wehub-resource-sync
197 lines
7.4 KiB
C++
197 lines
7.4 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 Yurii Shyrma (iuriish@yahoo.com), created on 04.06.2018
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//
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/helpers/axis.h>
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#include <ops/declarable/helpers/reductions.h>
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#if NOT_EXCLUDED(OP_reduce_variance)
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namespace sd {
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namespace ops {
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(reduce_variance, -1, 1, false, 0, 0) {
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_VARIABLE(0);
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bool biasCorrected = false; // block.getTArguments()->size() > 1 ? (bool)T_ARG(1) : false;
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auto dimensions = *block.getIArguments();
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if (block.width() > 1) {
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auto axesVector = INPUT_VARIABLE(1);
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helpers::adjustAxis(input->rankOf(), axesVector, dimensions);
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}
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if (block.getBArguments()->size()) {
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if (block.getBArguments()->size() > 1) biasCorrected = B_ARG(1);
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} else if (block.getTArguments()->size()) {
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if (block.getTArguments()->size() > 1) biasCorrected = (bool)T_ARG(1);
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}
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REQUIRE_TRUE(
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dimensions.size() <= static_cast<size_t>(input->rankOf()), 0,
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"REDUCE_VARIANCE OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead",
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dimensions.size());
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for (const auto& item : dimensions)
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REQUIRE_TRUE(
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item >= -input->rankOf() && item < input->rankOf(), 0,
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"REDUCE_VARIANCE OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !",
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input->rankOf(), input->rankOf(), item);
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sd::ops::helpers::variance(*input, *output, dimensions, biasCorrected);
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return sd::Status::OK;
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}
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DECLARE_SHAPE_FN(reduce_variance) {
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bool keepDims = false; // block.getTArguments()->size() > 0 ? (bool)T_ARG(0) : false;
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auto dimensions = *block.getIArguments();
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if (block.width() > 1) {
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auto axesVector = INPUT_VARIABLE(1);
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helpers::adjustAxis(INPUT_VARIABLE(0)->rankOf(), axesVector, dimensions);
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}
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if (block.getBArguments()->size()) {
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keepDims = B_ARG(0);
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} else if (block.getTArguments()->size()) {
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keepDims = (bool)T_ARG(0);
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}
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REQUIRE_TRUE(
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dimensions.size() <= static_cast<size_t>(INPUT_VARIABLE(0)->rankOf()), 0,
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"REDUCE_VARIANCE OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead",
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dimensions.size());
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for (const auto& item : dimensions)
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REQUIRE_TRUE(
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item >= -inputShape->at(0)[0] && item < inputShape->at(0)[0], 0,
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"REDUCE_VARIANCE OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !",
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inputShape->at(0)[0], inputShape->at(0)[0], item);
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auto outShapeInfo = ShapeUtils::evalReduceShapeInfo(shape::order(inputShape->at(0)), &dimensions, inputShape->at(0),
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keepDims, false, block.getWorkspace());
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return SHAPELIST(outShapeInfo);
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}
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DECLARE_TYPES(reduce_variance) {
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getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
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}
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(reduce_variance_bp, -1, 1, false, 0, 0) {
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auto input = INPUT_VARIABLE(0);
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auto gradO = INPUT_VARIABLE(1);
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auto gradI = OUTPUT_VARIABLE(0);
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bool keepDims = true;
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bool biasCorrected = false;
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auto dimensions = *block.getIArguments();
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if (block.width() > 2) {
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auto axesVector = INPUT_VARIABLE(2);
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helpers::adjustAxis(input->rankOf(), axesVector, dimensions);
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}
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if (block.getBArguments()->size()) {
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keepDims = B_ARG(0);
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if (block.getBArguments()->size() > 1) biasCorrected = B_ARG(1);
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} else if (block.getTArguments()->size()) {
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keepDims = (bool)T_ARG(0);
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if (block.getTArguments()->size() > 1) biasCorrected = (bool)T_ARG(1);
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}
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REQUIRE_TRUE(
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dimensions.size() <= static_cast<size_t>(input->rankOf()), 0,
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"REDUCE_VARIANCE_BP OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead",
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dimensions.size());
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for (const auto& item : dimensions) {
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REQUIRE_TRUE(
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item >= -input->rankOf() && item < input->rankOf(), 0,
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"REDUCE_VARIANCE_BP OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !",
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input->rankOf(), input->rankOf(), item);
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sd_debug("Dimension item is %d\n", item);
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}
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auto inputLen = input->lengthOf();
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//avoid divide by zero
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auto grad0Length = gradO->isScalar() || gradO->lengthOf() < 1 ? 1 : gradO->lengthOf();
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const sd::LongType N = inputLen / grad0Length;
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const sd::LongType NminusOne = biasCorrected ? N - 1 : N;
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// Break down: (*input - mean) * (2.0f / NminusOne)
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auto* mean = input->reduceAlongDimension(reduce::Mean, &dimensions, true);
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auto* inputMinusMean = (*input) - (*mean);
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delete mean;
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auto* assign = (*inputMinusMean) * (2.0f / NminusOne);
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delete inputMinusMean;
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gradI->assign(assign); // automatic broadcasting happens here
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delete assign;
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if (!keepDims) {
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auto gradOShapeKeepDims = ShapeUtils::evalReduceShapeInfo(gradO->ordering(), &dimensions, *input, true, false, block.getWorkspace());
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auto grad0Shape = ShapeUtils::pullShapeFromShapeInfo(gradOShapeKeepDims);
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auto* reshaped = !gradO->isScalar() ? gradO->reshape(gradO->ordering(), grad0Shape) : gradO; // for example could be something like [a,b] -> [1,a,1,b];
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*gradI *= (*reshaped); // for example could be something like [a,b] -> [1,a,1,b]
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//reshape can vary and may have the same buffer as the original
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if(reshaped != gradO && reshaped->buffer() != gradO->buffer() && reshaped->specialBuffer() != gradI->specialBuffer())
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delete reshaped;
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} else {
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*gradI *= (*gradO); // automatic broadcasting happens here
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}
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return sd::Status::OK;
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}
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DECLARE_SHAPE_FN(reduce_variance_bp) {
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auto in = inputShape->at(0);
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auto rank = shape::rank(in);
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auto dimensions = *block.getIArguments();
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if (block.width() > 2) {
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auto axesVector = INPUT_VARIABLE(2);
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helpers::adjustAxis(rank, axesVector, dimensions);
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}
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REQUIRE_TRUE(
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dimensions.size() <= static_cast<size_t>(rank), 0,
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"REDUCE_VARIANCE_BP OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead",
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dimensions.size());
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for (const auto& item : dimensions)
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REQUIRE_TRUE(
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item >= -inputShape->at(0)[0] && item < inputShape->at(0)[0], 0,
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"REDUCE_VARIANCE_BP OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !",
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inputShape->at(0)[0], inputShape->at(0)[0], item);
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return SHAPELIST(CONSTANT(in));
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}
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DECLARE_TYPES(reduce_variance_bp) {
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getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
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}
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} // namespace ops
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} // namespace sd
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#endif
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