337 lines
22 KiB
C++
337 lines
22 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2019 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author Yurii Shyrma, created on 05.02.2018
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//
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#include <system/op_boilerplate.h>
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#if NOT_EXCLUDED(OP_conv3dnew)
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/helpers/convolutions.h>
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#include <ops/declarable/helpers/addBias.h>
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#include <helpers/MmulHelper.h>
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namespace sd {
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namespace ops {
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CUSTOM_OP_IMPL(conv3dnew, 2, 1, false, 0, 13) {
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auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
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auto weights = INPUT_VARIABLE(1); // [kD, kH, kW, iC, oC] always
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auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
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auto output = OUTPUT_VARIABLE(0); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW)
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REQUIRE_TRUE(input->rankOf() == 5, 0, "CUSTOM CONV3D OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf());
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REQUIRE_TRUE(weights->rankOf() == 5, 0, "CUSTOM CONV3D OP: rank of weights array must be equal to 5, but got %i instead !", weights->rankOf());
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int kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) depth
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int kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1));// filter(kernel) height
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int kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<int>(weights->sizeAt(2));// filter(kernel) width
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int sD = INT_ARG(3); // strides depth
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int sH = INT_ARG(4); // strides height
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int sW = INT_ARG(5); // strides width
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int pD = INT_ARG(6); // paddings depth
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int pH = INT_ARG(7); // paddings height
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int pW = INT_ARG(8); // paddings width
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int dD = INT_ARG(9); // dilations depth
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int dH = INT_ARG(10); // dilations height
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int dW = INT_ARG(11); // dilations width
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int paddingMode = INT_ARG(12); // 0-SAME, 1-VALID
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int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
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int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
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int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD);
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REQUIRE_TRUE(paddingMode < 2, 0, "CUSTOM CONV3D OP: causal padding mode (paddingMode = 2) is not allowed for this operation !");
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std::vector<Nd4jLong> expectedWeightsShape = {kD, kH, kW, iC, oC};
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REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM CONV3D OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
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if (bias)
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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());
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ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW, paddingMode);
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nd4j_debug("MKL-DNN is not used for conv3dnew!\n", 0);
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std::vector<int> permutForOutput;
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if (isNCDHW)
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permutForOutput = {0,2,3,4,1}; // [bS, oC, oD, oH, oW] -> [bS, oD, oH, oW, oC]
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else
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input = new NDArray(input->permute({0,4,1,2,3}));
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NDArray columns(input->ordering(), {bS, iC, kD, kH, kW, oD, oH, oW}, input->dataType(), block.launchContext());
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ConvolutionUtils::vol2col(block, *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]
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// [bS, iC, kD, kH, kW, oD, oH, oW] x [kD, kH, kW, iC, oC] = [bS, oD, oH, oW, oC]
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MmulHelper::tensorDot(&columns, weights, output, {1,2,3,4}, {3,0,1,2}, permutForOutput);
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if(bias)
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// output->applyBroadcast(broadcast::Add, {indIOioC}, bias);
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helpers::addBias(block, *output, *bias, *output, isNCDHW);
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if(!isNCDHW)
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delete input;
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return Status::OK();
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}
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DECLARE_TYPES(conv3dnew) {
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getOpDescriptor()
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->setAllowedInputTypes(0, sd::DataType::ANY)
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->setAllowedInputTypes(1, {ALL_FLOATS})
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->setAllowedInputTypes(2, {ALL_FLOATS})
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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DECLARE_SHAPE_FN(conv3dnew) {
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auto inputShapeInfo = inputShape->at(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
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auto weightsShapeInfo = inputShape->at(1); // [kD, kH, kW, iC, oC] always
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auto biasShapeInfo = block.width() > 2 ? inputShape->at(2) : nullptr; // [oC]
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int kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 0));// filter(kernel) depth
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int kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 1));// filter(kernel) height
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int kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 2));// filter(kernel) width
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int sD = INT_ARG(3); // strides depth
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int sH = INT_ARG(4); // strides height
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int sW = INT_ARG(5); // strides width
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int pD = INT_ARG(6); // paddings depth
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int pH = INT_ARG(7); // paddings height
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int pW = INT_ARG(8); // paddings width
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int dD = INT_ARG(9); // dilations depth
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int dH = INT_ARG(10); // dilations height
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int dW = INT_ARG(11); // dilations width
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int paddingMode = INT_ARG(12); // 1-SAME, 0-VALID;
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int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
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const int rank = 5;
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REQUIRE_TRUE(paddingMode < 2, 0, "CUSTOM CONV3D OP: causal padding mode (paddingMode = 2) is not allowed for this operation !");
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REQUIRE_TRUE(inputShapeInfo[0] == rank, 0, "CUSTOM CONV3D OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo);
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REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0, "CUSTOM CONV3D OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo);
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int indIOioC, indIiD, indWoC(4);
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if(!isNCDHW) {
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indIOioC = 4; indIiD = 1;
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}
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else {
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indIOioC = 1; indIiD = 2;
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}
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int bS = inputShapeInfo[1]; // batch size
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int iD = inputShapeInfo[indIiD+1]; // input depth
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int iH = inputShapeInfo[indIiD+2]; // input height
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int iW = inputShapeInfo[indIiD+3]; // input width
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int iC = inputShapeInfo[indIOioC+1]; // input channels
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int oC = weightsShapeInfo[indWoC+1]; // output channels
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std::vector<Nd4jLong> expectedWeightsShape = {kD, kH, kW, iC, oC};
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REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsShapeInfo, expectedWeightsShape), 0, "CUSTOM CONV3D OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
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if (biasShapeInfo)
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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));
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int oD, oH, oW; // output depth, height, width
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ConvolutionUtils::calcOutSizePool3D(oD, oH, oW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, paddingMode);
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Nd4jLong* outputShapeInfo = nullptr;
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ALLOCATE(outputShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inputShapeInfo), Nd4jLong);
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outputShapeInfo[0] = rank;
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outputShapeInfo[1] = bS;
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if (isNCDHW) {
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outputShapeInfo[2] = oC;
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outputShapeInfo[3] = oD;
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outputShapeInfo[4] = oH;
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outputShapeInfo[5] = oW;
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} else {
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outputShapeInfo[2] = oD;
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outputShapeInfo[3] = oH;
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outputShapeInfo[4] = oW;
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outputShapeInfo[5] = oC;
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}
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ShapeUtils::updateStridesAndType(outputShapeInfo, weightsShapeInfo, shape::order(inputShapeInfo));
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return SHAPELIST(CONSTANT(outputShapeInfo));
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}
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(conv3dnew_bp, 3, 2, false, 0, 13) {
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auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
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auto weights = INPUT_VARIABLE(1); // [kD, kH, kW, iC, oC] always
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auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC]
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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
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auto gradI = OUTPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW), epsilon
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auto gradW = OUTPUT_VARIABLE(1); // [kD, kH, kW, iC, oC] always
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auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
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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());
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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());
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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());
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int kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) depth
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int kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1));// filter(kernel) height
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int kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<int>(weights->sizeAt(2));// filter(kernel) width
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int sD = INT_ARG(3); // strides depth
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int sH = INT_ARG(4); // strides height
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int sW = INT_ARG(5); // strides width
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int pD = INT_ARG(6); // paddings depth
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int pH = INT_ARG(7); // paddings height
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int pW = INT_ARG(8); // paddings width
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int dD = INT_ARG(9); // dilations depth
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int dH = INT_ARG(10); // dilations height
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int dW = INT_ARG(11); // dilations width
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int paddingMode = INT_ARG(12); // 1-SAME, 0-VALID
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int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
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int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
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int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD);
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int trueoD, trueoH, trueoW; // true output depth/height/width
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ConvolutionUtils::calcOutSizePool3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, paddingMode);
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REQUIRE_TRUE(paddingMode < 2, 0, "CUSTOM CONV3D_BP OP: causal padding mode (paddingMode = 2) is not allowed for this operation !");
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std::vector<Nd4jLong> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoD,trueoH,trueoW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2});
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std::vector<Nd4jLong> expectedWeightsShape = {kD, kH, kW, iC, oC};
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REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "CUSTOM CONV3D_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());
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REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM CONV3D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
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if(bias)
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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());
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ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW, paddingMode);
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nd4j_debug("MKL-DNN is not used for conv3dnew_bp!\n", 0);
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std::vector<int> gradOaxesForDot;
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if(!isNCDHW) {
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gradOaxesForDot = {0,1,2,3}; // bS, oD, oH, oW
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input = new NDArray(input->permute({0,4,1,2,3})); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW]
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gradI = new NDArray(gradI->permute({0,4,1,2,3})); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW]
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}
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else {
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gradOaxesForDot = {0,2,3,4}; // bS, oD, oH, oW
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}
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// ----- calculation of gradW and gradB ----- //
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NDArray columns(input->ordering(), {bS, iC, kD, kH, kW, oD, oH, oW}, input->dataType(), block.launchContext());
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ConvolutionUtils::vol2col(block, *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]
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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]
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//----- calculation of gradO -----//
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if(gradB) {
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if(gradB->rankOf() == 2)
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gradB = new NDArray(gradB->reshape(gradB->ordering(), {(int)gradB->lengthOf()}, false));
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gradO->reduceAlongDimension(reduce::Sum, *gradB, gradOaxesForDot); // sum over bS oD oH oW
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if(gradB != OUTPUT_VARIABLE(2))
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delete gradB;
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}
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//----- calculation of gradI -----//
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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]
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ConvolutionUtils::col2vol(block, 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]
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if(!isNCDHW) {
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delete input;
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delete gradI;
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}
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return Status::OK();
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}
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DECLARE_TYPES(conv3dnew_bp) {
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getOpDescriptor()
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->setAllowedInputTypes(0, sd::DataType::ANY)
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->setAllowedInputTypes(1, {ALL_FLOATS})
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->setAllowedInputTypes(2, {ALL_FLOATS})
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->setAllowedInputTypes(3, {ALL_FLOATS})
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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DECLARE_SHAPE_FN(conv3dnew_bp) {
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Nd4jLong* inputShapeInfo = inputShape->at(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
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Nd4jLong* weightsShapeInfo = inputShape->at(1); // [kD, kH, kW, iC, oC] always
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Nd4jLong* biasShapeInfo = block.width() > 3 ? inputShape->at(2) : nullptr; // [oC]
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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
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int kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 0));// filter(kernel) depth
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int kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 1));// filter(kernel) height
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int kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 2));// filter(kernel) width
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int sD = INT_ARG(3); // strides depth
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int sH = INT_ARG(4); // strides height
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int sW = INT_ARG(5); // strides width
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int pD = INT_ARG(6); // paddings depth
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int pH = INT_ARG(7); // paddings height
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int pW = INT_ARG(8); // paddings width
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int dD = INT_ARG(9); // dilations depth
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int dH = INT_ARG(10); // dilations height
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int dW = INT_ARG(11); // dilations width
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int paddingMode = INT_ARG(12); // 1-SAME, 0-VALID
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int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
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const int rank = 5;
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REQUIRE_TRUE(paddingMode < 2, 0, "CUSTOM CONV3D OP: causal padding mode (paddingMode = 2) is not allowed for this operation !");
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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);
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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);
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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);
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int indIOioC, indIiD, indWoC(4);
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if(!isNCDHW) {
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indIOioC = 4; indIiD = 1;
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}
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else {
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indIOioC = 1; indIiD = 2;
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}
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int bS = inputShapeInfo[1]; // batch size
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int iD = inputShapeInfo[indIiD+1]; // input depth
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int iH = inputShapeInfo[indIiD+2]; // input height
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int iW = inputShapeInfo[indIiD+3]; // input width
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int iC = inputShapeInfo[indIOioC+1]; // input channels
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int oC = weightsShapeInfo[indWoC+1]; // output channels
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int trueoD, trueoH, trueoW; // true output depth/height/width
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ConvolutionUtils::calcOutSizePool3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, paddingMode);
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std::vector<Nd4jLong> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoD,trueoH,trueoW, 0,indIOioC,indIiD,indIiD+1,indIiD+2});
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std::vector<Nd4jLong> expectedWeightsShape = {kD, kH, kW, iC, oC};
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REQUIRE_TRUE(ShapeUtils::areShapesEqual(gradOShapeInfo, expectedGradOShape), 0, "CUSTOM CONV3D_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());
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REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsShapeInfo, expectedWeightsShape), 0, "CUSTOM CONV3D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
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if(biasShapeInfo)
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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));
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auto gradIshapeInfo = ShapeBuilders::copyShapeInfoAndType(inputShapeInfo, gradOShapeInfo, false, block.getWorkspace());
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auto gradWshapeInfo = ShapeBuilders::copyShapeInfoAndType(weightsShapeInfo, gradOShapeInfo, false, block.getWorkspace());
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if(biasShapeInfo) {
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auto gradBshapeInfo = ShapeBuilders::copyShapeInfoAndType(biasShapeInfo, gradOShapeInfo, false, block.getWorkspace());
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return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWshapeInfo), CONSTANT(gradBshapeInfo));
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}
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return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWshapeInfo));
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}
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}
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}
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#endif
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