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Shyrma depthconv (#156) 

* - implementation of depthwise_conv2d (both ff/bp) based on mkl dnn api

* - minor corrections in deconv3d

Signed-off-by: Yurii <iuriish@yahoo.com>

* - remove unnecessary time test

Signed-off-by: Yurii <iuriish@yahoo.com>

* - update mkl dnn version in cmake

Signed-off-by: Yurii <iuriish@yahoo.com>

* - take into account several notes given by pr reviewer

Signed-off-by: Yurii <iuriish@yahoo.com>

* - fix bug in depthwise conv2d op based on mkl

Signed-off-by: Yurii <iuriish@yahoo.com>
master
Yurii Shyrma 2020-01-11 06:36:40 +02:00 committed by raver119
parent bbf88b53dd
commit cae5ef4180
9 changed files with 1019 additions and 323 deletions
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2
libnd4j/CMakeLists.txt.mkldnn.in
View File

@ -5,7 +5,7 @@ project(mkldnn-download NONE)
include(ExternalProject) include(ExternalProject)
ExternalProject_Add(mkldnn ExternalProject_Add(mkldnn
GIT_REPOSITORY https://github.com/intel/mkl-dnn.git GIT_REPOSITORY https://github.com/intel/mkl-dnn.git
GIT_TAG v1.1.1 GIT_TAG v1.1.2
SOURCE_DIR "${CMAKE_CURRENT_BINARY_DIR}/mkldnn-src" SOURCE_DIR "${CMAKE_CURRENT_BINARY_DIR}/mkldnn-src"
BINARY_DIR "${CMAKE_CURRENT_BINARY_DIR}/mkldnn-build" BINARY_DIR "${CMAKE_CURRENT_BINARY_DIR}/mkldnn-build"
CONFIGURE_COMMAND "" CONFIGURE_COMMAND ""

64
libnd4j/include/ops/declarable/generic/nn/convo/depthwiseConv2d.cpp
View File

@ -15,7 +15,7 @@
******************************************************************************/ ******************************************************************************/
// //
// created by Yurii Shyrma on 08.03.2018 // @author Yurii Shyrma (iuriish@yahoo.com)
// //
#include <op_boilerplate.h> #include <op_boilerplate.h>
@ -56,8 +56,8 @@ CUSTOM_OP_IMPL(depthwise_conv2d, 2, 1, false, 0, 9) {
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWmC, indWkH, indOoH); ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWmC, indWkH, indOoH);
mC = weights->sizeAt(indWmC); // channels multiplier mC = weights->sizeAt(indWmC); // channels multiplier
std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, iC, mC}); std::vector<Nd4jLong> expectedWeightsShape = {kH, kW, iC, mC};
REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM DEPTHWISECONV2D OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str()); REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM DEPTHWISECONV2D OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
REQUIRE_TRUE(output->sizeAt(indIOioC) == iC*mC, 0, "CUSTOM DEPTHWISECONV2D OP: the output_channels must be equal to input_channels * channels_multiplier = %i !", iC*mC); REQUIRE_TRUE(output->sizeAt(indIOioC) == iC*mC, 0, "CUSTOM DEPTHWISECONV2D OP: the output_channels must be equal to input_channels * channels_multiplier = %i !", iC*mC);
if (bias) if (bias)
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM DEPTHWISECONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf()); REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM DEPTHWISECONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
@ -79,8 +79,8 @@ DECLARE_SHAPE_FN(depthwise_conv2d) {
Nd4jLong* biasShapeInfo = block.width() > 2 ? inputShape->at(2) : nullptr; // [oC] = iC*mC Nd4jLong* biasShapeInfo = block.width() > 2 ? inputShape->at(2) : nullptr; // [oC] = iC*mC
const int rank = 4; const int rank = 4;
REQUIRE_TRUE(inputShapeInfo[0] == rank, 0, "CUSTOM DEPTHWISECONV2D OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo[0]); REQUIRE_TRUE(shape::rank(inputShapeInfo) == rank, 0, "CUSTOM DEPTHWISECONV2D OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo[0]);
REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0, "CUSTOM DEPTHWISECONV2D OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo[0]); REQUIRE_TRUE(shape::rank(weightsShapeInfo) == rank, 0, "CUSTOM DEPTHWISECONV2D OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo[0]);
int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 0));// filter(kernel) height int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 0));// filter(kernel) height
int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 1));// filter(kernel) width int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 1));// filter(kernel) width
@ -101,17 +101,18 @@ DECLARE_SHAPE_FN(depthwise_conv2d) {
indIOioC = 1; indIiH = 2; indIOioC = 1; indIiH = 2;
} }
const int bS = inputShapeInfo[1]; // batch size const int bS = shape::sizeAt(inputShapeInfo, 0); // batch size
const int iH = inputShapeInfo[indIiH+1]; // input height const int iH = shape::sizeAt(inputShapeInfo, indIiH); // input height
const int iW = inputShapeInfo[indIiH+2]; // input width const int iW = shape::sizeAt(inputShapeInfo, indIiH+1); // input width
const int iC = inputShapeInfo[indIOioC+1]; // input channels const int iC = shape::sizeAt(inputShapeInfo, indIOioC); // input channels
const int mC = weightsShapeInfo[indWmC+1]; // channels multiplier(oC = iC*mC) const int mC = shape::sizeAt(weightsShapeInfo, indWmC); // channels multiplier(oC = iC*mC)
const int oC = iC*mC; // output channels const int oC = iC*mC; // output channels
std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, iC, mC});
REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weightsShapeInfo), 0, "DEPTHWISECONV2D OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str()); std::vector<Nd4jLong> expectedWeightsShape = {kH, kW, iC, mC};
REQUIRE_TRUE(shape::shapeEquals(4, expectedWeightsShape.data(), shape::rank(weightsShapeInfo), shape::shapeOf(weightsShapeInfo)), 0, "DEPTHWISECONV2D OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
if (biasShapeInfo) if (biasShapeInfo)
REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "DEPTHWISECONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo)); REQUIRE_TRUE(shape::rank(biasShapeInfo) <= 2 && oC == shape::length(biasShapeInfo), 0, "DEPTHWISECONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, shape::rank(biasShapeInfo), shape::length(biasShapeInfo));
int oH, oW; // output height, width int oH, oW; // output height, width
ConvolutionUtils::calcOutSizePool2D(oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode); ConvolutionUtils::calcOutSizePool2D(oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
@ -178,10 +179,10 @@ CUSTOM_OP_IMPL(depthwise_conv2d_bp, 3, 2, false, 0, 9) {
int trueoH, trueoW; // correct output height, width int trueoH, trueoW; // correct output height, width
ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode); ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1})); std::vector<Nd4jLong> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1});
std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, iC, mC}); std::vector<Nd4jLong> expectedWeightsShape = {kH, kW, iC, mC};
REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0, "CUSTOM DEPTHWISECONV2D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradO).c_str()); REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "CUSTOM DEPTHWISECONV2D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str());
REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM DEPTHWISECONV2D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str()); REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM DEPTHWISECONV2D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
if(bias) if(bias)
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM DEPTHWISECONV2D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf()); REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM DEPTHWISECONV2D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
@ -190,8 +191,7 @@ CUSTOM_OP_IMPL(depthwise_conv2d_bp, 3, 2, false, 0, 9) {
return Status::OK(); return Status::OK();
} }
//////////////////////////////////////////////////////////////////////
DECLARE_SHAPE_FN(depthwise_conv2d_bp) { DECLARE_SHAPE_FN(depthwise_conv2d_bp) {
Nd4jLong* inputShapeInfo = inputShape->at(0); Nd4jLong* inputShapeInfo = inputShape->at(0);
@ -200,9 +200,9 @@ DECLARE_SHAPE_FN(depthwise_conv2d_bp) {
Nd4jLong* gradOShapeInfo = block.width() > 3 ? inputShape->at(3) : inputShape->at(2); Nd4jLong* gradOShapeInfo = block.width() > 3 ? inputShape->at(3) : inputShape->at(2);
const int rank = 4; const int rank = 4;
REQUIRE_TRUE(inputShapeInfo[0] == rank, 0, "CUSTOM DEPTHWISECONV2D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo[0]); REQUIRE_TRUE(shape::rank(inputShapeInfo) == rank, 0, "CUSTOM DEPTHWISECONV2D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank, shape::rank(inputShapeInfo));
REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0, "CUSTOM DEPTHWISECONV2D_BP OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo[0]); REQUIRE_TRUE(shape::rank(weightsShapeInfo) == rank, 0, "CUSTOM DEPTHWISECONV2D_BP OP: rank of weights array must be equal to %i, but got %i instead !", rank, shape::rank(weightsShapeInfo));
REQUIRE_TRUE(gradOShapeInfo[0] == rank, 0, "CUSTOM DEPTHWISECONV2D_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got %i instead !", rank, gradOShapeInfo[0]); REQUIRE_TRUE(shape::rank(gradOShapeInfo) == rank, 0, "CUSTOM DEPTHWISECONV2D_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got %i instead !", rank, shape::rank(gradOShapeInfo));
int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 0));// filter(kernel) height int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 0));// filter(kernel) height
int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 1));// filter(kernel) width int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 1));// filter(kernel) width
@ -223,22 +223,22 @@ DECLARE_SHAPE_FN(depthwise_conv2d_bp) {
indIOioC = 1; indIiH = 2; indIOioC = 1; indIiH = 2;
} }
const int bS = inputShapeInfo[1]; // batch size const int bS = shape::sizeAt(inputShapeInfo, 0); // batch size
const int iH = inputShapeInfo[indIiH+1]; // input height const int iH = shape::sizeAt(inputShapeInfo, indIiH); // input height
const int iW = inputShapeInfo[indIiH+2]; // input width const int iW = shape::sizeAt(inputShapeInfo, indIiH+1); // input width
const int iC = inputShapeInfo[indIOioC+1]; // input channels const int iC = shape::sizeAt(inputShapeInfo, indIOioC); // input channels
const int mC = weightsShapeInfo[indWmC+1]; // channels multiplier(oC = iC*mC) const int mC = shape::sizeAt(weightsShapeInfo, indWmC); // channels multiplier(oC = iC*mC)
const int oC = iC*mC; // output channels const int oC = iC*mC; // output channels
int trueoH, trueoW; // correct output height, width int trueoH, trueoW; // correct output height, width
ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode); ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indIiH,indIiH+1})); std::vector<Nd4jLong> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indIiH,indIiH+1});
std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, iC, mC}); std::vector<Nd4jLong> expectedWeightsShape = {kH, kW, iC, mC};
REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradOShapeInfo), 0, "CUSTOM DEPTHWISECONV2D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradOShapeInfo).c_str()); REQUIRE_TRUE(shape::shapeEquals(4, expectedGradOShape.data(), shape::rank(gradOShapeInfo), shape::shapeOf(gradOShapeInfo)), 0, "CUSTOM DEPTHWISECONV2D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradOShapeInfo).c_str());
REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weightsShapeInfo), 0, "CUSTOM DEPTHWISECONV2D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str()); REQUIRE_TRUE(shape::shapeEquals(4, expectedWeightsShape.data(), shape::rank(weightsShapeInfo), shape::shapeOf(weightsShapeInfo)), 0, "CUSTOM DEPTHWISECONV2D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
if(biasShapeInfo) if(biasShapeInfo)
REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM DEPTHWISECONV2D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo)); REQUIRE_TRUE(shape::rank(biasShapeInfo) <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM DEPTHWISECONV2D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, shape::rank(biasShapeInfo), shape::length(biasShapeInfo));
auto gradIshapeInfo = ShapeBuilders::copyShapeInfoAndType(inputShapeInfo, gradOShapeInfo, false, block.getWorkspace()); auto gradIshapeInfo = ShapeBuilders::copyShapeInfoAndType(inputShapeInfo, gradOShapeInfo, false, block.getWorkspace());
auto gradWshapeInfo = ShapeBuilders::copyShapeInfoAndType(weightsShapeInfo, gradOShapeInfo, false, block.getWorkspace()); auto gradWshapeInfo = ShapeBuilders::copyShapeInfoAndType(weightsShapeInfo, gradOShapeInfo, false, block.getWorkspace());

34
libnd4j/include/ops/declarable/platform/mkldnn/deconv2d.cpp
View File

@ -34,13 +34,13 @@ namespace platforms {
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
static void deconv2dMKLDNN(const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, static void deconv2dMKLDNN(const NDArray* input, const NDArray* weights, const NDArray* bias, 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 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 isSameMode) { const int paddingMode) {
// input [bS, iH, iW, iC] nchw, mkl doesn't support format nhwc // input [bS, iC, iH, iW] nchw, mkl doesn't support format nhwc
// weights [oC, iC, kH, kW] always, mkl doesn't support weights format [kH, kW, oC, iC] // weights [oC, iC, kH, kW] always, mkl doesn't support weights format [kH, kW, oC, iC]
// bias [oC], may be nullptr // bias [oC], may be nullptr
// output [bS, oH, oW, oC] nchw, mkl doesn't support format nhwc // output [bS, oC, oH, oW] nchw, mkl doesn't support format nhwc
int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width; int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
@ -179,12 +179,12 @@ static void deconv2dMKLDNN(const NDArray* input, const NDArray* weights, const N
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
static void deconv2dBackPropMKLDNN(const NDArray* input, const NDArray* weights, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, static void deconv2dBackPropMKLDNN(const NDArray* input, const NDArray* weights, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB,
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 kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW,
const int isSameMode) { const int paddingMode) {
// input and gradI [bS, iH, iW, iC], mkl doesn't support ndhwc format // input and gradI [bS, iC, iH, iW], mkl doesn't support ndhwc format
// weights and gradW [oC, iC, kH, kW] always, mkl doesn't support weights format [kH, kW, oC, iC] // weights and gradW [oC, iC, kH, kW] always, mkl doesn't support weights format [kH, kW, oC, iC]
// gradB [oC], may be nullptr // gradB [oC], may be nullptr
// gradO [bS, oH, oW, oC] // gradO [bS, oC, oH, oW]
int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width; int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
@ -368,7 +368,7 @@ PLATFORM_IMPL(deconv2d) {
int pW = INT_ARG(5); // paddings width int pW = INT_ARG(5); // paddings width
int dH = INT_ARG(6); // dilations height int dH = INT_ARG(6); // dilations height
int dW = INT_ARG(7); // dilations width int dW = INT_ARG(7); // dilations width
int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME
int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width; int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
@ -380,7 +380,7 @@ PLATFORM_IMPL(deconv2d) {
if (bias) if (bias)
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM DECONV2D_MKLDNN OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf()); REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM DECONV2D_MKLDNN OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
if(isSameMode){ // SAME if(paddingMode){ // SAME
//Note: we're intentionally swapping iH and oH, to calculated the padding for a"normal" conv (not deconv) forward pass //Note: we're intentionally swapping iH and oH, to calculated the padding for a"normal" conv (not deconv) forward pass
ConvolutionUtils::calcPadding2D(pH, pW, iH, iW, oH, oW, kH, kW, sH, sW, dH, dW); ConvolutionUtils::calcPadding2D(pH, pW, iH, iW, oH, oW, kH, kW, sH, sW, dH, dW);
} }
@ -394,7 +394,7 @@ PLATFORM_IMPL(deconv2d) {
output = new NDArray(output->permute({0,3,1,2})); // [bS, oH, oW, oC] -> [bS, oC, oH, oW] output = new NDArray(output->permute({0,3,1,2})); // [bS, oH, oW, oC] -> [bS, oC, oH, oW]
} }
deconv2dMKLDNN(input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode); deconv2dMKLDNN(input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode);
delete weights; delete weights;
@ -419,14 +419,14 @@ PLATFORM_CHECK(deconv2d) {
int dH = INT_ARG(6); // dilations height int dH = INT_ARG(6); // dilations height
int dW = INT_ARG(7); // dilations width int dW = INT_ARG(7); // dilations width
int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME
const DataType xType = input->dataType(); const DataType xType = input->dataType();
const DataType wType = weights->dataType(); const DataType wType = weights->dataType();
const DataType zType = output->dataType(); const DataType zType = output->dataType();
const DataType bType = bias != nullptr ? bias->dataType() : zType; const DataType bType = bias != nullptr ? bias->dataType() : zType;
return block.isUseMKLDNN() && (dH <= 1 && dW <= 1 && !isSameMode) && return block.isUseMKLDNN() && (dH <= 1 && dW <= 1 && !paddingMode) &&
( (
(xType==DataType::FLOAT32 && wType==DataType::FLOAT32 && bType==DataType::FLOAT32 && zType==DataType::FLOAT32) || (xType==DataType::FLOAT32 && wType==DataType::FLOAT32 && bType==DataType::FLOAT32 && zType==DataType::FLOAT32) ||
((xType==DataType::UINT8 || xType==DataType::INT8) && wType==DataType::INT8 && (zType==DataType::UINT8 || zType==DataType::INT8 || zType==DataType::INT32 || zType==DataType::FLOAT32) && bType == zType) ((xType==DataType::UINT8 || xType==DataType::INT8) && wType==DataType::INT8 && (zType==DataType::UINT8 || zType==DataType::INT8 || zType==DataType::INT32 || zType==DataType::FLOAT32) && bType == zType)
@ -459,7 +459,7 @@ PLATFORM_IMPL(deconv2d_bp) {
int pW = INT_ARG(5); // paddings width int pW = INT_ARG(5); // paddings width
int dH = INT_ARG(6); // dilations height int dH = INT_ARG(6); // dilations height
int dW = INT_ARG(7); // dilations width int dW = INT_ARG(7); // dilations width
int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME
int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 1-NHWC, 0-NCHW int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 1-NHWC, 0-NCHW
int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width; int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
@ -467,7 +467,7 @@ PLATFORM_IMPL(deconv2d_bp) {
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH); ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH);
int trueoH, trueoW; // true output height, width int trueoH, trueoW; // true output height, width
ConvolutionUtils::calcOutSizeDeconv2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode); ConvolutionUtils::calcOutSizeDeconv2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, paddingMode);
std::vector<Nd4jLong> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1}); std::vector<Nd4jLong> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1});
std::vector<Nd4jLong> expectedWeightsShape = {kH, kW, oC, iC}; std::vector<Nd4jLong> expectedWeightsShape = {kH, kW, oC, iC};
@ -476,7 +476,7 @@ PLATFORM_IMPL(deconv2d_bp) {
if(bias) if(bias)
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM DECONV2D_MKLDNN_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf()); REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM DECONV2D_MKLDNN_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
if(isSameMode){ // SAME if(paddingMode){ // SAME
//Note: we're intentionally swapping iH and oH, to calculated the padding for a"normal" conv (not deconv) forward pass //Note: we're intentionally swapping iH and oH, to calculated the padding for a"normal" conv (not deconv) forward pass
ConvolutionUtils::calcPadding2D(pH, pW, iH, iW, oH, oW, kH, kW, sH, sW, dH, dW); ConvolutionUtils::calcPadding2D(pH, pW, iH, iW, oH, oW, kH, kW, sH, sW, dH, dW);
} }
@ -492,7 +492,7 @@ PLATFORM_IMPL(deconv2d_bp) {
gradO = new NDArray(gradO->permute({0,3,1,2})); // [bS, oH, oW, oC] -> [bS, oC, oH, oW] gradO = new NDArray(gradO->permute({0,3,1,2})); // [bS, oH, oW, oC] -> [bS, oC, oH, oW]
} }
deconv2dBackPropMKLDNN(input, weights, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode); deconv2dBackPropMKLDNN(input, weights, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode);
delete weights; delete weights;
delete gradW; delete gradW;
@ -518,7 +518,7 @@ PLATFORM_CHECK(deconv2d_bp) {
int dH = INT_ARG(6); // dilations height int dH = INT_ARG(6); // dilations height
int dW = INT_ARG(7); // dilations width int dW = INT_ARG(7); // dilations width
int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME
const DataType xType = input->dataType(); const DataType xType = input->dataType();
const DataType wType = weights->dataType(); const DataType wType = weights->dataType();
@ -528,7 +528,7 @@ PLATFORM_CHECK(deconv2d_bp) {
const DataType gradWType = gradW->dataType(); const DataType gradWType = gradW->dataType();
const DataType gradBType = gradB != nullptr ? gradB->dataType() : DataType::FLOAT32; const DataType gradBType = gradB != nullptr ? gradB->dataType() : DataType::FLOAT32;
return block.isUseMKLDNN() && (dH <= 1 && dW <= 1 && !isSameMode) && ((xType==DataType::FLOAT32 || xType==DataType::BFLOAT16) && (wType==DataType::FLOAT32 || wType==DataType::BFLOAT16) && (gradOType==DataType::FLOAT32 || gradOType==DataType::BFLOAT16) && (gradIType==DataType::FLOAT32 || gradIType==DataType::BFLOAT16) && (gradWType==DataType::FLOAT32 || gradWType==DataType::BFLOAT16) && (gradBType==DataType::FLOAT32 || gradBType==DataType::BFLOAT16) ); return block.isUseMKLDNN() && (dH <= 1 && dW <= 1 && !paddingMode) && ((xType==DataType::FLOAT32 || xType==DataType::BFLOAT16) && (wType==DataType::FLOAT32 || wType==DataType::BFLOAT16) && (gradOType==DataType::FLOAT32 || gradOType==DataType::BFLOAT16) && (gradIType==DataType::FLOAT32 || gradIType==DataType::BFLOAT16) && (gradWType==DataType::FLOAT32 || gradWType==DataType::BFLOAT16) && (gradBType==DataType::FLOAT32 || gradBType==DataType::BFLOAT16) );
} }

13
libnd4j/include/ops/declarable/platform/mkldnn/deconv3d.cpp
View File

@ -34,8 +34,7 @@ namespace platforms {
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
static void deconv3dMKLDNN(const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, static void deconv3dMKLDNN(const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output,
const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, 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 pD, const int pH, const int pW, const int dD, const int dH, const int dW) {
const int isSameMode) {
// input [bS, iD, iH, iW, iC] ncdhw, mkl doesn't support format ndhwc // input [bS, iD, iH, iW, iC] ncdhw, mkl doesn't support format ndhwc
// weights [oC, iC, kD, kH, kW] always, mkl doesn't support weights format [kD, kH, kW, oC, iC] // weights [oC, iC, kD, kH, kW] always, mkl doesn't support weights format [kD, kH, kW, oC, iC]
@ -182,8 +181,10 @@ static void deconv3dMKLDNN(const NDArray* input, const NDArray* weights, const N
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
static void deconv3dBackPropMKLDNN(const NDArray* input, const NDArray* weights, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, static void deconv3dBackPropMKLDNN(const NDArray* input, const NDArray* weights, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB,
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 kD, const int kH, const int kW,
const int isSameMode) { 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) {
// input and gradI [bS, iD, iH, iW, iC], mkl doesn't support ndhwc format // input and gradI [bS, iD, iH, iW, iC], mkl doesn't support ndhwc format
// weights and gradW [oC, iC, kD, kH, kW] always, mkl doesn't support weights format [kD, kH, kW, oC, iC] // weights and gradW [oC, iC, kD, kH, kW] always, mkl doesn't support weights format [kD, kH, kW, oC, iC]
@ -408,7 +409,7 @@ PLATFORM_IMPL(deconv3d) {
output = new NDArray(output->permute({0,4,1,2,3})); // [bS, oD, oH, oW, oC] -> [bS, oC, oD, oH, oW] output = new NDArray(output->permute({0,4,1,2,3})); // [bS, oD, oH, oW, oC] -> [bS, oC, oD, oH, oW]
} }
deconv3dMKLDNN(input, weights, bias, output, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, isSameMode); deconv3dMKLDNN(input, weights, bias, output, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW);
delete weights; delete weights;
@ -509,7 +510,7 @@ PLATFORM_IMPL(deconv3d_bp) {
gradO = new NDArray(gradO->permute({0,4,1,2,3})); // [bS, oD, oH, oW, oC] -> [bS, oC, oD, oH, oW] gradO = new NDArray(gradO->permute({0,4,1,2,3})); // [bS, oD, oH, oW, oC] -> [bS, oC, oD, oH, oW]
} }
deconv3dBackPropMKLDNN(input, weights, gradO, gradI, gradW, gradB, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, isSameMode); deconv3dBackPropMKLDNN(input, weights, gradO, gradI, gradW, gradB, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW);
delete weights; delete weights;
delete gradW; delete gradW;

505
libnd4j/include/ops/declarable/platform/mkldnn/depthwiseConv2d.cpp Normal file
View File

@ -0,0 +1,505 @@
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
* Copyright (c) 2019 Konduit K.K.
*
* 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
******************************************************************************/
//
// @author Yurii Shyrma (iuriish@yahoo.com)
//
#include <ops/declarable/PlatformHelper.h>
#include <ops/declarable/OpRegistrator.h>
#include <platform_boilerplate.h>
#include <helpers/MKLDNNStream.h>
#include <ops/declarable/helpers/convolutions.h>
#include "mkldnnUtils.h"
using namespace dnnl;
namespace nd4j {
namespace ops {
namespace platforms {
//////////////////////////////////////////////////////////////////////////
static void depthwiseConv2dMKLDNN(const NDArray* input, const NDArray* weights, const NDArray* bias, 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 int paddingMode, const bool isNCHW) {
// mkl supports only following case: mC = 1, oC = iC
// input [bS, iC, iH, iW] nchw or [bS, iH, iW, iC] nhwc, since mkl doesn't support nhwc format we'll permute when nhwc is given
// weights [kH, kW, iC, mC], mkl doesn't support this format, so we'll make permute
// bias [oC], may be nullptr
// output [bS, oC, oH, oW] nchw or [bS, oH, oW, oC] nhwc
// oC = iC*mC
int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
int indIOioC, indIiH, indWmC, indWiC, indWkH, indOoH; // corresponding indexes
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWmC, indWkH, indOoH);
mC = weights->sizeAt(indWmC); // channels multiplier
const int pWSame = (paddingMode == 2 && dW > 1) ? ((oW - 1) * sW + (kW - 1) * dW + 1 - iW) / 2 : pW; // dH == 1 for causal mode in conv1d
dnnl::memory::dims strides = { sH, sW };
dnnl::memory::dims padding = { pH, pW };
dnnl::memory::dims padding_r = { (oH - 1) * sH - iH + kH - pH, (oW - 1) * sW - iW + kW - pWSame };
dnnl::memory::dims dilation = { dH-1, dW-1};
// input type
dnnl::memory::data_type xType;
if(input->dataType() == DataType::FLOAT32)
xType = dnnl::memory::data_type::f32;
else if(input->dataType() == DataType::HALF)
xType = dnnl::memory::data_type::f16;
else if(input->dataType() == DataType::UINT8)
xType = dnnl::memory::data_type::u8;
else
xType = dnnl::memory::data_type::s8;
// weights type
dnnl::memory::data_type wType = xType;
if(xType == dnnl::memory::data_type::u8)
wType = dnnl::memory::data_type::s8;
// output and bias type (have the same types)
dnnl::memory::data_type zType;
if(output->dataType() == DataType::FLOAT32)
zType = dnnl::memory::data_type::f32;
else if(output->dataType() == DataType::HALF)
zType = dnnl::memory::data_type::f16;
else if(output->dataType() == DataType::UINT8)
zType = dnnl::memory::data_type::u8;
else if(output->dataType() == DataType::INT8)
zType = dnnl::memory::data_type::s8;
else
zType = dnnl::memory::data_type::s32;
dnnl::memory::format_tag xzFrmat = dnnl::memory::format_tag::nchw;
dnnl::memory::format_tag wFormat = dnnl::memory::format_tag::goihw;
dnnl::memory::dims xDims = {bS, iC, iH, iW};
dnnl::memory::dims wDims = {iC, mC, 1, kH, kW};
dnnl::memory::dims zDims = {bS, oC, oH, oW};
// memory descriptors for arrays
// input
dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, xType, dnnl::memory::format_tag::any);
dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, xType, xzFrmat);
x_user_md.data.format_kind = dnnl_blocked; // overrides format NHWC -> NCHW
x_user_md.data.format_desc.blocking.strides[0] = input->strideAt(0);
x_user_md.data.format_desc.blocking.strides[1] = input->strideAt(isNCHW ? 1 : 3);
x_user_md.data.format_desc.blocking.strides[2] = input->strideAt(isNCHW ? 2 : 1);
x_user_md.data.format_desc.blocking.strides[3] = input->strideAt(isNCHW ? 3 : 2);
// weights, make permute [kH, kW, iC, mC] -> [iC, mC, 1, kH, kW];
dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, wType, dnnl::memory::format_tag::any);
dnnl::memory::desc w_user_md = dnnl::memory::desc(wDims, wType, wFormat);
w_user_md.data.format_kind = dnnl_blocked; // overrides format
w_user_md.data.format_desc.blocking.strides[0] = weights->strideAt(2); // permute
w_user_md.data.format_desc.blocking.strides[1] = weights->strideAt(3);
w_user_md.data.format_desc.blocking.strides[2] = 0;
w_user_md.data.format_desc.blocking.strides[3] = weights->strideAt(0);
w_user_md.data.format_desc.blocking.strides[4] = weights->strideAt(1);
// bias
dnnl::memory::desc b_mkl_md;
if(bias != nullptr)
b_mkl_md = dnnl::memory::desc({oC}, zType, dnnl::memory::format_tag::x);
// output
dnnl::memory::desc z_mkl_md = dnnl::memory::desc(zDims, zType, dnnl::memory::format_tag::any);
dnnl::memory::desc z_user_md = dnnl::memory::desc(zDims, zType, xzFrmat);
z_user_md.data.format_kind = dnnl_blocked; // overrides format
z_user_md.data.format_desc.blocking.strides[0] = output->strideAt(0);
z_user_md.data.format_desc.blocking.strides[1] = output->strideAt(isNCHW ? 1 : 3);
z_user_md.data.format_desc.blocking.strides[2] = output->strideAt(isNCHW ? 2 : 1);
z_user_md.data.format_desc.blocking.strides[3] = output->strideAt(isNCHW ? 3 : 2);
auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
// operation primitive description
dnnl::convolution_forward::desc op_desc(dnnl::prop_kind::forward_inference, dnnl::algorithm::convolution_auto,
x_mkl_md, w_mkl_md, b_mkl_md, z_mkl_md, strides, dilation, padding, padding_r);
dnnl::convolution_forward::primitive_desc op_prim_desc(op_desc, engine);
// arguments (memory buffers) necessary for calculations
std::unordered_map<int, dnnl::memory> args;
dnnl::stream stream(engine);
// provide memory buffers and check whether reorder is required
// input
auto x_user_mem = dnnl::memory(x_user_md, engine, input->getBuffer());
const bool xReorder = op_prim_desc.src_desc() != x_user_mem.get_desc();
auto x_mkl_mem = xReorder ? dnnl::memory(op_prim_desc.src_desc(), engine) : x_user_mem;
if (xReorder)
dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem);
args[DNNL_ARG_SRC] = x_mkl_mem;
// weights
auto w_user_mem = dnnl::memory(w_user_md, engine, weights->getBuffer());
const bool wReorder = op_prim_desc.weights_desc() != w_user_mem.get_desc();
auto w_mkl_mem = wReorder ? dnnl::memory(op_prim_desc.weights_desc(), engine) : w_user_mem;
if (wReorder)
dnnl::reorder(w_user_mem, w_mkl_mem).execute(stream, w_user_mem, w_mkl_mem);
args[DNNL_ARG_WEIGHTS] = w_mkl_mem;
// bias
if(bias != nullptr) {
auto b_mkl_mem = dnnl::memory(b_mkl_md, engine, bias->getBuffer());
args[DNNL_ARG_BIAS] = b_mkl_mem;
}
// output
auto z_user_mem = dnnl::memory(z_user_md, engine, output->getBuffer());
const bool zReorder = op_prim_desc.dst_desc() != z_user_mem.get_desc();
auto z_mkl_mem = zReorder ? dnnl::memory(op_prim_desc.dst_desc(), engine) : z_user_mem;
args[DNNL_ARG_DST] = z_mkl_mem;
// run calculations
dnnl::convolution_forward(op_prim_desc).execute(stream, args);
// reorder outputs if necessary
if (zReorder)
dnnl::reorder(z_mkl_mem, z_user_mem).execute(stream, z_mkl_mem, z_user_mem);
stream.wait();
// shape::printArray(z_mkl_mem.map_data<float>(),8);
}
//////////////////////////////////////////////////////////////////////////
static void depthwiseConv2dNackPropMKLDNN(const NDArray* input, const NDArray* weights, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB,
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 paddingMode, const bool isNCHW) {
// mkl supports only following case: mC = 1, oC = iC
// input, gradI [bS, iC, iH, iW] nchw or [bS, iH, iW, iC] nhwc, since mkl doesn't support nhwc format we'll permute when nhwc is given
// weights, gradW [kH, kW, iC, mC], mkl doesn't support this format, so we'll make permute
// gradB [oC], may be nullptr
// gradO [bS, oC, oH, oW] nchw or [bS, oH, oW, oC] nhwc
// oC = iC*mC
int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
int indIOioC, indIiH, indWmC, indWiC, indWkH, indOoH; // corresponding indexes
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWmC, indWkH, indOoH);
mC = weights->sizeAt(indWmC);
const int pWSame = (paddingMode == 2 && dW > 1) ? ((oW - 1) * sW + (kW - 1) * dW + 1 - iW) / 2 : pW; // dH == 1 for causal mode in conv1d
dnnl::memory::dims strides = { sH, sW };
dnnl::memory::dims padding = { pH, pW };
dnnl::memory::dims padding_r = { (oH - 1) * sH - iH + kH - pH, (oW - 1) * sW - iW + kW - pWSame };
dnnl::memory::dims dilation = { dH-1, dW-1};
// input type
dnnl::memory::data_type xType = input->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16;
// weights type
dnnl::memory::data_type wType = weights->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16;
// gradO type
dnnl::memory::data_type gradOType = gradO->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16;
// gradI type
dnnl::memory::data_type gradIType = gradI->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16;
// gradW type
dnnl::memory::data_type gradWType = gradW->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16;
// gradB type
dnnl::memory::data_type gradBType = gradB != nullptr ? (gradB->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16) : dnnl::memory::data_type::f32;
dnnl::memory::format_tag xFormat = dnnl::memory::format_tag::nchw; // isNCHW ? dnnl::memory::format_tag::nchw : dnnl::memory::format_tag::nhwc;
dnnl::memory::format_tag wFormat = dnnl::memory::format_tag::goihw;
dnnl::memory::dims xDims = {bS, iC, iH, iW};
dnnl::memory::dims wDims = {iC, mC, 1, kH, kW};
dnnl::memory::dims zDims = {bS, oC, oH, oW};
// memory descriptors for arrays
// input
dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, xType, dnnl::memory::format_tag::any);
dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, xType, xFormat);
x_user_md.data.format_kind = dnnl_blocked; // overrides format
x_user_md.data.format_desc.blocking.strides[0] = input->strideAt(0);
x_user_md.data.format_desc.blocking.strides[1] = input->strideAt(isNCHW ? 1 : 3);
x_user_md.data.format_desc.blocking.strides[2] = input->strideAt(isNCHW ? 2 : 1);
x_user_md.data.format_desc.blocking.strides[3] = input->strideAt(isNCHW ? 3 : 2);
// weights, make permute [kH, kW, iC, mC] -> [iC, mC, 1, kH, kW];
dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, wType, dnnl::memory::format_tag::any);
dnnl::memory::desc w_user_md = dnnl::memory::desc(wDims, wType, wFormat);
w_user_md.data.format_kind = dnnl_blocked; // overrides format
w_user_md.data.format_desc.blocking.strides[0] = weights->strideAt(2); // permute
w_user_md.data.format_desc.blocking.strides[1] = weights->strideAt(3);
w_user_md.data.format_desc.blocking.strides[2] = 0;
w_user_md.data.format_desc.blocking.strides[3] = weights->strideAt(0);
w_user_md.data.format_desc.blocking.strides[4] = weights->strideAt(1);
// gradO
dnnl::memory::desc gradO_mkl_md = dnnl::memory::desc(zDims, gradOType, dnnl::memory::format_tag::any);
dnnl::memory::desc gradO_user_md = dnnl::memory::desc(zDims, gradOType, xFormat);
gradO_user_md.data.format_kind = dnnl_blocked; // overrides format
gradO_user_md.data.format_desc.blocking.strides[0] = gradO->strideAt(0);
gradO_user_md.data.format_desc.blocking.strides[1] = gradO->strideAt(isNCHW ? 1 : 3);
gradO_user_md.data.format_desc.blocking.strides[2] = gradO->strideAt(isNCHW ? 2 : 1);
gradO_user_md.data.format_desc.blocking.strides[3] = gradO->strideAt(isNCHW ? 3 : 2);
// gradI
dnnl::memory::desc gradI_mkl_md = dnnl::memory::desc(xDims, gradIType, dnnl::memory::format_tag::any);
dnnl::memory::desc gradI_user_md = dnnl::memory::desc(xDims, gradIType, xFormat);
gradI_user_md.data.format_kind = dnnl_blocked; // overrides format
gradI_user_md.data.format_desc.blocking.strides[0] = gradI->strideAt(0);
gradI_user_md.data.format_desc.blocking.strides[1] = gradI->strideAt(isNCHW ? 1 : 3);
gradI_user_md.data.format_desc.blocking.strides[2] = gradI->strideAt(isNCHW ? 2 : 1);
gradI_user_md.data.format_desc.blocking.strides[3] = gradI->strideAt(isNCHW ? 3 : 2);
// gradW, make permute [kH, kW, iC, mC] -> [iC, mC, 1, kH, kW];
dnnl::memory::desc gradW_mkl_md = dnnl::memory::desc(wDims, gradWType, dnnl::memory::format_tag::any);
dnnl::memory::desc gradW_user_md = dnnl::memory::desc(wDims, gradWType, wFormat);
gradW_user_md.data.format_kind = dnnl_blocked; // overrides format
gradW_user_md.data.format_desc.blocking.strides[0] = gradW->strideAt(2); // permute
gradW_user_md.data.format_desc.blocking.strides[1] = gradW->strideAt(3);
gradW_user_md.data.format_desc.blocking.strides[2] = 0;
gradW_user_md.data.format_desc.blocking.strides[3] = gradW->strideAt(0);
gradW_user_md.data.format_desc.blocking.strides[4] = gradW->strideAt(1);
// gradB
dnnl::memory::desc gradB_mkl_md;
if(gradB != nullptr)
gradB_mkl_md = dnnl::memory::desc({oC}, gradBType, dnnl::memory::format_tag::x);
auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
// forward primitive description
dnnl::convolution_forward::desc op_ff_desc(dnnl::prop_kind::forward_inference, dnnl::algorithm::convolution_auto, x_mkl_md, w_mkl_md, gradB_mkl_md, gradO_mkl_md, strides, dilation, padding, padding_r);
dnnl::convolution_forward::primitive_desc op_ff_prim_desc(op_ff_desc, engine);
// backward data primitive description
dnnl::convolution_backward_data::desc op_data_bp_desc(dnnl::algorithm::convolution_auto, gradI_mkl_md, w_mkl_md, gradO_mkl_md, strides, dilation, padding, padding_r);
dnnl::convolution_backward_data::primitive_desc op_data_bp_prim_desc(op_data_bp_desc, engine, op_ff_prim_desc);
// backward weights primitive description
dnnl::convolution_backward_weights::desc op_weights_bp_desc(dnnl::algorithm::convolution_auto, x_mkl_md, gradW_mkl_md, gradB_mkl_md, gradO_mkl_md, strides, dilation, padding, padding_r);
dnnl::convolution_backward_weights::primitive_desc op_weights_bp_prim_desc(op_weights_bp_desc, engine, op_ff_prim_desc);
// arguments (memory buffers) necessary for calculations
std::unordered_map<int, dnnl::memory> args;
dnnl::stream stream(engine);
// provide memory buffers and check whether reorder is required
// input
auto x_user_mem = dnnl::memory(x_user_md, engine, input->getBuffer());
const bool xReorder = op_weights_bp_prim_desc.src_desc() != x_user_mem.get_desc();
auto x_mkl_mem = xReorder ? dnnl::memory(op_weights_bp_prim_desc.src_desc(), engine) : x_user_mem;
if (xReorder)
dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem);
args[DNNL_ARG_SRC] = x_mkl_mem;
// weights
auto w_user_mem = dnnl::memory(w_user_md, engine, weights->getBuffer());
const bool wReorder = op_data_bp_prim_desc.weights_desc() != w_user_mem.get_desc();
auto w_mkl_mem = wReorder ? dnnl::memory(op_data_bp_prim_desc.weights_desc(), engine) : w_user_mem;
if (wReorder)
dnnl::reorder(w_user_mem, w_mkl_mem).execute(stream, w_user_mem, w_mkl_mem);
args[DNNL_ARG_WEIGHTS] = w_mkl_mem;
// gradO
auto gradO_user_mem = dnnl::memory(gradO_user_md, engine, gradO->getBuffer());
const bool gradOReorder = op_data_bp_prim_desc.diff_dst_desc() != gradO_user_mem.get_desc();
auto gradO_mkl_mem = gradOReorder ? dnnl::memory(op_data_bp_prim_desc.diff_dst_desc(), engine) : gradO_user_mem;
if (gradOReorder)
dnnl::reorder(gradO_user_mem, gradO_mkl_mem).execute(stream, gradO_user_mem, gradO_mkl_mem);
args[DNNL_ARG_DIFF_DST] = gradO_mkl_mem;
// gradI
auto gradI_user_mem = dnnl::memory(gradI_user_md, engine, gradI->getBuffer());
const bool gradIReorder = op_data_bp_prim_desc.diff_src_desc() != gradI_user_mem.get_desc();
auto gradI_mkl_mem = gradIReorder ? dnnl::memory(op_data_bp_prim_desc.diff_src_desc(), engine) : gradI_user_mem;
args[DNNL_ARG_DIFF_SRC] = gradI_mkl_mem;
// gradW
auto gradW_user_mem = dnnl::memory(gradW_user_md, engine, gradW->getBuffer());
const bool gradWReorder = op_weights_bp_prim_desc.diff_weights_desc() != gradW_user_mem.get_desc();
auto gradW_mkl_mem = gradWReorder ? dnnl::memory(op_weights_bp_prim_desc.diff_weights_desc(), engine) : gradW_user_mem;
args[DNNL_ARG_DIFF_WEIGHTS] = gradW_mkl_mem;
// gradB
if(gradB != nullptr) {
auto gradB_mkl_mem = dnnl::memory(gradB_mkl_md, engine, gradB->getBuffer());
args[DNNL_ARG_DIFF_BIAS] = gradB_mkl_mem;
}
// run backward data calculations
dnnl::convolution_backward_data(op_data_bp_prim_desc).execute(stream, args);
// run backward weights calculations
dnnl::convolution_backward_weights(op_weights_bp_prim_desc).execute(stream, args);
// reorder gradI if necessary
if (gradIReorder)
dnnl::reorder(gradI_mkl_mem, gradI_user_mem).execute(stream, gradI_mkl_mem, gradI_user_mem);
if (gradWReorder)
dnnl::reorder(gradW_mkl_mem, gradW_user_mem).execute(stream, gradW_mkl_mem, gradW_user_mem);
stream.wait();
// shape::printArray(z_mkl_mem.map_data<float>(),8);
}
//////////////////////////////////////////////////////////////////////
PLATFORM_IMPL(depthwise_conv2d) {
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, mC] always
auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC] = iC*mC
auto output = OUTPUT_VARIABLE(0); // [bS, oH, oW, iC*mC] (NHWC) or [bS, iC*mC, oH, oW] (NCHW)
int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) height
int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1));// filter(kernel) width
int sH = INT_ARG(2); // strides height
int sW = INT_ARG(3); // strides width
int pH = INT_ARG(4); // paddings height
int pW = INT_ARG(5); // paddings width
int dH = INT_ARG(6); // dilations height
int dW = INT_ARG(7); // dilations width
int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME
int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, channels multiplier(oC = iC*mC), output channels, output height/width
int indIOioC, indIiH, indWmC, indWiC, indWkH, indOoH; // corresponding indexes
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWmC, indWkH, indOoH);
mC = weights->sizeAt(indWmC); // channels multiplier
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);
std::vector<Nd4jLong> expectedWeightsShape = {kH, kW, iC, mC};
REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM DEPTHWISECONV2D MKL OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
REQUIRE_TRUE(output->sizeAt(indIOioC) == iC*mC, 0, "CUSTOM DEPTHWISECONV2D MKL OP: the output_channels must be equal to input_channels * channels_multiplier = %i !", iC*mC);
if (bias)
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM DEPTHWISECONV2D MKL OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
depthwiseConv2dMKLDNN(input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW);
return Status::OK();
}
//////////////////////////////////////////////////////////////////////
PLATFORM_CHECK(depthwise_conv2d) {
// we don't want to use mkldnn if cpu doesn't support avx/avx2
if (::optimalLevel() < 2)
return false;
auto input = INPUT_VARIABLE(0);
auto weights = INPUT_VARIABLE(1);
auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr;
auto output = INPUT_VARIABLE(0);
const DataType xType = input->dataType();
const DataType wType = weights->dataType();
const DataType zType = output->dataType();
const DataType bType = bias != nullptr ? bias->dataType() : zType;
const int mC = weights->sizeAt(3);
return block.isUseMKLDNN() && mC == 1 &&
(
(xType==DataType::FLOAT32 && wType==DataType::FLOAT32 && bType==DataType::FLOAT32 && zType==DataType::FLOAT32) ||
(xType==DataType::HALF && wType==DataType::HALF && bType==DataType::HALF && zType==DataType::HALF) ||
((xType==DataType::UINT8 || xType==DataType::INT8) && wType==DataType::INT8 && (zType==DataType::UINT8 || zType==DataType::INT8 || zType==DataType::INT32 || zType==DataType::FLOAT32) && bType == zType)
);
}
//////////////////////////////////////////////////////////////////////////
PLATFORM_IMPL(depthwise_conv2d_bp) {
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NDHWC) or [bS, iC, iH, iW] (NCDHW)
auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, mC] always
auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC] = [iC*mC]
auto gradO = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NDHWC) or [bS, oC, oH, oW] (NCDHW), epsilon_next
auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NDHWC) or [bS, iC, iH, iW] (NCDHW), epsilon
auto gradW = OUTPUT_VARIABLE(1); // [kH, kW, iC, mC] always
auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
REQUIRE_TRUE(input->rankOf() == 4, 0, "CUSTOM DEPTHWISECONV2D_BP MKL OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf());
REQUIRE_TRUE(weights->rankOf() == 4, 0, "CUSTOM DEPTHWISECONV2D_BP MKL OP: rank of weights array must be equal to 4, but got %i instead !", weights->rankOf());
REQUIRE_TRUE(gradO->rankOf() == 4, 0, "CUSTOM DEPTHWISECONV2D_BP MKL OP: rank of output gradients (next epsilon) array must be equal to 4, but got %i instead !", gradO->rankOf());
int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) height
int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1));// filter(kernel) width
int sH = INT_ARG(2); // strides height
int sW = INT_ARG(3); // strides width
int pH = INT_ARG(4); // paddings height
int pW = INT_ARG(5); // paddings width
int dH = INT_ARG(6); // dilations height
int dW = INT_ARG(7); // dilations width
int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME
int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 1-NHWC, 0-NCHW
int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, channels multiplier(oC = iC*mC), output channels, output height/width
int indIOioC, indIiH, indWmC, indWiC, indWkH, indOoH; // corresponding indexes
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWmC, indWkH, indOoH);
mC = weights->sizeAt(indWmC); // channels multiplier
int trueoH, trueoW; // correct output height, width
ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, paddingMode);
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);
std::vector<Nd4jLong> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1});
std::vector<Nd4jLong> expectedWeightsShape = {kH, kW, iC, mC};
REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "CUSTOM DEPTHWISECONV2D_BP MKL OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str());
REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM DEPTHWISECONV2D_BP MKL OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
if(bias)
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM DEPTHWISECONV2D_BP MKL OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
depthwiseConv2dNackPropMKLDNN(input, weights, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW);
return Status::OK();
}
//////////////////////////////////////////////////////////////////////
PLATFORM_CHECK(depthwise_conv2d_bp) {
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NDHWC) or [bS, iC, iH, iW] (NCDHW)
auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, mC] always
auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC] = [iC*mC]
auto gradO = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NDHWC) or [bS, oC, oH, oW] (NCDHW), epsilon_next
auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NDHWC) or [bS, iC, iH, iW] (NCDHW), epsilon
auto gradW = OUTPUT_VARIABLE(1); // [kH, kW, iC, mC] always
auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
const DataType xType = input->dataType();
const DataType wType = weights->dataType();
const DataType gradOType = gradO->dataType();
const DataType gradIType = gradI->dataType();
const DataType gradWType = gradW->dataType();
const DataType gradBType = gradB != nullptr ? gradB->dataType() : DataType::FLOAT32;
const int mC = weights->sizeAt(3);
return block.isUseMKLDNN() && mC == 1 && ((xType==DataType::FLOAT32 || xType==DataType::BFLOAT16) && (wType==DataType::FLOAT32 || wType==DataType::BFLOAT16) && (gradOType==DataType::FLOAT32 || gradOType==DataType::BFLOAT16) && (gradIType==DataType::FLOAT32 || gradIType==DataType::BFLOAT16) && (gradWType==DataType::FLOAT32 || gradWType==DataType::BFLOAT16) && (gradBType==DataType::FLOAT32 || gradBType==DataType::BFLOAT16) );
}
}
}
}

3
libnd4j/include/ops/declarable/platform/mkldnn/mkldnnUtils.h
View File

@ -77,6 +77,9 @@ namespace nd4j{
DECLARE_PLATFORM(deconv2d_bp); DECLARE_PLATFORM(deconv2d_bp);
DECLARE_PLATFORM(deconv3d_bp); DECLARE_PLATFORM(deconv3d_bp);
DECLARE_PLATFORM(depthwise_conv2d);
DECLARE_PLATFORM(depthwise_conv2d_bp);
} }
} }

371
libnd4j/tests_cpu/layers_tests/ConvolutionTests1.cpp
View File

@ -1469,223 +1469,6 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_bp_test3) {
delete results; delete results;
} }
//////////////////////////////////////////////////////////////////////
TYPED_TEST(TypedConvolutionTests1, depthwise_conv2d_1) {
int bS=2, iH=4,iW=3, iC=2,mC=2, kH=3,kW=2, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
int oC=iC*mC;
int oH=4,oW=3;
int paddingMode = 1; // 1-SAME, 0-VALID;
int dataFormat = 1; // 1-NHWC, 0-NCHW
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iH, iW, iC});
auto weights = NDArrayFactory::create<TypeParam>('c', {kH, kW, iC, mC});
auto expOutput = NDArrayFactory::create<TypeParam>('c', {bS, oH, oW, oC},{12.f, 12.8f, 13.6f, 14.4f, 12.f, 12.8f, 13.6f, 14.4f, 5.2f, 5.6f, 6.f, 6.4f, 13.2f, 14.4f, 15.6f, 16.8f, 13.2f, 14.4f, 15.6f, 16.8f, 5.4f, 6.f, 6.6f, 7.2f,
13.2f, 14.4f, 15.6f, 16.8f, 13.2f, 14.4f, 15.6f, 16.8f, 5.4f, 6.f, 6.6f, 7.2f, 5.6f, 6.4f, 7.2f, 8.f, 5.6f, 6.4f, 7.2f, 8.f, 2.f, 2.4f, 2.8f, 3.2f,
12.f, 12.8f, 13.6f, 14.4f, 12.f, 12.8f, 13.6f, 14.4f, 5.2f, 5.6f, 6.f, 6.4f, 13.2f, 14.4f, 15.6f, 16.8f, 13.2f, 14.4f, 15.6f, 16.8f, 5.4f, 6.f, 6.6f, 7.2f,
13.2f, 14.4f, 15.6f, 16.8f, 13.2f, 14.4f, 15.6f, 16.8f, 5.4f, 6.f, 6.6f, 7.2f, 5.6f, 6.4f, 7.2f, 8.f, 5.6f, 6.4f, 7.2f, 8.f, 2.f, 2.4f, 2.8f, 3.2f});
input = 2.;
weights.linspace(0.1, 0.1);
nd4j::ops::depthwise_conv2d op;
auto results = op.execute({&input, &weights}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat});
auto* output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expOutput.isSameShape(output));
ASSERT_TRUE(expOutput.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(ConvolutionTests1, depthwise_conv2d_2) {
int bS=2, iH=4,iW=3, iC=2,mC=2, kH=3,kW=2, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
int oC=iC*mC;
int oH=2,oW=2;
int paddingMode = 0; // 1-SAME, 0-VALID;
int dataFormat = 1; // 1-NHWC, 0-NCHW
auto input = NDArrayFactory::create<double>('c', {bS, iH, iW, iC});
auto weights = NDArrayFactory::create<double>('c', {kH, kW, iC, mC});
auto expOutput = NDArrayFactory::create<double>('c', {bS, oH, oW, oC},{13.2f, 14.4f, 15.6f, 16.8f, 13.2f, 14.4f, 15.6f, 16.8f, 13.2f, 14.4f, 15.6f, 16.8f, 13.2f, 14.4f, 15.6f, 16.8f,
13.2f, 14.4f, 15.6f, 16.8f, 13.2f, 14.4f, 15.6f, 16.8f, 13.2f, 14.4f, 15.6f, 16.8f, 13.2f, 14.4f, 15.6f, 16.8f});
input = 2.;
weights.linspace(0.1, 0.1);
nd4j::ops::depthwise_conv2d op;
auto results = op.execute({&input, &weights}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat});
auto* output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expOutput.isSameShape(output));
ASSERT_TRUE(expOutput.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(ConvolutionTests1, depthwise_conv2d_3) {
int bS=2, iH=4,iW=3, iC=2,mC=2, kH=3,kW=2, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
int oC=iC*mC;
int oH=2,oW=2;
int paddingMode = 0; // 1-SAME, 0-VALID;
int dataFormat = 0; // 1-NHWC, 0-NCHW
auto input = NDArrayFactory::create<double>('c', {bS, iC, iH, iW});
auto weights = NDArrayFactory::create<double>('c', {mC, iC, kH, kW});
auto biases = NDArrayFactory::create<double>('c', {iC*mC}, {1,2,3,4});
auto expOutput = NDArrayFactory::create<double>('c', {bS, oC, oH, oW},{5.2, 5.2, 5.2, 5.2,20.6,20.6,20.6,20.6,14.4,14.4,14.4,14.4,29.8,29.8,29.8,29.8, 5.2, 5.2, 5.2, 5.2,20.6,20.6,20.6,20.6,14.4,14.4,14.4,14.4,29.8,29.8,29.8,29.8});
input = 2.;
weights.linspace(0.1, 0.1);
weights.permutei({2,3,1,0});
nd4j::ops::depthwise_conv2d op;
auto results = op.execute({&input, &weights, &biases}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat});
auto* output = results->at(0);
// output->printIndexedBuffer();
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expOutput.isSameShape(output));
ASSERT_TRUE(expOutput.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(ConvolutionTests1, depthwise_conv2d_4) {
int bS=1, iH=111,iW=111, iC=32,mC=1, kH=7,kW=7, sH=2,sW=2, pH=0,pW=0, dH=1,dW=1;
int oC=iC*mC;
int oH=56,oW=56;
int paddingMode = 1; // 1-SAME, 0-VALID;
int dataFormat = 1; // 1-NHWC, 0-NCHW
const float unique = -1000000;
NDArray input('c', {bS, iH, iW, iC}, nd4j::DataType::FLOAT32);
NDArray weights('c', {kH, kW, iC, mC}, nd4j::DataType::FLOAT32);
NDArray output('c', {bS, oH, oW, oC}, nd4j::DataType::FLOAT32);
input.linspace(0.1, 0.0001);
weights = 0.5;
output = unique;
nd4j::ops::depthwise_conv2d op;
Nd4jStatus status = op.execute({&input, &weights}, {&output} , {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}, {});
ASSERT_EQ(Status::OK(), status);
for(Nd4jLong i=output.lengthOf()/1.5; i < output.lengthOf(); ++i)
ASSERT_EQ(output.e<float>(i) != unique, true);
}
//////////////////////////////////////////////////////////////////////
TEST_F(ConvolutionTests1, depthwise_conv2d_5) {
int bS=1, iH=3,iW=3, iC=2,mC=1, kH=2,kW=2, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
int oC=iC*mC;
int oH=3,oW=3;
int paddingMode = 1; // 1-SAME, 0-VALID;
int dataFormat = 1; // 1-NHWC, 0-NCHW
auto input = NDArrayFactory::create<double>('c', {bS, iH, iW, iC});
auto weights = NDArrayFactory::create<double>('c', {kH, kW, iC, mC});
auto expOutput = NDArrayFactory::create<double>('c', {bS, oH, oW, oC}, {20., 24.,28., 32.,16., 18.,44., 48.,52., 56.,28., 30.,28., 30.,32., 34.,17., 18.});
input.linspace(1.);
weights = 1.;
nd4j::ops::depthwise_conv2d op;
auto results = op.execute({&input, &weights}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat});
auto output = results->at(0);
// output->printIndexedBuffer();
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expOutput.isSameShape(output));
ASSERT_TRUE(expOutput.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(ConvolutionTests1, depthwise_conv2d_6) {
int bS=1, iH=3,iW=3, iC=2,mC=1, kH=2,kW=2, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
int oC=iC*mC;
int oH=3,oW=3;
int paddingMode = 1; // 1-SAME, 0-VALID;
int dataFormat = 1; // 1-NHWC, 0-NCHW
NDArray input('c', {bS, iH, iW, iC}, nd4j::DataType::DOUBLE);
NDArray weights('c', {kH, kW, iC, mC}, nd4j::DataType::DOUBLE);
NDArray expOutput('c', {bS, oH, oW, oC}, {20., 24.,28., 32.,16., 18.,44., 48.,52., 56.,28., 30.,28., 30.,32., 34.,17., 18.});
input.linspace(1.);
weights = 1.;
nd4j::ops::depthwise_conv2d op;
ResultSet* results = op.execute({&input, &weights}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat});
NDArray* output = results->at(0);
// output.printIndexedBuffer();
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expOutput.isSameShape(output));
ASSERT_TRUE(expOutput.equalsTo(output));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(ConvolutionTests1, depthwise_conv2d_7) {
int bS=1, iH=3,iW=3, iC=2,mC=2, kH=1,kW=1, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
int oC=iC*mC;
int oH=3,oW=3;
int paddingMode = 0; // 1-SAME, 0-VALID;
int dataFormat = 0; // 1-NHWC, 0-NCHW
NDArray input('c', {bS, iC, iH, iW}, {0.6793503761291504, 0.35508695244789124, 0.842789351940155, 0.20031332969665527, 0.7014986872673035, 0.3106933832168579,
0.44793984293937683, 0.9380097389221191, 0.3266739547252655, 0.15187257528305054, 0.3833175301551819, 0.7821229696273804,
0.19880719482898712, 0.7985635995864868, 0.16326339542865753, 0.14696824550628662, 0.2608966827392578, 0.13505761325359344});
NDArray weights('c', {kH, kW, iC, mC}, {0.1308445781469345, 0.6442840099334717, 0.5698848366737366, 0.19896849989891052});
NDArray biases('c', {1,iC*mC}, {0.6123566627502441, 0.37637925148010254, 0.17464971542358398, 0.4270855486392975});
NDArray expOutput('c', {bS, oC, oH, oW}, {0.7012459761288241, 0.6588178652487691, 0.722631079971582, 0.6385665758716108, 0.7041439625563628, 0.6530092074102978,
0.670967162534851, 0.735090151337225, 0.6551001785478623, 0.8140738359624038, 0.6051560970782859, 0.9193749546773375, 0.5054379267801892, 0.8283436386757472,
0.5765540302788565, 0.6649797296980537, 0.9807239274294943, 0.586850056971322, 0.261199593183985, 0.3930965634902499, 0.6203697362284615, 0.28794692117826504,
0.6297390019475202, 0.26769104886224415, 0.25840469001015975, 0.3233307788551656, 0.25161700129415276, 0.4573034071191504, 0.5033536625992294, 0.5827033826425385,
0.4666419179635315, 0.585974550122895, 0.4595698215161401, 0.45632759998045813, 0.4789957702325296, 0.4539577593482922});
nd4j::ops::depthwise_conv2d op;
auto results = op.execute({&input, &weights, &biases}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat});
auto* output = results->at(0);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expOutput.isSameShape(output));
ASSERT_TRUE(expOutput.equalsTo(output));
delete results;
}
////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////
TEST_F(ConvolutionTests1, depthwise_conv2d_bp_test1) { TEST_F(ConvolutionTests1, depthwise_conv2d_bp_test1) {
@ -1695,15 +1478,15 @@ TEST_F(ConvolutionTests1, depthwise_conv2d_bp_test1) {
int paddingMode = 1; // 1-SAME, 0-VALID; int paddingMode = 1; // 1-SAME, 0-VALID;
int dataFormat = 1; // 1-NHWC, 0-NCHW int dataFormat = 1; // 1-NHWC, 0-NCHW
auto input = NDArrayFactory::create<double>('c', {bS, iH, iW, iC}); auto input = NDArrayFactory::create<float>('c', {bS, iH, iW, iC});
auto weights = NDArrayFactory::create<double>('c', {kH, kW, iC, mC}); auto weights = NDArrayFactory::create<float>('c', {kH, kW, iC, mC});
auto bias = NDArrayFactory::create<double>('c', {oC}, {1,2,3,4}); auto bias = NDArrayFactory::create<float>('c', {oC}, {1,2,3,4});
auto gradO = NDArrayFactory::create<double>('c', {bS, oH, oW, oC}); auto gradO = NDArrayFactory::create<float>('c', {bS, oH, oW, oC});
auto expGradI = NDArrayFactory::create<double>('c', {bS, iH, iW, iC},{0.07 , 0.19 , 0.348, 0.652, 0.588, 0.956, 0.387, 0.687, 1.326, 2.022, 1.878, 2.67 , 1.071, 1.515, 2.982, 3.966, 3.534, 4.614, 1.606, 1.982, 3.932, 4.748, 4.428, 5.308, NDArray expGradI('c', {bS, iH, iW, iC},{0.07 , 0.19 , 0.348, 0.652, 0.588, 0.956, 0.387, 0.687, 1.326, 2.022, 1.878, 2.67 , 1.071, 1.515, 2.982, 3.966, 3.534, 4.614, 1.606, 1.982, 3.932, 4.748, 4.428, 5.308,
1.126, 1.63 , 3.228, 4.3 , 3.468, 4.604, 3.123, 3.999, 7.95 , 9.798, 8.502, 10.446, 3.807, 4.827, 9.606, 11.742,10.158, 12.39 , 4.198, 4.958, 9.884, 11.468,10.38 , 12.028}); 1.126, 1.63 , 3.228, 4.3 , 3.468, 4.604, 3.123, 3.999, 7.95 , 9.798, 8.502, 10.446, 3.807, 4.827, 9.606, 11.742,10.158, 12.39 , 4.198, 4.958, 9.884, 11.468,10.38 , 12.028}, nd4j::DataType::FLOAT32);
auto expGradW = NDArrayFactory::create<double>('c', {kH, kW, iC, mC},{19.08, 19.44,19.8 , 20.16,12.24, 12.48,12.72, 12.96,22.56, 23.04,23.52, 24. ,14.4 , 14.72,15.04, 15.36,14.76, 15.12,15.48, 15.84, 9.36, 9.6 , 9.84, 10.08}); NDArray expGradW('c', {kH, kW, iC, mC},{19.08, 19.44,19.8 , 20.16,12.24, 12.48,12.72, 12.96,22.56, 23.04,23.52, 24. ,14.4 , 14.72,15.04, 15.36,14.76, 15.12,15.48, 15.84, 9.36, 9.6 , 9.84, 10.08}, nd4j::DataType::FLOAT32);
input = 2.; input = 2.;
weights.linspace(0.1, 0.1); weights.linspace(0.1, 0.1);
@ -1734,14 +1517,14 @@ TEST_F(ConvolutionTests1, depthwise_conv2d_bp_test2) {
int paddingMode = 0; // 1-SAME, 0-VALID; int paddingMode = 0; // 1-SAME, 0-VALID;
int dataFormat = 1; // 1-NHWC, 0-NCHW int dataFormat = 1; // 1-NHWC, 0-NCHW
auto input = NDArrayFactory::create<double>('c', {bS, iH, iW, iC}); auto input = NDArrayFactory::create<float>('c', {bS, iH, iW, iC});
auto weights = NDArrayFactory::create<double>('c', {kH, kW, iC, mC}); auto weights = NDArrayFactory::create<float>('c', {kH, kW, iC, mC});
auto bias = NDArrayFactory::create<double>('c', {oC}, {1,2,3,4}); auto bias = NDArrayFactory::create<float>('c', {oC}, {1,2,3,4});
auto gradO = NDArrayFactory::create<double>('c', {bS, oH, oW, oC}); auto gradO = NDArrayFactory::create<float>('c', {bS, oH, oW, oC});
auto expGradI = NDArrayFactory::create<double>('c', {bS, iH, iW, iC},{0.005, 0.025,0.034, 0.106,0.061, 0.113,0.058, 0.162,0.292, 0.564,0.298, 0.466,0.234, 0.402,0.772, 1.172,0.602, 0.834,0.333, 0.449,0.882, 1.146,0.581, 0.729, NDArray expGradI('c', {bS, iH, iW, iC},{0.005, 0.025,0.034, 0.106,0.061, 0.113,0.058, 0.162,0.292, 0.564,0.298, 0.466,0.234, 0.402,0.772, 1.172,0.602, 0.834,0.333, 0.449,0.882, 1.146,0.581, 0.729,
0.053, 0.137,0.258, 0.458,0.237, 0.353,0.41 , 0.642,1.252, 1.78 ,0.906, 1.202,1.098, 1.394,2.756, 3.412,1.722, 2.082,0.893, 1.073,2.13 , 2.522,1.269, 1.481}); 0.053, 0.137,0.258, 0.458,0.237, 0.353,0.41 , 0.642,1.252, 1.78 ,0.906, 1.202,1.098, 1.394,2.756, 3.412,1.722, 2.082,0.893, 1.073,2.13 , 2.522,1.269, 1.481}, nd4j::DataType::FLOAT32);
auto expGradW = NDArrayFactory::create<double>('c', {kH, kW, iC, mC},{2.4 , 2.56,2.72, 2.88,2.4 , 2.56,2.72, 2.88,2.4 , 2.56,2.72, 2.88,2.4 , 2.56,2.72, 2.88,2.4 , 2.56,2.72, 2.88,2.4 , 2.56,2.72, 2.88}); NDArray expGradW('c', {kH, kW, iC, mC},{2.4 , 2.56,2.72, 2.88,2.4 , 2.56,2.72, 2.88,2.4 , 2.56,2.72, 2.88,2.4 , 2.56,2.72, 2.88,2.4 , 2.56,2.72, 2.88,2.4 , 2.56,2.72, 2.88}, nd4j::DataType::FLOAT32);
input = 2.; input = 2.;
weights.linspace(0.1, 0.1); weights.linspace(0.1, 0.1);
@ -1763,6 +1546,132 @@ TEST_F(ConvolutionTests1, depthwise_conv2d_bp_test2) {
delete results; delete results;
} }
//////////////////////////////////////////////////////////////////////
TEST_F(ConvolutionTests1, depthwise_conv2d_bp_test3) {
auto in = NDArrayFactory::create<float>('c', {4, 8, 64, 64});
auto w = NDArrayFactory::create<float>('c', {2, 2, 8, 2});
auto b = NDArrayFactory::create<float>('c', {1, 16});
auto grad = NDArrayFactory::create<float>('c', {4, 16, 64, 64});
auto gradI = in.like();
auto gradW = w.like();
auto gradB = b.like();
nd4j:ops::depthwise_conv2d_bp op;
auto status = op.execute({&in, &w, &b, &grad}, {&gradI, &gradW, &gradB}, {}, {2, 2, 1, 1, 0, 0, 1, 1, 1, 0}, {});
ASSERT_EQ(Status::OK(), status);
}
//////////////////////////////////////////////////////////////////////
TEST_F(ConvolutionTests1, depthwise_conv2d_bp_test4) {
int bS=1, iH=10,iW=10, iC=8,mC=1, kH=3,kW=3, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
int oC=iC*mC;
int oH=10,oW=10;
int paddingMode = 1; // 1-SAME, 0-VALID;
int dataFormat = 1; // 1-NHWC, 0-NCHW
NDArray input('c', {bS, iH, iW, iC}, nd4j::DataType::FLOAT32);
NDArray weights('c', {kH, kW, iC, mC}, nd4j::DataType::FLOAT32);
NDArray gradO('c', {bS, oH, oW, oC}, nd4j::DataType::FLOAT32);
NDArray bias('c', {oC}, nd4j::DataType::FLOAT32);
input.linspace(-10, 0.1);
weights.linspace(-2, 0.1);
gradO.linspace(10, -0.1);
NDArray expGradI('c', {bS, iH, iW, iC},{10.880001, 13.239998, 15.520001, 17.719997, 19.840000, 21.880001, 23.839998, 25.720001, 31.360004, 34.420002, 37.360001, 40.180004, 42.880005, 45.460003, 47.919994, 50.260002, 31.360001, 33.939999, 36.400002, 38.739998, 40.959999, 43.059998, 45.040001, 46.900005, 31.359997, 33.459999, 35.439999, 37.300003, 39.040001, 40.660000, 42.160000, 43.539997, 31.360001, 32.980000, 34.480000, 35.860001, 37.119999, 38.259998, 39.279999, 40.180000, 31.360001, 32.499996, 33.520000, 34.419998, 35.200001, 35.860001, 36.400002, 36.820000, 31.360001, 32.019997, 32.560001, 32.979996, 33.280003, 33.459999, 33.520000, 33.459999, 31.360001, 31.540001, 31.599998, 31.539999, 31.360001, 31.059999, 30.639999, 30.100000, 31.360001, 31.060001, 30.639999, 30.099998, 29.440002, 28.660000, 27.759998, 26.740000, 18.559999, 18.040001, 17.440001, 16.760000, 16.000000, 15.160000, 14.240001, 13.240000, 85.439995, 85.860001, 86.159996, 86.339996, 86.400002, 86.340012, 86.159996, 85.860008, 132.000000, 131.910004, 131.639999, 131.190002, 130.559998, 129.750000, 128.760010, 127.589996, 123.360001, 122.550003, 121.559998, 120.389999, 119.040009, 117.510002, 115.799988, 113.910004, 114.720001, 113.189995, 111.480003, 109.590004, 107.520004, 105.270004, 102.839996, 100.230011, 106.079994, 103.830002, 101.400009, 98.790009, 96.000008,
93.030006, 89.879990, 86.549988, 97.439995, 94.469994, 91.319992, 87.990005, 84.479996, 80.789993, 76.919998, 72.870003, 88.800003, 85.110001, 81.239998, 77.190002, 72.960007, 68.550003, 63.959999, 59.190002, 80.160004, 75.750000, 71.160004, 66.389999, 61.440002, 56.309994, 51.000000, 45.510002, 71.519997, 66.389999, 61.079998, 55.590000, 49.919998, 44.070000, 38.040001, 31.830002, 31.680000, 27.780003, 23.760000, 19.619999, 15.360001, 10.980000, 6.480000, 1.859999, 47.040001, 42.660004, 38.160000, 33.540001, 28.799999, 23.939999, 18.960001, 13.860001, 45.599998, 38.310001, 30.840000, 23.190002, 15.360001, 7.349998, -0.840002, -9.210003, 36.959999, 28.950003, 20.759998, 12.390001, 3.839998, -4.889999, -13.799999, -22.890003, 28.320002, 19.589998, 10.680000, 1.590002, -7.680002, -17.129999, -26.759998, -36.570007, 19.680002, 10.230003, 0.599998, -9.210001, -19.199999, -29.370003, -39.720001, -50.250008, 11.039999, 0.869999, -9.480000, -20.010002, -30.719994, -41.610001, -52.679996, -63.930008, 2.400005, -8.489998, -19.560005, -30.809998, -42.239998, -53.849991, -65.639992, -77.610001, -6.239998, -17.849998, -29.639988, -41.609985, -53.760002, -66.090004, -78.599991, -91.290009, -14.879990, -27.209995, -39.720009, -52.410007, -65.279999, -78.330002, -91.559998, -104.969986, -45.119995, -53.820000, -62.639999, -71.580002, -80.640007, -89.819992, -99.119995, -108.540009, 8.639999, -0.540001, -9.839996, -19.259998, -28.799995, -38.459999, -48.240002, -58.140003, -40.799999, -55.289997, -69.960007, -84.810013, -99.840004, -115.050011, -130.440018, -146.010010, -49.439991, -64.650009, -80.040009, -95.610016, -111.360008, -127.290001, -143.399994, -159.690018, -58.080009, -74.009987, -90.119995, -106.409988, -122.880005, -139.530014, -156.360001, -173.369995, -66.720001, -83.369995, -100.199997,
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-788.250000, -843.239990, -898.410034, -524.639954, -579.449951, -634.440002, -689.609985, -744.960022, -800.489990, -856.200012, -912.090027, -533.280029, -588.810059, -644.520081, -700.409973, -756.480042, -812.730103, -869.159912, -925.769958, -505.920013, -543.420044, -581.040039, -618.780029, -656.640015, -694.620056, -732.719971, -770.940002, -447.359985, -471.559998, -495.840027, -520.200012, -544.640015, -569.159973, -593.760010, -618.440002, -815.359985, -852.140015, -889.040039, -926.059937, -963.200073, -1000.460022, -1037.839966, -1075.339966, -826.879944, -864.139954, -901.519958, -939.019958, -976.640076, -1014.379944, -1052.239990, -1090.219971, -838.400024, -876.140015, -913.999939, -951.979919, -990.080017, -1028.299927, -1066.640015, -1105.099976, -849.919983, -888.140015, -926.479980, -964.939941, -1003.520081, -1042.219971, -1081.040039, -1119.979980, -861.440063, -900.140015, -938.960022,-977.899963, -1016.960022, -1056.140015, -1095.440063, -1134.859985, -872.960022, -912.140015, -951.439941, -990.859985, -1030.400024, -1070.060059, -1109.839844, -1149.739990, -884.479980, -924.140015, -963.919922, -1003.819946, -1043.839966, -1083.979980, -1124.239990, -1164.619995, -896.000000, -936.140015, -976.399963, -1016.780029, -1057.280029, -1097.899902, -1138.640015, -1179.500122, -705.919983, -733.000000, -760.159912, -787.400024, -814.719971, -842.119995, -869.599976, -897.160034}, nd4j::DataType::FLOAT32);
NDArray expGradW('c', {kH, kW, iC, mC},{-104306.421875, -104786.734375, -105268.687500, -105752.250000, -106237.421875, -106724.242188, -107212.671875,
-107702.734375, -116289.593750, -116823.296875, -117358.781250, -117896.109375, -118435.210938, -118976.109375, -119518.796875, -120063.296875, -104824.789062,
-105305.117188, -105787.070312, -106270.640625, -106755.843750, -107242.640625, -107731.078125, -108221.117188, -126744.000000, -127277.710938, -127813.187500,
-128350.484375, -128889.601562, -129430.515625, -129973.210938, -130517.703125, -140944.000000, -141536.984375, -142131.984375, -142729.000000, -143328.000000,
-143929.015625, -144532.000000, -145137.000000, -126744.000000, -127277.710938, -127813.187500, -128350.484375, -128889.601562, -129430.515625, -129973.210938, -130517.703125, -104824.789062, -105305.117188, -105787.070312, -106270.640625, -106755.843750, -107242.640625, -107731.078125, -108221.117188, -116289.593750, -116823.296875, -117358.781250, -117896.109375, -118435.210938, -118976.109375, -119518.796875, -120063.296875, -104306.421875, -104786.734375, -105268.687500, -105752.250000, -106237.421875, -106724.242188, -107212.671875, -107702.734375}, nd4j::DataType::FLOAT32);
NDArray expGradB('c', {oC}, {-2960., -2970., -2980., -2990., -3000., -3010., -3020., -3030.}, nd4j::DataType::FLOAT32);
nd4j::ops::depthwise_conv2d_bp op;
ResultSet* results = op.execute({&input, &weights, &bias, &gradO}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat});
NDArray* gradI = results->at(0);
NDArray* gradW = results->at(1);
NDArray* gradB = results->at(2);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expGradI.isSameShape(gradI));
ASSERT_TRUE(expGradI.equalsTo(gradI));
ASSERT_TRUE(expGradW.isSameShape(gradW));
ASSERT_TRUE(expGradW.equalsTo(gradW));
ASSERT_TRUE(expGradB.isSameShape(gradB));
ASSERT_TRUE(expGradB.equalsTo(gradB));
delete results;
}
//////////////////////////////////////////////////////////////////////
TEST_F(ConvolutionTests1, depthwise_conv2d_bp_test5) {
int bS=1, iH=10,iW=10, iC=8,mC=1, kH=3,kW=3, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
int oC=iC*mC;
int oH=10,oW=10;
int paddingMode = 1; // 1-SAME, 0-VALID;
int dataFormat = 0; // 1-NHWC, 0-NCHW
NDArray input('c', {bS, iC, iH, iW}, nd4j::DataType::FLOAT32);
NDArray weights('c', {kH, kW, iC, mC}, nd4j::DataType::FLOAT32);
NDArray gradO('c', {bS, oC, oH, oW}, nd4j::DataType::FLOAT32);
NDArray bias('c', {oC}, nd4j::DataType::FLOAT32);
input.linspace(-10, 0.1);
weights.linspace(-2, 0.1);
gradO.linspace(10, -0.1);
NDArray expGradI('c', {bS, iC, iH, iW}, {-12.639999, 3.920004, 3.920000, 3.920000, 3.920002, 3.920000, 3.920000, 3.919998, 3.919998, 16.319998, 52.680004, 111.000015, 109.919991, 108.840004, 107.760002, 106.680008, 105.600006, 104.519997, 103.440018, 87.960007, 47.880001, 100.200005, 99.119995, 98.040001, 96.959999, 95.879990, 94.799995, 93.720001, 92.639999, 78.360001, 43.079998, 89.399994, 88.320007, 87.240005, 86.159996, 85.079994, 84.000000, 82.919998, 81.840004, 68.759995, 38.279999, 78.600006, 77.519997, 76.440010, 75.360001, 74.279999, 73.200005, 72.120003, 71.040001, 59.160004, 33.480000, 67.799995, 66.720009, 65.639999, 64.559998, 63.480000, 62.399994, 61.320007, 60.240002, 49.559998, 28.680004, 57.000004, 55.919998, 54.839993, 53.759998, 52.680000, 51.600002, 50.519997, 49.440002, 39.959999, 23.880001, 46.200001, 45.120003, 44.039997, 42.959999, 41.880001, 40.799999, 39.719994, 38.639999, 30.360001, 19.079998, 35.400002, 34.320000, 33.239998, 32.159996, 31.080000, 29.999998, 28.919998, 27.840000, 20.759998, 14.079999, 24.080000, 22.639997, 21.200001, 19.759998, 18.320002, 16.880001, 15.440001, 14.000000, 9.759999, 3.140000, 3.560000, 3.500000, 3.440000, 3.380000, 3.320000, 3.260000, 3.200000, 3.140000, -0.220000, 4.050000, 2.010000, 0.840000, -0.330000, -1.499999, -2.670000, -3.840000, -5.010000, -6.179998, -9.150000, -1.350000, -9.690001, -10.859999, -12.029998, -13.200001, -14.370001, -15.539999, -16.710001, -17.879999, -19.349998, -6.750000, -21.389997, -22.560003, -23.730003, -24.900002, -26.069998, -27.239998, -28.410007, -29.580002, -29.550003, -12.150001, -33.089996, -34.260002, -35.430000, -36.600002, -37.770000, -38.939995, -40.110001, -41.280003, -39.749996, -17.550003, -44.790005, -45.959991, -47.129993, -48.300003, -49.470001, -50.640003, -51.809990, -52.979996, -49.950001, -22.949999, -56.490005, -57.660000, -58.829998, -60.000000, -61.170002, -62.340004, -63.510002, -64.680000,
-60.149994, -28.349998, -68.189987, -69.360001, -70.529999, -71.700005, -72.870010, -74.039993, -75.209999, -76.379990, -70.349998, -33.749996, -79.889999, -81.059990, -82.229988, -83.399994, -84.570007, -85.740005, -86.910004, -88.079994, -80.549995, -69.340004, -125.080002, -126.580002, -128.080002, -129.580002, -131.080002, -132.580002, -134.080002, -135.580002, -105.979996, 10.919998, -8.799997, -8.919998, -9.040003, -9.160004, -9.279999, -9.400002, -9.520002, -9.640003, -24.760000, -56.580009, -124.980003, -126.240005, -127.499992, -128.759995, -130.020020, -131.279999, -132.540009, -133.800003, -118.260002, -62.580009, -137.580002, -138.840012, -140.099991, -141.360001, -142.620010, -143.879974, -145.139999, -146.399994, -129.060013, -68.580002, -150.179993, -151.439987, -152.699997, -153.959991, -155.219986, -156.480011, -157.740005, -159.000000, -139.860001, -74.579994, -162.779999, -164.040024, -165.300003, -166.560028, -167.819977, -169.080002, -170.339996, -171.599991, -150.660004, -80.580002, -175.379990, -176.639999, -177.899994, -179.160019, -180.419998, -181.679993, -182.940002, -184.199997, -161.459991, -86.580002, -187.979996, -189.240005, -190.499985, -191.759995, -193.020020, -194.279999, -195.540024, -196.800018, -172.260010, -92.580002, -200.579987, -201.839981, -203.100006, -204.359970, -205.620010, -206.880005, -208.139999, -209.399994, -183.060013, -98.580002, -213.180023, -214.440002, -215.700012, -216.959991, -218.220001, -219.480011, -220.739975, -222.000000, -193.860001, -160.760010, -286.239990, -287.799988, -289.360016, -290.920013, -292.480011, -294.040009, -295.599976, -297.160004, -229.719986, 10.700003, -33.160004, -33.339996, -33.519993, -33.700001,
-33.879997, -34.059994, -34.239994, -34.419994, -57.299995, -129.209991, -269.969971, -271.319977, -272.670044, -274.019989, -275.369995, -276.720001, -278.070007, -279.420013, -239.369980, -135.809998, -283.470001, -284.820007, -286.169983, -287.520020, -288.869995, -290.220001, -291.570038, -292.919983, -250.770004, -142.410004, -296.969971, -298.320007, -299.669983, -301.020020, -302.369995, -303.719971, -305.070007, -306.419983, -262.169983, -149.009995, -310.470001, -311.820007, -313.170013, -314.519989, -315.869995, -317.220001, -318.570007, -319.919983, -273.570007, -155.610016, -323.969971, -325.320038, -326.669983, -328.020020, -329.369965, -330.719971, -332.070007, -333.419983, -284.970001, -162.209991, -337.469971, -338.820007, -340.169983, -341.519958, -342.869995, -344.220001, -345.570007, -346.920013, -296.369995, -168.809998, -350.970001, -352.320007, -353.669983, -355.019989, -356.369995, -357.719971, -359.070038, -360.419983, -307.769989, -175.410004, -364.469971, -365.820007, -367.169983, -368.520020, -369.869995, -371.219971, -372.570007, -373.919983, -319.169983, -260.179993, -459.399994, -461.019958, -462.639984, -464.260010, -465.880005, -467.500000, -469.119995, -470.739990, -361.459991, 2.480003, -69.520004, -69.760025, -70.000000, -70.239990, -70.479996, -70.720001, -70.960007, -71.200005, -97.839996, -213.840012, -432.960022, -434.400055, -435.840027, -437.279999, -438.720001, -440.160065, -441.599976, -443.040039, -372.480011, -221.040009, -447.360016, -448.800018, -450.239990, -451.679993, -453.119995, -454.559967, -456.000061, -457.440033, -384.480011, -228.239990, -461.759979, -463.200012, -464.639984, -466.079956, -467.520081, -468.960052, -470.399963, -471.839996, -396.479980, -235.440002, -476.159912,
-477.600006, -479.040039, -480.479980, -481.919952, -483.360046, -484.800079, -486.239990, -408.480042, -242.639999, -490.559967, -491.999969, -493.440063, -494.880035, -496.319946, -497.759979, -499.200012, -500.639984, -420.480011, -249.840012, -504.960052, -506.399963, -507.839996, -509.280029, -510.720001, -512.159973, -513.599976, -515.040039, -432.480011, -257.040009, -519.360046, -520.800049, -522.239990, -523.680054, -525.120056, -526.559998, -527.999939, -529.440002, -444.480011, -264.239990, -533.760010, -535.200012, -536.640015, -538.079956, -539.520020, -540.960022, -542.399963, -543.839966, -456.479980, -367.599976, -644.559998, -646.239929, -647.920044, -649.599976, -651.280029, -652.960022, -654.640076, -656.320007, -501.200043, -13.740002, -117.880005, -118.179993, -118.479996, -118.780014, -119.080002, -119.379990, -119.680008, -119.979996, -146.379990, -310.470001, -613.950012, -615.479980, -617.010071, -618.539978, -620.069946, -621.599976, -623.130005, -624.660034, -517.589966, -318.269958, -629.250000, -630.779968, -632.309937, -633.840027, -635.369995, -636.899902, -638.429993, -639.959961, -530.190063, -326.070038, -644.550049, -646.079956, -647.609985, -649.140015, -650.669922, -652.200012, -653.729980, -655.260010, -542.789978, -333.870026, -659.849976, -661.380005, -662.910034, -664.439941, -665.970093, -667.500000, -669.029968, -670.559937, -555.390015, -341.669983, -675.149902, -676.679993, -678.209961, -679.740051, -681.270020, -682.800049, -684.329956, -685.859985, -567.989990, -349.470001, -690.450012, -691.979980, -693.510010, -695.039978, -696.569946, -698.099976, -699.630005, -701.160034, -580.589966, -357.269958, -705.750000, -707.279968, -708.809937, -710.340027, -711.869995, -713.399902, -714.929993, -716.459961, -593.190002, -365.070038, -721.050049, -722.579956, -724.109985, -725.640015, -727.169922, -728.700012,
-730.229980, -731.760010, -605.789978, -483.019958, -841.719971, -843.460022, -845.200073, -846.939941, -848.680054, -850.419983, -852.159973, -853.899963, -648.940002, -37.960014, -178.240021, -178.599976, -178.959991, -179.320007, -179.679993, -180.039978, -180.399994, -180.759964, -202.919983, -419.099915, -812.939941, -814.559937, -816.179993, -817.800049, -819.419922, -821.040039, -822.660034, -824.279968, -674.699951, -427.500031, -829.140015, -830.759949, -832.380005, -833.999939, -835.619995, -837.240051, -838.859924, -840.479980, -687.899963, -435.899994, -845.339966, -846.959961, -848.579956, -850.200012, -851.819885, -853.439941, -855.059937, -856.679993, -701.100037, -444.299927, -861.540039, -863.160034, -864.779968, -866.399963, -868.020020, -869.640015, -871.259949, -872.880005, -714.299988, -452.700012, -877.740051, -879.359924, -880.979980, -882.599915, -884.219971, -885.839966, -887.459961, -889.079956, -727.500000, -461.099915, -893.939941, -895.559937, -897.179993, -898.800049, -900.419922, -902.040039, -903.660034, -905.279968, -740.700012, -469.499969, -910.140015, -911.759949, -913.380005, -914.999939, -916.620056, -918.239990, -919.860046, -921.479919, -753.899963, -477.899902, -926.339905, -927.959961, -929.579956, -931.200012, -932.819946, -934.439880, -936.059937, -937.679932, -767.100037, -606.439941, -1050.880005, -1052.680054, -1054.479980, -1056.280029, -1058.079956, -1059.880005, -1061.679932, -1063.479980, -804.679993, -70.180008, -250.600006, -251.019958, -251.440033, -251.860001, -252.280029, -252.700043, -253.120026, -253.540039, -267.459991, -539.730042, -1029.929932, -1031.640137, -1033.350098, -1035.060059, -1036.770020, -1038.479980, -1040.190063, -1041.900024, -843.809998, -548.729980, -1047.030029, -1048.740112, -1050.449829, -1052.160034, -1053.870117, -1055.580078, -1057.289917, -1059.000122, -857.609985, -557.729980,
-1064.130005, -1065.840088, -1067.550049, -1069.260010, -1070.969849, -1072.679932, -1074.390137, -1076.100098, -871.410034, -566.729980, -1081.229980, -1082.940063, -1084.650024, -1086.359985, -1088.069946, -1089.780029, -1091.489990, -1093.199951, -885.210022, -575.729980, -1098.329956, -1100.040039, -1101.750122, -1103.460205, -1105.170166, -1106.879883, -1108.589966, -1110.300049, -899.010071, -584.730042, -1115.429932, -1117.140137, -1118.850098, -1120.560059, -1122.270020, -1123.979980, -1125.689941, -1127.400024, -912.810059, -593.730042, -1132.530029, -1134.240234, -1135.949951, -1137.659912, -1139.370117, -1141.079956, -1142.790039, -1144.500122, -926.610046, -602.730042, -1149.629883, -1151.339966, -1153.050049, -1154.760132, -1156.469971, -1158.179810, -1159.890137, -1161.600098, -940.410034, -737.859985, -1272.040039, -1273.899902, -1275.760010, -1277.619995, -1279.479980, -1281.340088, -1283.200195, -1285.060059, -968.420044}, nd4j::DataType::FLOAT32);
NDArray expGradW('c', {kH, kW, iC, mC}, {-2586.600586, -2505.600098, -18624.595703, -50943.605469, -99462.601562, -164181.609375, -245100.609375, -342219.625000,
-2880.149902, -2790.150146, -20700.152344, -56610.148438, -110520.156250, -182430.156250, -272340.156250, -380250.125000, -2594.701416, -2513.699951,
-18632.699219, -50951.695312, -99470.695312, -164189.703125, -245108.687500, -342227.750000, -3043.501465, -2953.500244, -20863.500000, -56773.492188,
-110683.515625, -182593.515625, -272503.531250, -380413.562500, -3383.499756, -3283.500000, -23183.501953, -63083.500000, -122983.500000, -202883.515625,
-302783.531250, -422683.468750, -3043.501465, -2953.500244, -20863.500000, -56773.492188, -110683.515625, -182593.515625, -272503.531250, -380413.562500,
-2594.701416, -2513.699951, -18632.699219, -50951.695312, -99470.695312, -164189.703125, -245108.687500, -342227.750000, -2880.149902, -2790.150146, -20700.152344, -56610.148438, -110520.156250, -182430.156250, -272340.156250, -380250.125000, -2586.600586, -2505.600098, -18624.595703, -50943.605469, -99462.601562, -164181.609375, -245100.609375, -342219.625000}, nd4j::DataType::FLOAT32);
NDArray expGradB('c', {oC}, {505., -495., -1495., -2495., -3495., -4494.999512, -5495., -6495.}, nd4j::DataType::FLOAT32);
nd4j::ops::depthwise_conv2d_bp op;
ResultSet* results = op.execute({&input, &weights, &bias, &gradO}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat});
NDArray* gradI = results->at(0);
NDArray* gradW = results->at(1);
NDArray* gradB = results->at(2);
ASSERT_EQ(Status::OK(), results->status());
ASSERT_TRUE(expGradI.isSameShape(gradI));
ASSERT_TRUE(expGradI.equalsTo(gradI));
ASSERT_TRUE(expGradW.isSameShape(gradW));
ASSERT_TRUE(expGradW.equalsTo(gradW));
ASSERT_TRUE(expGradB.isSameShape(gradB));
ASSERT_TRUE(expGradB.equalsTo(gradB));
delete results;
}
////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////
TYPED_TEST(TypedConvolutionTests1, conv3d_test1) { TYPED_TEST(TypedConvolutionTests1, conv3d_test1) {

293
libnd4j/tests_cpu/layers_tests/ConvolutionTests2.cpp
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15
libnd4j/tests_cpu/layers_tests/DeclarableOpsTests15.cpp
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@ -591,21 +591,6 @@ TEST_F(DeclarableOpsTests15, Test_BitCast_7) {
delete result; delete result;
} }
TEST_F(DeclarableOpsTests15, Test_depthwise_bp_1) {
auto in = NDArrayFactory::create<float>('c', {4, 8, 64, 64});
auto w = NDArrayFactory::create<float>('c', {2, 2, 8, 2});
auto b = NDArrayFactory::create<float>('c', {1, 16});
auto grad = NDArrayFactory::create<float>('c', {4, 16, 64, 64});
auto gradI = in.like();
auto gradW = w.like();
auto gradB = b.like();
nd4j:ops::depthwise_conv2d_bp op;
auto status = op.execute({&in, &w, &b, &grad}, {&gradI, &gradW, &gradB}, {}, {2, 2, 1, 1, 0, 0, 1, 1, 1, 0}, {});
ASSERT_EQ(Status::OK(), status);
}
TEST_F(DeclarableOpsTests15, test_matmul_bp_1) { TEST_F(DeclarableOpsTests15, test_matmul_bp_1) {
auto a = NDArrayFactory::create<double>('c', {1, 3}); auto a = NDArrayFactory::create<double>('c', {1, 3});
auto b = NDArrayFactory::create<double>('c', {1, 4}); auto b = NDArrayFactory::create<double>('c', {1, 4});