/* ****************************************************************************** * * * 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 16.10.2017. // #include #include #include #ifdef LEGACY_REDUCE_SAME_ONLY namespace sd { namespace ops { LegacyReduceOp::LegacyReduceOp() : LegacyOp::LegacyOp(1) { // } LegacyReduceOp::LegacyReduceOp(int opNum) : LegacyOp::LegacyOp(1, opNum) { //this->_opNum = opNum; } LegacyOp* LegacyReduceOp::clone() { return new LegacyReduceOp(this->_opNum); } Nd4jStatus LegacyReduceOp::validateAndExecute(Context &block) { auto x = INPUT_VARIABLE(0); int opNum = block.opNum() < 0 ? this->_opNum : block.opNum(); nd4j_debug("Executing LegacyReduceOp: [%i]\n", opNum); bool allAxes = false; if (block.width() == 1) { auto z = OUTPUT_VARIABLE(0); if (block.getIArguments()->size() == x->rankOf()) allAxes = true; if ((block.getIArguments()->size() == 0) || (block.getIArguments()->size() == 1 && INT_ARG(0) == MAX_INT) || allAxes) { // scalar NativeOpExcutioner::execReduceFloatScalar(opNum, x->buffer(), x->shapeInfo(), block.getTArguments()->data(), z->buffer(), z->shapeInfo()); } else { // TAD std::vector dims(*block.getIArguments()); for (int e = 0; e < dims.size(); e++) if (dims[e] < 0) dims[e] += x->rankOf(); std::sort(dims.begin(), dims.end()); REQUIRE_TRUE(dims.size() > 0, 0, "Some dimensions required for reduction!"); shape::TAD tad(x->shapeInfo(), dims.data(), dims.size()); tad.createTadOnlyShapeInfo(); tad.createOffsets(); NativeOpExcutioner::execReduceFloat(opNum, x->buffer(), x->shapeInfo(), block.getTArguments()->data(), z->buffer(), z->shapeInfo(), dims.data(), (int) dims.size(), tad.tadOnlyShapeInfo, tad.tadOffsets); } STORE_RESULT(*z); } else { auto indices = INPUT_VARIABLE(1); if (indices->lengthOf() == x->rankOf()) allAxes = true; //indices->printIndexedBuffer("indices"); std::vector axis(indices->lengthOf()); for (int e = 0; e < indices->lengthOf(); e++) { // lol otherwise we segfault on macOS int f = indices->e(e); axis[e] = f >= 0 ? f : f += x->rankOf(); } if ((block.getIArguments()->size() == 1 && INT_ARG(0) == MAX_INT) || allAxes) { auto z = OUTPUT_VARIABLE(0); auto b = x->buffer(); auto s = x->shapeInfo(); auto e = block.numT() > 0 ? block.getTArguments()->data() : nullptr; //x->printIndexedBuffer("x"); // scalar NativeOpExcutioner::execReduceFloatScalar(opNum, b, s, e, z->buffer(), z->shapeInfo()); } else { // TAD if (indices->lengthOf() > 1) std::sort(axis.begin(), axis.end()); REQUIRE_TRUE(axis.size() > 0, 0, "Some dimensions required for reduction!"); shape::TAD tad(x->shapeInfo(), axis.data(), axis.size()); tad.createTadOnlyShapeInfo(); tad.createOffsets(); auto newShape = ShapeUtils::evalReduceShapeInfo(x->ordering(), axis, *x); auto z = new NDArray(newShape, x->getWorkspace()); NativeOpExcutioner::execReduceFloat(opNum, x->buffer(), x->shapeInfo(), block.getTArguments()->data(), z->buffer(), z->shapeInfo(), axis.data(), (int) axis.size(), tad.tadOnlyShapeInfo, tad.tadOffsets); // keepDims processing, for TF compatibility if (block.getIArguments()->size() > 0 && block.getIArguments()->at(0) == 1) { // z->printShapeInfo("z shape before"); std::vector newshape(z->getShapeAsVector()); for (int e = 0; e < axis.size(); e++) { auto a = axis.at(e); newshape.insert(newshape.begin() + a, 1); } z->reshapei(z->ordering(), newshape); // z->printShapeInfo("z shape after"); } OVERWRITE_RESULT(z); } } return ND4J_STATUS_OK; } /** * For all reductions rules are simple: either you return scalar, or you return reduced NDArray. * It solely depends on input shape, and requested dimensions */ ShapeList *LegacyReduceOp::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { auto inShape = inputShape->at(0); Nd4jLong *newShape; bool allAxes = false; if (block.getIArguments()->size() == shape::rank(inShape)) allAxes = true; if (block.getIArguments()->size() == 0 || (block.getIArguments()->size() == 1 && INT_ARG(0) == MAX_INT) || allAxes) { if (block.getIArguments()->size() > 0 && block.getIArguments()->at(0) == 1) { // in this case we just return legacy scalar ALLOCATE(newShape, block.getWorkspace(), shape::shapeInfoLength(2), Nd4jLong); newShape[0] = 2; newShape[1] = 1; newShape[2] = 1; newShape[3] = 1; newShape[4] = 1; newShape[5] = 0; newShape[6] = 1; newShape[7] = 99; //ArrayOptions::setDataType(newShape, block.dataType() == DataType::BOOL?block.dataType():ArrayOptions::dataType(inShape)); } else { ALLOCATE(newShape, block.getWorkspace(), shape::shapeInfoLength(0), Nd4jLong); newShape[0] = 0; newShape[1] = 0; newShape[2] = 1; newShape[3] = 99; //ArrayOptions::setDataType(newShape, block.dataType() == DataType::BOOL?block.dataType():ArrayOptions::dataType(inShape)); } } else { // in this case we're building proper shape for reduction auto array = new NDArray(nullptr, inShape, block.getWorkspace()); newShape = ShapeUtils::evalReduceShapeInfo(shape::order(inShape), *block.getIArguments(), *array, false, false, block.workspace()); delete array; } return SHAPELIST(newShape); } } } #endif