/******************************************************************************* * Copyright (c) 2015-2018 Skymind, Inc. * * 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. * * 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 george@skymind.io on 6/4/2018. // #include #include namespace sd { namespace ops { #if NOT_EXCLUDED(OP_reduce_sqnorm) ////////////////////////////////////////////////////////////////////////// CUSTOM_OP_IMPL(reduce_sqnorm, 1, 1, false, 0, 0) { auto input = INPUT_VARIABLE(0); auto gradI = OUTPUT_VARIABLE(0); bool keepDims = false; auto dimensions = *block.getIArguments(); if (block.width() > 1) { auto axesVector = INPUT_VARIABLE(1); helpers::adjustAxis(input->rankOf(), axesVector, dimensions); } if (block.getBArguments()->size()) keepDims = B_ARG(0); else if (block.getTArguments()->size()) keepDims = (bool)T_ARG(0); REQUIRE_TRUE(dimensions.size() <= input->rankOf(), 0, "REDUCE_SQNORM OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead" , dimensions.size()); for(const auto& item : dimensions) REQUIRE_TRUE(item >= -input->rankOf() && item < input->rankOf(), 0, "REDUCE_SQNORM OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !" , input->rankOf(), input->rankOf(), item); input->reduceAlongDimension(reduce::SquaredNorm, *gradI, dimensions, keepDims); return Status::OK(); } DECLARE_SHAPE_FN(reduce_sqnorm) { auto dimensions = *block.getIArguments(); bool keepDims = false; if (block.width() > 1) { auto axesVector = INPUT_VARIABLE(1); helpers::adjustAxis(INPUT_VARIABLE(0)->rankOf(), axesVector, dimensions); } if (block.getBArguments()->size()) keepDims = B_ARG(0); else if (block.getTArguments()->size()) keepDims = (bool)T_ARG(0); REQUIRE_TRUE(dimensions.size() <= inputShape->at(0)[0], 0, "REDUCE_SQNORM OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead" , dimensions.size()); for(const auto& item : dimensions) REQUIRE_TRUE(item >= -inputShape->at(0)[0] && item < inputShape->at(0)[0], 0, "REDUCE_SQNORM OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !" , inputShape->at(0)[0], inputShape->at(0)[0], item); Nd4jLong* outShapeInfo = ShapeUtils::evalReduceShapeInfo(shape::order(inputShape->at(0)), dimensions, inputShape->at(0), keepDims, false, block.getWorkspace()); return SHAPELIST(outShapeInfo); } DECLARE_TYPES(reduce_sqnorm) { getOpDescriptor() ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } #endif #if NOT_EXCLUDED(OP_reduce_sqnorm_bp) ////////////////////////////////////////////////////////////////////////// CUSTOM_OP_IMPL(reduce_sqnorm_bp, 2, 1, false, 0, 0) { auto input = INPUT_VARIABLE(0); auto gradO = INPUT_VARIABLE(1); auto gradI = OUTPUT_VARIABLE(0); if (gradO->lengthOf() == 1) { gradI->assign( 2 * (*input) * gradO->e(0)); } else { bool keepDims = false; auto dimensions = *block.getIArguments(); if (block.width() > 2) { auto axesVector = INPUT_VARIABLE(2); helpers::adjustAxis(input->rankOf(), axesVector, dimensions); } if (block.getBArguments()->size()) keepDims = B_ARG(0); else if (block.getTArguments()->size()) keepDims = (bool)T_ARG(0); REQUIRE_TRUE(dimensions.size() <= input->rankOf(), 0, "REDUCE_SQNORM_BP OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead" , dimensions.size()); for(const auto& item : dimensions) REQUIRE_TRUE(item >= -input->rankOf() && item < input->rankOf(), 0, "REDUCE_SQNORM_BP OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !" , input->rankOf(), input->rankOf(), item); // *** calculations *** // if(!keepDims) { auto gradOShapeKeepDims = ShapeUtils::evalReduceShapeInfo(gradO->ordering(), dimensions, *input, true, false, block.getWorkspace()); gradI->assign(2. * (*input) *gradO->reshape(gradO->ordering(), ShapeUtils::pullShapeFromShapeInfo(gradOShapeKeepDims))); // for example could be something like [a,b] -> [1,a,1,b] } else gradI->assign(2. * (*input) * *gradO); } return Status::OK(); } DECLARE_SHAPE_FN(reduce_sqnorm_bp) { if(shape::length(inputShape->at(1)) > 1) { auto dimensions = *block.getIArguments(); if (block.width() > 2) { auto axesVector = INPUT_VARIABLE(2); helpers::adjustAxis(INPUT_VARIABLE(0)->rankOf(), axesVector, dimensions); } REQUIRE_TRUE(dimensions.size() <= inputShape->at(0)[0], 0, "REDUCE_SQNORM_BP OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead" , dimensions.size()); for(const auto& item : dimensions) REQUIRE_TRUE(item >= -inputShape->at(0)[0] && item < inputShape->at(0)[0], 0, "REDUCE_SQNORM_BP OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !" , inputShape->at(0)[0], inputShape->at(0)[0], item); } Nd4jLong* gradIshapeInfo(nullptr); COPY_SHAPE(inputShape->at(0), gradIshapeInfo); return SHAPELIST(CONSTANT(gradIshapeInfo)); } DECLARE_TYPES(reduce_sqnorm_bp) { getOpDescriptor() ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } #endif } }