/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author Yurii Shyrma, created on 05.02.2018
//
#include <op_boilerplate.h>
#if NOT_EXCLUDED(OP_conv3dnew)
#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/helpers/convolutions.h>
#include <MmulHelper.h>
namespace nd4j {
namespace ops {
#ifdef HAVE_MKLDNN
using namespace mkldnn;
#endif
CUSTOM_OP_IMPL(conv3dnew, 2, 1, false, 0, 13) {
auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
auto weights = INPUT_VARIABLE(1); // [kD, kH, kW, iC, oC] always
auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
auto output = OUTPUT_VARIABLE(0); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW)
REQUIRE_TRUE(input->rankOf() == 5, 0, "CUSTOM CONV3D OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf());
REQUIRE_TRUE(weights->rankOf() == 5, 0, "CUSTOM CONV3D OP: rank of weights array must be equal to 5, but got %i instead !", weights->rankOf());
int kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) depth
int kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1));// filter(kernel) height
int kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<int>(weights->sizeAt(2));// filter(kernel) width
int sD = INT_ARG(3); // strides depth
int sH = INT_ARG(4); // strides height
int sW = INT_ARG(5); // strides width
int pD = INT_ARG(6); // paddings depth
int pH = INT_ARG(7); // paddings height
int pW = INT_ARG(8); // paddings width
int dD = INT_ARG(9); // dilations depth
int dH = INT_ARG(10); // dilations height
int dW = INT_ARG(11); // dilations width
int isSameMode = INT_ARG(12); // 0-SAME, 1-VALID
int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD);
std::string expectedWeightsShape = ShapeUtils::shapeAsString({kD, kH, kW, iC, oC});
REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM CONV3D OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str());
if (bias)
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM CONV3D 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
ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW);
#ifdef HAVE_MKLDNN
if (block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, weights, bias, output})) {
std::vector<nd4j::MKLDNNStream>& streams = block.getMKLDNNStreams();
if (streams.empty()) {
streams.push_back(MKLDNNStream("conv3dnew"));
}
if (streams[0].checkAndReset({input, weights, bias}, {output}, {}, {kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, isSameMode, isNCDHW})) {
mkldnn_memory_desc_t empty;
mkldnn::memory::desc conv_src_md(empty), conv_weights_md(empty), conv_bias_md(empty), conv_dst_md(empty);
mkldnn::memory::desc user_src_md(empty), user_weights_md(empty), user_bias_md(empty), user_dst_md(empty);
mkldnn::memory::dims conv_strides, conv_padding, conv_padding_r;
ConvolutionUtils::getMKLDNNMemoryDescConv3d(kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, isSameMode, isNCDHW,
bS, iC, iD, iH, iW, oC, oD, oH, oW, input, nullptr, weights, nullptr, bias, output,
&conv_src_md, nullptr, &conv_weights_md, nullptr, &conv_bias_md, &conv_dst_md,
&user_src_md, nullptr, &user_weights_md, nullptr, &user_bias_md, &user_dst_md,
conv_strides, conv_padding, conv_padding_r);
auto conv_desc = bias != nullptr
? convolution_forward::desc(prop_kind::forward,
convolution_direct, conv_src_md, conv_weights_md, conv_bias_md,
conv_dst_md, conv_strides, conv_padding, conv_padding_r, padding_kind::zero)
: convolution_forward::desc(prop_kind::forward,
convolution_direct, conv_src_md, conv_weights_md,
conv_dst_md, conv_strides, conv_padding, conv_padding_r, padding_kind::zero);
auto engine = streams[0].getEngine();
auto conv_prim_desc = convolution_forward::primitive_desc(conv_desc, engine);
auto user_src_memory = mkldnn::memory({user_src_md, engine}, const_cast<NDArray*>(input)->buffer());
auto user_weights_memory = mkldnn::memory({user_weights_md, engine}, const_cast<NDArray*>(weights)->buffer());
auto user_dst_memory = mkldnn::memory({user_dst_md, engine}, output->buffer());
auto conv_src_memory = user_src_memory;
streams[0].addMemory(user_src_memory);
if (mkldnn::memory::primitive_desc(conv_prim_desc.src_primitive_desc())
!= user_src_memory.get_primitive_desc()) {
conv_src_memory = mkldnn::memory(conv_prim_desc.src_primitive_desc());
streams[0].addMemory(conv_src_memory);
streams[0].addOperation(reorder(user_src_memory, conv_src_memory));
}
auto conv_weights_memory = user_weights_memory;
streams[0].addMemory(user_weights_memory);
if (mkldnn::memory::primitive_desc(conv_prim_desc.weights_primitive_desc())
!= user_weights_memory.get_primitive_desc()) {
conv_weights_memory = mkldnn::memory(conv_prim_desc.weights_primitive_desc());
streams[0].addMemory(conv_weights_memory);
streams[0].addOperation(reorder(user_weights_memory, conv_weights_memory));
}
auto conv_dst_memory = user_dst_memory;
streams[0].addMemory(user_dst_memory);
if (mkldnn::memory::primitive_desc(conv_prim_desc.dst_primitive_desc())
!= user_dst_memory.get_primitive_desc()) {
conv_dst_memory = mkldnn::memory(conv_prim_desc.dst_primitive_desc());
streams[0].addMemory(conv_dst_memory);
}
if (bias != nullptr) {
auto conv_bias_memory = mkldnn::memory(conv_prim_desc.bias_primitive_desc(), bias->buffer());
streams[0].addMemory(conv_bias_memory);
streams[0].addOperation(convolution_forward(conv_prim_desc, conv_src_memory, conv_weights_memory, conv_bias_memory, conv_dst_memory));
} else {
streams[0].addOperation(convolution_forward(conv_prim_desc, conv_src_memory, conv_weights_memory, conv_dst_memory));
}
if (mkldnn::memory::primitive_desc(conv_prim_desc.dst_primitive_desc())
!= user_dst_memory.get_primitive_desc()) {
streams[0].addOperation(reorder(conv_dst_memory, user_dst_memory));
}
}
streams[0].submitAndWait();
return Status::OK();
}
#endif
nd4j_debug("MKL-DNN is not used for conv3dnew!\n", 0);
std::vector<int> permutForOutput;
if(!isNCDHW)
input = input->permute({0,4,1,2,3}); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW]
else
permutForOutput = {0,2,3,4,1}; // [bS, oC, oD, oH, oW] -> [bS, oD, oH, oW, oC]
NDArray columns(input->ordering(), {bS, iC, kD, kH, kW, oD, oH, oW}, input->dataType(), block.launchContext());
ConvolutionUtils::vol2col(*block.launchContext(), *input, columns, sD, sH, sW, pD, pH, pW, dD, dH, dW); // [bS, iC, iD, iH, iW] is convoluted to [bS, iC, kD, kH, kW, oD, oH, oW]
// [bS, iC, kD, kH, kW, oD, oH, oW] x [kD, kH, kW, iC, oC] = [bS, oD, oH, oW, oC]
MmulHelper::tensorDot(&columns, weights, output, {1,2,3,4}, {3,0,1,2}, permutForOutput);
if(bias)
output->applyBroadcast(broadcast::Add, {indIOioC}, bias);
if(!isNCDHW)
delete input;
return Status::OK();
}
DECLARE_TYPES(conv3dnew) {
getOpDescriptor()
->setAllowedInputTypes(0, nd4j::DataType::ANY)
->setAllowedInputTypes(1, {ALL_FLOATS})
->setAllowedInputTypes(2, {ALL_FLOATS})
->setAllowedOutputTypes({ALL_FLOATS});
}
DECLARE_SHAPE_FN(conv3dnew) {
auto inputShapeInfo = inputShape->at(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
auto weightsShapeInfo = inputShape->at(1); // [kD, kH, kW, iC, oC] always
auto biasShapeInfo = block.width() > 2 ? inputShape->at(2) : nullptr; // [oC]
int kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 0));// filter(kernel) depth
int kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 1));// filter(kernel) height
int kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 2));// filter(kernel) width
int sD = INT_ARG(3); // strides depth
int sH = INT_ARG(4); // strides height
int sW = INT_ARG(5); // strides width
int pD = INT_ARG(6); // paddings depth
int pH = INT_ARG(7); // paddings height
int pW = INT_ARG(8); // paddings width
int dD = INT_ARG(9); // dilations depth
int dH = INT_ARG(10); // dilations height
int dW = INT_ARG(11); // dilations width
int isSameMode = INT_ARG(12); // 1-SAME, 0-VALID;
int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
const int rank = 5;
REQUIRE_TRUE(inputShapeInfo[0] == rank, 0, "CUSTOM CONV3D OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo);
REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0, "CUSTOM CONV3D OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo);
int indIOioC, indIiD, indWoC(4);
if(!isNCDHW) {
indIOioC = 4; indIiD = 1;
}
else {
indIOioC = 1; indIiD = 2;
}
int bS = inputShapeInfo[1]; // batch size
int iD = inputShapeInfo[indIiD+1]; // input depth
int iH = inputShapeInfo[indIiD+2]; // input height
int iW = inputShapeInfo[indIiD+3]; // input width
int iC = inputShapeInfo[indIOioC+1]; // input channels
int oC = weightsShapeInfo[indWoC+1]; // output channels
std::string expectedWeightsShape = ShapeUtils::shapeAsString({kD, kH, kW, iC, oC});
REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weightsShapeInfo), 0, "CUSTOM CONV3D OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
if (biasShapeInfo)
REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM CONV3D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo));
int oD, oH, oW; // output depth, height, width
ConvolutionUtils::calcOutSizePool3D(oD, oH, oW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, isSameMode);
Nd4jLong* outputShapeInfo = nullptr;
ALLOCATE(outputShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inputShapeInfo), Nd4jLong);
outputShapeInfo[0] = rank;
outputShapeInfo[1] = bS;
if (isNCDHW) {
outputShapeInfo[2] = oC;
outputShapeInfo[3] = oD;
outputShapeInfo[4] = oH;
outputShapeInfo[5] = oW;
} else {
outputShapeInfo[2] = oD;
outputShapeInfo[3] = oH;
outputShapeInfo[4] = oW;
outputShapeInfo[5] = oC;
}
ShapeUtils::updateStridesAndType(outputShapeInfo, weightsShapeInfo, shape::order(inputShapeInfo));
return SHAPELIST(CONSTANT(outputShapeInfo));
}
//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(conv3dnew_bp, 3, 2, false, 0, 13) {
auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
auto weights = INPUT_VARIABLE(1); // [kD, kH, kW, iC, oC] always
auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC]
auto gradO = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next
auto gradI = OUTPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW), epsilon
auto gradW = OUTPUT_VARIABLE(1); // [kD, kH, kW, iC, oC] always
auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
REQUIRE_TRUE(input->rankOf() == 5, 0, "CUSTOM CONV3D_BP OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf());
REQUIRE_TRUE(weights->rankOf() == 5, 0, "CUSTOM CONV3D_BP OP: rank of weights array must be equal to 5, but got %i instead !", weights->rankOf());
REQUIRE_TRUE(gradO->rankOf() == 5, 0, "CUSTOM CONV3D_BP OP: rank of output gradients (next epsilon) array must be equal to 5, but got %i instead !", gradO->rankOf());
int kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) depth
int kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1));// filter(kernel) height
int kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<int>(weights->sizeAt(2));// filter(kernel) width
int sD = INT_ARG(3); // strides depth
int sH = INT_ARG(4); // strides height
int sW = INT_ARG(5); // strides width
int pD = INT_ARG(6); // paddings depth
int pH = INT_ARG(7); // paddings height
int pW = INT_ARG(8); // paddings width
int dD = INT_ARG(9); // dilations depth
int dH = INT_ARG(10); // dilations height
int dW = INT_ARG(11); // dilations width
int isSameMode = INT_ARG(12); // 1-SAME, 0-VALID
int isNDHWC = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
ConvolutionUtils::getSizesAndIndexesConv3d(isNDHWC, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD);
int trueoD, trueoH, trueoW; // true output depth/height/width
ConvolutionUtils::calcOutSizePool3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, isSameMode);
std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoD,trueoH,trueoW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2}));
std::string expectedWeightsShape = ShapeUtils::shapeAsString({kD, kH, kW, iC, oC});
REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0, "CUSTOM CONV3D_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(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM CONV3D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str());
if(bias)
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM CONV3D_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
ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW);
#ifdef HAVE_MKLDNN
if (block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, weights, bias, gradO, gradI, gradW, gradB})) {
std::vector<nd4j::MKLDNNStream>& streams = block.getMKLDNNStreams();
if (streams.empty()) {
streams.push_back(MKLDNNStream("conv3dnew_bp_weights"));
streams.push_back(MKLDNNStream("conv3dnew_bp_data"));
}
bool resetW = streams[0].checkAndReset({input, weights, bias, gradO}, {gradI, gradW, gradB}, {}, {kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, isSameMode, isNDHWC});
bool resetI = streams[1].checkAndReset({input, weights, bias, gradO}, {gradI, gradW, gradB}, {}, {kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, isSameMode, isNDHWC});
if (resetW || resetI) {
mkldnn_memory_desc_t empty;
mkldnn::memory::desc conv_src_md(empty), conv_diff_src_md(empty), conv_weights_md(empty),
conv_diff_weights_md(empty), conv_bias_md(empty), conv_dst_md(empty);
mkldnn::memory::desc user_src_md(empty), user_diff_src_md(empty), user_weights_md(empty),
user_diff_weights_md(empty), user_bias_md(empty), user_dst_md(empty);
mkldnn::memory::dims conv_strides, conv_padding, conv_padding_r;
ConvolutionUtils::getMKLDNNMemoryDescConv3d(kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, isSameMode, isNDHWC,
bS, iC, iD, iH, iW, oC, oD, oH, oW, input, gradI, weights, gradW, gradB, gradO,
&conv_src_md, &conv_diff_src_md, &conv_weights_md, &conv_diff_weights_md, &conv_bias_md, &conv_dst_md,
&user_src_md, &user_diff_src_md, &user_weights_md, &user_diff_weights_md, &user_bias_md, &user_dst_md,
conv_strides, conv_padding, conv_padding_r);
auto conv_desc = gradB != nullptr
? convolution_forward::desc(prop_kind::forward,
convolution_direct, conv_src_md, conv_weights_md, conv_bias_md,
conv_dst_md, conv_strides, conv_padding, conv_padding_r, padding_kind::zero)
: convolution_forward::desc(prop_kind::forward,
convolution_direct, conv_src_md, conv_weights_md,
conv_dst_md, conv_strides, conv_padding, conv_padding_r, padding_kind::zero);
auto conv_prim_desc = convolution_forward::primitive_desc(conv_desc, streams[0].getEngine());
if (gradW != nullptr) {
auto convW_desc = gradB != nullptr
? convolution_backward_weights::desc(
convolution_direct, conv_src_md, conv_diff_weights_md, conv_bias_md,
conv_dst_md, conv_strides, conv_padding, conv_padding_r, padding_kind::zero)
: convolution_backward_weights::desc(
convolution_direct, conv_src_md, conv_diff_weights_md,
conv_dst_md, conv_strides, conv_padding, conv_padding_r, padding_kind::zero);
auto engine = streams[0].getEngine();
auto convW_prim_desc = convolution_backward_weights::primitive_desc(convW_desc, engine, conv_prim_desc);
auto userW_src_memory = mkldnn::memory({user_src_md, engine}, const_cast<NDArray*>(input)->buffer());
auto userW_weights_memory = mkldnn::memory({user_diff_weights_md, engine}, gradW->buffer());
auto userW_dst_memory = mkldnn::memory({user_dst_md, engine}, const_cast<NDArray*>(gradO)->buffer());
auto convW_src_memory = userW_src_memory;
streams[0].addMemory(userW_src_memory);
if (mkldnn::memory::primitive_desc(convW_prim_desc.src_primitive_desc())
!= userW_src_memory.get_primitive_desc()) {
convW_src_memory = mkldnn::memory(convW_prim_desc.src_primitive_desc());
streams[0].addMemory(convW_src_memory);
streams[0].addOperation(reorder(userW_src_memory, convW_src_memory));
}
auto convW_weights_memory = userW_weights_memory;
streams[0].addMemory(userW_weights_memory);
if (mkldnn::memory::primitive_desc(convW_prim_desc.diff_weights_primitive_desc())
!= userW_weights_memory.get_primitive_desc()) {
convW_weights_memory = mkldnn::memory(convW_prim_desc.diff_weights_primitive_desc());
streams[0].addMemory(convW_weights_memory);
}
auto convW_dst_memory = userW_dst_memory;
streams[0].addMemory(userW_dst_memory);
if (mkldnn::memory::primitive_desc(convW_prim_desc.diff_dst_primitive_desc())
!= userW_dst_memory.get_primitive_desc()) {
convW_dst_memory = mkldnn::memory(convW_prim_desc.diff_dst_primitive_desc());
streams[0].addMemory(convW_dst_memory);
streams[0].addOperation(reorder(userW_dst_memory, convW_dst_memory));
}
if (gradB != nullptr) {
auto convW_bias_memory = mkldnn::memory(convW_prim_desc.diff_bias_primitive_desc(), gradB->buffer());
streams[0].addMemory(convW_bias_memory);
streams[0].addOperation(convolution_backward_weights(convW_prim_desc, convW_src_memory, convW_dst_memory, convW_weights_memory, convW_bias_memory));
} else {
streams[0].addOperation(convolution_backward_weights(convW_prim_desc, convW_src_memory, convW_dst_memory, convW_weights_memory));
}
if (mkldnn::memory::primitive_desc(convW_prim_desc.diff_weights_primitive_desc())
!= userW_weights_memory.get_primitive_desc()) {
streams[0].addOperation(reorder(convW_weights_memory, userW_weights_memory));
}
}
if (gradI != nullptr) {
auto convI_desc =
convolution_backward_data::desc(
convolution_direct, conv_diff_src_md, conv_weights_md,
conv_dst_md, conv_strides, conv_padding, conv_padding_r, padding_kind::zero);
auto engine = streams[1].getEngine();
auto convI_prim_desc = convolution_backward_data::primitive_desc(convI_desc, engine, conv_prim_desc);
auto userI_src_memory = mkldnn::memory({user_diff_src_md, engine}, gradI->buffer());
auto userI_weights_memory = mkldnn::memory({user_weights_md, engine}, const_cast<NDArray*>(weights)->buffer());
auto userI_dst_memory = mkldnn::memory({user_dst_md, engine}, const_cast<NDArray*>(gradO)->buffer());
auto convI_src_memory = userI_src_memory;
streams[1].addMemory(userI_src_memory);
if (mkldnn::memory::primitive_desc(convI_prim_desc.diff_src_primitive_desc())
!= userI_src_memory.get_primitive_desc()) {
convI_src_memory = mkldnn::memory(convI_prim_desc.diff_src_primitive_desc());
streams[1].addMemory(convI_src_memory);
}
auto convI_weights_memory = userI_weights_memory;
streams[1].addMemory(userI_weights_memory);
if (mkldnn::memory::primitive_desc(convI_prim_desc.weights_primitive_desc())
!= userI_weights_memory.get_primitive_desc()) {
convI_weights_memory = mkldnn::memory(convI_prim_desc.weights_primitive_desc());
streams[1].addMemory(convI_weights_memory);
streams[1].addOperation(reorder(userI_weights_memory, convI_weights_memory));
}
auto convI_dst_memory = userI_dst_memory;
streams[1].addMemory(userI_dst_memory);
if (mkldnn::memory::primitive_desc(convI_prim_desc.diff_dst_primitive_desc())
!= userI_dst_memory.get_primitive_desc()) {
convI_dst_memory = mkldnn::memory(convI_prim_desc.diff_dst_primitive_desc());
streams[1].addMemory(convI_dst_memory);
streams[1].addOperation(reorder(userI_dst_memory, convI_dst_memory));
}
streams[1].addOperation(convolution_backward_data(convI_prim_desc, convI_dst_memory, convI_weights_memory, convI_src_memory));
if (mkldnn::memory::primitive_desc(convI_prim_desc.diff_src_primitive_desc())
!= userI_src_memory.get_primitive_desc()) {
streams[1].addOperation(reorder(convI_src_memory, userI_src_memory));
}
}
}
if (gradW != nullptr) {
streams[0].submitAndWait();
}
if (gradI != nullptr) {
streams[1].submitAndWait();
}
return Status::OK();
}
#endif
nd4j_debug("MKL-DNN is not used for conv3dnew_bp!\n", 0);
std::vector<int> gradOaxesForDot;
if(!isNDHWC) {
input = input->permute({0,4,1,2,3}); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW]
gradI = gradI->permute({0,4,1,2,3}); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW]
gradOaxesForDot = {0,1,2,3}; // bS, oD, oH, oW
}
else
gradOaxesForDot = {0,2,3,4}; // bS, oD, oH, oW
// ----- calculation of gradW and gradB ----- //
NDArray columns(input->ordering(), {bS, iC, kD, kH, kW, oD, oH, oW}, input->dataType(), block.launchContext());
ConvolutionUtils::vol2col(*block.launchContext(), *input, columns, sD, sH, sW, pD, pH, pW, dD, dH, dW); // [bS, iC, iD, iH, iW] is convoluted to [bS, iC, kD, kH, kW, oD, oH, oW]
MmulHelper::tensorDot(&columns, gradO, gradW, {0,5,6,7}, gradOaxesForDot, {3,0,1,2,4}); // [bS, iC, kD, kH, kW, oD, oH, oW] x [bS, oD, oH, oW, oC]/[bS, oC, oD, oH, oW] = [iC, kD, kH, kW, oC]
if(gradB) {
if(gradB->rankOf() == 2)
gradB = gradB->reshape(gradB->ordering(), {(int)gradB->lengthOf()});
gradO->reduceAlongDimension(reduce::Sum, gradB, gradOaxesForDot); // sum over bS oD oH oW
if(gradB != OUTPUT_VARIABLE(2))
delete gradB;
}
//----- calculation of gradI -----//
MmulHelper::tensorDot(weights, gradO, &columns, {indWoC}, {indIOioC}, {2,3,4,1,0,5,6,7}); // [kD, kH, kW, iC, oC] x [bS, oD, oH, oW, oC]/[bS, oC, oD, oH, oW] = [kD, kH, kW, iC, bS, oD, oH, oW]
ConvolutionUtils::col2vol(*block.launchContext(), columns, *gradI, sD, sH, sW, pD, pH, pW, dD, dH, dW); // columns [bS, iC, kD, kH, kW, oD, oH, oW] is de-convoluted to [bS, iC, iD, iH, iW]
if(!isNDHWC) {
delete input;
delete gradI;
}
return Status::OK();
}
DECLARE_TYPES(conv3dnew_bp) {
getOpDescriptor()
->setAllowedInputTypes(0, nd4j::DataType::ANY)
->setAllowedInputTypes(1, {ALL_FLOATS})
->setAllowedInputTypes(2, {ALL_FLOATS})
->setAllowedInputTypes(3, {ALL_FLOATS})
->setAllowedOutputTypes({ALL_FLOATS});
}
DECLARE_SHAPE_FN(conv3dnew_bp) {
Nd4jLong* inputShapeInfo = inputShape->at(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
Nd4jLong* weightsShapeInfo = inputShape->at(1); // [kD, kH, kW, iC, oC] always
Nd4jLong* biasShapeInfo = block.width() > 3 ? inputShape->at(2) : nullptr; // [oC]
Nd4jLong* gradOShapeInfo = block.width() > 3 ? inputShape->at(3) : inputShape->at(2); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next
int kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 0));// filter(kernel) depth
int kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 1));// filter(kernel) height
int kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 2));// filter(kernel) width
int sD = INT_ARG(3); // strides depth
int sH = INT_ARG(4); // strides height
int sW = INT_ARG(5); // strides width
int pD = INT_ARG(6); // paddings depth
int pH = INT_ARG(7); // paddings height
int pW = INT_ARG(8); // paddings width
int dD = INT_ARG(9); // dilations depth
int dH = INT_ARG(10); // dilations height
int dW = INT_ARG(11); // dilations width
int isSameMode = INT_ARG(12); // 1-SAME, 0-VALID
int isNDHWC = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
const int rank = 5;
REQUIRE_TRUE(inputShapeInfo[0] == rank, 0, "CUSTOM CONV3D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo);
REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0, "CUSTOM CONV3D_BP OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo);
REQUIRE_TRUE(gradOShapeInfo[0] == rank, 0, "CUSTOM CONV3D_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got %i instead !", rank, gradOShapeInfo);
int indIOioC, indIiD, indWoC(4);
if(!isNDHWC) {
indIOioC = 4; indIiD = 1;
}
else {
indIOioC = 1; indIiD = 2;
}
int bS = inputShapeInfo[1]; // batch size
int iD = inputShapeInfo[indIiD+1]; // input depth
int iH = inputShapeInfo[indIiD+2]; // input height
int iW = inputShapeInfo[indIiD+3]; // input width
int iC = inputShapeInfo[indIOioC+1]; // input channels
int oC = weightsShapeInfo[indWoC+1]; // output channels
int trueoD, trueoH, trueoW; // true output depth/height/width
ConvolutionUtils::calcOutSizePool3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, isSameMode);
std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoD,trueoH,trueoW, 0,indIOioC,indIiD,indIiD+1,indIiD+2}));
std::string expectedWeightsShape = ShapeUtils::shapeAsString({kD, kH, kW, iC, oC});
REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradOShapeInfo), 0, "CUSTOM CONV3D_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(expectedWeightsShape == ShapeUtils::shapeAsString(weightsShapeInfo), 0, "CUSTOM CONV3D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
if(biasShapeInfo)
REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM CONV3D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo));
auto gradIshapeInfo = ShapeBuilders::copyShapeInfoAndType(inputShapeInfo, gradOShapeInfo, false, block.getWorkspace());
auto gradWshapeInfo = ShapeBuilders::copyShapeInfoAndType(weightsShapeInfo, gradOShapeInfo, false, block.getWorkspace());
if(biasShapeInfo) {
auto gradBshapeInfo = ShapeBuilders::copyShapeInfoAndType(biasShapeInfo, gradOShapeInfo, false, block.getWorkspace());
return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWshapeInfo), CONSTANT(gradBshapeInfo));
}
return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWshapeInfo));
}
}
}
#endif