cavis/libnd4j/include/ops/declarable/generic/nn/convo/conv1d.cpp

/*******************************************************************************
 * Copyright (c) 2015-2018 Skymind, Inc.
 *
 * This program and the accompanying materials are made available under the
 * terms of the Apache License, Version 2.0 which is available at
 * https://www.apache.org/licenses/LICENSE-2.0.
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
 * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
 * License for the specific language governing permissions and limitations
 * under the License.
 *
 * SPDX-License-Identifier: Apache-2.0
 ******************************************************************************/

//
//  @author raver119@gmail.com
//  @author Yurii Shyrma


#include <system/op_boilerplate.h>
#if NOT_EXCLUDED(OP_conv1d)

#include <ops/declarable/DeclarableOp.h>
#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/helpers/convolutions.h>

namespace sd {
    namespace ops  {



        CUSTOM_OP_IMPL(conv1d, 2, 1, false, 0, 5) {

            auto input   = INPUT_VARIABLE(0);                                    // [bS, iW, iC] (NWC) or [bS, iC, iW] (NCW)
            auto weights = INPUT_VARIABLE(1);                                    // [kW, iC, oC], [oC, iC, kW], [oC, kW, iC]
            auto bias    = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr;      // [oC]

            auto output  = OUTPUT_NULLIFIED(0);                                   // [bS, oW, oC] (NWC) or [bS, oC, oW] (NCW)

            int kW = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) width
            int sW = INT_ARG(1);                                                        // strides width
            int pW = INT_ARG(2);                                                        // paddings width
            int dW = INT_ARG(3);                                                        // dilations width
            int paddingMode = INT_ARG(4);                                               // 0-VALID, 1-SAME, 2-CAUSAL
            int isNCW       = block.getIArguments()->size() > 5 ? !INT_ARG(5) : 1;      // INT_ARG(4): 0-NCW,  1-NWC
            int wFormat = block.getIArguments()->size() > 6 ? INT_ARG(6) : 0;           // 0 - [kW, iC, oC], 1 - [oC, iC, kW], 2 - [oC, kW, iC]

            const int rank = 3;
            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());
            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());

            int indIOioC, indIiW, indWoC(0 == wFormat ? 2 : 0);
            if(!isNCW) {
                indIOioC = 2; indIiW = 1;
            }
            else {
                indIOioC = 1; indIiW = 2;
            }

            int bS = input->sizeAt(0);                        // batch size
            int iW = input->sizeAt(indIiW);                   // input width
            int iC = input->sizeAt(indIOioC);                 // input channels
            int oC = weights->sizeAt(indWoC);                 // output channels
            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}));

            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());
            if (bias)
            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());

            std::vector<Nd4jLong> reshapeForInput, reshapeForOutput;
            if(!isNCW) {
                reshapeForInput   = {input->sizeAt(0), 1, input->sizeAt(1), input->sizeAt(2)};                  // [bS, iW, iC] -> [bS, 1, iW, iC]
                reshapeForOutput  = {output->sizeAt(0), 1, output->sizeAt(1), output->sizeAt(2)};               // [bS, oW, oC] -> [bS, 1, oW, oC]
            }
            else {
                reshapeForInput   = {input->sizeAt(0),  input->sizeAt(1),  1, input->sizeAt(2)};                // [bS, iC, iW] -> [bS, iC, 1, iW]
                reshapeForOutput  = {output->sizeAt(0), output->sizeAt(1), 1, output->sizeAt(2)};               // [bS, oC, oW] -> [bS, oC, 1, oW]
            }

            auto inputReshaped   = input  ->reshape(input->ordering(),   reshapeForInput);
            auto outputReshaped  = output ->reshape(output->ordering(),  reshapeForOutput, false);
            auto weightsReshaped = weights->reshape(weights->ordering(), {1, weights->sizeAt(0), weights->sizeAt(1), weights->sizeAt(2)});   // [kW, iC, oC] -> [1, kW, iC, oC]

            sd::ops::conv2d conv2d;
            const Nd4jStatus status = conv2d.execute({&inputReshaped, &weightsReshaped, bias}, {&outputReshaped}, {}, {1,kW,  1,sW,  0,pW,  1,dW,  paddingMode, !isNCW, wFormat}, {});
            if (status != ND4J_STATUS_OK)
                return status;

            // ConvolutionUtils::conv2d(block, &inputReshaped, &weightsReshaped, bias, &outputReshaped, 1,kW,  1,sW,  0,pW,  1,dW,  paddingMode, isNCW, wFormat);

            return Status::OK();
        }


        DECLARE_SHAPE_FN(conv1d) {

            auto inputShapeInfo   = inputShape->at(0);
            auto weightsShapeInfo = inputShape->at(1);
            Nd4jLong const* biasShapeInfo    = block.width() > 2 ? inputShape->at(2) : nullptr;

            int kW = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 0)); // filter(kernel) width
            int sW = INT_ARG(1);                                                        // strides width
            int pW = INT_ARG(2);                                                        // paddings width
            int dW = INT_ARG(3);                                                        // dilations width
            int paddingMode = INT_ARG(4);                                               // 0-VALID, 1-SAME
            int isNCW  = block.getIArguments()->size() > 5 ? !INT_ARG(5) : 1;           // INT_ARG(4): 1-NWC, 0-NCW
            int wFormat = block.getIArguments()->size() > 6 ? INT_ARG(6) : 0;           // 0 - [kW, iC, oC], 1 - [oC, iC, kW], 2 - [oC, kW, iC]

            int indIOioC, indIiW, indWoC(0 == wFormat ? 2 : 0);
            if(!isNCW) {
                indIOioC = 2; indIiW = 1;
            }
            else {
                indIOioC = 1; indIiW = 2;
            }

            const int rank = 3;
            REQUIRE_TRUE(inputShapeInfo[0]   == rank, 0, "CUSTOM CONV1D OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo);
            REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0, "CUSTOM CONV1D OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo);

            int bS = inputShapeInfo[1];                         // batch size
            int iW = inputShapeInfo[indIiW+1];                   // input width
            int iC = inputShapeInfo[indIOioC+1];                   // input channels
            int oC = weightsShapeInfo[indWoC+1];                 // output channels

            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}));
            //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());
            if (biasShapeInfo)
            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));

            int oH, oW;                                         // output height, width
            ConvolutionUtils::calcOutSizePool2D(oH,oW,  1,kW,  1,sW,  0,pW,  1,dW,  1,iW, paddingMode);

            Nd4jLong* outputShapeInfo = nullptr;
            ALLOCATE(outputShapeInfo, block.getWorkspace(), shape::shapeInfoLength(rank), Nd4jLong);

            outputShapeInfo[0] = 3;
            outputShapeInfo[1] = bS;

            if (isNCW) {
                outputShapeInfo[2] = oC;
                outputShapeInfo[3] = oW;
            } else {
                outputShapeInfo[2] = oW;
                outputShapeInfo[3] = oC;
            }

            ShapeUtils::updateStridesAndType(outputShapeInfo, weightsShapeInfo, shape::order(weightsShapeInfo));
            return SHAPELIST(CONSTANT(outputShapeInfo));
        }

        DECLARE_TYPES(conv1d) {
            getOpDescriptor()
                    ->setAllowedInputTypes(0, {ALL_FLOATS, ALL_INTS, DataType::QINT8, DataType::QINT16})
                    ->setAllowedInputTypes(1, {ALL_FLOATS})
                    ->setAllowedInputTypes(2, {ALL_FLOATS})
                    ->setAllowedOutputTypes(0, {ALL_FLOATS});
        }


//////////////////////////////////////////////////////////////////////////
        CUSTOM_OP_IMPL(conv1d_bp, 3, 2, false, 0, 5) {

            auto input   = INPUT_VARIABLE(0);                                                // [bS, iW, iC] (NWC) or [bS, iC, iW] (NCW)
            auto weights = INPUT_VARIABLE(1);                                                // [kW, iC, oC], [oC, iC, kW], [oC, kW, iC]
            auto bias    = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr;                  // [oC]
            auto gradO   = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2);        // [bS, oW, oC] (NWC) or [bS, oC, oW] (NCW), epsilon_next

            auto gradI = OUTPUT_NULLIFIED(0);                                                 // [bS, iW, iC] (NWC) or [bS, iC, iW] (NCW), epsilon
            auto gradW = OUTPUT_NULLIFIED(1);                                                 // [kW, iC, oC], [oC, iC, kW], [oC, kW, iC]
            auto gradB = block.width() > 3 ? OUTPUT_NULLIFIED(2) : nullptr;                   // [oC]

            int kW = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) width
            int sW = INT_ARG(1);                                                        // strides width
            int pW = INT_ARG(2);                                                        // paddings width
            int dW = INT_ARG(3);                                                        // dilations width
            int paddingMode = INT_ARG(4);                                               // 0-VALID, 1-SAME, 2-CAUSAL
            int isNCW  = block.getIArguments()->size() > 5 ? !INT_ARG(5) : 1;           // INT_ARG(4): 1-NWC, 0-NCW
            int wFormat = block.getIArguments()->size() > 6 ? INT_ARG(6) : 0;           // 0 - [kW, iC, oC], 1 - [oC, iC, kW], 2 - [oC, kW, iC]

            const int rank = 3;
            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());
            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());
            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());

            int indIOioC, indIiW, indWoC(0 == wFormat ? 2 : 0);
            if(!isNCW) {
                indIOioC = 2; indIiW = 1;
            }
            else {
                indIOioC = 1; indIiW = 2;
            }

            const int bS = input->sizeAt(0);                          // batch size
            const int iW = input->sizeAt(indIiW);                     // input width
            const int iC = input->sizeAt(indIOioC);                   // input channels
            const int oC = weights->sizeAt(indWoC);                    // output channels

            int trueoH, trueoW;          // true output height, width
            ConvolutionUtils::calcOutSizePool2D(trueoH,trueoW, 1,kW, 1,sW, 0,pW, 1,dW, 1,iW, paddingMode);

            std::vector<Nd4jLong> expectedGradOShape   = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoW,  0,indIOioC,indIiW});
            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}));
            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());
            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());
            if(bias)
            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());

            std::vector<Nd4jLong> reshapeForInput, reshapeForGradO;
            if(!isNCW) {
                reshapeForInput   = {input->sizeAt(0), 1, input->sizeAt(1), input->sizeAt(2)};                  // [bS, iW, iC] -> [bS, 1, iW, iC]
                reshapeForGradO   = {gradO->sizeAt(0), 1, gradO->sizeAt(1), gradO->sizeAt(2)};                  // [bS, oW, oC] -> [bS, 1, oW, oC]
            }
            else {
                reshapeForInput   = {input->sizeAt(0), input->sizeAt(1), 1, input->sizeAt(2)};                  // [bS, iC, iW] -> [bS, iC, 1, iW]
                reshapeForGradO   = {gradO->sizeAt(0), gradO->sizeAt(1), 1, gradO->sizeAt(2)};                  // [bS, oC, oW] -> [bS, oC, 1, oW]
            }

            auto inputReshaped   = input  ->reshape(input->ordering(),  reshapeForInput);
            auto gradIReshaped   = gradI  ->reshape(gradI->ordering(),  reshapeForInput, false);
            auto gradOReshaped   = gradO  ->reshape(gradO->ordering(),  reshapeForGradO);
            auto weightsReshaped = weights->reshape(weights->ordering(),{1, weights->sizeAt(0), weights->sizeAt(1), weights->sizeAt(2)});       // [kW, iC, oC] -> [1, kW, iC, oC]
            auto gradWReshaped   = gradW  ->reshape(gradW->ordering(),  {1, weights->sizeAt(0), weights->sizeAt(1), weights->sizeAt(2)}, false);// [kW, iC, oC] -> [1, kW, iC, oC]

            sd::ops::conv2d_bp conv2dBP;
            auto status = conv2dBP.execute({&inputReshaped, &weightsReshaped, bias, &gradOReshaped}, {&gradIReshaped, &gradWReshaped, gradB}, {}, {1,kW,  1,sW,  0,pW,  1,dW,  paddingMode, !isNCW, wFormat}, {});
            if (status != ND4J_STATUS_OK)
                return status;

            // ConvolutionUtils::conv2dBP(block, &inputReshaped, &weightsReshaped, bias, &gradOReshaped, &gradIReshaped, &gradWReshaped, gradB, 1,kW,  1,sW,  0,pW,  1,dW,  paddingMode, isNCW, wFormat);

            return Status::OK();
        }


        DECLARE_SHAPE_FN(conv1d_bp) {

            auto inputShapeInfo   = inputShape->at(0);                                               // [bS, iW, iC] (NWC) or [bS, iC, iW] (NCW)
            auto weightsShapeInfo = inputShape->at(1);                                               // [kW, iC, oC], [oC, iC, kW], [oC, kW, iC]
            Nd4jLong const* biasShapeInfo    = block.width() > 3 ? inputShape->at(2) : nullptr;            // [oC]
            Nd4jLong const* gradOShapeInfo   = block.width() > 3 ? inputShape->at(3) : inputShape->at(2);  // [bS, oW, oC] (NWC) or [bS, oC, oW] (NCW), epsilon_next

            const int rank = 3;
            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]);
            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]);
            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]);

            int kW = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 0));// filter(kernel) width
            int sW = INT_ARG(1);                                                        // strides width
            int pW = INT_ARG(2);                                                        // paddings width
            int dW = INT_ARG(3);                                                        // dilations width
            int paddingMode = INT_ARG(4);                                               // 0-VALID, 1-SAME
            int isNCW  = block.getIArguments()->size() > 5 ? !INT_ARG(5) : 1;           // INT_ARG(4): 1-NWC, 0-NCW
            int wFormat = block.getIArguments()->size() > 6 ? INT_ARG(6) : 0;           // 0 - [kW, iC, oC], 1 - [oC, iC, kW], 2 - [oC, kW, iC]

            int indIOioC, indIiW, indWoC(0 == wFormat ? 2 : 0);
            if(!isNCW) {
                indIOioC = 2; indIiW = 1;
            }
            else {
                indIOioC = 1; indIiW = 2;
            }

            const int bS = inputShapeInfo[1];                            // batch size
            const int iW = inputShapeInfo[indIiW+1];                     // input width
            const int iC = inputShapeInfo[indIOioC+1];                   // input channels
            const int oC = weightsShapeInfo[indWoC+1];                   // output channels

            int trueoH, trueoW;          // true output height, width
            ConvolutionUtils::calcOutSizePool2D(trueoH,trueoW, 1,kW, 1,sW, 0,pW, 1,dW, 1,iW, paddingMode);

            std::vector<Nd4jLong> expectedGradOShape   = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoW,  0,indIOioC,indIiW});
            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}));
            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());
            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());
            if(biasShapeInfo)
            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));

            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));
        }

        DECLARE_TYPES(conv1d_bp) {
            getOpDescriptor()
                    ->setAllowedInputTypes(0, {ALL_FLOATS, ALL_INTS, DataType::QINT8, DataType::QINT16})
                    ->setAllowedInputTypes(1, {ALL_FLOATS})
                    ->setAllowedInputTypes(2, {ALL_FLOATS})
                    ->setAllowedInputTypes(3, {ALL_FLOATS})
                    ->setAllowedOutputTypes(0, {ALL_FLOATS})
                    ->setAllowedOutputTypes(1, {ALL_FLOATS});
        }



    }
}

#endif