wehub-resource-sync
106 lines
3.3 KiB
Plaintext
106 lines
3.3 KiB
Plaintext
/* ******************************************************************************
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*
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* See the NOTICE file distributed with this work for additional
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* information regarding copyright ownership.
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author sgazeos@gmail.com
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//
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#include <array/NDArray.h>
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#include <helpers/ShapeUtils.h>
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#include <system/op_boilerplate.h>
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namespace sd {
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namespace ops {
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namespace helpers {
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template <typename T>
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void maximumBPFunctor_(NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY) {
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auto lambdaX = LAMBDA_TTT(_e, _x, _y) { return _x >= _y ? _e : (T)0.; });
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auto lambdaY = LAMBDA_TTT(_e, _x, _y) { return _x <= _y ? _e : (T)0.; });
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if (x->isSameShape(y)) {
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// PWT case case
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// X gradient
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epsNext->applyTriplewiseLambda(x, y, lambdaX, gradX);
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// Y gradient
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epsNext->applyTriplewiseLambda(x, y, lambdaY, gradY);
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} else if (y->isScalar()) {
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T s = y->e<T>(0);
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auto lambdaS = LAMBDA_TT(_e, _x, s) { return _x >= s ? _e : (T)0.; });
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float zero = 0.f;
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// scalar case
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auto tmp = epsNext->reduceNumber(reduce::Sum);
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if (x <= y)
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gradY->assign(&tmp);
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else
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gradY->assign(zero);
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epsNext->applyPairwiseLambda(x, lambdaS, gradX);
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} else {
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// broadcast case
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// 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
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// shape)
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auto preX = x->dup();
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auto preY = y->dup();
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auto* targetShapePtr = epsNext->getShapeAsVector();
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std::vector<LongType> targetShape = *targetShapePtr;
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delete targetShapePtr;
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preX.tileToShape(targetShape, preX);
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preY.tileToShape(targetShape, preY);
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epsNext->applyTriplewiseLambda(&preX, &preY, lambdaX, &preX);
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epsNext->applyTriplewiseLambda(&preX, &preY, lambdaY, &preY);
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auto axisX = ShapeUtils::evalBroadcastBackwardAxis(x->shapeInfo(), epsNext->shapeInfo());
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auto axisY = ShapeUtils::evalBroadcastBackwardAxis(y->shapeInfo(), epsNext->shapeInfo());
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if (axisX.size() > 0) {
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auto sum = preX.reduceAlongDimension(reduce::Sum, &axisX);
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gradX->assign(&sum);
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} else
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gradX->assign(&preX);
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if (axisY.size() > 0) {
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auto sum = preY.reduceAlongDimension(reduce::Sum, &axisY);
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gradY->assign(&sum);
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} else
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gradY->assign(&preY);
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}
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}
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void maximumBPFunctor(LaunchContext* context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX,
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NDArray* gradY) {
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NDArray::prepareSpecialUse({gradX, gradY}, {x, y, epsNext});
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BUILD_SINGLE_SELECTOR(x->dataType(), maximumBPFunctor_, (x, y, epsNext, gradX, gradY), SD_NUMERIC_TYPES);
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NDArray::registerSpecialUse({gradX, gradY}, {x, y, epsNext});
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}
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} // namespace helpers
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} // namespace ops
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} // namespace sd
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