cavis/libnd4j/include/ops/declarable/platform/mkldnn/maxpooling2d.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 saudet
// @author raver119@gmail.com
// @author Yurii Shyrma (iuriish@yahoo.com)
//

#include <ops/declarable/PlatformHelper.h>
#include <ops/declarable/OpRegistrator.h>
#include <system/platform_boilerplate.h>

#include <helpers/MKLDNNStream.h>
#include "mkldnnUtils.h"
#include <ops/declarable/helpers/convolutions.h>

using namespace dnnl;

namespace sd      {
namespace ops       {
namespace platforms {


//////////////////////////////////////////////////////////////////////////
PLATFORM_IMPL(maxpool2d, ENGINE_CPU) {

    auto input = INPUT_VARIABLE(0);
    auto output = OUTPUT_VARIABLE(0);

    REQUIRE_TRUE(input->rankOf() == 4, 0, "MAXPOOL2D MKLDNN  OP: input array should have rank of 4, but got %i instead", input->rankOf());

    // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode;
    const int kH = INT_ARG(0);
    const int kW = INT_ARG(1);
    const int sH = INT_ARG(2);
    const int sW = INT_ARG(3);
          int pH = INT_ARG(4);
          int pW = INT_ARG(5);
    const int dH = INT_ARG(6);
    const int dW = INT_ARG(7);
    const int paddingMode = INT_ARG(8);
    // const int extraParam0 = INT_ARG(9);
    const int isNCHW  = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1;       // INT_ARG(10): 1-NHWC, 0-NCHW

    REQUIRE_TRUE(dH != 0 && dW != 0, 0, "MAXPOOL2D MKLDNN op: dilation must not be zero, but got instead {%i, %i}", dH, dW);

    int bS, iC, iH, iW, oC, oH, oW;                             // batch size, input channels, input height/width, output channels, output height/width;
    int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH;       // corresponding indexes
    ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, 0, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);

    if (paddingMode)
        ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);

    mkldnnUtils::poolingMKLDNN(input, output, 0,kH,kW, 0,sH,sW, 0,pH,pW, isNCHW, algorithm::pooling_max);

    return Status::OK();
}

//////////////////////////////////////////////////////////////////////////
PLATFORM_CHECK(maxpool2d, ENGINE_CPU) {
    auto input = INPUT_VARIABLE(0);
    auto output = OUTPUT_VARIABLE(0);

    return block.isUseMKLDNN() && sd::MKLDNNStream::isSupported({input, output});
}

//////////////////////////////////////////////////////////////////////////
PLATFORM_IMPL(maxpool2d_bp, ENGINE_CPU) {

    auto input = INPUT_VARIABLE(0);                          // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
    auto gradO = INPUT_VARIABLE(1);                          // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
    auto gradI = OUTPUT_VARIABLE(0);                         // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon

    int kH = INT_ARG(0);                                                        // filter(kernel) height
    int kW = INT_ARG(1);                                                        // filter(kernel) width
    int sH = INT_ARG(2);                                                        // strides height
    int sW = INT_ARG(3);                                                        // strides width
    int pH = INT_ARG(4);                                                        // paddings height
    int pW = INT_ARG(5);                                                        // paddings width
    int dH = INT_ARG(6);                                                        // dilations height
    int dW = INT_ARG(7);                                                        // dilations width
    int paddingMode = INT_ARG(8);                                                // 0-VALID, 1-SAME
    // int extraParam0 = INT_ARG(9);
    int isNCHW = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1;         // INT_ARG(10): 0-NCHW, 1-NHWC

    REQUIRE_TRUE(input->rankOf() == 4, 0, "MAXPOOL2D_BP MKLDNN op: input should have rank of 4, but got %i instead", input->rankOf());
    REQUIRE_TRUE(dH != 0 && dW != 0, 0, "MAXPOOL2D_BP MKLDNN op: dilation must not be zero, but got instead {%i, %i}", dH, dW);

    int bS, iC, iH, iW, oC, oH, oW;                             // batch size, input channels, input height/width, output channels, output height/width;
    int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH;       // corresponding indexes
    ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, 0, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);

    std::vector<Nd4jLong> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS, iC, oH, oW, 0, indIOioC, indIiH, indIiH + 1});
    REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "MAXPOOL2D_BP MKLDNN op: wrong shape of output's gradients array (next epsilon), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str());

    if (paddingMode)                       // SAME
        ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);

    mkldnnUtils::poolingBpMKLDNN(input, gradO, gradI, 0,kH,kW, 0,sH,sW, 0,pH,pW, isNCHW, algorithm::pooling_max);

    return Status::OK();
}

PLATFORM_CHECK(maxpool2d_bp, ENGINE_CPU) {
    auto input = INPUT_VARIABLE(0);
    auto output = OUTPUT_VARIABLE(0);

    return block.isUseMKLDNN() && sd::MKLDNNStream::isSupported({input, output});
}

}
}
}