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