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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.tensor
/**
* It provides multiple math operation functions for manipulating Tensor objects.
* All functions support both allocating a new Tensor to return the result
* and treating the caller as a target Tensor, in which case the target Tensor(s)
* will be resized accordingly and filled with the result. This property is especially
* useful when one wants to have tight control over when memory is allocated.
*
* @tparam T should be double or float
*/
trait TensorMath[T] {
// scalastyle:off methodName
/**
* Add all elements of this with value not in place.
* It will allocate new memory.
* @param s
* @return
*/
def +(s: T): Tensor[T]
/**
* Add a Tensor to another one, return the result in new allocated memory.
* The number of elements in the Tensors must match, but the sizes do not matter.
* The size of the returned Tensor will be the size of the first Tensor
* @param t
* @return
*/
def +(t: Tensor[T]): Tensor[T]
def +(e: Either[Tensor[T], T]): Tensor[T] = {
e match {
case Right(scalar) => this + scalar
case Left(tensor) => this + tensor
}
}
/**
* subtract all elements of this with the value not in place.
* It will allocate new memory.
* @param s
* @return
*/
def -(s: T): Tensor[T]
/**
* Subtract a Tensor from another one, return the result in new allocated memory.
* The number of elements in the Tensors must match, but the sizes do not matter.
* The size of the returned Tensor will be the size of the first Tensor
* @param t
* @return
*/
def -(t: Tensor[T]): Tensor[T]
def unary_-(): Tensor[T]
/**
* divide all elements of this with value not in place.
* It will allocate new memory.
* @param s
* @return
*/
def /(s: T): Tensor[T]
/**
* Divide a Tensor by another one, return the result in new allocated memory.
* The number of elements in the Tensors must match, but the sizes do not matter.
* The size of the returned Tensor will be the size of the first Tensor
* @param t
* @return
*/
def /(t: Tensor[T]): Tensor[T]
/**
* multiply all elements of this with value not in place.
* It will allocate new memory.
* @param s
* @return
*/
def *(s: T): Tensor[T]
/**
* Multiply a Tensor by another one, return the result in new allocated memory.
* The number of elements in the Tensors must match, but the sizes do not matter.
* The size of the returned Tensor will be the size of the first Tensor
* @param t
* @return
*/
def *(t: Tensor[T]): Tensor[T]
// scalastyle:on methodName
/**
* returns the sum of the elements of this
* @return
*/
def sum(): T
/**
* performs the sum operation over the dimension dim
* @param dim
* @return
*/
def sum(dim: Int): Tensor[T]
def sum(x: Tensor[T], dim: Int): Tensor[T]
/**
* returns the mean of all elements of this.
* @return
*/
def mean(): T
/**
* performs the mean operation over the dimension dim.
*
* @param dim
* @return
*/
def mean(dim: Int): Tensor[T]
/**
* returns the single biggest element of x
* @return
*/
def max(): T
/**
* performs the max operation over the dimension n
* @param dim
* @return
*/
def max(dim: Int): (Tensor[T], Tensor[T])
/**
* performs the max operation over the dimension n
* @param values
* @param indices
* @param dim
* @return
*/
def max(values: Tensor[T], indices: Tensor[T], dim: Int): (Tensor[T], Tensor[T])
/**
* returns the single minimum element of x
* @return
*/
def min(): T
/**
* performs the min operation over the dimension n
* @param dim
* @return
*/
def min(dim: Int): (Tensor[T], Tensor[T])
/**
* performs the min operation over the dimension n
* @param values
* @param indices
* @param dim
* @return
*/
def min(values: Tensor[T], indices: Tensor[T], dim: Int): (Tensor[T], Tensor[T])
/**
* Writes all values from tensor src into this tensor at the specified indices
* @param dim
* @param index
* @param src
* @return this
*/
def scatter(dim: Int, index: Tensor[T], src: Tensor[T]): Tensor[T]
/**
* change this tensor with values from the original tensor by gathering a number of values
* from each "row", where the rows are along the dimension dim.
* @param dim
* @param index
* @param src
* @return this
*/
def gather(dim: Int, index: Tensor[T], src: Tensor[T]): Tensor[T]
/**
* This function computes 2 dimensional convolution of a single image
* with a single kernel (2D output). the dimensions of input and kernel
* need to be 2, and Input image needs to be bigger than kernel. The
* last argument controls if the convolution is a full ('F') or valid
* ('V') convolution. The default is valid convolution.
*
* @param kernel
* @param vf full ('F') or valid ('V') convolution.
* @return
*/
def conv2(kernel: Tensor[T], vf: Char = 'V'): Tensor[T]
/**
* This function operates with same options and input/output configurations as conv2,
* but performs cross-correlation of the input with the kernel k.
*
* @param kernel
* @param vf full ('F') or valid ('V') convolution.
* @return
*/
def xcorr2(kernel: Tensor[T], vf: Char = 'V'): Tensor[T]
/**
* replaces all elements in-place with the square root of the elements of this.
* @return
*/
def sqrt(): Tensor[T]
/**
* replaces all elements in-place with the absolute values of the elements of this.
* @return
*/
def abs(): Tensor[T]
/**
* x.add(value,y) multiply-accumulates values of y into x.
*
* @param value scalar
* @param y other tensor
* @return current tensor
*/
def add(value: T, y: Tensor[T]): Tensor[T]
/**
* accumulates all elements of y into this
*
* @param y other tensor
* @return current tensor
*/
def add(y: Tensor[T]): Tensor[T]
// Puts the result of x + value * y in current tensor
/**
* z.add(x, value, y) puts the result of x + value * y in z.
*
* @param x
* @param value
* @param y
* @return
*/
def add(x: Tensor[T], value: T, y: Tensor[T]): Tensor[T]
/**
* x.add(value) : add value to all elements of x in place.
* @param value
* @return
*/
def add(value: T): Tensor[T]
def add(x: Tensor[T], y: Tensor[T]): Tensor[T]
/**
* Performs the dot product. The number of elements must match: both Tensors are seen as a 1D
* vector.
*
* @param y
* @return
*/
def dot(y: Tensor[T]): T
/**
* For each elements of the tensor, performs the max operation compared with the given value
* vector.
*
* @param value
* @return
*/
def cmax(value: T): Tensor[T]
/**
* Performs the p-norm distance calculation between two tensors
* @param y the secode Tensor
* @param norm the norm of distance
* @return
*/
def dist(y: Tensor[T], norm: Int): T
/**
* Performs the element-wise multiplication of tensor1 by tensor2, multiply the result by the
* scalar value (1 if not present) and add it to x. The number of elements must match, but sizes
* do not matter.
*
* @param value
* @param tensor1
* @param tensor2
*/
def addcmul(value: T, tensor1: Tensor[T], tensor2: Tensor[T]): Tensor[T]
def addcmul(tensor1: Tensor[T], tensor2: Tensor[T]): Tensor[T]
/**
* Performs the element-wise division of tensor1 by tensor2, multiply the result by the scalar
* value and add it to x.
* The number of elements must match, but sizes do not matter.
*
* @param value
* @param tensor1
* @param tensor2
* @return
*/
def addcdiv(value: T, tensor1: Tensor[T], tensor2: Tensor[T]): Tensor[T]
def sub(value : T, y : Tensor[T]) : Tensor[T]
// Puts the result of x - value * y in current tensor
def sub(x : Tensor[T], value : T, y : Tensor[T]) : Tensor[T]
/**
* subtracts all elements of y from this
*
* @param y other tensor
* @return current tensor
*/
def sub(y : Tensor[T]) : Tensor[T]
def sub(x : Tensor[T], y : Tensor[T]) : Tensor[T]
def sub(value : T) : Tensor[T]
/**
* Element-wise multiply
* x.cmul(y) multiplies all elements of x with corresponding elements of y.
* x = x * y
*
* @param y tensor
* @return current tensor
*/
def cmul(y: Tensor[T]): Tensor[T]
/**
* Element-wise multiply
* z.cmul(x, y) equals z = x * y
*
* @param x tensor
* @param y tensor
* @return current tensor
*/
def cmul(x: Tensor[T], y: Tensor[T]): Tensor[T]
/**
* Element-wise divide
* x.cdiv(y) all elements of x divide all elements of y.
* x = x / y
*
* @param y tensor
* @return current tensor
*/
def cdiv(y: Tensor[T]): Tensor[T]
/**
* Element-wise divide
* z.cdiv(x, y) means z = x / y
*
* @param x tensor
* @param y tensor
* @return current tensor
*/
def cdiv(x: Tensor[T], y: Tensor[T]): Tensor[T]
/**
* multiply all elements of this with value in-place.
*
* @param value
* @return
*/
def mul(value: T): Tensor[T]
/**
* divide all elements of this with value in-place.
*
* @param value
* @return
*/
def div(value: T): Tensor[T]
/**
* put the result of x * value in current tensor
*
* @param value
* @return
*/
def mul(x: Tensor[T], value: T): Tensor[T]
/**
* Performs a matrix-matrix multiplication between mat1 (2D tensor) and mat2 (2D tensor).
* Optional values v1 and v2 are scalars that multiply M and mat1 * mat2 respectively.
* Optional value beta is a scalar that scales the result tensor, before accumulating the result
* into the tensor. Defaults to 1.0.
* If mat1 is a n x m matrix, mat2 a m x p matrix, M must be a n x p matrix.
*
* res = (v1 * M) + (v2 * mat1*mat2)
*
* @param v1
* @param M
* @param v2
* @param mat1
* @param mat2
*/
def addmm(v1: T, M: Tensor[T], v2: T, mat1: Tensor[T], mat2: Tensor[T]): Tensor[T]
/** res = M + (mat1*mat2) */
def addmm(M: Tensor[T], mat1: Tensor[T], mat2: Tensor[T]): Tensor[T]
/** res = res + mat1 * mat2 */
def addmm(mat1: Tensor[T], mat2: Tensor[T]): Tensor[T]
/** res = res + v2 * mat1 * mat2 */
def addmm(v2: T, mat1: Tensor[T], mat2: Tensor[T]): Tensor[T]
/** res = v1 * res + v2 * mat1*mat2 */
def addmm(v1: T, v2: T, mat1: Tensor[T], mat2: Tensor[T]): Tensor[T]
/** res = mat1*mat2 */
def mm(mat1: Tensor[T], mat2: Tensor[T]): Tensor[T]
/**
* 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 t1
* @param t2
* @return
*/
def addr(t1: Tensor[T], t2: Tensor[T]): Tensor[T]
def addr(v1: T, t1: Tensor[T], t2: Tensor[T]): Tensor[T]
def addr(v1: T, t1: Tensor[T], v2: T, t2: Tensor[T]): Tensor[T]
/**
* 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
*/
def addr(v1: T, t1: Tensor[T], v2: T, t2: Tensor[T], t3: Tensor[T]): Tensor[T]
/**
* 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
*/
def uniform(args: T*): T
/**
* Performs a matrix-vector multiplication between mat (2D Tensor) and vec2 (1D Tensor) and add
* it to vec1. Optional values v1 and v2 are scalars that multiply vec1 and vec2 respectively.
*
* In other words,
* res = (beta * vec1) + alpha * (mat * vec2)
*
* Sizes must respect the matrix-multiplication operation: if mat is a n × m matrix,
* vec2 must be vector of size m and vec1 must be a vector of size n.
*/
def addmv(beta: T, vec1: Tensor[T], alpha: T, mat: Tensor[T], vec2: Tensor[T]): Tensor[T]
/** res = beta * res + alpha * (mat * vec2) */
def addmv(beta: T, alpha: T, mat: Tensor[T], vec2: Tensor[T]): Tensor[T]
/** res = res + alpha * (mat * vec2) */
def addmv(alpha: T, mat: Tensor[T], vec2: Tensor[T]): Tensor[T]
/** res = res + (mat * vec2) */
def mv(mat: Tensor[T], vec2: Tensor[T]): Tensor[T]
/**
* Perform a batch matrix matrix multiplication of matrices and stored in batch1 and batch2
* with batch add. batch1 and batch2 must be 3D Tensors each containing the same number of
* matrices. If batch1 is a b × n × m Tensor, batch2 a b × m × p Tensor, res will be a
* b × n × p Tensor.
*
* In other words,
* res_i = (beta * M_i) + (alpha * batch1_i * batch2_i)
*/
def baddbmm(beta: T, M: Tensor[T], alpha: T, batch1: Tensor[T], batch2: Tensor[T]): Tensor[T]
/** res_i = (beta * res_i) + (alpha * batch1_i * batch2_i) */
def baddbmm(beta: T, alpha: T, batch1: Tensor[T], batch2: Tensor[T]): Tensor[T]
/** res_i = res_i + (alpha * batch1_i * batch2_i) */
def baddbmm(alpha: T, batch1: Tensor[T], batch2: Tensor[T]): Tensor[T]
/** res_i = res_i + batch1_i * batch2_i */
def bmm(batch1: Tensor[T], batch2: Tensor[T]): Tensor[T]
/**
* Replaces all elements in-place with the elements of x to the power of n
*
* @param y
* @param n
* @return current tensor reference
*/
def pow(y: Tensor[T], n : T): Tensor[T]
def pow(n: T): Tensor[T]
/**
* Get the top k smallest values and their indices.
*
* @param result result buffer
* @param indices indices buffer
* @param k
* @param dim dimension, default is the last dimension
* @param increase sort order, set it to true if you want to get the smallest top k values
* @return
*/
def topk(k: Int, dim: Int = -1, increase: Boolean = true, result: Tensor[T] = null,
indices: Tensor[T] = null)
: (Tensor[T], Tensor[T])
/**
* Replaces all elements in-place with the elements of lnx
*
* @param y
* @return current tensor reference
*/
def log(y: Tensor[T]): Tensor[T]
def exp(y: Tensor[T]): Tensor[T]
def sqrt(y: Tensor[T]): Tensor[T]
def log1p(y: Tensor[T]): Tensor[T]
def log(): Tensor[T]
def exp(): Tensor[T]
def log1p(): Tensor[T]
def abs(x: Tensor[T]): Tensor[T]
/**
* returns the p-norms of the Tensor x computed over the dimension dim.
* @param y result buffer
* @param value
* @param dim
* @return
*/
def norm(y: Tensor[T], value: Int, dim: Int): Tensor[T]
/**
* Implements > operator comparing each element in x with y
*
* @param x
* @param y
* @return current tensor reference
*/
def gt(x: Tensor[T], y: Tensor[T]): Tensor[T]
/**
* Implements < operator comparing each element in x with y
*
* @param x
* @param y
* @return current tensor reference
*/
def lt(x: Tensor[T], y: Tensor[T]): Tensor[T]
/**
* Implements <= operator comparing each element in x with y
*
* @param x
* @param y
* @return current tensor reference
*/
def le(x: Tensor[T], y: Tensor[T]): Tensor[T]
/**
* Implements == operator comparing each element in x with y
*
* @param y
* @return current tensor reference
*/
def eq(x: Tensor[T], y: T): Tensor[T]
/**
* Fills the masked elements of itself with value val
*
* @param mask
* @param e
* @return current tensor reference
*/
def maskedFill(mask: Tensor[T], e: T): Tensor[T]
/**
* Copies the elements of tensor into mask locations of itself.
*
* @param mask
* @param y
* @return current tensor reference
*/
def maskedCopy(mask: Tensor[T], y: Tensor[T]): Tensor[T]
/**
* Returns a new Tensor which contains all elements aligned to a 1 in the corresponding mask.
*
* @param mask
* @param y
* @return current tensor reference
*/
def maskedSelect(mask: Tensor[T], y: Tensor[T]): Tensor[T]
/**
* returns the sum of the n-norms on the Tensor x
* @param value the n-norms
* @return
*/
def norm(value: Int): T
/**
* returns a new Tensor with the sign (+/- 1 or 0) of the elements of x.
* @return
*/
def sign(): Tensor[T]
/**
* Implements >= operator comparing each element in x with value
* @param x
* @param value
* @return
*/
def ge(x: Tensor[T], value: Double): Tensor[T]
/**
* 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
*/
def indexAdd(dim: Int, index: Tensor[T], y: Tensor[T]): Tensor[T]
/**
* 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
*/
def index(dim: Int, index: Tensor[T], y: Tensor[T]): Tensor[T]
/**
* stores the element-wise maximum of x and y in x.
* x.cmax(y) = max(x, y)
*
* @param y tensor
* @return current tensor
*/
def cmax(y: Tensor[T]): Tensor[T]
/**
* 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
*/
def cmax(x: Tensor[T], y: Tensor[T]): Tensor[T]
/**
* 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
*/
def range(xmin: Double, xmax: Double, step: Int = 1): Tensor[T]
}
