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deeplearning4j--deeplearning4j/libnd4j/include/ops/declarable/generic/broadcastable/floormod.cpp
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2026-07-13 12:47:05 +08:00

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/* ******************************************************************************
*
*
* 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 raver119@gmail.com
// modified by sgazeos@gmail.com with backprop implementation.
//
#include <system/op_boilerplate.h>
#if NOT_EXCLUDED(OP_floormod)
#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/generic/helpers/BroadcastHelper.h>
namespace sd {
namespace ops {
BROADCASTABLE_OP_IMPL(floormod, 0, 0) {
auto x = INPUT_VARIABLE(0);
auto y = INPUT_VARIABLE(1);
auto z = OUTPUT_VARIABLE(0);
BROADCAST_CHECK_EMPTY(x, y, z);
REQUIRE_TRUE(!y->isB(), 0, "FLOORMOD OP: you can't divide by bool array!");
auto tZ = BroadcastHelper::broadcastApply(BROADCAST(FloorMod), x, y, z);
if (tZ == nullptr)
return Status::KERNEL_FAILURE;
else if (tZ != z) {
OVERWRITE_RESULT(tZ);
}
return Status::OK;
}
DECLARE_TYPES(floormod) {
getOpDescriptor()
->setAllowedInputTypes(0, ANY)
->setAllowedInputTypes(1, ANY)
->setAllowedOutputTypes(0, INHERIT);
}
DECLARE_TYPES(floormod_bp) {
getOpDescriptor()->setAllowedInputTypes(ANY)->setAllowedOutputTypes({ALL_FLOATS});
}
CUSTOM_OP_IMPL(floormod_bp, 3, 2, false, 0, 0) {
auto x = INPUT_VARIABLE(0);
auto y = INPUT_VARIABLE(1);
auto epsNext = INPUT_VARIABLE(2);
auto gradX = OUTPUT_VARIABLE(0);
auto gradY = OUTPUT_VARIABLE(1);
gradX->assign(epsNext);
NDArray temp(*epsNext);
BroadcastHelper::broadcastApply(BROADCAST(FloorMod), x, y, &temp);
if (gradY->rankOf() == gradX->rankOf()) {
epsNext->applyPairwiseTransform(pairwise::Multiply, &temp, gradY);
} else { // epsNext is greater than gradY
std::vector<LongType> dims(epsNext->rankOf() * 2);
LongType gap = epsNext->rankOf() - gradY->rankOf();
for (LongType d = 0; d < gap; d++) {
dims[d * 2 + 1] = 1;
}
auto tempIn((temp)(dims));
NDArray negTempIn = -*tempIn;
auto get= (*epsNext)(dims);
get->applyPairwiseTransform(pairwise::Multiply, &negTempIn, gradY);
delete get;
delete tempIn;
}
return Status::OK;
}
DECLARE_SHAPE_FN(floormod_bp) {
auto x = inputShape->at(0);
auto y = inputShape->at(1);
auto e = inputShape->at(2);
// eps always has shape of x
// grad always has shape of y
return SHAPELIST(CONSTANT(x), CONSTANT(y));
}
} // namespace ops
} // namespace sd
#endif