7783012f39
* initial commit Signed-off-by: raver119 <raver119@gmail.com> * one file Signed-off-by: raver119 <raver119@gmail.com> * few more includes Signed-off-by: raver119 <raver119@gmail.com> * m? Signed-off-by: raver119 <raver119@gmail.com> * const Signed-off-by: raver119 <raver119@gmail.com> * cudnn linkage in tests Signed-off-by: raver119 <raver119@gmail.com> * culibos Signed-off-by: raver119 <raver119@gmail.com> * static reminder Signed-off-by: raver119 <raver119@gmail.com> * platform engine tag Signed-off-by: raver119 <raver119@gmail.com> * HAVE_CUDNN moved to config.h.in Signed-off-by: raver119 <raver119@gmail.com> * include Signed-off-by: raver119 <raver119@gmail.com> * include Signed-off-by: raver119 <raver119@gmail.com> * skip cudnn handle creation if there's not cudnn Signed-off-by: raver119 <raver119@gmail.com> * meh Signed-off-by: raver119 <raver119@gmail.com> * target device in context Signed-off-by: raver119 <raver119@gmail.com> * platform engines Signed-off-by: raver119 <raver119@gmail.com> * platform engines Signed-off-by: raver119 <raver119@gmail.com> * allow multiple -h args Signed-off-by: raver119 <raver119@gmail.com> * allow multiple -h args Signed-off-by: raver119 <raver119@gmail.com> * move mkldnn out of CPU block Signed-off-by: raver119 <raver119@gmail.com> * link to mkldnn on cuda Signed-off-by: raver119 <raver119@gmail.com> * less prints Signed-off-by: raver119 <raver119@gmail.com> * minor tweaks Signed-off-by: raver119 <raver119@gmail.com> * next step Signed-off-by: raver119 <raver119@gmail.com> * conv2d NCHW draft Signed-off-by: raver119 <raver119@gmail.com> * conv2d biasAdd Signed-off-by: raver119 <raver119@gmail.com> * test for MKL/CUDNN combined use Signed-off-by: raver119 <raver119@gmail.com> * - provide additional code for conv2d ff based on cudnn api, not tested yet Signed-off-by: Yurii <iuriish@yahoo.com> * - further work on conv2d helper based on using cudnn api Signed-off-by: Yurii <iuriish@yahoo.com> * - fixing several cuda bugs which appeared after cudnn lib had been started to use Signed-off-by: Yurii <iuriish@yahoo.com> * - implementation of conv2d backprop op based on cudnn api Signed-off-by: Yurii <iuriish@yahoo.com> * - implementaion of conv3d and conv3d_bp ops based on cudnn api Signed-off-by: Yurii <iuriish@yahoo.com> * - bugs fixing in conv3d/conv3d_bp ops (cudnn in use) Signed-off-by: Yurii <iuriish@yahoo.com> * - implementation of depthwiseConv2d (ff/bp) op based on cudnn api Signed-off-by: Yurii <iuriish@yahoo.com> * - implementation of batchnorm ff op based on cudnn api Signed-off-by: Yurii <iuriish@yahoo.com> * - disable cudnn batchnorm temporary Signed-off-by: Yurii <iuriish@yahoo.com> * - add minor change in cmake Signed-off-by: Yurii <iuriish@yahoo.com> * engine for depthwise mkldnn Signed-off-by: raver119 <raver119@gmail.com> * couple of includes Signed-off-by: raver119 <raver119@gmail.com> * - provide permutation to cudnn batchnorm ff when format is NHWC Signed-off-by: Yurii <iuriish@yahoo.com> * lgamma fix Signed-off-by: raver119 <raver119@gmail.com> * - eliminate memory leak in two tests Signed-off-by: Yurii <iuriish@yahoo.com> Co-authored-by: Yurii Shyrma <iuriish@yahoo.com>
295 lines
17 KiB
C++
295 lines
17 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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// Based on PyTorch - https://github.com/pytorch/pytorch
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//
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#ifndef LIBND4J_CONVOLUTIONS_H
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#define LIBND4J_CONVOLUTIONS_H
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#include <NDArray.h>
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#include <graph/Context.h>
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#include <dll.h>
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#include <execution/LaunchContext.h>
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namespace nd4j {
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namespace ops {
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enum PoolingType {
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MAX_POOL = 0,
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AVG_POOL = 1,
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PNORM_POOL = 2,
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};
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class ND4J_EXPORT ConvolutionUtils {
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public:
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static inline void calcOutSizePool2D(int& oH, int& oW, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int iH, const int iW, const int paddingMode) {
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if(paddingMode == 0) { // valid
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// oH = (iH - (kH + (kH-1)*(dH-1)) + 2*pH)/sH + 1;
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// oW = (iW - (kW + (kW-1)*(dW-1)) + 2*pW)/sW + 1;
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oH = (iH - ((kH - 1) * dH + 1) + 2 * pH) / sH + 1;
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oW = (iW - ((kW - 1) * dW + 1) + 2 * pW) / sW + 1;
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}
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else if (paddingMode == 1) { // same
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oH = (int) math::nd4j_ceil<double, double>(iH * 1. / sH);
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oW = (int) math::nd4j_ceil<double, double>(iW * 1. / sW);
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}
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else { // causal
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oH = (iH - 1) / sH + 1; // 2*pH = (kH-1)*dH
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oW = (iW - 1) / sW + 1;
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}
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}
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static inline void calcOutSizePool3D(int& oD, int& oH, int& oW, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int iD, const int iH, const int iW, const int paddingMode) {
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if(paddingMode == 0) { // valid
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oD = (iD - ((kD - 1) * dD + 1) + 2 * pD) / sD + 1;
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oH = (iH - ((kH - 1) * dH + 1) + 2 * pH) / sH + 1;
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oW = (iW - ((kW - 1) * dW + 1) + 2 * pW) / sW + 1;
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}
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else if(paddingMode == 1) { // same
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oD = (int) nd4j::math::nd4j_ceil<double, double>(iD * 1. / sD);
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oH = (int) nd4j::math::nd4j_ceil<double, double>(iH * 1. / sH);
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oW = (int) nd4j::math::nd4j_ceil<double, double>(iW * 1. / sW);
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}
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else { // causal
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oD = (iD - 1) / sD + 1;
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oH = (iH - 1) / sH + 1; // 2*pH = (kH-1)*dH
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oW = (iW - 1) / sW + 1;
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}
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}
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static inline void calcPadding2D(int& pH, int& pW, int oH, int oW, int iH, int iW, int kH, int kW, int sH, int sW, int dH, int dW, const int paddingMode = 1 /* default is same mode*/) {
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if(paddingMode == 0) // valid
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return;
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if(paddingMode == 1) { // same
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const int eKH = (kH - 1) * dH + 1;
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const int eKW = (kW - 1) * dW + 1;
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pH = ((oH - 1) * sH + eKH - iH) / 2; //Note that padBottom is 1 bigger than this if bracketed term is not divisible by 2
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pW = ((oW - 1) * sW + eKW - iW) / 2;
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}
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else { // causal
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pH = (kH - 1) * dH;
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pW = (kW - 1) * dW;
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}
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}
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static inline void calcPadding3D(int& pD, int& pH, int& pW, const int oD, const int oH, const int oW, const int iD, const int iH, const int iW, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int dD, const int dH, const int dW, const int paddingMode = 1 /* default is same mode*/) {
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if(paddingMode == 0) // valid
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return;
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if(paddingMode == 1) { // same
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const int eKD = (kD - 1) * dD + 1;
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const int eKH = (kH - 1) * dH + 1;
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const int eKW = (kW - 1) * dW + 1;
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pD = ((oD - 1) * sD + eKD - iD) / 2;
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pH = ((oH - 1) * sH + eKH - iH) / 2; //Note that padBottom is 1 bigger than this if bracketed term is not divisible by 2
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pW = ((oW - 1) * sW + eKW - iW) / 2;
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}
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else { // causal
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pD = (kD - 1) * dD;
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pH = (kH - 1) * dH;
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pW = (kW - 1) * dW;
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}
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}
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// calculation of output height and width in 2D deconvolution procedure
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static inline void calcOutSizeDeconv2D(int& oH, int& oW, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int iH, const int iW, const int paddingMode) {
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if (paddingMode) {
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oH = sH * iH;
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oW = sW * iW;
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}
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else {
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const int ekH = (kH - 1) * dH + 1;
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const int ekW = (kW - 1) * dW + 1;
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oH = sH * (iH - 1) + ekH - 2 * pH;
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oW = sW * (iW - 1) + ekW - 2 * pW;
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}
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}
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// calculation of output height and width in 3D deconvolution procedure
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static inline void calcOutSizeDeconv3D(int& oD, int& oH, int& oW, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int iD, const int iH, const int iW, const int paddingMode) {
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if (paddingMode) {
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oD = sD * iD;
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oH = sH * iH;
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oW = sW * iW;
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}
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else {
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const int ekD = (kD - 1) * dD + 1;
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const int ekH = (kH - 1) * dH + 1;
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const int ekW = (kW - 1) * dW + 1;
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oD = sD * (iD - 1) + ekD - 2 * pD;
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oH = sH * (iH - 1) + ekH - 2 * pH;
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oW = sW * (iW - 1) + ekW - 2 * pW;
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}
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}
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// evaluates sizes values and indexes using input and output arrays depending on data format
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static inline void getSizesAndIndexesConv2d(const bool isNCHW, const NDArray& input, const NDArray& output, int& bS, int& iC, int& iH, int& iW, int& oC, int& oH, int& oW, int& indIOioC, int& indIiH, int& indWiC, int& indWoC, int& indWkH, int& indOoH) {
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getSizesAndIndexesConv2d(isNCHW, input.getShapeInfo(), output.getShapeInfo(), bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
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}
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static inline void getSizesAndIndexesConv2d(const bool isNCHW, const Nd4jLong* inShapeInfo, const Nd4jLong* outShapeInfo, int& bS, int& iC, int& iH, int& iW, int& oC, int& oH, int& oW, int& indIOioC, int& indIiH, int& indWiC, int& indWoC, int& indWkH, int& indOoH) {
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// input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
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// weights [kH, kW, iC, oC] always
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// output [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
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indWkH = 0; indWiC = 2; indWoC = 3;
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if(!isNCHW) {
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indIOioC = 3; indIiH = 1; indOoH = 1;
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}
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else {
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indIOioC = 1; indIiH = 2; indOoH = 2;
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}
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bS = inShapeInfo[1]; // batch size
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iC = inShapeInfo[indIOioC+1]; // input channels
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iH = inShapeInfo[indIiH+1]; // input height
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iW = inShapeInfo[indIiH+2]; // input width
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oC = outShapeInfo[indIOioC+1]; // output channels
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oH = outShapeInfo[indOoH+1]; // output height
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oW = outShapeInfo[indOoH+2]; // output width
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}
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// evaluates sizes values and indexes using input and output arrays depending on data format
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static inline void getSizesAndIndexesConv3d(const bool isNCDHW, const NDArray& input, const NDArray& output, int& bS, int& iC, int& iD, int& iH, int& iW, int& oC, int& oD, int& oH, int& oW, int& indIOioC, int& indIOioD, int& indWiC, int& indWoC, int& indWkD) {
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// input [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
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// weights [kD, kH, kW, iC, oC] (NDHWC) or [oC, iC, kD, kH, kW] (NCDHW)
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// output [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW)
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indWkD = 0; indWiC = 3; indWoC = 4;
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if(!isNCDHW) {
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indIOioC = 4; indIOioD = 1;
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}
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else {
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indIOioC = 1; indIOioD = 2;
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}
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bS = input.sizeAt(0); // batch size
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iC = input.sizeAt(indIOioC); // input channels
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iD = input.sizeAt(indIOioD); // input depth
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iH = input.sizeAt(indIOioD+1); // input height
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iW = input.sizeAt(indIOioD+2); // input width
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oC = output.sizeAt(indIOioC); // output channels
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oD = output.sizeAt(indIOioD); // output depth
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oH = output.sizeAt(indIOioD+1); // output height
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oW = output.sizeAt(indIOioD+2); // output width
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}
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// static inline void calcPaddingAndDilationForConv2DMKL(const int iH, const int iW, const int oH, const int oW, const int kH, const int kW, const int sH, const int sW, const int paddingMode, int& pH, int& pW, int& dH, int& dW) {
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// if(kH != 1) {
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// if(paddingMode) {
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// pH = (oH - 1) * sH - iH + kH - pH;
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// dH = dH - 1;
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// }
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// else
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// dH = (iH + 2*pH - (oH - 1) * sH - kH) / (kH - 1);
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// }
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// if(kW != 1) {
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// if(paddingMode) {
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// pW = (oW - 1) * sW - iW + kW - pW;
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// dW = dW - 1;
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// }
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// else
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// dW = (iW + 2*pW - (oW - 1) * sW - kW) / (kW - 1);
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// }
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// }
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// static inline void calcPaddingAndDilationForConv3DMKL(const int iD, const int iH, const int iW, const int oD, const int oH, const int oW, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int paddingMode, int& pD, int& pH, int& pW, int& dD, int& dH, int& dW) {
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// if(kD != 1) {
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// if(paddingMode) {
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// pD = (oD - 1) * sD - iD + kD - pD;
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// dD = dD - 1;
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// }
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// else
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// dD = (iD + 2*pD - (oD - 1) * sD - kD) / (kD - 1);
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// }
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// if(kH != 1) {
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// if(paddingMode) {
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// pH = (oH - 1) * sH - iH + kH - pH;
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// dH = dH - 1;
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// }
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// else
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// dH = (iH + 2*pH - (oH - 1) * sH - kH) / (kH - 1);
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// }
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// if(kW != 1) {
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// if(paddingMode) {
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// pW = (oW - 1) * sW - iW + kW - pW;
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// dW = dW - 1;
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// }
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// else
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// dW = (iW + 2*pW - (oW - 1) * sW - kW) / (kW - 1);
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// }
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// }
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static void conv2d(nd4j::graph::Context &context, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW);
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// static void conv2d(nd4j::graph::Context & block, const std::vector<NDArray*>& inArrs, NDArray* output, const std::vector<int>& intArgs);
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// static void conv2dBP(nd4j::graph::Context & block, const std::vector<NDArray*>& inArrs, const std::vector<NDArray*>& outArrs, const std::vector<int>& intArgs);
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static void conv2dBP(nd4j::graph::Context & block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW);
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static void depthwiseConv2d(nd4j::graph::Context & block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW);
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static void depthwiseConv2dBP(nd4j::graph::Context & block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW);
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static void sconv2d(nd4j::graph::Context & block, const NDArray* input, const NDArray* weightsDepth, const NDArray* weightsPoint, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW);
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static void vol2col(nd4j::graph::Context & block, const NDArray& vol, NDArray& col, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW);
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static void col2vol(nd4j::graph::Context & block, const NDArray& col, NDArray& vol, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW);
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static void upsampling2d(nd4j::graph::Context & block, const NDArray& input, NDArray& output, const int factorH, const int factorW, const bool isNCHW);
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static void upsampling3d(nd4j::graph::Context & block, const NDArray& input, NDArray& output, const int factorD, const int factorH, const int factorW, const bool isNCDHW);
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static void upsampling2dBP(nd4j::graph::Context & block, const NDArray& gradO, NDArray& gradI, const bool isNCHW);
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static void upsampling3dBP(nd4j::graph::Context & block, const NDArray& gradO, NDArray& gradI, const bool isNCDHW);
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static void pooling2d(nd4j::graph::Context & block, const NDArray& input, NDArray& output, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const PoolingType poolingMode, const int extraParam0);
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static void pooling3d(nd4j::graph::Context & block, const NDArray& input, NDArray& output, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int poolingMode, const int extraParam0);
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static void pooling2dBP(nd4j::graph::Context & block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int poolingMode, const int extraParam0);
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static void pooling3dBP(nd4j::graph::Context & block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int poolingMode, const int extraParam0);
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};
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}
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}
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#endif //LIBND4J_CONVOLUTIONS_H
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