cavis/libnd4j/include/ops/declarable/generic/nn/activations/crelu.cpp

114 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 <system/op_boilerplate.h>
#if NOT_EXCLUDED(OP_crelu)
#include <ops/declarable/CustomOperations.h>
#include<ops/declarable/helpers/transforms.h>
#include <ops/declarable/helpers/legacy_helpers.h>
namespace sd {
namespace ops {
CUSTOM_OP_IMPL(crelu, 1, 1, false, 0, 0) {
auto x = INPUT_VARIABLE(0);
REQUIRE_TRUE(x->isR(), 0, "CRELU: input must be real type");
auto tmp = x->dup();
tmp.applyTransform(sd::transform::Neg, tmp);
auto z = OUTPUT_VARIABLE(0);
helpers::concat(block.launchContext(), {x, &tmp}, *z, x->rankOf()-1);
// NDArrayFactory<T>::concat({x, tmp}, -1, z);
// TODO: make this configurable?
double threshold = 0.0;
z->applyScalar(sd::scalar::RELU, threshold, *z);
STORE_RESULT(z);
return Status::OK();
}
DECLARE_TYPES(crelu) {
getOpDescriptor()
->setAllowedInputTypes(0, DataType::ANY)
->setSameMode(true);
}
DECLARE_SHAPE_FN(crelu) {
auto inShape = inputShape->at(0);
std::vector<Nd4jLong> shape;
for (int e = 0; e < shape::rank(inShape); e++)
shape.emplace_back(shape::shapeOf(inShape)[e]);
shape[shape.size()-1] *= 2;
auto newShape = ConstantShapeHelper::getInstance()->createShapeInfo(ArrayOptions::dataType(inShape), shape::order(inShape), shape);
return SHAPELIST(newShape);
}
CUSTOM_OP_IMPL(crelu_bp, 2, 1, false, 0, 0) {
auto input = INPUT_VARIABLE(0);
auto epsilonNext = INPUT_VARIABLE(1);
auto epsilon = OUTPUT_VARIABLE(0);
// at first step we build fwd activation
sd::ops::crelu op;
auto tmpResult = op.evaluate({input});
if (tmpResult.status() != ND4J_STATUS_OK)
return tmpResult.status();
auto actv = tmpResult.at(0);
// now we do RELU backward pass
//actv->applyPairwiseTransform(pairwise::RELUDerivativeE, *epsilon, nullptr);
helpers::reluDerivative(block.launchContext(), actv, epsilonNext);
// now we split updated array into 2 chunks along last dimension
sd::ops::concat_bp opc;
auto dec = opc.evaluate({input, input, actv}, {-1});
if (dec.status() != ND4J_STATUS_OK)
return dec.status();
// and now we subtract two parts of epsilons and pass result out
auto pos = dec.at(0);
auto neg = dec.at(1);
pos->applyPairwiseTransform(sd::pairwise::Subtract, *neg, *epsilon);
return ND4J_STATUS_OK;
}
DECLARE_TYPES(crelu_bp) {
getOpDescriptor()
->setAllowedInputTypes(0, DataType::ANY)
->setAllowedInputTypes(1, {DataType::FLOAT32, DataType ::DOUBLE, DataType::HALF})
->setAllowedOutputTypes(0, {DataType::FLOAT32, DataType ::DOUBLE, DataType::HALF});
}
DECLARE_SHAPE_FN(crelu_bp) {
auto inShape = inputShape->at(0);
return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(inShape)));
}
}
}
#endif