deeplearning4j/deeplearning4j

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libnd4j/include/ops/declarable/generic/nn/recurrent/sruCell.cpp

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/*******************************************************************************
 * 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 Yurii Shyrma, created on 05.12.2017
//

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

#include <ops/declarable/CustomOperations.h>
#include<ops/declarable/helpers/sru.h>


namespace sd {
namespace ops  {


//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(sruCell, 4, 2, false, 0, 0) {
    auto xt   = INPUT_VARIABLE(0);               // input [bS x inSize], bS - batch size, inSize - number of features
    auto ct_1 = INPUT_VARIABLE(1);               // previous cell state ct  [bS x inSize], that is at previous time step t-1
    auto w    = INPUT_VARIABLE(2);               // weights [inSize x 3*inSize]
    auto b    = INPUT_VARIABLE(3);               // biases [2*inSize]

    auto ht   = OUTPUT_VARIABLE(0);              // current cell output [bS x inSize], that is at current time step t
    auto ct   = OUTPUT_VARIABLE(1);              // current cell state  [bS x inSize], that is at current time step t

    const int rank   = xt->rankOf();
    const int bS     = xt->sizeAt(0);
    const int inSize = xt->sizeAt(1);                   // inSize - number of features

    // input shapes validation
    const std::vector<Nd4jLong> correctCt_1Shape = {bS, inSize};
    const std::vector<Nd4jLong> correctWShape    = {inSize, 3*inSize};
    const std::vector<Nd4jLong> correctBShape    = {2*inSize};

    REQUIRE_TRUE(ct_1->isSameShape(correctCt_1Shape), 0, "SRUCELL operation: wrong shape of previous cell state, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctCt_1Shape).c_str(), ShapeUtils::shapeAsString(ct_1).c_str());
    REQUIRE_TRUE(w->isSameShape(correctWShape),    0, "SRUCELL operation: wrong shape of weights, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctWShape).c_str(), ShapeUtils::shapeAsString(w).c_str());
    REQUIRE_TRUE(b->isSameShape(correctBShape),    0, "SRUCELL operation: wrong shape of biases, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctBShape).c_str(), ShapeUtils::shapeAsString(b).c_str());


    // fixme: shitty initializer lists
    helpers::sruCell(block.launchContext(), xt, ct_1, w, b, ht, ct);

    return Status::OK();
}

        DECLARE_TYPES(sruCell) {
            getOpDescriptor()
                    ->setAllowedInputTypes(sd::DataType::ANY)
                    ->setAllowedOutputTypes({ALL_FLOATS});
        }

DECLARE_SHAPE_FN(sruCell) {

    auto xtShapeInfo   = inputShape->at(0);               // input [bS x inSize], bS - batch size, inSize - number of features
    auto ct_1ShapeInfo = inputShape->at(1);               // previous cell state ct  [bS x inSize], that is at previous time step t-1
    auto wShapeInfo    = inputShape->at(2);               // weights [inSize x 3*inSize]
    auto bShapeInfo    = inputShape->at(3);               // biases [2*inSize]

    const int rank   = xtShapeInfo[0];
    const int bS     = xtShapeInfo[1];
    const int inSize = xtShapeInfo[2];                   // inSize - number of features

    // input shapes validation
    const std::vector<Nd4jLong> correctCt_1Shape = {bS, inSize};
    const std::vector<Nd4jLong> correctWShape    = {inSize, 3*inSize};
    const std::vector<Nd4jLong> correctBShape    = {2*inSize};

    REQUIRE_TRUE(ShapeUtils::areShapesEqual(ct_1ShapeInfo, correctCt_1Shape) , 0, "SRUCELL operation: wrong shape of previous cell state, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctCt_1Shape).c_str(), ShapeUtils::shapeAsString(ct_1ShapeInfo).c_str());
    REQUIRE_TRUE(ShapeUtils::areShapesEqual(wShapeInfo ,correctWShape),    0, "SRUCELL operation: wrong shape of weights, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctWShape).c_str(), ShapeUtils::shapeAsString(wShapeInfo).c_str());
    REQUIRE_TRUE(ShapeUtils::areShapesEqual(bShapeInfo ,correctBShape),    0, "SRUCELL operation: wrong shape of biases, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctBShape).c_str(), ShapeUtils::shapeAsString(bShapeInfo).c_str());

    // evaluate output shapeInfos
    Nd4jLong *hShapeInfo(nullptr), *cShapeInfo(nullptr);
    ALLOCATE(hShapeInfo, block.getWorkspace(), shape::shapeInfoLength(rank), Nd4jLong);      // [bS x numProj]
    ALLOCATE(cShapeInfo, block.getWorkspace(), shape::shapeInfoLength(rank), Nd4jLong);      // [bS x numUnits]

    hShapeInfo[0] = cShapeInfo[0] = rank;
    hShapeInfo[1] = cShapeInfo[1] = bS;
    hShapeInfo[2] = cShapeInfo[2] = inSize;

    ShapeUtils::updateStridesAndType(hShapeInfo, ct_1ShapeInfo, shape::order(ct_1ShapeInfo));
    ShapeUtils::updateStridesAndType(cShapeInfo, ct_1ShapeInfo, shape::order(ct_1ShapeInfo));

    return SHAPELIST(ConstantShapeHelper::getInstance()->createFromExisting(hShapeInfo, block.workspace()), ConstantShapeHelper::getInstance()->createFromExisting(cShapeInfo, block.workspace()));
}




}
}

#endif