cavis/libnd4j/include/ops/declarable/generic/parity_ops/bias_add.cpp

121 lines
4.0 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
******************************************************************************/
//
// @author raver119@gmail.com
//
#include <op_boilerplate.h>
#if NOT_EXCLUDED(OP_biasadd)
#include <ops/declarable/CustomOperations.h>
namespace nd4j {
namespace ops {
DECLARE_TYPES(biasadd) {
getOpDescriptor()
->setAllowedInputTypes(nd4j::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
CUSTOM_OP_IMPL(biasadd, 2, 1, true, 0, 0) {
//REQUIRE_OK(this->validateInput2D(block));
auto input = INPUT_VARIABLE(0);
auto bias = INPUT_VARIABLE(1);
REQUIRE_TRUE(bias->isRowVector(), 0, "Bias array should be a vector");
auto z = OUTPUT_VARIABLE(0);
if (input->isMatrix())
input->addRowVector(bias, z);
else {
// TODO: we might want to use NDArray::applyTrueBroadcast here, like AddOp does
std::vector<Nd4jLong> shape({-1, bias->lengthOf()});
//nd4j_debug("Reshaping to: [%i, %i]\n", -1, (int) bias->lengthOf());
auto tArr = input->reshape(input->ordering(), shape);
auto zArr = z->reshape(z->ordering(), shape);
tArr.addRowVector(bias, &zArr);
}
STORE_RESULT(*z);
return Status::OK();
}
DECLARE_SYN(bias_add, biasadd);
DECLARE_SHAPE_FN(biasadd) {
auto xShape = inputShape->at(0);
auto yShape = inputShape->at(1);
auto dtype = ArrayOptions::dataType(yShape);
return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(xShape, dtype)));
}
DECLARE_TYPES(biasadd_bp) {
getOpDescriptor()
->setAllowedInputTypes(nd4j::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
CUSTOM_OP_IMPL(biasadd_bp, 3, 2, false, 0, 0) {
auto input = INPUT_VARIABLE(0);
auto bias = INPUT_VARIABLE(1);
auto epsilonNext = INPUT_VARIABLE(2);
auto epsilon = OUTPUT_VARIABLE(0);
auto gradB = OUTPUT_VARIABLE(1);
epsilon->assign(epsilonNext);
// cnn case
if (input->rankOf() == 4) {
auto epsilonNext2d = epsilonNext->permute({1, 0, 2, 3});
epsilonNext2d.reshapei('c', {(int) bias->lengthOf(), -1});
auto sum = epsilonNext2d.reduceAlongDimension(reduce::Sum, {1});
gradB->assign(sum);
delete sum;
} else if (input->rankOf() == 2) {
// regular fully-connected case
auto sum = epsilonNext->reduceAlongDimension(reduce::Sum, {0});
gradB->assign(sum);
delete sum;
}
return ND4J_STATUS_OK;
}
DECLARE_SYN(BiasAddGrad, biasadd_bp);
DECLARE_SHAPE_FN(biasadd_bp) {
auto input = inputShape->at(0);
auto bias = inputShape->at(1);
Nd4jLong* epsShape;
Nd4jLong* gradShape;
COPY_SHAPE(input, epsShape);
COPY_SHAPE(bias, gradShape);
return SHAPELIST(CONSTANT(epsShape), CONSTANT(gradShape));
}
}
}
#endif