0cdb5750e0
* - provide possibility to pass axis as last input array in concat op - corrcect sumation in bias_add_bp op for NHWC case Signed-off-by: Yurii <iuriish@yahoo.com> * - write code for deconv2d op based on mkl dnn api * no unsafe math Signed-off-by: raver119 <raver119@gmail.com> * no unsafe math Signed-off-by: raver119 <raver119@gmail.com> * - get rid of e<> and p<> methods in svd helper Signed-off-by: Yurii <iuriish@yahoo.com> * - provide mkl api support for deconvolution 3d Signed-off-by: Yurii <iuriish@yahoo.com> * - write deconv2d_bp based on mkl api Signed-off-by: Yurii <iuriish@yahoo.com> * - write deconv3d_bp based on mkl api Signed-off-by: Yurii <iuriish@yahoo.com> * - testing and fixing deconv based on mkl api Signed-off-by: Yurii <iuriish@yahoo.com> * - remove dilation form conv2d/3d mkl Signed-off-by: Yurii <iuriish@yahoo.com> * - minor changes Signed-off-by: Yurii <iuriish@yahoo.com> * - further corrections of deconv ops based on mkl dnn api Signed-off-by: Yurii <iuriish@yahoo.com> * - provide deconv2d_tf based on mkl dnn api Signed-off-by: Yurii <iuriish@yahoo.com> * - add minor corrections required by reviewer Signed-off-by: Yurii <iuriish@yahoo.com>
115 lines
3.9 KiB
C++
115 lines
3.9 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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// @author Yurii Shyrma (iuriish@yahoo.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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#include<ops/declarable/helpers/addBias.h>
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namespace nd4j {
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namespace ops {
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////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(biasadd, 2, 1, true, 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 output = OUTPUT_VARIABLE(0);
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const bool isNCHW = !block.getBArguments()->empty() ? B_ARG(0) : false;
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const int channelDim = isNCHW ? 1 : input->rankOf() - 1; // second or last
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REQUIRE_TRUE(bias->rankOf() == 1, 0, "BIASADD CUSTOM_OP: bias array should have rank = 1, but got %i instead !", bias->rankOf());
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REQUIRE_TRUE(bias->sizeAt(0) == input->sizeAt(channelDim), 0, "BIASADD CUSTOM_OP: shapes of bias %s and input %s arrays are not suitable for broadcast operation along channel dimension %i !", ShapeUtils::shapeAsString(bias).c_str(), ShapeUtils::shapeAsString(input).c_str(), channelDim);
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REQUIRE_TRUE(output->isSameShape(input), 0, "BIASADD CUSTOM_OP: wrong shape of output array, expected is %s but got %s instead !", ShapeUtils::shapeAsString(input).c_str(), ShapeUtils::shapeAsString(output).c_str());
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helpers::addBias(block, *input, *bias, *output, isNCHW);
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// input->applyBroadcast(nd4j::broadcast::Add, {channelDim}, bias, output);
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return Status::OK();
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}
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DECLARE_SYN(bias_add, biasadd);
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////////////////////////////////////////////////////////////////////
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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) {
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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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////////////////////////////////////////////////////////////////////
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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 gradO = INPUT_VARIABLE(2);
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auto gradI = OUTPUT_VARIABLE(0);
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auto gradB = OUTPUT_VARIABLE(1);
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const bool isNCHW = !block.getBArguments()->empty() ? B_ARG(0) : false;
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const int channelDim = isNCHW ? 1 : input->rankOf() - 1; // second or last
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gradI->assign(gradO);
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gradO->reduceAlongDimension(nd4j::reduce::Sum, gradB, ShapeUtils::evalDimsToExclude(gradO->rankOf(), {channelDim}));
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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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////////////////////////////////////////////////////////////////////
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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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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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}
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}
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#endif |