118 lines
4.1 KiB
C++
118 lines
4.1 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author raver119@gmail.com
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//
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#include <op_boilerplate.h>
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#if NOT_EXCLUDED(OP_crelu)
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#include <ops/declarable/CustomOperations.h>
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#include<ops/declarable/helpers/transforms.h>
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#include <ops/declarable/helpers/legacy_helpers.h>
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namespace nd4j {
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namespace ops {
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CUSTOM_OP_IMPL(crelu, 1, 1, false, 0, 0) {
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auto x = INPUT_VARIABLE(0);
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REQUIRE_TRUE(x->isR(), 0, "CRELU: input must be real type");
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auto tmp = x->dup();
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tmp->applyTransform(nd4j::transform::Neg, nullptr, nullptr);
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auto z = OUTPUT_VARIABLE(0);
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helpers::concat(block.launchContext(), {x, tmp}, *z, x->rankOf()-1);
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// NDArrayFactory<T>::concat({x, tmp}, -1, z);
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// TODO: make this configurable?
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double threshold = 0.0;
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z->applyScalar(nd4j::scalar::RELU, threshold);
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STORE_RESULT(z);
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delete tmp;
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return Status::OK();
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}
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DECLARE_TYPES(crelu) {
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getOpDescriptor()
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->setAllowedInputTypes(0, DataType::ANY)
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->setSameMode(true);
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}
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DECLARE_SHAPE_FN(crelu) {
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auto inShape = inputShape->at(0);
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std::vector<Nd4jLong> shape;
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for (int e = 0; e < shape::rank(inShape); e++)
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shape.emplace_back(shape::shapeOf(inShape)[e]);
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shape[shape.size()-1] *= 2;
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auto newShape = ConstantShapeHelper::getInstance()->createShapeInfo(ArrayOptions::dataType(inShape), shape::order(inShape), shape);
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return SHAPELIST(newShape);
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}
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CUSTOM_OP_IMPL(crelu_bp, 2, 1, false, 0, 0) {
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auto input = INPUT_VARIABLE(0);
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auto epsilonNext = INPUT_VARIABLE(1);
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auto epsilon = OUTPUT_VARIABLE(0);
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// at first step we build fwd activation
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nd4j::ops::crelu op;
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auto tmpResult = op.execute({input}, {}, {}, {});
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if (tmpResult->status() != ND4J_STATUS_OK)
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return tmpResult->status();
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auto actv = tmpResult->at(0);
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// now we do RELU backward pass
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//actv->applyPairwiseTransform(pairwise::RELUDerivativeE, *epsilon, nullptr);
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helpers::reluDerivative(block.launchContext(), actv, epsilonNext);
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// now we split updated array into 2 chunks along last dimension
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nd4j::ops::concat_bp opc;
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auto dec = opc.execute({input, input, actv}, {}, {-1}, {});
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if (dec->status() != ND4J_STATUS_OK)
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return dec->status();
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// and now we subtract two parts of epsilons and pass result out
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auto pos = dec->at(0);
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auto neg = dec->at(1);
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pos->applyPairwiseTransform(nd4j::pairwise::Subtract, neg, epsilon, nullptr);
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delete tmpResult;
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delete dec;
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return ND4J_STATUS_OK;
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}
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DECLARE_TYPES(crelu_bp) {
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getOpDescriptor()
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->setAllowedInputTypes(0, DataType::ANY)
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->setAllowedInputTypes(1, {DataType::FLOAT32, DataType ::DOUBLE, DataType::HALF})
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->setAllowedOutputTypes(0, {DataType::FLOAT32, DataType ::DOUBLE, DataType::HALF});
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}
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DECLARE_SHAPE_FN(crelu_bp) {
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auto inShape = inputShape->at(0);
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return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(inShape)));
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}
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}
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}
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#endif |