113 lines
3.9 KiB
C++
113 lines
3.9 KiB
C++
/* ******************************************************************************
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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 <ops/declarable/helpers/roll.h>
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#if NOT_EXCLUDED(OP_roll)
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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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static void rollFunctorLinear_(NDArray* input, NDArray* output, int shift, bool inplace) {
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auto source = input;
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if (!inplace) output->assign(input);
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int fullLen = source->lengthOf();
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int actualShift = shift; // % fullLen; // shift already non-negative then
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if (actualShift < 0) {
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actualShift -= fullLen * (actualShift / fullLen - 1);
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} else
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actualShift %= fullLen;
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if (actualShift) {
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int shiftCount = fullLen / actualShift - 1;
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int remainShift = fullLen % actualShift;
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// stage 1) swap last actualShift elements with first ones.
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for (int e = 0; e < actualShift; ++e) {
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int sourceIndex = fullLen - actualShift + e;
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auto _e0 = output->e<T>(e);
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auto _e1 = output->e<T>(sourceIndex);
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output->p<T>(e, _e1);
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output->p<T>(sourceIndex, _e0);
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}
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// stage 2) swap swapped actualShift elements with rest remainShiftCount times.
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for (int count = 1; count < shiftCount; ++count) {
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for (int e = 0; e < actualShift; ++e) {
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int destinationIndex = fullLen - (count + 1) * actualShift + e;
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int sourceIndex = fullLen - count * actualShift + e;
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auto _e0 = output->e<T>(destinationIndex);
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auto _e1 = output->e<T>(sourceIndex);
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output->p<T>(destinationIndex, _e1);
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output->p<T>(sourceIndex, _e0);
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}
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}
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// stage 3) swap remainder of items.
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if (remainShift && shiftCount)
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for (int i = actualShift; i < 2 * actualShift; ++i) {
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auto _e0 = output->e<T>(i);
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auto _e1 = output->e<T>(i + remainShift);
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output->p<T>(i, _e1);
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output->p<T>(i + remainShift, _e0);
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}
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}
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}
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void rollFunctorFull(sd::LaunchContext* context, NDArray* input, NDArray* output, const std::vector<LongType>& shifts,
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const std::vector<LongType>& axes, bool inplace) {
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if (!inplace) output->assign(input);
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auto source = output; // input;
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for (size_t i = 0; i < axes.size(); i++) {
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int axe = axes[i];
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ResultSet listOfTensors = source->allTensorsAlongDimension({axe});
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ResultSet listOfOutTensors = output->allTensorsAlongDimension({axe});
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int fullLen = listOfTensors.size();
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sd_debug("Roll: fullLen at last dimension is %d\n", fullLen);
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int theShift = shifts[i];
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if (theShift > 0) {
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theShift %= fullLen;
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} else {
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theShift -= fullLen * (theShift / fullLen - 1);
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}
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for (int k = 0; k < fullLen; k++) {
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rollFunctorLinear(context, listOfTensors.at(k), listOfOutTensors.at(k), theShift, true);
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}
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}
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}
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void rollFunctorLinear(sd::LaunchContext* context, NDArray* input, NDArray* output, int shift, bool inplace) {
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BUILD_SINGLE_SELECTOR(input->dataType(), rollFunctorLinear_, (input, output, shift, inplace), SD_COMMON_TYPES);
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}
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BUILD_SINGLE_TEMPLATE( void rollFunctorLinear_, (NDArray * input, NDArray* output, int shift, bool inplace),
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SD_COMMON_TYPES);
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} // namespace helpers
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} // namespace ops
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} // namespace sd
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#endif |