Int): Array[Tensor[T]] = {
val result = new ArrayBuffer[Tensor[T]]()
val dimLength = this.size(dim)
var start = 1
while (start <= dimLength) {
result.append(this.select(dim, start))
start += 1
}
result.toArray
}
// scalastyle:off methodName
override def +(s: T): Tensor[T] = DenseTensorMath.add(s, this)
override def +(t: Tensor[T]): Tensor[T] = DenseTensorMath.add(this, t)
override def -(s: T): Tensor[T] = DenseTensorMath.sub(s, this)
override def -(t: Tensor[T]): Tensor[T] = DenseTensorMath.sub(this, t)
override def unary_-(): Tensor[T] = DenseTensorMath.neg(this)
override def /(s: T): Tensor[T] = DenseTensorMath.divide(s, this)
override def /(t: Tensor[T]): Tensor[T] = DenseTensorMath.divide(this, t)
override def *(s: T): Tensor[T] = DenseTensorMath.mul(s, this)
override def *(t: Tensor[T]): Tensor[T] = DenseTensorMath.mul(this, t)
// scalastyle:on methodName
override def prod(): T = DenseTensorMath.prodAll(this)
override def prod(x: Tensor[T], dim: Int): Tensor[T] = DenseTensorMath.prod(this, x, dim - 1)
override def sum(): T = DenseTensorMath.sumAll(this)
override def sum(dim: Int): Tensor[T] = DenseTensorMath.sum(null, this, dim - 1)
override def sum(x: Tensor[T], dim: Int): Tensor[T] = DenseTensorMath.sum(this, x, dim - 1)
override def mean(): T = DenseTensorMath.meanAll(this)
override def mean(dim: Int): Tensor[T] = DenseTensorMath.mean(this, dim - 1)
override def max(): T = DenseTensorMath.maxAll(this)
override def max(dim: Int): (Tensor[T], Tensor[T]) = {
require(dim > 0 && dim <= this.nDimension, "dimension out of range")
max(Tensor[T](), Tensor[T](), dim)
}
override def max(values: Tensor[T], indices: Tensor[T], dim: Int): (Tensor[T], Tensor[T]) = {
require(dim > 0 && dim <= this.nDimension, "dimension out of range")
val sizes = this.size() // here slice
sizes(dim - 1) = 1
values.resize(sizes)
indices.resize(sizes)
// TODO: the performance of contiguous tensor should be optimize
DenseTensorDimApply.dimApply3[T](this, values, indices, dim, (tdata, toffset, tstride,
tsize, vdata, voffset, vstride, vsize, idata, ioffset, istride, isize) => {
var max = tdata(toffset)
var index = 1
var i = 0
while (i < tsize) {
if (ev.toType[Double](ev.minus(tdata(toffset + i * tstride), max)) > 0) {
index = i + 1
max = tdata(toffset + i * tstride)
}
i += 1
}
vdata(voffset) = max
idata(ioffset) = ev.fromType[Float](index)
})
(values, indices)
}
override def min(): T = DenseTensorMath.minAll(this)
override def min(dim: Int): (Tensor[T], Tensor[T]) = {
require(dim > 0 && dim <= this.nDimension, "dimension out of range")
min(Tensor[T](), Tensor[T](), dim)
}
override def min(values: Tensor[T], indices: Tensor[T], dim: Int): (Tensor[T], Tensor[T]) = {
require(dim > 0 && dim <= this.nDimension, "dimension out of range")
val sizes = this.size()
sizes(dim - 1) = 1
values.resize(sizes)
indices.resize(sizes)
// TODO: the performance of contiguous tensor should be optimize
DenseTensorDimApply.dimApply3[T](this, values, indices, dim, (tdata, toffset, tstride,
tsize, vdata, voffset, vstride, vsize, idata, ioffset, istride, isize) => {
var min = tdata(toffset)
var index = 1
var i = 0
while (i < tsize) {
if (ev.isGreater(min, tdata(toffset + i * tstride))) {
index = i + 1
min = tdata(toffset + i * tstride)
}
i += 1
}
vdata(voffset) = min
idata(ioffset) = ev.fromType[Float](index)
})
(values, indices)
}
override def sumSquare(): T = {
this.dot(this)
}
override def clamp(min: Double, max: Double): Tensor[T] = {
val maxT = ev.fromType[Double](max)
val minT = ev.fromType[Double](min)
val func = new TensorFunc2[T] {
override def apply(data1: Array[T], offset1: Int): Unit = {
if (ev.isGreater(data1(offset1), maxT)) data1(offset1) = maxT
else if (ev.isGreater(minT, data1(offset1))) data1(offset1) = minT
}
}
DenseTensorApply.apply1[T](this, func)
this
}
def scatter(dim: Int, index: Tensor[T], src: Tensor[T]): Tensor[T] = {
require(src.dim() == this.dim(), "Input tensor must have same dimensions as output tensor")
require(dim <= this.dim(), "Index dimension is out of bounds")
require(index.dim() == src.dim(), "Index tensor must have same dimensions as input tensor")
val elementsPerRow = index.size(dim)
// TODO: the performance of contiguous tensor should be optimize
DenseTensorDimApply.dimApply3[T](this, src, index, dim, (tdata, toffset, tstride,
tsize, vdata, voffset, vstride, vsize, idata, ioffset, istride, isize) => {
var i = 0
while (i < elementsPerRow) {
val idx = ev.toType[Int](idata(ioffset + i * istride))
require(idx >= 1 && idx <= this.size(dim))
tdata((idx - 1) * tstride + toffset) = vdata(i * vstride + voffset)
i += 1
}
})
this
}
def gather(dim: Int, index: Tensor[T], src: Tensor[T]): Tensor[T] = {
require(src.dim() == this.dim(), "Input tensor must have same dimensions as output tensor")
require(dim <= this.dim(), "Index dimension is out of bounds")
require(index.dim() == src.dim(), "Index tensor must have same dimensions as input tensor")
val elementsPerRow = index.size(dim)
// TODO: the performance of contiguous tensor should be optimize
DenseTensorDimApply.dimApply3[T](this, src, index, dim, (tdata, toffset, tstride,
tsize, vdata, voffset, vstride, vsize, idata, ioffset, istride, isize) => {
var i = 0
while (i < elementsPerRow) {
val idx = ev.toType[Int](idata(ioffset + i * istride))
require(idx >= 1 && idx <= src.size(dim), "invalid index in gather")
tdata(i * tstride + toffset) = vdata((idx - 1) * vstride + voffset)
i += 1
}
})
this
}
override def add(value: T, y: Tensor[T]): Tensor[T] = DenseTensorMath.cadd(this, this, value, y)
override def add(x: Tensor[T]): Tensor[T] = {
require(x.isInstanceOf[DenseTensor[_]], "Only support dense tensor in this operation")
if (this.nElement() == x.nElement()) {
if (MKL.isMKLLoaded && this.isContiguous() && x.isContiguous()) {
ev.vAdd(this.nElement(), this.storage().array(), this.storageOffset() - 1,
x.storage().array(), x.storageOffset() - 1,
this.storage().array(), this.storageOffset() - 1)
} else {
val func = new TensorFunc4[T] {
override def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int): Unit = {
data1(offset1) = ev.plus(data1(offset1), data2(offset2))
}
}
DenseTensorApply.apply2[T](this, x, func)
}
} else if (DenseTensor.canFastBroadcast(this, x)) {
// recursive add
var i = 0
while(i < this.size(1)) {
this.select(1, i + 1).add(x)
i += 1
}
} else {
this.add(expandTensor(x.asInstanceOf[DenseTensor[T]]))
}
this
}
private[tensor] def expandTensor(x: DenseTensor[T]): Tensor[T] = {
val targetSize = DenseTensor.expandSize(this, x)
val expandStrides = new Array[Int](targetSize.length)
val expandStridesX = new Array[Int](targetSize.length)
var i = targetSize.length - 1
val delta2 = targetSize.length - x.nDimension
while(i >= delta2) {
if (x.size(i + 1- delta2) != 1) expandStridesX(i) = x.stride(i + 1- delta2)
i -= 1
}
val expandX = new DenseTensor[T](
x.storage().asInstanceOf[ArrayStorage[T]],
x.storageOffset(),
targetSize,
expandStridesX
)
if (targetSize.product != this.nElement()) {
i = targetSize.length - 1
val delta1 = targetSize.length - this.nDimension
while (i >= delta1) {
if (this.size(i + 1 - delta1) != 1) expandStrides(i) = this.stride(i + 1 - delta1)
i -= 1
}
val tensor1 = new DenseTensor[T](
this._storage,
this.storageOffset(),
targetSize,
expandStrides
)
val newTensor = new DenseTensor[T]().resize(targetSize).add(tensor1)
this.set(newTensor)
}
expandX
}
override def add(x: Tensor[T], y: Tensor[T]): Tensor[T] = {
require(this.nElement() == x.nElement() && this.nElement() == y.nElement())
if (MKL.isMKLLoaded && this.isContiguous() && x.isContiguous() && y.isContiguous()) {
ev.vAdd(this.nElement(), y.storage().array(), y.storageOffset() - 1,
x.storage().array(), x.storageOffset() - 1,
this.storage().array(), this.storageOffset() - 1)
} else {
val func = new TensorFunc6[T] {
override def apply (data: Array[T], offset: Int, data1: Array[T],
offset1: Int, data2: Array[T], offset2: Int): Unit = {
data(offset1) = ev.plus(data1(offset1), data2(offset2))
}
}
DenseTensorApply.apply3[T](this, x, y, func)
}
this
}
// Puts the result of x + value * y in current tensor
override def add(x: Tensor[T], value: T, y: Tensor[T]): Tensor[T] =
DenseTensorMath.cadd(this, x, value, y)
override def add(value: T): Tensor[T] = {
if (this.isContiguous()) {
ev.add(this.nElement(), this.storage().array(), this.storageOffset() - 1, value, 1)
this
} else {
this.apply1(ev.plus(_, value))
}
}
override def sub(value: T, y: Tensor[T]): Tensor[T] =
DenseTensorMath.csub(this, this, ev.negative(value), y)
override def sub(x: Tensor[T]): Tensor[T] = {
require(x.isInstanceOf[DenseTensor[T]], "Only dense tensor is supported in this operation")
if (this.nElement() == x.nElement()) {
if (MKL.isMKLLoaded && this.isContiguous() && x.isContiguous() &&
(x.getType() == DoubleType || x.getType() == FloatType)) {
ev.vSub(this.nElement(), this.storage().array(), this.storageOffset() - 1,
x.storage().array(), x.storageOffset() - 1,
this.storage().array(), this.storageOffset() - 1)
}
else {
val func = new TensorFunc4[T] {
override def apply (data1: Array[T], offset1: Int,
data2: Array[T], offset2: Int): Unit = {
data1(offset1) = ev.minus(data1(offset1), data2(offset2))
}
}
DenseTensorApply.apply2[T](this, x, func)
}
} else if (DenseTensor.canFastBroadcast(this, x)) {
// recursive add
var i = 0
while(i < this.size(1)) {
this.select(1, i + 1).sub(x)
i += 1
}
} else {
this.sub(expandTensor(x.asInstanceOf[DenseTensor[T]]))
}
this
}
override def sub(x: Tensor[T], y: Tensor[T]): Tensor[T] = {
require(this.nElement() == x.nElement() && this.nElement() == y.nElement())
if (MKL.isMKLLoaded && this.isContiguous() && x.isContiguous() && y.isContiguous()) {
ev.vSub(this.nElement(), x.storage().array(), x.storageOffset() - 1,
y.storage().array(), y.storageOffset() - 1,
this.storage().array(), this.storageOffset() - 1)
} else {
val func = new TensorFunc6[T] {
override def apply (data: Array[T], offset: Int, data1: Array[T],
offset1: Int, data2: Array[T], offset2: Int): Unit = {
data(offset) = ev.minus(data1(offset1), data2(offset2))
}
}
DenseTensorApply.apply3[T](this, x, y, func)
}
this
}
// Puts the result of x - value * y in current tensor
override def sub(x: Tensor[T], value: T, y: Tensor[T]): Tensor[T] =
DenseTensorMath.csub(this, x, value, y)
override def sub(value: T): Tensor[T] = {
if (this.isContiguous()) {
ev.sub(this.nElement(), this.storage().array(), this.storageOffset() - 1, value, 1)
this
} else {
this.apply1(ev.minus(_, value))
}
}
override def dot(y: Tensor[T]): T = {
require(this.nElement() == y.nElement())
if (MKL.isMKLLoaded && this.isContiguous() && y.isContiguous()) {
ev.dot(this.nElement(), this.storage().array(), this.storageOffset() - 1, 1,
y.storage().array(), y.storageOffset() - 1, 1)
}
else {
var sum = ev.fromType[Int](0)
this.map(y, (a, b) => {
sum = ev.plus(sum, ev.times(a, b))
a
})
sum
}
}
override def cmax(value: T): Tensor[T] = {
this.apply1(x => ev.max(x, value))
}
override def dist(y: Tensor[T], norm: Int): T = {
var sum = ev.fromType[Int](0)
this.map(y, (a, b) => {
sum = ev.plus(sum, ev.pow(ev.abs(ev.minus(b, a)), ev.fromType[Int](norm)))
a
})
ev.pow(sum, ev.divide(ev.fromType[Int](1), ev.fromType[Int](norm)))
}
override def addcmul(value: T, tensor1: Tensor[T], tensor2: Tensor[T]): Tensor[T] = {
require(tensor1.nElement() == tensor2.nElement() && this.nElement() == tensor1.nElement())
if (this.isContiguous() && tensor1.isContiguous() && tensor2.isContiguous()) {
ev.addcmul(value, this.nElement(), this.storage().array(), this.storageOffset() - 1,
tensor1.storage().array(), tensor1.storageOffset() - 1,
tensor2.storage().array(), tensor2.storageOffset() - 1)
} else {
val func = new TensorFunc6[T] {
override def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int,
data3: Array[T], offset3: Int): Unit = {
data1(offset1) = ev.plus(data1(offset1), ev.times(ev.times(data2(offset2),
data3(offset3)), value))
}
}
DenseTensorApply.apply3[T](this, tensor1, tensor2, func)
}
this
}
override def addcmul(tensor1: Tensor[T], tensor2: Tensor[T]): Tensor[T] = {
addcmul(ev.fromType(1), tensor1, tensor2)
}
override def addcdiv(value: T, tensor1: Tensor[T], tensor2: Tensor[T]): Tensor[T] = {
if (this.isContiguous() && tensor1.isContiguous() && tensor2.isContiguous()) {
ev.addcdiv(value, this.nElement(), this.storage().array(), this.storageOffset() - 1,
tensor1.storage().array(), tensor1.storageOffset() - 1,
tensor2.storage().array(), tensor2.storageOffset() - 1)
} else {
val func = new TensorFunc6[T] {
override def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int,
data3: Array[T], offset3: Int): Unit = {
data1(offset1) = ev.plus(data1(offset1), ev.times(ev.divide(data2(offset2),
data3(offset3)), value))
}
}
DenseTensorApply.apply3[T](this, tensor1, tensor2, func)
}
this
}
override def cmul(y: Tensor[T]): Tensor[T] = {
require(y.isInstanceOf[DenseTensor[_]], "Only support dense tensor in this operation")
DenseTensorMath.cmul(this, this, y.asInstanceOf[DenseTensor[T]])
}
override def cmul(x: Tensor[T], y: Tensor[T]): Tensor[T] = {
require(x.isInstanceOf[DenseTensor[_]], "Only support dense tensor in this operation")
require(y.isInstanceOf[DenseTensor[_]], "Only support dense tensor in this operation")
DenseTensorMath.cmul(this, x.asInstanceOf[DenseTensor[T]], y.asInstanceOf[DenseTensor[T]])
}
override def cdiv(y: Tensor[T]): Tensor[T] = DenseTensorMath.cdiv(this, this, y)
override def cdiv(x: Tensor[T], y: Tensor[T]): Tensor[T] = DenseTensorMath.cdiv(this, x, y)
/**
* stores the element-wise maximum of x and y in x.
* x.cmax(y) = max(x, y)
*
* @param y tensor
* @return current tensor
*/
override def cmax(y: Tensor[T]): Tensor[T] = DenseTensorMath.cmax(this, this, y)
/**
* stores the element-wise maximum of x and y in z.
* z.cmax(x, y) means z = max(x, y)
*
* @param x tensor
* @param y tensor
*/
override def cmax(x: Tensor[T], y: Tensor[T]): Tensor[T] = DenseTensorMath.cmax(this, x, y)
override def cmin(y: Tensor[T]): Tensor[T] = DenseTensorMath.cmin(this, this, y)
override def cmin(x: Tensor[T], y: Tensor[T]): Tensor[T] = DenseTensorMath.cmin(this, x, y)
override def mul(x: Tensor[T], value: T): Tensor[T] = DenseTensorMath.mul(this, x, value)
override def mul(value: T): Tensor[T] = DenseTensorMath.mul(this, null, value)
override def div(value: T): Tensor[T] = DenseTensorMath.mul(this, null, ev.inv(value))
override def div(x: Tensor[T]): Tensor[T] = {
require(x.isInstanceOf[DenseTensor[_]], "Only dense tensor is supported in this operation")
if (this.nElement() == x.nElement()) {
if (MKL.isMKLLoaded && this.isContiguous() && x.isContiguous()) {
ev.vDiv(this.nElement(), this.storage().array(), this.storageOffset() - 1,
x.storage().array(), x.storageOffset() - 1,
this.storage().array(), this.storageOffset() - 1)
}
else {
val func = new TensorFunc4[T] {
override def apply (data1: Array[T], offset1: Int,
data2: Array[T], offset2: Int): Unit = {
data1(offset1) = ev.divide(data1(offset1), data2(offset2))
}
}
DenseTensorApply.apply2[T](this, x, func)
}
} else if (DenseTensor.canFastBroadcast(this, x)) {
// recursive add
var i = 0
while(i < this.size(1)) {
this.select(1, i + 1).div(x)
i += 1
}
} else {
this.div(expandTensor(x.asInstanceOf[DenseTensor[T]]))
}
this
}
override def conv2(kernel: Tensor[T], vf: Char = 'V'): Tensor[T] =
DenseTensorConv.conv2Dmul[T](ev.fromType[Int](1), this, kernel, 1, 1, vf, 'C')
override def xcorr2(kernel: Tensor[T], vf: Char = 'V'): Tensor[T] =
DenseTensorConv.conv2Dmul[T](ev.fromType[Int](1), this, kernel, 1, 1, vf, 'X')
override def addmm(v1: T, M: Tensor[T], v2: T, mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] =
DenseTensorMath.addmm(this, v1, M, v2, mat1, mat2)
override def addmm(M: Tensor[T], mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] =
DenseTensorMath.addmm[T](this, ev.fromType[Int](1), M, ev.fromType[Int](1), mat1, mat2)
override def addmm(mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] =
DenseTensorMath.addmm[T](this, ev.fromType[Int](1), this, ev.fromType[Int](1), mat1, mat2)
override def addmm(v2: T, mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] =
DenseTensorMath.addmm[T](this, ev.fromType[Int](1), this, v2, mat1, mat2)
override def addmm(v1: T, v2: T, mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] =
DenseTensorMath.addmm(this, v1, this, v2, mat1, mat2)
override def mm(mat1: Tensor[T], mat2: Tensor[T]): Tensor[T] =
DenseTensorMath.addmm(this, ev.zero, this, ev.fromType[Int](1), mat1, mat2)
override def addr(t1: Tensor[T], t2: Tensor[T]): Tensor[T] =
DenseTensorMath.addr[T](this, ev.fromType[Int](1), this, ev.fromType[Int](1), t1, t2)
override def addr(v2: T, t1: Tensor[T], t2: Tensor[T]): Tensor[T] =
DenseTensorMath.addr[T](this, ev.fromType[Int](1), this, v2, t1, t2)
override def addr(v1: T, t1: Tensor[T], v2: T, t2: Tensor[T]): Tensor[T] =
DenseTensorMath.addr(this, v1, this, v2, t1, t2)
/**
* Performs the outer-product between vec1 (1D Tensor) and vec2 (1D Tensor).
* Optional values v1 and v2 are scalars that multiply mat and vec1 [out] vec2 respectively.
* In other words,res_ij = (v1 * mat_ij) + (v2 * vec1_i * vec2_j)
*
* @param v1
* @param t1
* @param v2
* @param t2
* @param t3
* @return
*/
override def addr(v1: T, t1: Tensor[T], v2: T, t2: Tensor[T], t3: Tensor[T]): Tensor[T] =
DenseTensorMath.addr(this, v1, t1, v2, t2, t3)
override def addmv(beta: T, vec1: Tensor[T], alpha: T, mat: Tensor[T],
vec2: Tensor[T]): Tensor[T] =
DenseTensorMath.addmv(this, beta, vec1, alpha, mat, vec2)
override def addmv(beta: T, alpha: T, mat: Tensor[T], vec2: Tensor[T]): Tensor[T] =
DenseTensorMath.addmv(this, beta, this, alpha, mat, vec2)
/**
* return pseudo-random numbers, require 0<=args.length<=2
* if args.length = 0, return [0, 1)
* if args.length = 1, return [1, args(0)] or [args(0), 1]
* if args.length = 2, return [args(0), args(1)]
*
* @param args
*/
override def uniform(args: T*): T = {
require(args.length <= 2, s"invalid arguments, excepted ${args.length} <= 2.")
if (args.length == 0) {
ev.rand()
} else if (args.length == 1) {
ev.plus(ev.times(ev.rand(), ev.minus(args(0), ev.fromType[Int](1))),
ev.fromType[Int](1))
} else {
require(ev.toType[Double](ev.minus(args(0), args(1))) <= 0.0,
s"invalid arguments, excepted ${args(0)} <= ${args(1)}.")
ev.plus(ev.times(ev.rand(), ev.minus(args(1), args(0))), args(0))
}
}
override def repeatTensor(sizes: Array[Int]): Tensor[T] = {
require(sizes.length >= this.nDimension,
"Number of dimensions of repeat dims can not be smaller than number of dimensions of tensor")
val result = new DenseTensor[T]()
val xTensor = this.clone()
var xSize = xTensor.size()
var i = 1
while (i <= sizes.length - this.dim()) {
xSize = Array(1) ++ xSize
i += 1
}
val size = new DenseTensor(new ArrayStorage[T](xSize.map(x => ev.fromType[Int](x)))).
cmul(new DenseTensor(new ArrayStorage[T](sizes.map(x => ev.fromType[Int](x))))).
storage().array().map(x => ev.toType[Int](x))
xTensor.resize(xSize)
result.resize(size)
var urTensor = Tensor(result)
i = 1
while (i <= xTensor.dim()) {
urTensor = urTensor.unfold(i, xTensor.size(i), xTensor.size(i))
i += 1
}
i = 1
while (i <= urTensor.dim() - xTensor.dim()) {
xSize = Array(1) ++ xSize
i += 1
}
xTensor.resize(xSize)
val xxTensor = xTensor.expandAs(urTensor)
urTensor.copy(xxTensor)
result
}
override def expandAs(template: Tensor[T]): Tensor[T] = {
this.expand(template.size())
}
override def expand(sizes: Array[Int]): Tensor[T] = {
require(sizes.length == this.dim(),
s"the number of dimensions provided must equal ${this.dim()}")
val tensorDim = this.dim()
val tensorStride = this.stride()
val tensorSize = this.size()
var i = 0
while (i < tensorDim) {
if (tensorSize(i) == 1) {
tensorSize(i) = sizes(i)
tensorStride(i) = 0
} else if (tensorSize(i) != sizes(i)) {
throw new UnsupportedOperationException(
"incorrect size: only supporting singleton expansion (size=1)")
}
i += 1
}
set(this.storage(), this.storageOffset(), tensorSize, tensorStride)
}
override def addmv(alpha: T, mat: Tensor[T], vec2: Tensor[T]): Tensor[T] =
DenseTensorMath.addmv(this, ev.fromType[Int](1), this, alpha, mat, vec2)
override def mv(mat: Tensor[T], vec2: Tensor[T]): Tensor[T] =
DenseTensorMath.addmv(this, ev.fromType[Int](1), this, ev.fromType[Int](1), mat, vec2)
override def baddbmm(beta: T, M: Tensor[T], alpha: T, batch1: Tensor[T],
batch2: Tensor[T]): Tensor[T] = DenseTensorMath.baddbmm(this, beta, M, alpha, batch1, batch2)
override def baddbmm(beta: T, alpha: T, batch1: Tensor[T], batch2: Tensor[T]): Tensor[T] =
DenseTensorMath.baddbmm(this, beta, this, alpha, batch1, batch2)
override def baddbmm(alpha: T, batch1: Tensor[T], batch2: Tensor[T]): Tensor[T] =
DenseTensorMath.baddbmm(this, ev.fromType[Int](1), this, alpha, batch1, batch2)
override def bmm(batch1: Tensor[T], batch2: Tensor[T]): Tensor[T] =
DenseTensorMath.baddbmm(this, ev.fromType[Int](1), this, ev.fromType[Int](1), batch1, batch2)
override def abs(): Tensor[T] = this.apply1(ev.abs(_))
override def toBreezeVector(): BrzDenseVector[T] = {
require(this.nDimension == 1, "tensor is not 1D")
new BrzDenseVector(this.storage().array(), this.storageOffset() - 1, this.stride(1),
this.nElement())
}
override def getType(): TensorDataType = ev.getType()
override def toMLlibMatrix(): Matrix = {
require(this.nDimension == 2, "tensor is not 2D")
require((this.stride(1) == 1 && this.stride(2) == this.size(1))
|| (this.stride(1) == this.size(2) && this.stride(2) == 1), "tensor is not continuous")
new DenseMatrix(this.size(1), this.size(2), this.storage().array().asInstanceOf[Array[Double]],
this.stride(2) == 1) // column major
}
override def toBreezeMatrix(): BrzDenseMatrix[T] = {
require(this.nDimension == 2, "tensor is not 2D")
val majorStride = if (this.stride(2) == 1) this.stride(1) else this.stride(2)
new BrzDenseMatrix[T](this.size(1), this.size(2), this.storage().array(),
this.storageOffset() - 1,
majorStride, this.stride(2) == 1)
}
override def toMLlibVector(): Vector = {
require(this.nDimension == 1, "tensor is not 1D")
require(this.stride(1) == 1, "tensor is not continuous")
new DenseVector(this.storage().array().asInstanceOf[Array[Double]])
}
override def equals(obj: Any): Boolean = {
if (obj == null) {
return false
}
if (!obj.isInstanceOf[Tensor[T]]) {
return false
}
val other = obj.asInstanceOf[Tensor[T]]
if (this.eq(other)) {
return true
}
if (this.nDimension != other.nDimension) {
return false
}
var d = 1
while (d <= this.nDimension) {
if (this.size(d) != other.size(d)) {
return false
}
d += 1
}
var result = true
this.map(other, (a, b) => {
if (result) {
result = ev.nearlyEqual(a, b, DenseTensorMath.floatEpsilon)
}
a
})
return result
}
override def hashCode(): Int = {
val seed = 37
var hash = 1
hash = hash * seed + this.nDimension
var d = 1
while (d <= this.nDimension) {
hash = hash * seed + this.size(d)
d += 1
}
this.apply1(e => {
hash = hash * seed + e.hashCode()
e
})
hash
}
override def toString(): String = {
val foldThreshold = System.getProperty("bigdl.tensor.fold", "1000").toInt
this.nDimension match {
case 0 =>
if (this.isScalar) {
s"Scalar(${this.value()})"
} else {
s"Empty Tensor"
}
case 1 =>
val sb = new StringBuilder
if (this.size().product < foldThreshold) {
this.apply1(e => {
sb.append(e).append('\n')
e
})
} else {
var i = 0
this.apply1(e => {
i = i + 1
if (i < 3 || i > this.size(1) - 3) {
sb.append(e).append('\n')
} else if (i == 3) sb.append(e).append("\n...\n")
e
})
}
s"${sb}[${this.getClass.getName} of size ${this.size(1)}]"
case 2 =>
val sb = new StringBuilder
val indexer = Array(0, 0)
if (this.size().product < foldThreshold) {
var i = 1
while (i <= this.size(1)) {
var j = 1
while (j <= this.size(2)) {
indexer(0) = i
indexer(1) = j
sb.append(this.apply(indexer)).append('\t')
j += 1
}
sb.append('\n')
i += 1
}
} else {
var i = 1
while (i <= this.size(1)) {
var j = 1
if (i <= 3 || i > this.size(1) - 3) {
while (j <= this.size(2)) {
indexer(0) = i
indexer(1) = j
if (j < 3 || j > this.size(2) - 3) {
sb.append(this.apply(indexer)).append('\t')
} else if (j == 3) {
sb.append(this.apply(indexer)).append("\t...\t")
}
j += 1
}
sb.append('\n')
if (i == 3) sb.append("...\n")
}
i += 1
}
}
s"${sb}[${this.getClass.getName} of size ${this.size(1)}x${this.size(2)}]"
case _ =>
val sb = new StringBuilder
val size = this.size()
val indexer = Array.fill(this.nDimension)(1)
var done = false
val _lastDim = this.nDimension - 1
val _secLastDim = _lastDim - 1
var d = _secLastDim - 1
val total = this.nElement()
while (!done) {
var i = 0
var needPrint = true
if (this.size.product > foldThreshold) {
while (i < _secLastDim) {
if (indexer(i) <= 2 || indexer(i) > size(i) - 2) i += 1
else {
needPrint = false
i = _secLastDim
}
if (indexer(i) == size(i) - 1) sb.append("...\n\n")
}
}
if (needPrint) {
// print header
sb.append('(')
i = 0
while (i < _secLastDim) {
sb.append(indexer(i)).append(',')
i += 1
}
sb.append(".,.) =\n")
// print current matrix
i = 1
if (this.size(_secLastDim + 1) * this.size(_lastDim + 1) < foldThreshold) {
while (i <= this.size(_secLastDim + 1)) {
var j = 1
while (j <= this.size(_lastDim + 1)) {
indexer(_lastDim) = j
indexer(_secLastDim) = i
sb.append(this.apply(indexer)).append('\t')
j += 1
}
sb.append('\n')
i += 1
}
} else {
while (i <= this.size(_secLastDim + 1)) {
var j = 1
if (i <= 3 || i > this.size(_secLastDim + 1) - 3) {
while (j <= this.size(_lastDim + 1)) {
indexer(_lastDim) = j
indexer(_secLastDim) = i
if (j < 3 || j > this.size(_lastDim + 1) - 3) {
sb.append(this.apply(indexer)).append('\t')
}
else if (j == 3) {
sb.append(this.apply(indexer)).append("\t...\t")
}
j += 1
}
sb.append('\n')
if (i == 3) sb.append("...\n")
}
i += 1
}
}
sb.append('\n')
}
indexer(d) = indexer(d) + 1
while (d >= 0 && indexer(d) > size(d)) {
indexer(d) = 1
d = d - 1
if (d >= 0) indexer(d) = indexer(d) + 1
}
if (d == -1) {
done = true
} else {
d = _secLastDim - 1
}
}
s"${sb}[${this.getClass.getName} of size ${size.mkString("x")}]"
}
}
override def diff(other: Tensor[T], count: Int, reverse: Boolean): Boolean = {
if (this.nDimension != other.nDimension()) {
println("Dimension number is different")
return true
}
var d = 1
while (d <= this.nDimension) {
if (this.size(d) != other.size(d)) {
println(s"Dimension $d is different, left is ${this.size(d)}, right is ${other.size(d)}")
return true
}
d += 1
}
val buffer = new Array[(T, T, Int)](count)
var result = false
var catchNum = 0
val func2 = new TensorFunc4[T] {
override def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int): Unit = {
if (data1(offset1) != data2(offset2)) {
require(offset1 == offset2)
if (reverse || catchNum < count) {
buffer(catchNum % count) = (data1(offset1), data2(offset2), offset1)
}
catchNum += 1
result = true
}
}
}
DenseTensorApply.apply2[T](this, other, func2)
if (result == true) {
var i = 0
while (i < buffer.length) {
println(
s"Find difference => this is ${buffer(i)._1} other is ${buffer(i)._2} " +
s"offset is (${buffer(i)._3}/${this.nElement()}})")
i += 1
}
}
result
}
override def reshape(sizes: Array[Int]): Tensor[T] = {
require(sizes.product == this.nElement(),
"DenseTensor: nElement of this tensor is not equal to nElement specified by sizes," +
s" specified sizes = (${sizes.mkString(",")})," +
s" nElement specified by sizes = ${sizes.reduce(_ * _)}," +
s" nElement of this tensor = ${this.nElement()}")
val result = new DenseTensor[T]()
result.resize(sizes)
result.copy(this)
result
}
override def topk(k: Int, dim: Int, increase: Boolean, result: Tensor[T],
indices: Tensor[T], sortedResult: Boolean = true): (Tensor[T], Tensor[T]) = {
val selectDim = if (dim == -1) this.dim() else dim
require(selectDim > 0 && selectDim <= this.nDimension)
val sliceSize = this.size(selectDim)
require(k > 0 && k <= sliceSize)
val tmpResult = new Array[(T, Int)](sliceSize)
val topKSize = this.size()
topKSize(selectDim - 1) = k
val resultTensor = if (result == null) Tensor[T]() else result
resultTensor.resize(topKSize)
val indicesTensor = if (indices == null) Tensor[T]() else indices
indicesTensor.resize(topKSize)
DenseTensorDimApply.dimApply3[T](this, resultTensor, indicesTensor, selectDim,
(tdata, toffset, tstride, tsize, vdata, voffset, vstride, vsize, idata,
ioffset, istride, isize) => {
var i = 0
while (i < tsize) {
tmpResult(i) = (tdata(toffset + i * tstride), i + 1)
i += 1
}
val sorted = tmpResult.sortWith((l, r) =>
if (increase) ev.isGreater(r._1, l._1) else ev.isGreater(l._1, r._1))
i = 0
while (i < k) {
if (sortedResult) {
vdata(voffset + i * vstride) = sorted(i)._1
idata(ioffset + i * istride) = ev.fromType(sorted(i)._2)
} else {
vdata(voffset + (k - i - 1) * vstride) = sorted(i)._1
idata(ioffset + (k - i - 1) * istride) = ev.fromType(sorted(i)._2)
}
i += 1
}
})
(resultTensor, indicesTensor)
}
override def pow(x: Tensor[T], n: T): Tensor[T] = DenseTensorMath.pow[T](this, x, n)
override def pow(n: T): Tensor[T] = DenseTensorMath.pow[T](this, this, n)
override def square(): Tensor[T] = pow(ev.fromType(2.0))
override def log(x: Tensor[T]): Tensor[T] = DenseTensorMath.log[T](this, x)
override def log(): Tensor[T] = DenseTensorMath.log[T](this, this)
override def exp(x: Tensor[T]): Tensor[T] = DenseTensorMath.exp[T](this, x)
override def exp(): Tensor[T] = DenseTensorMath.exp[T](this, this)
override def sqrt(x: Tensor[T]): Tensor[T] = DenseTensorMath.sqrt[T](this, x)
override def sqrt(): Tensor[T] = DenseTensorMath.sqrt[T](this, this)
override def tanh(): Tensor[T] = DenseTensorMath.tanh[T](this, this)
override def tanh(x: Tensor[T]): Tensor[T] = DenseTensorMath.tanh[T](this, x)
override def log1p(x: Tensor[T]): Tensor[T] = DenseTensorMath.log1p[T](this, x)
override def log1p(): Tensor[T] = DenseTensorMath.log1p[T](this, this)
override def norm(y: Tensor[T], value: Int, dim: Int): Tensor[T] =
DenseTensorMath.norm(this, y, value, dim - 1)
override def abs(x: Tensor[T]): Tensor[T] = {
require(this.nElement() == x.nElement())
if (MKL.isMKLLoaded && this.isContiguous() && x.isContiguous()) {
ev.vAbs(this.nElement(), x.storage().array(), x.storageOffset() - 1,
this.storage().array(), this.storageOffset() - 1)
} else {
val func = new TensorFunc4[T] {
override def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int): Unit = {
data1(offset1) = ev.abs(data2(offset2))
}
}
DenseTensorApply.apply2[T](this, x, func)
}
this
}
override def save(path: String, overWrite: Boolean): this.type = {
File.save(this, path, overWrite)
this
}
/**
* Fills the masked elements of itself with value val
*
* @param mask
* @param value
* @return current tensor reference
*/
override def maskedFill(mask: Tensor[T], value: T): Tensor[T] = {
require(this.nElement() == mask.nElement())
// todo: the performance of contiguous tensor should be optimized
val func = new TensorFunc4[T] {
def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int): Unit = {
require(ev.toType[Int](data2(offset2)) == 1 || ev.toType[Int](data2(offset2)) == 0,
"Mask tensor can take 0 and 1 values only")
if (ev.toType[Int](data2(offset2)) == 1) {
data1(offset1) = value
}
}
}
DenseTensorApply.apply2[T](this, mask, func)
this
}
/**
* Copies the elements of tensor into mask locations of itself.
*
* @param mask
* @param y
* @return current tensor reference
*/
override def maskedCopy(mask: Tensor[T], y: Tensor[T]): Tensor[T] = {
require(this.nElement() == mask.nElement())
require(y.isContiguous())
val data3 = y.storage().array()
var offset = 0
// todo: the performance of contiguous tensor should be optimized
val func = new TensorFunc4[T] {
override def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int): Unit = {
require(ev.toType[Int](data2(offset2)) == 1 || ev.toType[Int](data2(offset2)) == 0,
"Mask tensor can take 0 and 1 values only")
if (ev.toType[Int](data2(offset2)) == 1) {
require(offset < data3.length, "Number of elements of y < number of ones in mask")
data1(offset1) = data3(offset)
offset += 1
}
}
}
DenseTensorApply.apply2[T](this, mask, func)
this
}
/**
* Returns a new Tensor which contains all elements aligned to a 1 in the corresponding mask.
*
* @param mask
* @param res
* @return current tensor reference
*/
override def maskedSelect(mask: Tensor[T], res: Tensor[T]): Tensor[T] = {
require(this.nElement() == mask.nElement())
require(ev.isGreater(mask.sum(), ev.fromType(0)))
val length = mask.sum()
var offset = 0
res.resize(ev.toType[Double](length).toInt)
val result = res.storage().array()
// todo: the performance of contiguous tensor should be optimized
val func = new TensorFunc4[T] {
override def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int): Unit = {
require(ev.toType[Int](data2(offset2)) == 1 || ev.toType[Int](data2(offset2)) == 0,
"Mask tensor can take 0 and 1 values only")
if (ev.toType[Int](data2(offset2)) == 1) {
result(offset) = data1(offset1)
offset += 1
}
}
}
DenseTensorApply.apply2[T](this, mask, func)
res
}
/**
* Implements > operator comparing each element in x with y
*
* @param x
* @param y
* @return current tensor reference
*/
override def gt(x: Tensor[T], y: Tensor[T]): Tensor[T] = {
// todo: the performance of contiguous tensor should be optimized
val func = new TensorFunc6[T] {
def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int,
data3: Array[T], offset3: Int): Unit = {
if (ev.isGreater(data2(offset1), data3(offset2))) {
data1(offset1) = ev.fromType(1)
} else {
data1(offset1) = ev.fromType(0)
}
}
}
DenseTensorApply.apply3[T](this, x, y, func)
this
}
/**
* Implements < operator comparing each element in x with y
*
* @param x
* @param y
* @return current tensor reference
*/
override def lt(x: Tensor[T], y: Tensor[T]): Tensor[T] = {
// todo: the performance of contiguous tensor should be optimized
val func = new TensorFunc6[T] {
def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int,
data3: Array[T], offset3: Int): Unit = {
if (ev.toType[Double](ev.minus(data2(offset1), data3(offset2))) < 0) {
data1(offset1) = ev.fromType(1)
} else {
data1(offset1) = ev.fromType(0)
}
}
}
DenseTensorApply.apply3[T](this, x, y, func)
this
}
/**
* Implements <= operator comparing each element in x with y
*
* @param x
* @param y
* @return current tensor reference
*/
override def le(x: Tensor[T], y: Tensor[T]): Tensor[T] = {
// todo: the performance of contiguous tensor should be optimized
val func = new TensorFunc6[T] {
def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int,
data3: Array[T], offset3: Int): Unit = {
if (ev.toType[Double](ev.minus(data2(offset1), data3(offset2))) <= 0) {
data1(offset1) = ev.fromType(1)
} else {
data1(offset1) = ev.fromType(0)
}
}
}
DenseTensorApply.apply3[T](this, x, y, func)
this
}
/**
* Implements == operator comparing each element in a with b
*
* @param x
* @param value
* @return
*/
override def eq(x: Tensor[T], value: T): Tensor[T] = {
// todo: the performance of contiguous tensor should be optimized
val func = new TensorFunc4[T] {
def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int): Unit = {
if (data2(offset1) == value) {
data1(offset1) = ev.fromType(1)
} else {
data1(offset1) = ev.fromType(0)
}
}
}
DenseTensorApply.apply2[T](this, x, func)
this
}
/**
* returns the sum of the n-norms on the Tensor x
*
* @param value the n-norms
* @return
*/
override def norm(value: Int): T = {
require(value > 0, "norm value should be greater than 0")
var res: T = ev.fromType(0)
val func = new TensorFunc2[T] {
override def apply(data1: Array[T], offset1: Int): Unit = {
res = ev.plus(res, ev.pow(ev.abs(data1(offset1)), ev.fromType(value)))
}
}
DenseTensorApply.apply1[T](this, func)
ev.pow(res, ev.fromType(1.0 / value))
}
/**
* returns a new Tensor with the sign (+/- 1 or 0) of the elements of x.
*
* @return
*/
override def sign(): Tensor[T] = {
val func = new TensorFunc2[T] {
override def apply(data1: Array[T], offset1: Int): Unit = {
if (ev.isGreater(data1(offset1), ev.zero)) {
data1(offset1) = ev.one
} else if (ev.isGreater(ev.zero, data1(offset1))) {
data1(offset1) = ev.fromType(-1)
} else {
data1(offset1) = ev.zero
}
}
}
DenseTensorApply.apply1[T](this, func)
this
}
/**
* resize this tensor size to floor((xmax - xmin) / step) + 1 and set values from
* xmin to xmax with step (default to 1).
* @param xmin
* @param xmax
* @param step
* @return this tensor
*/
override def range(xmin: Double, xmax: Double, step: Int = 1): Tensor[T] = {
require((xmax >= xmin) && (step > 0),
"upper bound and larger bound incoherent with step sign")
val size = math.floor((xmax-xmin)/ step + 1).toInt
if (this.nElement() != size) this.resize(size)
var i = 0
// TODO: the performance of contiguous tensor should be optimize
val func = new TensorFunc2[T] {
override def apply(data1: Array[T], offset1: Int): Unit = {
data1(offset1) = ev.fromType(xmin + i * step)
i += 1
}
}
DenseTensorApply.apply1[T](this, func)
this
}
override def addSingletonDimension(t: Tensor[T], dim: Int = 1): Tensor[T] = {
require(dim > 0 && dim <= t.dim() + 1, s"invalid dimension: $dim. " +
s"Tensor is of ${t.dim()} dimensions.")
val size = new Array[Int](t.dim() + 1)
val stride = new Array[Int](t.dim() + 1)
var d = 0
while (d < dim - 1) {
size(d) = t.size(d + 1)
stride(d) = t.stride(d + 1)
d += 1
}
size(dim - 1) = 1
stride(dim - 1) = 1
d += 1
while (d < t.dim + 1) {
size(d) = t.size(d)
stride(d) = t.stride(d)
d += 1
}
this.set(t.storage(), t.storageOffset(), size, stride)
}
/**
* Implements >= operator comparing each element in x with value
*
* @param x
* @param value
* @return
*/
override def ge(x: Tensor[T], value: Double): Tensor[T] = {
// todo: the performance of contiguous tensor should be optimized
val func = new TensorFunc4[T] {
def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int): Unit = {
if (ev.toType[Double](data2(offset2)) >= value) {
data1(offset1) = ev.fromType(1)
} else {
data1(offset1) = ev.fromType(0)
}
}
}
DenseTensorApply.apply2[T](this, x, func)
this
}
/**
* Accumulate the elements of tensor into the original tensor by adding to the indices
* in the order given in index. The shape of tensor must exactly match the elements indexed
* or an error will be thrown.
*
* @param dim
* @param index
* @param y
* @return
*/
override def indexAdd(dim: Int, index: Tensor[T], y: Tensor[T]): Tensor[T] = {
require(dim <= y.nDimension(), "Indexing dim is out of bounds of tensor y")
require(index.nElement() == y.size(dim),
"Number of indices should be equal to source:size(dim)")
require(index.nDimension() == 1, "Index is supposed to be a vector")
val indexC = index.contiguous()
val numEle = indexC.nElement()
var i = 1
if (this.nDimension > 1) {
while (i <= numEle) {
this.select(dim, ev.toType[Double](indexC(Array(i))).toInt).add(y.select(dim, i))
i += 1
}
} else {
while (i <= numEle) {
this.narrow(1, ev.toType[Double](indexC(Array(i))).toInt, 1).add(y.narrow(1, i, 1))
i += 1
}
}
this
}
/**
* create a new Tensor which indexes the original Tensor along dimension dim using the entries
* in torch.LongTensor index. The returned Tensor has the same number of dimensions as the
* original Tensor.
*
* @param dim
* @param index
* @param y
* @return
*/
override def index(dim: Int, index: Tensor[T], y: Tensor[T]): Tensor[T] = {
require(dim <= y.nDimension(), "Indexing dim is out of bounds of tensor y")
require(index.nDimension() == 1, "Index is supposed to be a vector")
require(y.nDimension() > 0, "Source tensor is empty")
val indexC = index.contiguous()
val numEle = indexC.nElement()
val newSize = y.size()
newSize(dim - 1) = numEle
this.resize(newSize)
var i = 1
if (y.nDimension() == 1) {
while (i <= numEle) {
this.narrow(1, i, 1).add(y.narrow(1, ev.toType[Double](indexC(Array(i))).toInt, 1))
i += 1
}
} else {
while (i <= numEle) {
this.select(dim, i).copy(y.select(dim, ev.toType[Double](indexC(Array(i))).toInt))
i += 1
}
}
this
}
override def toTensor[D](implicit env: TensorNumeric[D]): Tensor[D] = {
if (ev.getType() == env.getType()) {
this.asInstanceOf[Tensor[D]]
} else {
throw new IllegalArgumentException(s"The type ${env.getType().getClass}" +
s" in toTensor[${env.getType().getClass}] is not same" +
s"as the numeric type ${ev.getType().getClass} of the " +
"corresponding module, please keep them same.")
}
}
override def getTensorNumeric(): TensorNumeric[T] = ev
override def getTensorType: TensorType = DenseType
override def floor(y: Tensor[T]): Tensor[T] = {
this.map(y, (a, b) => ev.floor(b))
}
override def floor(): Tensor[T] = {
this.apply1(a => ev.floor(a))
}
override def ceil(): Tensor[T] = {
this.apply1(a => ev.ceil(a))
}
override def negative(x: Tensor[T]): Tensor[T] = {
this.map(x, (a, b) => ev.negative(b))
this
}
override def inv(): Tensor[T] = {
this.apply1(a => ev.inv(a))
}
override def reduce(dim: Int, result: Tensor[T], reducer: (T, T) => T): Tensor[T] = {
DenseTensorDimApply.dimApply2[T](result.asInstanceOf[DenseTensor[T]], this, dim - 1,
(r, rOffset, rStride, rSize, t, tOffset, tStride, tSize) => {
r(rOffset) = t(tOffset)
var i = 1
while(i < tSize) {
r(rOffset) = reducer(r(rOffset), t(tOffset + i * tStride))
i += 1
}
})
result
}
override def toArray(): Array[T] = {
require(this.dim() == 1, "toArray only support 1D tensor")
val n = this.nElement()
val array = new Array[T](n)
var i = 0
while(i < n) {
array(i) = this.valueAt(i + 1)
i += 1
}
array
}
override def erf(): Tensor[T] = {
this.apply1(a => ev.erf(a))
}
override def erfc(): Tensor[T] = {
this.apply1(a => ev.erfc(a))
}
override def logGamma(): Tensor[T] = {
this.apply1(a => ev.logGamma(a))
}
override def digamma(): Tensor[T] = {
this.apply1(a => ev.digamma(a))
}
override private[bigdl] def toQuantizedTensor: QuantizedTensor[T] =
throw new IllegalArgumentException("DenseTensor cannot be cast to QuantizedTensor")
}
object DenseTensor {
def apply[@specialized(Float, Double) T: ClassTag](value: T)(
implicit ev: TensorNumeric[T]): Tensor[T] = {
new DenseTensor[T](new ArrayStorage[T](Array(value)), 0, Array[Int](),
Array[Int](), 0)
}
private[tensor] def squeeze[@specialized(Float, Double) T](self: DenseTensor[T]): Tensor[T] = {
var ndim = 0
var d = 0
while (d < self.nDimension) {
if (self._size(d) != 1) {
if (d != ndim) {
self._size(ndim) = self._size(d)
self._stride(ndim) = self._stride(d)
}
ndim += 1
}
d += 1
}
if (ndim == 0 && self.nDimension > 0) {
self._size(0) = 1
self._stride(0) = 1
ndim = 1
}
self.nDimension = ndim
self
}
private[tensor] def squeeze[@specialized(Float, Double) T](self: DenseTensor[T],
_dim: Int): Tensor[T] = {
require(_dim >= 0 && _dim < self.nDimension, "dimension out of range")
if (self._size(_dim) == 1 && self.nDimension > 1) {
var d = _dim
while (d < self.nDimension - 1) {
self._size(d) = self._size(d + 1)
self._stride(d) = self._stride(d + 1)
d += 1
}
self.nDimension -= 1
}
self
}
private[tensor] def newWithStorage[@specialized(Float, Double) T: ClassTag](
tensor: DenseTensor[T], storage: ArrayStorage[T], storageOffset: Int, size: Array[Int],
stride: Array[Int], ev: TensorNumeric[T]): DenseTensor[T] = {
if (size != null && stride != null) {
require(size.length == stride.length, "inconsistent size")
}
implicit val ev2 = ev
val self = if (tensor == null) new DenseTensor[T]() else tensor
val nDimension = if (size != null) size.length else if (stride != null) stride.length else 0
DenseTensor.rawSet[T](self, storage, storageOffset, nDimension, size, stride)
}
private[tensor] def newWithTensor[@specialized(Float, Double) T: ClassTag](
other: DenseTensor[T])(implicit ev: TensorNumeric[T]): DenseTensor[T] = {
val self = new DenseTensor[T]()
DenseTensor.rawSet[T](self, other._storage, other._storageOffset,
other.nDimension, other._size, other._stride)
}
private[tensor] def rawSet[@specialized(Float, Double) T: ClassTag](
self: DenseTensor[T], storage: ArrayStorage[T], storageOffset: Int,
nDimension: Int, _size: Array[Int], _stride: Array[Int]): DenseTensor[T] = {
self._storage = storage
require(storageOffset >= 0, "Tensor: invalid storage offset")
self._storageOffset = storageOffset
rawResize[T](self, nDimension, _size, _stride)
}
private[tensor] def rawResize[@specialized(Float, Double) T: ClassTag](
self: DenseTensor[T], nDim: Int, _size: Array[Int], _stride: Array[Int])
: DenseTensor[T] = {
// resize as a scalar
if (nDim == 0 && _size.isEmpty) {
self._size = Array[Int]()
self._stride = Array[Int]()
self.nDimension = nDim
val totalSize = 1
if (self._storage == null ) {
self._storage = new ArrayStorage(new Array[T](totalSize + self._storageOffset))
} else if (totalSize + self._storageOffset > self._storage.length) {
self._storage.resize(totalSize + self._storageOffset)
}
return self
}
var hasCorrectSize = true
var nDim_ = 0
var d = 0
while (d < nDim) {
nDim_ = nDim_ + 1
if (self.nDimension > d && _size(d) != self._size(d)) {
hasCorrectSize = false
}
if (self.nDimension > d && _stride != null && _stride(d) >= 0 &&
_stride(d) != self._stride(d)) {
hasCorrectSize = false
}
d += 1
}
if (nDim_ != self.nDimension) hasCorrectSize = false
if (hasCorrectSize) return self
if (nDim_ > 0) {
if (nDim_ != self.nDimension) {
self._size = new Array[Int](nDim)
self._stride = new Array[Int](nDim)
self.nDimension = nDim
}
var totalSize = 1
var d = self.nDimension - 1
while (d >= 0) {
self._size(d) = _size(d)
if (_stride != null && _stride(d) >= 0) {
self._stride(d) = _stride(d)
} else {
if (d == self.nDimension - 1) {
self._stride(d) = 1
} else {
self._stride(d) = self._size(d + 1) * self._stride(d + 1)
}
}
totalSize = totalSize + (self._size(d) - 1) * self._stride(d)
d -= 1
}
if (totalSize + self._storageOffset > 0) {
if (self._storage == null ) {
self._storage = new ArrayStorage(new Array[T](totalSize + self._storageOffset))
} else if (totalSize + self._storageOffset > self._storage.length) {
self._storage.resize(totalSize + self._storageOffset)
}
}
} else {
self.nDimension = 0
}
self
}
private[tensor] def newClone[@specialized(Float, Double) T: ClassTag](self: DenseTensor[T])(
implicit ev: TensorNumeric[T]): DenseTensor[T] = {
val tensor = new DenseTensor[T]()
resizeAs(tensor, self)
copy(tensor, self)
tensor
}
private[tensor] def newContiguous[@specialized(Float, Double) T: ClassTag](self: DenseTensor[T])(
implicit ev: TensorNumeric[T]): DenseTensor[T] = {
if (!isContiguous(self)) {
newClone(self)
} else {
self
}
}
private[tensor] def newSelect[@specialized(Float, Double) T: ClassTag](
self: DenseTensor[T], _dimension: Int,
_sliceIndex: Int)(implicit ev: TensorNumeric[T]): DenseTensor[T] = {
val tensor = DenseTensor.newWithTensor(self)
select(tensor, null, _dimension, _sliceIndex)
tensor
}
private[tensor] def newNarrow[@specialized(Float, Double) T: ClassTag](
self: DenseTensor[T], _dimension: Int,
_firstIndex: Int, _size: Int)(implicit ev: TensorNumeric[T]): DenseTensor[T] = {
val tensor = DenseTensor.newWithTensor(self)
narrow(tensor, null, _dimension, _firstIndex, _size)
tensor
}
private[tensor] def newTranspose[@specialized(Float, Double) T: ClassTag](
self: DenseTensor[T], _dimension1: Int,
_dimension2: Int)(implicit ev: TensorNumeric[T]): DenseTensor[T] = {
val tensor = DenseTensor.newWithTensor(self)
transpose(tensor, null, _dimension1, _dimension2)
tensor
}
private[tensor] def resizeAs[@specialized(Float, Double) T: ClassTag](
self: DenseTensor[T], src: Tensor[_]): Unit = {
if (!isSameSizeAs(self, src)) rawResize(self, src.nDimension(), src.size(), null)
}
private[tensor] def resize[@specialized(Float, Double) T: ClassTag](
self: DenseTensor[T], sizes: Array[Int], strides: Array[Int] = null) = {
require(sizes != null, "invalid size")
if (strides != null) {
require(sizes.length == strides.length, "invalid stride")
}
rawResize(self, sizes.length, sizes, strides)
}
private[tensor] def isSameSizeAs[@specialized T](
self: DenseTensor[T], src: Tensor[_]): Boolean = {
if (self.nDimension != src.nDimension()) {
return false
}
if (self.isEmpty != src.isEmpty) {
return false
}
var d = 0
while (d < self.nDimension) {
if (self.size(d + 1) != src.size(d + 1)) {
return false
}
d += 1
}
return true
}
private[tensor] def isContiguous[@specialized(Float, Double) T](
self: DenseTensor[T]): Boolean = {
var s = 1
var d = self.nDimension - 1
while (d >= 0) {
if (self._size(d) != 1) {
if (s != self._stride(d)) {
return false
} else {
s = s * self._size(d)
}
}
d -= 1
}
return true
}
private[tensor] def size2Stride(sizes: Array[Int]): Array[Int] = {
val strides = new Array[Int](sizes.length)
var jump = 1
var i = strides.length - 1
while (i >= 0) {
strides(i) = jump
jump = jump * sizes(i)
i -= 1
}
strides
}
private[tensor] def set[@specialized(Float, Double) T: ClassTag](
self: DenseTensor[T], other: DenseTensor[T]): Tensor[T] = {
if (self != other) {
DenseTensor.rawSet(self, other.storage.asInstanceOf[ArrayStorage[T]], other.storageOffset,
other.nDimension, other.size, other.stride)
} else {
self
}
}
private[tensor] def offsetFromIndice[@specialized(Float, Double) T](
self: DenseTensor[T], indexes: Array[Int]): Int = {
var offset = self._storageOffset
var d = 0
while (d < indexes.length) {
offset = offset + (indexes(d) - 1) * self._stride(d)
d += 1
}
offset
}
private[tensor] def select[@specialized(Float, Double) T: ClassTag](
self: DenseTensor[T], source: DenseTensor[T], _dimension: Int, _sliceIndex: Int): Unit = {
var src = source
if (src == null) src = self
require(src.nDimension > 0, "cannot select on a scalar")
require(_dimension >= 0 && _dimension < src.nDimension, "out of range")
require(_sliceIndex >= 0 && _sliceIndex < src.size(_dimension + 1),
s"${_sliceIndex} out of range 0 to ${src.size(_dimension + 1) - 1}")
set(self, src)
narrow(self, null, _dimension, _sliceIndex, 1)
var d = _dimension
while (d < self.nDimension - 1) {
self._size(d) = self._size(d + 1)
self._stride(d) = self._stride(d + 1)
d += 1
}
self.nDimension = self.nDimension - 1
}
private[tensor] def narrow[@specialized(Float, Double) T: ClassTag](
self: DenseTensor[T], source: DenseTensor[T], _dimension: Int, _firstIndex: Int, size: Int)
: Unit = {
var src = source
if (src == null) {
src = self
}
require(_dimension >= 0 && _dimension < src.nDimension, "dimension out of range")
require(_firstIndex >= 0 && _firstIndex < src.size(_dimension + 1),
s"firstIndex(${_firstIndex}) out of range [0, ${src.size(_dimension + 1)})")
require(size > 0 && _firstIndex + size <= src.size(_dimension + 1),
s"size out of range $size (0, ${src.size(_dimension + 1)} - ${_firstIndex}]")
set(self, src)
if (_firstIndex > 0) {
self._storageOffset = self._storageOffset + _firstIndex * self._stride(_dimension)
}
self._size(_dimension) = size
}
private[tensor] def transpose[@specialized(Float, Double) T: ClassTag](
self: DenseTensor[T], source: DenseTensor[T], _dimension1: Int, _dimension2: Int): Unit = {
var src = source
if (src == null) src = self
require(_dimension1 >= 0 && _dimension1 < src.nDimension, "out of range")
require(_dimension2 >= 0 && _dimension2 < src.nDimension, "out of range")
set(self, src)
if (_dimension1 == _dimension2) {
return
}
var z = self._stride(_dimension1)
self._stride(_dimension1) = self._stride(_dimension2)
self._stride(_dimension2) = z
z = self._size(_dimension1)
self._size(_dimension1) = self._size(_dimension2)
self._size(_dimension2) = z
}
private[tensor] def get1d[@specialized(Float, Double) T](self: DenseTensor[T], x0: Int): T = {
require(self.nDimension != 0, "tensor must have one dimension")
require(x0 >= 0 && x0 < self._size(0), "out of range")
self._storage(self._storageOffset + x0 * self._stride(0))
}
private[tensor] def get1dTensor[@specialized(Float, Double) T: ClassTag](
self: DenseTensor[T], x0: Int)(implicit ev: TensorNumeric[T]): DenseTensor[T] = {
new DenseTensor(new ArrayStorage(Array(get1d(self, x0))))
}
private[tensor] def copy[@specialized T](
self: DenseTensor[T], src: Tensor[T]): Unit = {
require(self.nElement() == src.nElement(), s"self element number(${self.nElement()}) is not" +
s" equal to source element number(${src.nElement()})")
if (self.isEmpty) {
return
}
if (self.isContiguous() && src.isContiguous() && sameStride(self.stride(), src.stride())) {
System.arraycopy(src.storage().array(), src.storageOffset - 1, self.storage().array(),
self.storageOffset - 1, self.nElement())
return
}
val func2 = new TensorFunc4[T] {
override def apply(data1: Array[T], offset1: Int, data2: Array[T], offset2: Int): Unit = {
data1(offset1) = data2(offset2)
}
}
DenseTensorApply.apply2[T](self, src, func2)
}
private[tensor] def randperm[@specialized(Float, Double) T: ClassTag](size: Int)(
implicit ev: TensorNumeric[T]): Tensor[T] = {
require(size >= 1, "invalid size")
// create an ordinal array
val array = new Array[T](size)
var i = 1
while (i <= size) {
array(i - 1) = ev.fromType[Int](i)
i = i + 1
}
// Randomly exchange the elements
i = 0
while (i < size - 1) {
val rand = Math.floor(RNG.random() % (size - i)).toInt
val tmp = array(i)
array(i) = array(rand + i)
array(rand + i) = tmp
i += 1
}
Tensor(new ArrayStorage(array))
}
private[tensor] def sameStride(l: Array[Int], r: Array[Int]): Boolean = {
if (l.length != r.length) return false
var i = 0
while (i < l.length) {
if (l(i) != r(i)) {
return false
}
i += 1
}
return true
}
private[tensor] def range[@specialized(Float, Double) T: ClassTag]
(xmin: Double, xmax: Double, step: Int = 1)(
implicit ev: TensorNumeric[T]): Tensor[T] = {
val newTensor = Tensor[T]()
newTensor.range(xmin, xmax, step)
}
private[tensor] def ones[@specialized(Float, Double) T: ClassTag](sizes: Array[Int])(
implicit ev: TensorNumeric[T]): Tensor[T] = {
val length = sizes.product
Tensor(Storage(new Array[T](length)), 1, sizes).fill(ev.fromType[Int](1))
}
private[tensor] def gaussian1D[@specialized T: ClassTag](
size: Int = 3,
sigma: Double = 0.25,
amplitude: Int = 1,
normalize: Boolean = false,
mean: Double = 0.5,
tensor: Tensor[T] = null)(implicit ev: TensorNumeric[T]): Tensor[T] = {
val gauss = if (null != tensor) {
require(tensor.dim() == 1, "expecting 1D tensor")
require(tensor.nElement() > 0, "expecting non-empty tensor")
tensor
} else {
Tensor[T](size)
}
val center = mean * gauss.nElement() + 0.5
// generate kernel
var i = 1
while (i <= gauss.nElement()) {
gauss.setValue(i, ev.fromType[Double](amplitude * math.exp(-(math.pow((i - center)
/ (sigma * size), 2) / 2)))
)
i += 1
}
if (normalize) {
gauss.div(gauss.sum())
}
gauss
}
private[tensor] def canFastBroadcast[T](tensor: Tensor[T],
other: Tensor[T]): Boolean = {
if (tensor.nDimension < other.nDimension()) return false
val delta = tensor.nDimension - other.nDimension()
var d = other.nDimension()
// Check dimensions
var broadcasting = false
while(d > 0) {
if (broadcasting) {
if (other.size(d) != 1) return false
} else if (tensor.size(delta + d) != other.size(d)) {
if (other.size(d) != 1) return false
broadcasting = true
}
d -= 1
}
return true
}
private[tensor] def expandSize[T: ClassTag](tensor: Tensor[T],
other: Tensor[T]): Array[Int] = {
val errorMsg = s"tensor size not match ${tensor.size.mkString("x")} " +
s"${other.size.mkString("x")}"
val longTensor = if (tensor.dim() > other.dim()) tensor else other
val shortTensor = if (tensor.dim() > other.dim()) other else tensor
val ndim = longTensor.nDimension()
val delta = longTensor.nDimension() - shortTensor.nDimension()
val size = new Array[Int](ndim)
var i = ndim - 1
while (i >= delta) {
require(longTensor.size(i + 1) == shortTensor.size(i + 1 - delta) ||
longTensor.size(i + 1) == 1 ||
shortTensor.size(i + 1 - delta) == 1, errorMsg)
size(i) = math.max(longTensor.size(i + 1), shortTensor.size(i + 1 - delta))
i -= 1
}
while (i >= 0) {
size(i) = longTensor.size(i + 1)
i -= 1
}
size
}
private[tensor] def apply[T: ClassTag](
sparseTensor: SparseTensor[T],
res: Tensor[T] = null)(implicit ev: TensorNumeric[T]): Tensor[T] = {
val dt = if (null == res) Tensor(sparseTensor.size()) else res
val srcIndex = new Array[Int](dt.dim())
val tgtIndex = new Array[Int](dt.dim())
// fill DenseTensor with sparseTensors' active values one by one
(0 until sparseTensor._nElement).foreach { i =>
// targetIndex = sourceIndex - indicesOffset
srcIndex.indices.foreach { j =>
srcIndex(j) = sparseTensor._indices(j)(i + sparseTensor._storageOffset) + 1
tgtIndex(j) = srcIndex(j) - sparseTensor._indicesOffset(j)
}
dt(tgtIndex) = sparseTensor(srcIndex)
}
dt
}
}
