raver119 98e2814879
Platform helpers (#8216)
* platform helpers draft

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* typo

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* disable platform cmake

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* another draft

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* mkldnn convolution refactored

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* minor tweaks

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* one more safety check

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* prototype works

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* meh

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* force static library mode for mkldnn

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* - ismax fix
- experimental arg fix
- don't enforce openblas on Apple hardware

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* bunch of small fixes

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* declare concurrent

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* - MKLDNN version upgrade to 1.0.2
- avgpool2d/maxpool2d APIs update

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* - avgpool2d_bp/maxpool2d_bp APIs update

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* - conv2d/batchnorm APIs update

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* - lrn/conv2d_bp/conv3d/conv3d_bp APIs update

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* all ops converted to MKLDNN 1.x

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* bunch of tweaks

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* namespace for platform helpers

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* make sure platform helpers aren't opimized out

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* build cpu_features on x86 systems

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* build cpu_features on x86 systems

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* more of cpu_features

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* - mkldnn removed from java
- cpu_features checks in CpuNDArrayFactory

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* F16C definition renamed

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* some mkldnn rearrangements

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* check supported instructions before doing anything

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* typo

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* missied impl

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* BUILD_PIC option

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* conv2d fix

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* avgpool3d fix

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* avgpool3d_bp fix

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* avgpool2d_bp leak fix

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* avgpool3d_bp leak fix

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* maxpool bp leaks fixed

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* printf removed

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* batchnorm fix

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* AVX warning/error polishing

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* Fix

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* More polish

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* Polish

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* remove previous MKL-DNN support layer

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* avx2 tweak

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* allow static for apple

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* exclude mkldnn in one more place

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* exclude mkldnn in one more place

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* restore OPENBLAS_PATH use

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* add runtime check for avx/avx2 support

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* convolution_auto

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* Add logic for helper argument

* minor test fix

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* few tweaks

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* few tweaks

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* skip OpTracker props for non-x86 builds

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* linux arm isn't x86 :)

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* avx-512

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* CUDA presets fix

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* BUILD_PIC

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* prefetchw for avx2

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* BUILD_PIC again

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2019-09-11 21:50:28 +03:00

235 lines
14 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
******************************************************************************/
//
// Based on PyTorch - https://github.com/pytorch/pytorch
//
#ifndef LIBND4J_CONVOLUTIONS_H
#define LIBND4J_CONVOLUTIONS_H
#include <NDArray.h>
#include <graph/Context.h>
#include <execution/LaunchContext.h>
namespace nd4j {
namespace ops {
enum PoolingType {
MAX_POOL = 0,
AVG_POOL = 1,
PNORM_POOL = 2,
};
class ConvolutionUtils {
public:
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 isSameMode) {
if(isSameMode > 0) {
oH = (int) math::nd4j_ceil<double, double>(iH * 1. / sH);
oW = (int) math::nd4j_ceil<double, double>(iW * 1. / sW);
}
else {
oH = (iH - (kH + (kH-1)*(dH-1)) + 2*pH)/sH + 1;
oW = (iW - (kW + (kW-1)*(dW-1)) + 2*pW)/sW + 1;
}
}
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 isSameMode) {
if(!isSameMode) { // valid
oD = (iD - (kD + (kD - 1) * (dD - 1)) + 2 * pD) / sD + 1;
oH = (iH - (kH + (kH - 1) * (dH - 1)) + 2 * pH) / sH + 1;
oW = (iW - (kW + (kW - 1) * (dW - 1)) + 2 * pW) / sW + 1;
}
else { // same
oD = (int) nd4j::math::nd4j_ceil<double, double>(iD * 1. / sD);
oH = (int) nd4j::math::nd4j_ceil<double, double>(iH * 1. / sH);
oW = (int) nd4j::math::nd4j_ceil<double, double>(iW * 1. / sW);
}
}
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) {
int eKH, eKW;
if (dH == 1 && dW == 1) {
eKH = kH;
eKW = kW;
} else {
eKH = kH + (kH - 1) * (dH - 1);
eKW = kW + (kW - 1) * (dW - 1);
}
pH = ((oH - 1) * sH + eKH - iH) / 2; //Note that padBottom is 1 bigger than this if bracketed term is not divisible by 2
pW = ((oW - 1) * sW + eKW - iW) / 2;
}
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) {
int eKD, eKH, eKW;
if (dD == 1 && dH == 1 && dW == 1) {
eKD = kD;
eKH = kH;
eKW = kW;
} else {
eKD = kD + (kD - 1) * (dD - 1);
eKH = kH + (kH - 1) * (dH - 1);
eKW = kW + (kW - 1) * (dW - 1);
}
pD = ((oD - 1) * sD + eKD - iD) / 2; // Note that padBottom is 1 bigger than this if bracketed term is not divisible by 2
pH = ((oH - 1) * sH + eKH - iH) / 2;
pW = ((oW - 1) * sW + eKW - iW) / 2;
}
// calculation of output height and width in 2D deconvolution procedure
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 isSameMode) {
if (isSameMode) {
oH = sH * iH;
oW = sW * iW;
}
else {
int ekH, ekW;
if (dH == 1 && dW == 1) {
ekH = kH;
ekW = kW;
} else {
ekH = kH + (kH - 1) * (dH - 1);
ekW = kW + (kW - 1) * (dW - 1);
}
oH = sH * (iH - 1) + ekH - 2 * pH;
oW = sW * (iW - 1) + ekW - 2 * pW;
}
}
// calculation of output height and width in 3D deconvolution procedure
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 isSameMode) {
if (isSameMode) {
oD = sD * iD;
oH = sH * iH;
oW = sW * iW;
}
else {
int ekD, ekH, ekW;
if (dD == 1 && dH == 1 && dW == 1) {
ekD = kD;
ekH = kH;
ekW = kW;
}
else {
ekD = kD + (kD - 1) * (dD - 1);
ekH = kH + (kH - 1) * (dH - 1);
ekW = kW + (kW - 1) * (dW - 1);
}
oD = sD * (iD - 1) + ekD - 2 * pD;
oH = sH * (iH - 1) + ekH - 2 * pH;
oW = sW * (iW - 1) + ekW - 2 * pW;
}
}
// evaluates sizes values and indexes using input and output arrays depending on data format
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) {
getSizesAndIndexesConv2d(isNCHW, input.getShapeInfo(), output.getShapeInfo(), bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
}
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) {
// input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
// weights [kH, kW, iC, oC] always
// output [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
indWkH = 0; indWiC = 2; indWoC = 3;
if(!isNCHW) {
indIOioC = 3; indIiH = 1; indOoH = 1;
}
else {
indIOioC = 1; indIiH = 2; indOoH = 2;
}
bS = inShapeInfo[1]; // batch size
iC = inShapeInfo[indIOioC+1]; // input channels
iH = inShapeInfo[indIiH+1]; // input height
iW = inShapeInfo[indIiH+2]; // input width
oC = outShapeInfo[indIOioC+1]; // output channels
oH = outShapeInfo[indOoH+1]; // output height
oW = outShapeInfo[indOoH+2]; // output width
}
// evaluates sizes values and indexes using input and output arrays depending on data format
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) {
// input [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
// weights [kD, kH, kW, iC, oC] (NDHWC) or [oC, iC, kD, kH, kW] (NCDHW)
// output [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW)
indWkD = 0; indWiC = 3; indWoC = 4;
if(!isNCDHW) {
indIOioC = 4; indIOioD = 1;
}
else {
indIOioC = 1; indIOioD = 2;
}
bS = input.sizeAt(0); // batch size
iC = input.sizeAt(indIOioC); // input channels
iD = input.sizeAt(indIOioD); // input depth
iH = input.sizeAt(indIOioD+1); // input height
iW = input.sizeAt(indIOioD+2); // input width
oC = output.sizeAt(indIOioC); // output channels
oD = output.sizeAt(indIOioD); // output depth
oH = output.sizeAt(indIOioD+1); // output height
oW = output.sizeAt(indIOioD+2); // output width
}
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 isSameMode, const int isNCHW);
// static void conv2d(nd4j::graph::Context & block, const std::vector<NDArray*>& inArrs, NDArray* output, const std::vector<int>& intArgs);
// static void conv2dBP(nd4j::graph::Context & block, const std::vector<NDArray*>& inArrs, const std::vector<NDArray*>& outArrs, const std::vector<int>& intArgs);
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 isSameMode, const int isNCHW);
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 isSameMode, const int isNCHW);
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 isSameMode, const int isNCHW);
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 isSameMode, const int isNCHW);
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);
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);
static void upsampling2d(nd4j::graph::Context & block, const NDArray& input, NDArray& output, const int factorH, const int factorW, const bool isNCHW);
static void upsampling3d(nd4j::graph::Context & block, const NDArray& input, NDArray& output, const int factorD, const int factorH, const int factorW, const bool isNCDHW);
static void upsampling2dBP(nd4j::graph::Context & block, const NDArray& gradO, NDArray& gradI, const bool isNCHW);
static void upsampling3dBP(nd4j::graph::Context & block, const NDArray& gradO, NDArray& gradI, const bool isNCDHW);
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);
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);
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);
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);
};
}
}
#endif //LIBND4J_CONVOLUTIONS_H