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chore: import upstream snapshot with attribution

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2026-07-13 12:47:05 +08:00
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libnd4j/include/ops/declarable/helpers/cpu/minimax.cpp
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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 sgazeos@gmail.com
//
#include <array/NDArray.h>
#include <helpers/ShapeUtils.h>
#include <ops/declarable/helpers/minimax.h>
#include <system/op_boilerplate.h>
namespace sd {
namespace ops {
namespace helpers {
template <typename T>
static void minimumBPFunctor_(LaunchContext* context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY) {
auto lambdaX = LAMBDA_TTT(_e, _x, _y) { return _x <= _y ? _e : (T)0.; });
auto lambdaY = LAMBDA_TTT(_e, _x, _y) { return _x >= _y ? _e : (T)0.; });
if (x->isSameShape(y)) {
// PWT case case
// X gradient
epsNext->applyTriplewiseLambda<T>(x, y, lambdaX, gradX);
// Y gradient
epsNext->applyTriplewiseLambda<T>(x, y, lambdaY, gradY);
} else if (y->isScalar()) {
T s = y->e<T>(0);
auto lambdaS = LAMBDA_TT(_e, _x, s) { return _x <= s ? _e : (T)0.; });
float zero = 0.0f;
// scalar case
auto tmp = epsNext->reduceNumber(reduce::Sum);
if (x <= y)
gradY->assign(tmp);
else
gradY->assign(zero);
epsNext->applyPairwiseLambda<T>(x, lambdaS, gradX);
delete tmp;
} else {
// broadcast case
// in this case we want to boost our X and Y shapes to the size of FF pass output (or epsNext, which has the same
// shape)
auto preX = x->dup();
auto preY = y->dup();
auto targetShape = epsNext->getShapeAsVector();
preX->tileToShape(*targetShape, *preX);
preY->tileToShape(*targetShape, *preY);
epsNext->applyTriplewiseLambda<T>(preX, preY, lambdaX, preX);
epsNext->applyTriplewiseLambda<T>(preX, preY, lambdaY, preY);
auto axisX = ShapeUtils::evalBroadcastBackwardAxis(x->shapeInfo(), epsNext->shapeInfo());
auto axisY = ShapeUtils::evalBroadcastBackwardAxis(y->shapeInfo(), epsNext->shapeInfo());
if (axisX.size() > 0) {
auto sum = preX->reduceAlongDimension(reduce::Sum, &axisX);
gradX->assign(sum);
} else
gradX->assign(preX);
if (axisY.size() > 0) {
auto sum = preY->reduceAlongDimension(reduce::Sum, &axisY);
gradY->assign(sum);
delete sum;
} else
gradY->assign(preY);
delete targetShape;
delete preX; // Clean up duped array
delete preY; // Clean up duped array
}
}
template <typename T>
void maximumBPFunctor_(LaunchContext* context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY) {
auto lambdaX = LAMBDA_TTT(_e, _x, _y) { return _x >= _y ? _e : (T)0.; });
auto lambdaY = LAMBDA_TTT(_e, _x, _y) { return _x <= _y ? _e : (T)0.; });
if (x->isSameShape(y)) {
// PWT case case
// X gradient
epsNext->applyTriplewiseLambda<T>(x, y, lambdaX, gradX);
// Y gradient
epsNext->applyTriplewiseLambda<T>(x, y, lambdaY, gradY);
} else if (y->isScalar()) {
T s = y->e<T>(0);
auto lambdaS = LAMBDA_TT(_e, _x, s) { return _x >= s ? _e : (T)0.; });
// scalar case
auto tmp = epsNext->reduceNumber(reduce::Sum);
float zero = 0.0f;
if (x <= y)
gradY->assign(tmp);
else
gradY->assign(zero);
delete tmp;
epsNext->applyPairwiseLambda<T>(x, lambdaS, gradX);
} else {
// broadcast case
// in this case we want to boost our X and Y shapes to the size of FF pass output (or epsNext, which has the same
// shape)
auto preX = x->dup();
auto preY = y->dup();
auto targetShape = epsNext->getShapeAsVector();
preX->tileToShape(*targetShape, *preX);
preY->tileToShape(*targetShape, *preY);
epsNext->applyTriplewiseLambda<T>(preX, preY, lambdaX, preX);
epsNext->applyTriplewiseLambda<T>(preX, preY, lambdaY, preY);
auto axisX = ShapeUtils::evalBroadcastBackwardAxis(x->shapeInfo(), epsNext->shapeInfo());
auto axisY = ShapeUtils::evalBroadcastBackwardAxis(y->shapeInfo(), epsNext->shapeInfo());
if (axisX.size() > 0) {
auto sum = preX->reduceAlongDimension(reduce::Sum, &axisX);
gradX->assign(sum);
delete sum;
} else
gradX->assign(preX);
if (axisY.size() > 0) {
auto sum = preY->reduceAlongDimension(reduce::Sum, &axisY);
gradY->assign(sum);
delete sum;
} else
gradY->assign(preY);
delete preX; // Clean up duped array
delete preY; // Clean up duped array
}
}
void minimumBPFunctor(LaunchContext* context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX,
NDArray* gradY) {
BUILD_SINGLE_SELECTOR(x->dataType(), minimumBPFunctor_, (context, x, y, epsNext, gradX, gradY), SD_NUMERIC_TYPES);
}
void maximumBPFunctor(LaunchContext* context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX,
NDArray* gradY) {
BUILD_SINGLE_SELECTOR(x->dataType(), maximumBPFunctor_, (context, x, y, epsNext, gradX, gradY), SD_NUMERIC_TYPES);
}
BUILD_SINGLE_TEMPLATE( void minimumBPFunctor_,
(LaunchContext* context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY), SD_NUMERIC_TYPES);
BUILD_SINGLE_TEMPLATE( void maximumBPFunctor_,
(LaunchContext* context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY), SD_NUMERIC_TYPES);
} // namespace helpers
} // namespace ops
} // namespace sd