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deeplearning4j--deeplearning4j/libnd4j/include/ops/declarable/generic/tensor/fill.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
//
#include <system/op_boilerplate.h>
#if NOT_EXCLUDED(OP_fill)
#include <ops/declarable/headers/parity_ops.h>
namespace sd {
namespace ops {
CUSTOM_OP_IMPL(fill, 1, 1, false, -2, 0) {
auto shapeArray = INPUT_VARIABLE(0);
auto output = OUTPUT_VARIABLE(0);
auto w = block.width();
auto i = block.numI();
auto t = block.numT();
REQUIRE_TRUE(w > 1 || t > 0 || i > 0, 0,
"Fill: either additional variable should exist, or scalar value should be present");
if (output->isEmpty()) {
// Empty output array - no-op
return Status::OK;
}
if (w > 1) {
output->assign(INPUT_VARIABLE(1));
} else {
if (t > 0) {
output->assign(T_ARG(0));
} else if (i > 0) {
output->assign(INT_ARG(0));
}
}
STORE_RESULT(output);
return Status::OK;
};
DECLARE_TYPES(fill) {
getOpDescriptor()
->setAllowedInputTypes(0, {ALL_INTS})
->setAllowedInputTypes(1, {ALL_INTS, ALL_FLOATS})
->setAllowedOutputTypes({ALL_INTS, ALL_FLOATS});
}
DECLARE_SHAPE_FN(fill) {
auto shapeArray = INPUT_VARIABLE(0);
const LongType len = shapeArray->lengthOf();
if (shapeArray->isEmpty()) {
std::vector<LongType> shape = {0};
return SHAPELIST(ConstantShapeHelper::getInstance().scalarShapeInfo(shapeArray->dataType()));
}
LongType *newShape = nullptr;
ALLOCATE(newShape, block.getWorkspace(), shape::shapeInfoLength(len), sd::LongType);
newShape[0] = len;
bool hasZeros = false;
LongType totalLen = 1;
for (int e = 0; e < shapeArray->lengthOf(); e++) {
newShape[e + 1] = shapeArray->e<LongType>(e);
if(newShape[e + 1] == 0)
hasZeros = true;
totalLen *= newShape[e + 1];
}
if(len > 1 && hasZeros) {
RELEASE(newShape, block.getWorkspace());
std::vector<LongType> shapeOnly = shapeArray->asVectorT<LongType>();
return SHAPELIST(ConstantShapeHelper::getInstance().emptyShapeInfoWithShape(shapeArray->dataType(),shapeOnly));
}
if (totalLen < 1) {
RELEASE(newShape, block.getWorkspace());
std::vector<LongType> shape = {0};
return SHAPELIST(ConstantShapeHelper::getInstance().emptyShapeInfoWithShape(shapeArray->dataType(), shape));
}
DataType dataType;
if (block.width() > 1) {
dataType = INPUT_VARIABLE(1)->dataType();
} else if (block.numT() > 0) {
dataType = Environment::getInstance().defaultFloatDataType();
} else if (block.numI() > 0) {
dataType = INT32;
} else if (block.numB() > 0) {
dataType = BOOL;
} else
THROW_EXCEPTION("Fill: missing value to fill output array with");
ShapeUtils::updateStridesAndType(newShape, dataType, 'c');
auto result = CONSTANT(newShape);
RELEASE(newShape, block.getWorkspace());
return SHAPELIST(result);
};
} // namespace ops
} // namespace sd
#endif