/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://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.
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author raver119@gmail.com
#include <ops/ops.h>
#include <helpers/shape.h>
#include <helpers/TAD.h>
#include <ops/declarable/helpers/prefix.h>
namespace nd4j {
namespace ops {
namespace helpers {
template <typename T>
static void prefix_(scalar::Ops op, const void* vx, Nd4jLong* xShapeInfo, void* vz, Nd4jLong* zShapeInfo, bool exclusive, bool reverse) {
const auto x = reinterpret_cast<const T *>(vx);
auto z = reinterpret_cast<T *>(vz);
auto length = shape::length(xShapeInfo);
T prevSum = op == scalar::Add ? (T) 0 : (T) 1;
T sum = prevSum;
if (reverse) {
if (shape::elementWiseStride(xShapeInfo) == 1 && shape::elementWiseStride(zShapeInfo) == 1 &&
shape::order(xShapeInfo) == 'c' && shape::order(zShapeInfo) == 'c') {
for (Nd4jLong e = length - 1; e >= 0; --e) {
sum = op == scalar::Add ? simdOps::Add<T, T, T>::op(sum, x[e]) : simdOps::Multiply<T, T, T>::op(sum, x[e]);
if (!exclusive)
prevSum = sum;
z[e] = prevSum;
}
else {
auto xOffset = shape::getIndexOffset(e, xShapeInfo);
auto zOffset = shape::getIndexOffset(e, zShapeInfo);
sum = op == scalar::Add ? simdOps::Add<T, T, T>::op(sum, x[xOffset]) : simdOps::Multiply<T, T, T>::op(sum, x[xOffset]);
z[zOffset] = prevSum;
} else {
for (int e = 0; e < length; e++) {
};
static void prefix_(scalar::Ops op, const NDArray* x, NDArray* z, const std::vector<int>& dims, bool exclusive, bool reverse) {
auto xTads = x->allTensorsAlongDimension(dims);
auto zTads = z->allTensorsAlongDimension(dims);
auto t = xTads->size();
for (int e = 0; e < t; e++) {
auto tx = xTads->at(e);
auto tz = zTads->at(e);
prefix_<T>(op, tx->buffer(), tx->shapeInfo(), tz->buffer(), tz->shapeInfo(), exclusive, reverse);
delete xTads;
delete zTads;
static void prefix_(scalar::Ops op, const NDArray* x, NDArray* z, bool exclusive, bool reverse) {
prefix_<T>(op, x->getBuffer(), x->getShapeInfo(), z->buffer(), z->shapeInfo(), exclusive, reverse);
void prefix(nd4j::LaunchContext * context, scalar::Ops op, const NDArray* x, NDArray* z, bool exclusive, bool reverse) {
BUILD_SINGLE_SELECTOR(x->dataType(), prefix_, (op, x, z, exclusive, reverse), LIBND4J_TYPES);
void prefix(nd4j::LaunchContext * context, scalar::Ops op, const NDArray* x, NDArray* z, const std::vector<int>& dims, bool exclusive, bool reverse) {
BUILD_SINGLE_SELECTOR(x->dataType(), prefix_, (op, x, z, dims, exclusive, reverse), LIBND4J_TYPES);
BUILD_SINGLE_TEMPLATE(template void prefix_, (scalar::Ops op, const void* vx, Nd4jLong* xShapeInfo, void* vz, Nd4jLong* zShapeInfo, bool exclusive, bool reverse), LIBND4J_TYPES);
BUILD_SINGLE_TEMPLATE(template void prefix_, (scalar::Ops op, const NDArray* x, NDArray* z, const std::vector<int>& dims, bool exclusive, bool reverse), LIBND4J_TYPES);
BUILD_SINGLE_TEMPLATE(template void prefix_, (scalar::Ops op, const NDArray* x, NDArray* z, bool exclusive, bool reverse), LIBND4J_TYPES);