309 lines
19 KiB
C++
309 lines
19 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author raver119@gmail.com
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// @author Yurii Shyrma
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#include <system/op_boilerplate.h>
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#if NOT_EXCLUDED(OP_conv1d)
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#include <ops/declarable/DeclarableOp.h>
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/helpers/convolutions.h>
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namespace sd {
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namespace ops {
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CUSTOM_OP_IMPL(conv1d, 2, 1, false, 0, 5) {
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auto input = INPUT_VARIABLE(0); // [bS, iW, iC] (NWC) or [bS, iC, iW] (NCW)
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auto weights = INPUT_VARIABLE(1); // [kW, iC, oC], [oC, iC, kW], [oC, kW, iC]
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auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
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auto output = OUTPUT_NULLIFIED(0); // [bS, oW, oC] (NWC) or [bS, oC, oW] (NCW)
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int kW = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) width
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int sW = INT_ARG(1); // strides width
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int pW = INT_ARG(2); // paddings width
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int dW = INT_ARG(3); // dilations width
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int paddingMode = INT_ARG(4); // 0-VALID, 1-SAME, 2-CAUSAL
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int isNCW = block.getIArguments()->size() > 5 ? !INT_ARG(5) : 1; // INT_ARG(4): 0-NCW, 1-NWC
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int wFormat = block.getIArguments()->size() > 6 ? INT_ARG(6) : 0; // 0 - [kW, iC, oC], 1 - [oC, iC, kW], 2 - [oC, kW, iC]
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const int rank = 3;
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REQUIRE_TRUE(input->rankOf() == rank, 0, "CUSTOM CONV1D OP: rank of input array must be equal to %i, but got %i instead !", rank, input->rankOf());
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REQUIRE_TRUE(weights->rankOf() == rank, 0, "CUSTOM CONV1D OP: rank of weights array must be equal to %i, but got %i instead !", rank, weights->rankOf());
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int indIOioC, indIiW, indWoC(0 == wFormat ? 2 : 0);
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if(!isNCW) {
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indIOioC = 2; indIiW = 1;
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}
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else {
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indIOioC = 1; indIiW = 2;
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}
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int bS = input->sizeAt(0); // batch size
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int iW = input->sizeAt(indIiW); // input width
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int iC = input->sizeAt(indIOioC); // input channels
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int oC = weights->sizeAt(indWoC); // output channels
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std::vector<Nd4jLong> expectedWeightsShape = 0 == wFormat ? std::vector<Nd4jLong>({kW, iC, oC}) : (1 == wFormat ? std::vector<Nd4jLong>({oC, iC, kW}) : std::vector<Nd4jLong>({oC, kW, iC}));
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REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM CONV1D 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 CONV1D 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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std::vector<Nd4jLong> reshapeForInput, reshapeForOutput;
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if(!isNCW) {
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reshapeForInput = {input->sizeAt(0), 1, input->sizeAt(1), input->sizeAt(2)}; // [bS, iW, iC] -> [bS, 1, iW, iC]
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reshapeForOutput = {output->sizeAt(0), 1, output->sizeAt(1), output->sizeAt(2)}; // [bS, oW, oC] -> [bS, 1, oW, oC]
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}
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else {
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reshapeForInput = {input->sizeAt(0), input->sizeAt(1), 1, input->sizeAt(2)}; // [bS, iC, iW] -> [bS, iC, 1, iW]
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reshapeForOutput = {output->sizeAt(0), output->sizeAt(1), 1, output->sizeAt(2)}; // [bS, oC, oW] -> [bS, oC, 1, oW]
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}
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auto inputReshaped = input ->reshape(input->ordering(), reshapeForInput);
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auto outputReshaped = output ->reshape(output->ordering(), reshapeForOutput, false);
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auto weightsReshaped = weights->reshape(weights->ordering(), {1, weights->sizeAt(0), weights->sizeAt(1), weights->sizeAt(2)}); // [kW, iC, oC] -> [1, kW, iC, oC]
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sd::ops::conv2d conv2d;
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const Nd4jStatus status = conv2d.execute({&inputReshaped, &weightsReshaped, bias}, {&outputReshaped}, {}, {1,kW, 1,sW, 0,pW, 1,dW, paddingMode, !isNCW, wFormat}, {});
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if (status != ND4J_STATUS_OK)
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return status;
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// ConvolutionUtils::conv2d(block, &inputReshaped, &weightsReshaped, bias, &outputReshaped, 1,kW, 1,sW, 0,pW, 1,dW, paddingMode, isNCW, wFormat);
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return Status::OK();
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}
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DECLARE_SHAPE_FN(conv1d) {
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auto inputShapeInfo = inputShape->at(0);
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auto weightsShapeInfo = inputShape->at(1);
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Nd4jLong* biasShapeInfo = block.width() > 2 ? inputShape->at(2) : nullptr;
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int kW = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 0)); // filter(kernel) width
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int sW = INT_ARG(1); // strides width
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int pW = INT_ARG(2); // paddings width
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int dW = INT_ARG(3); // dilations width
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int paddingMode = INT_ARG(4); // 0-VALID, 1-SAME
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int isNCW = block.getIArguments()->size() > 5 ? !INT_ARG(5) : 1; // INT_ARG(4): 1-NWC, 0-NCW
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int wFormat = block.getIArguments()->size() > 6 ? INT_ARG(6) : 0; // 0 - [kW, iC, oC], 1 - [oC, iC, kW], 2 - [oC, kW, iC]
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int indIOioC, indIiW, indWoC(0 == wFormat ? 2 : 0);
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if(!isNCW) {
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indIOioC = 2; indIiW = 1;
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}
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else {
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indIOioC = 1; indIiW = 2;
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}
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const int rank = 3;
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REQUIRE_TRUE(inputShapeInfo[0] == rank, 0, "CUSTOM CONV1D 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 CONV1D OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo);
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int bS = inputShapeInfo[1]; // batch size
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int iW = inputShapeInfo[indIiW+1]; // 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 = 0 == wFormat ? std::vector<Nd4jLong>({kW, iC, oC}) : (1 == wFormat ? std::vector<Nd4jLong>({oC, iC, kW}) : std::vector<Nd4jLong>({oC, kW, iC}));
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REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsShapeInfo, expectedWeightsShape), 0, "CUSTOM CONV1D 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 CONV1D 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 oH, oW; // output height, width
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ConvolutionUtils::calcOutSizePool2D(oH,oW, 1,kW, 1,sW, 0,pW, 1,dW, 1,iW, paddingMode);
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Nd4jLong* outputShapeInfo = nullptr;
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ALLOCATE(outputShapeInfo, block.getWorkspace(), shape::shapeInfoLength(rank), Nd4jLong);
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outputShapeInfo[0] = 3;
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outputShapeInfo[1] = bS;
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if (isNCW) {
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outputShapeInfo[2] = oC;
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outputShapeInfo[3] = oW;
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} else {
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outputShapeInfo[2] = oW;
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outputShapeInfo[3] = oC;
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}
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ShapeUtils::updateStridesAndType(outputShapeInfo, weightsShapeInfo, shape::order(weightsShapeInfo));
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return SHAPELIST(CONSTANT(outputShapeInfo));
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}
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DECLARE_TYPES(conv1d) {
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getOpDescriptor()
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->setAllowedInputTypes(0, {ALL_FLOATS, ALL_INTS, DataType::QINT8, DataType::QINT16})
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->setAllowedInputTypes(1, {ALL_FLOATS})
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->setAllowedInputTypes(2, {ALL_FLOATS})
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->setAllowedOutputTypes(0, {ALL_FLOATS});
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}
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(conv1d_bp, 3, 2, false, 0, 5) {
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auto input = INPUT_VARIABLE(0); // [bS, iW, iC] (NWC) or [bS, iC, iW] (NCW)
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auto weights = INPUT_VARIABLE(1); // [kW, iC, oC], [oC, iC, kW], [oC, kW, iC]
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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, oW, oC] (NWC) or [bS, oC, oW] (NCW), epsilon_next
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auto gradI = OUTPUT_NULLIFIED(0); // [bS, iW, iC] (NWC) or [bS, iC, iW] (NCW), epsilon
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auto gradW = OUTPUT_NULLIFIED(1); // [kW, iC, oC], [oC, iC, kW], [oC, kW, iC]
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auto gradB = block.width() > 3 ? OUTPUT_NULLIFIED(2) : nullptr; // [oC]
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int kW = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) width
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int sW = INT_ARG(1); // strides width
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int pW = INT_ARG(2); // paddings width
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int dW = INT_ARG(3); // dilations width
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int paddingMode = INT_ARG(4); // 0-VALID, 1-SAME, 2-CAUSAL
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int isNCW = block.getIArguments()->size() > 5 ? !INT_ARG(5) : 1; // INT_ARG(4): 1-NWC, 0-NCW
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int wFormat = block.getIArguments()->size() > 6 ? INT_ARG(6) : 0; // 0 - [kW, iC, oC], 1 - [oC, iC, kW], 2 - [oC, kW, iC]
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const int rank = 3;
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REQUIRE_TRUE(input->rankOf() == rank, 0, "CUSTOM CONV1D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank, input->rankOf());
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REQUIRE_TRUE(weights->rankOf() == rank, 0, "CUSTOM CONV1D_BP OP: rank of weights array must be equal to %i, but got %i instead !", rank, weights->rankOf());
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REQUIRE_TRUE(gradO->rankOf() == rank, 0, "CUSTOM CONV1D_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got %i instead !", rank, gradO->rankOf());
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int indIOioC, indIiW, indWoC(0 == wFormat ? 2 : 0);
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if(!isNCW) {
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indIOioC = 2; indIiW = 1;
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}
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else {
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indIOioC = 1; indIiW = 2;
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}
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const int bS = input->sizeAt(0); // batch size
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const int iW = input->sizeAt(indIiW); // input width
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const int iC = input->sizeAt(indIOioC); // input channels
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const int oC = weights->sizeAt(indWoC); // output channels
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int trueoH, trueoW; // true output height, width
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ConvolutionUtils::calcOutSizePool2D(trueoH,trueoW, 1,kW, 1,sW, 0,pW, 1,dW, 1,iW, paddingMode);
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std::vector<Nd4jLong> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoW, 0,indIOioC,indIiW});
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std::vector<Nd4jLong> expectedWeightsShape = 0 == wFormat ? std::vector<Nd4jLong>({kW, iC, oC}) : (1 == wFormat ? std::vector<Nd4jLong>({oC, iC, kW}) : std::vector<Nd4jLong>({oC, kW, iC}));
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REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "CUSTOM CONV1D_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 CONV1D_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 CONV1D_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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std::vector<Nd4jLong> reshapeForInput, reshapeForGradO;
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if(!isNCW) {
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reshapeForInput = {input->sizeAt(0), 1, input->sizeAt(1), input->sizeAt(2)}; // [bS, iW, iC] -> [bS, 1, iW, iC]
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reshapeForGradO = {gradO->sizeAt(0), 1, gradO->sizeAt(1), gradO->sizeAt(2)}; // [bS, oW, oC] -> [bS, 1, oW, oC]
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}
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else {
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reshapeForInput = {input->sizeAt(0), input->sizeAt(1), 1, input->sizeAt(2)}; // [bS, iC, iW] -> [bS, iC, 1, iW]
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reshapeForGradO = {gradO->sizeAt(0), gradO->sizeAt(1), 1, gradO->sizeAt(2)}; // [bS, oC, oW] -> [bS, oC, 1, oW]
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}
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auto inputReshaped = input ->reshape(input->ordering(), reshapeForInput);
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auto gradIReshaped = gradI ->reshape(gradI->ordering(), reshapeForInput, false);
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auto gradOReshaped = gradO ->reshape(gradO->ordering(), reshapeForGradO);
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auto weightsReshaped = weights->reshape(weights->ordering(),{1, weights->sizeAt(0), weights->sizeAt(1), weights->sizeAt(2)}); // [kW, iC, oC] -> [1, kW, iC, oC]
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auto gradWReshaped = gradW ->reshape(gradW->ordering(), {1, weights->sizeAt(0), weights->sizeAt(1), weights->sizeAt(2)}, false);// [kW, iC, oC] -> [1, kW, iC, oC]
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sd::ops::conv2d_bp conv2dBP;
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auto status = conv2dBP.execute({&inputReshaped, &weightsReshaped, bias, &gradOReshaped}, {&gradIReshaped, &gradWReshaped, gradB}, {}, {1,kW, 1,sW, 0,pW, 1,dW, paddingMode, !isNCW, wFormat}, {});
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if (status != ND4J_STATUS_OK)
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return status;
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// ConvolutionUtils::conv2dBP(block, &inputReshaped, &weightsReshaped, bias, &gradOReshaped, &gradIReshaped, &gradWReshaped, gradB, 1,kW, 1,sW, 0,pW, 1,dW, paddingMode, isNCW, wFormat);
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return Status::OK();
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}
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DECLARE_SHAPE_FN(conv1d_bp) {
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auto inputShapeInfo = inputShape->at(0); // [bS, iW, iC] (NWC) or [bS, iC, iW] (NCW)
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auto weightsShapeInfo = inputShape->at(1); // [kW, iC, oC], [oC, iC, kW], [oC, kW, iC]
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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, oW, oC] (NWC) or [bS, oC, oW] (NCW), epsilon_next
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const int rank = 3;
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REQUIRE_TRUE(inputShapeInfo[0] == rank, 0, "CUSTOM CONV1D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo[0]);
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REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0, "CUSTOM CONV1D_BP OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo[0]);
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REQUIRE_TRUE(gradOShapeInfo[0] == rank, 0, "CUSTOM CONV1D_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got %i instead !", rank, gradOShapeInfo[0]);
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int kW = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 0));// filter(kernel) width
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int sW = INT_ARG(1); // strides width
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int pW = INT_ARG(2); // paddings width
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int dW = INT_ARG(3); // dilations width
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int paddingMode = INT_ARG(4); // 0-VALID, 1-SAME
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int isNCW = block.getIArguments()->size() > 5 ? !INT_ARG(5) : 1; // INT_ARG(4): 1-NWC, 0-NCW
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int wFormat = block.getIArguments()->size() > 6 ? INT_ARG(6) : 0; // 0 - [kW, iC, oC], 1 - [oC, iC, kW], 2 - [oC, kW, iC]
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int indIOioC, indIiW, indWoC(0 == wFormat ? 2 : 0);
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if(!isNCW) {
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indIOioC = 2; indIiW = 1;
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}
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else {
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indIOioC = 1; indIiW = 2;
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}
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const int bS = inputShapeInfo[1]; // batch size
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const int iW = inputShapeInfo[indIiW+1]; // input width
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const int iC = inputShapeInfo[indIOioC+1]; // input channels
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const int oC = weightsShapeInfo[indWoC+1]; // output channels
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int trueoH, trueoW; // true output height, width
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ConvolutionUtils::calcOutSizePool2D(trueoH,trueoW, 1,kW, 1,sW, 0,pW, 1,dW, 1,iW, paddingMode);
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std::vector<Nd4jLong> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoW, 0,indIOioC,indIiW});
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std::vector<Nd4jLong> expectedWeightsShape = 0 == wFormat ? std::vector<Nd4jLong>({kW, iC, oC}) : (1 == wFormat ? std::vector<Nd4jLong>({oC, iC, kW}) : std::vector<Nd4jLong>({oC, kW, iC}));
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REQUIRE_TRUE(ShapeUtils::areShapesEqual(gradOShapeInfo, expectedGradOShape), 0, "CUSTOM CONV1D_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 CONV1D_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 CONV1D_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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|
|
|
DECLARE_TYPES(conv1d_bp) {
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getOpDescriptor()
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|
->setAllowedInputTypes(0, {ALL_FLOATS, ALL_INTS, DataType::QINT8, DataType::QINT16})
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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(0, {ALL_FLOATS})
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|
->setAllowedOutputTypes(1, {ALL_FLOATS});
|
|
}
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|
|
|
|
|
|
|
}
|
|
}
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|
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#endif
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