cavis/libnd4j/include/ops/declarable/impl/LegacyReduceOp.cpp

188 lines
7.7 KiB
C++

/*******************************************************************************
* 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 raver119 on 16.10.2017.
//
#include <ops/declarable/LegacyReduceOp.h>
#include <helpers/TAD.h>
#include <helpers/ShapeUtils.h>
#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->getBuffer(), x->getShapeInfo(), block.getTArguments()->data(), z->buffer(), z->shapeInfo());
} else {
// TAD
std::vector<int> 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->getShapeInfo(), dims.data(), dims.size());
tad.createTadOnlyShapeInfo();
tad.createOffsets();
NativeOpExcutioner::execReduceFloat(opNum, x->getBuffer(), x->getShapeInfo(), block.getTArguments()->data(), z->getBuffer(), z->getShapeInfo(), 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<int> axis(indices->lengthOf());
for (int e = 0; e < indices->lengthOf(); e++) {
// lol otherwise we segfault on macOS
int f = indices->e<int>(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->getBuffer();
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->getShapeInfo(), 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->getBuffer(), x->getShapeInfo(), block.getTArguments()->data(), z->getBuffer(), z->getShapeInfo(), 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<Nd4jLong> 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