Files
deeplearning4j--deeplearning4j/libnd4j/include/ops/declarable/helpers/cpu/triu.cpp
T
2026-07-13 12:47:05 +08:00

67 lines
2.3 KiB
C++

/* ******************************************************************************
*
*
* 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.
*
* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership.
* 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 Yurii Shyrma (iuriish@yahoo.com), created on 20.04.2018
//
#include <helpers/Loops.h>
#include <ops/declarable/helpers/transforms.h>
#if NOT_EXCLUDED(OP_triu)
namespace sd {
namespace ops {
namespace helpers {
//////////////////////////////////////////////////////////////////////////
template <typename T>
static void triuBP_(sd::LaunchContext* context, NDArray& input, NDArray& gradO, NDArray& gradI,
const int diagonal) {
if(gradO.isScalar()) {
auto firstElement = gradO.e(0);
gradI.assign(&firstElement);
} else {
auto dOdI = NDArray(&gradO); // dO/dI
char direction = diagonal <= 0 || diagonal > 0 ? 'l': 'u';
const_cast<NDArray&>(input).fillAsTriangular<T>(0, diagonal, diagonal, dOdI, direction,false);
int dLen = dOdI.lengthOf();
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i++) {
if (dOdI.t<T>(i) != static_cast<T>(0.f)) dOdI.r<T>(i) = static_cast<T>(1.f);
}
};
samediff::Threads::parallel_for(func, 0, dLen);
NDArray *ref = dOdI * gradO;
gradI.assign(ref); // chain rule: dLoss/dI = dO/dI * dLoss/dO
delete ref;
}
}
void triuBP(sd::LaunchContext* context, NDArray& input, NDArray& gradO, NDArray& gradI,
const int diagonal) {
BUILD_SINGLE_SELECTOR(gradO.dataType(), triuBP_, (context, input, gradO, gradI, diagonal), SD_COMMON_TYPES);
}
} // namespace helpers
} // namespace ops
} // namespace sd
#endif