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deeplearning4j--deeplearning4j/libnd4j/include/ops/declarable/generic/broadcastable/assign.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
******************************************************************************/
//
// Created by raver119 on 24.11.17.
//
#include <system/op_boilerplate.h>
#include <helpers/StringUtils.h>
#if NOT_EXCLUDED(OP_assign)
#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/generic/helpers/BroadcastHelper.h>
namespace sd {
namespace ops {
BROADCASTABLE_OP_IMPL(assign, 0, 0) {
auto x = INPUT_VARIABLE(0);
auto y = block.width() < 2 ? x: INPUT_VARIABLE(1);
auto z = OUTPUT_VARIABLE(0);
// Check if any array is of string type
if (x->isS() || y->isS() || z->isS()) {
// Handle string broadcast at high level
StringUtils::broadcastStringAssign(x,z);
return Status::OK;
}
NDArray *castedX = x->dataType() == z->dataType() ? x : x->cast(z->dataType());
NDArray *castedY = y->dataType() == z->dataType() ? y : y->cast(z->dataType());
ArrayOptions::validateSingleDataType(ArrayOptions::dataType(castedX->shapeInfo()));
ArrayOptions::validateSingleDataType(ArrayOptions::extra(castedY->shapeInfo()));
ArrayOptions::validateSingleDataType(ArrayOptions::extra(z->shapeInfo()));
auto tZ = BroadcastHelper::broadcastApply(BroadcastOpsTuple::Assign(), castedX, castedY, z);
if (tZ != z) {
OVERWRITE_RESULT(tZ);
}
// Cleanup casted arrays if they were allocated
if (castedX != x) delete castedX;
if (castedY != y) delete castedY;
return Status::OK;
}
DECLARE_SYN(set, assign);
DECLARE_SYN(copy, assign);
DECLARE_TYPES(assign) {
getOpDescriptor()
->setAllowedInputTypes(0, {ALL_INTS,ALL_FLOATS,ALL_STRINGS,BOOL})
->setAllowedInputTypes(1, {ALL_INTS,ALL_FLOATS,ALL_STRINGS,BOOL})
->setAllowedOutputTypes(0, {ALL_INTS,ALL_FLOATS,ALL_STRINGS,BOOL});
}
DECLARE_TYPES(assign_bp) {
getOpDescriptor()->setAllowedInputTypes(ANY)->setAllowedOutputTypes({ALL_INTS,ALL_FLOATS,ALL_STRINGS});
}
CUSTOM_OP_IMPL(assign_bp, 3, 2, false, 0, 0) {
auto x = INPUT_VARIABLE(0);
auto y = block.width() < 2 ? x->dup(x->ordering(), false) : INPUT_VARIABLE(1); // dup() already returns NDArray*
auto epsNext = INPUT_VARIABLE(2);
auto gradX = OUTPUT_VARIABLE(0);
auto gradY = OUTPUT_VARIABLE(1);
float zero = 0.0f;
gradX->assign(zero);
if (x->isSameShape(y)) {
gradY->assign(epsNext);
} else if (y->isScalar()) {
auto sum = epsNext->reduceNumber(reduce::Sum);
gradY->assign(sum);
delete sum;
} else {
// broadcastable
auto axisY = ShapeUtils::evalBroadcastBackwardAxis(y->shapeInfo(), epsNext->shapeInfo());
if (axisY.size() > 0) {
auto sum = epsNext->reduceAlongDimension(reduce::Sum, &axisY);
gradY->assign(sum);
delete sum;
} else
gradY->assign(epsNext);
}
return Status::OK;
}
DECLARE_SHAPE_FN(assign_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
auto shapeList = SHAPELIST(CONSTANT(x), CONSTANT(y));
return shapeList;
}
} // namespace ops
} // namespace sd
#endif