cavis/libnd4j/include/ops/declarable/platform/mkldnn/conv3d.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
//

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

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

using namespace dnnl;

namespace nd4j      {
namespace ops       {
namespace platforms {

//////////////////////////////////////////////////////////////////////
PLATFORM_IMPL(conv3dnew) {
    auto input = INPUT_VARIABLE(
            0);                                    // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
    auto weights = INPUT_VARIABLE(1);                                    // [kD, kH, kW, iC, oC] always
    auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr;      // [oC]
    auto output = OUTPUT_VARIABLE(
            0);                                   // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW)

    REQUIRE_TRUE(input->rankOf() == 5, 0,
                 "CUSTOM CONV3D OP: rank of input array must be equal to 5, but got %i instead !",
                 input->rankOf());
    REQUIRE_TRUE(weights->rankOf() == 5, 0,
                 "CUSTOM CONV3D OP: rank of weights array must be equal to 5, but got %i instead !",
                 weights->rankOf());

    int kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) depth
    int kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1));// filter(kernel) height
    int kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<int>(weights->sizeAt(2));// filter(kernel) width
    int sD = INT_ARG(3);                                                        // strides depth
    int sH = INT_ARG(4);                                                        // strides height
    int sW = INT_ARG(5);                                                        // strides width
    int pD = INT_ARG(6);                                                        // paddings depth
    int pH = INT_ARG(7);                                                        // paddings height
    int pW = INT_ARG(8);                                                        // paddings width
    int dD = INT_ARG(9);                                                        // dilations depth
    int dH = INT_ARG(10);                                                       // dilations height
    int dW = INT_ARG(11);                                                       // dilations width
    int isSameMode = INT_ARG(12);                                               // 0-SAME,  1-VALID
    int isNCDHW =
            block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1;       // INT_ARG(13): 1-NDHWC, 0-NCDHW

    int bS, iC, iD, iH, iW, oC, oD, oH, oW;                     // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
    int indIOioC, indIOioD, indWoC, indWiC, indWkD;             // corresponding indexes
    ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW,
                                               indIOioC, indIOioD, indWiC, indWoC, indWkD);

    std::string expectedWeightsShape = ShapeUtils::shapeAsString({kD, kH, kW, iC, oC});
    REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0,
                 "CUSTOM CONV3D OP: wrong shape of weights array, expected is %s, but got %s instead !",
                 expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str());
    if (bias)
        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());

    if (isSameMode)                       // SAME
        ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW);


    dnnl_memory_desc_t empty;
    dnnl::memory::desc conv_src_md(empty), conv_weights_md(empty), conv_bias_md(empty), conv_dst_md(
            empty);
    dnnl::memory::desc user_src_md(empty), user_weights_md(empty), user_bias_md(empty), user_dst_md(
            empty);
    dnnl::memory::dims conv_strides, conv_padding, conv_padding_r, conv_dilation;
    mkldnnUtils::getMKLDNNMemoryDescConv3d(kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, isSameMode,
                                           isNCDHW,
                                           bS, iC, iD, iH, iW, oC, oD, oH, oW, input, nullptr, weights,
                                           nullptr, bias, output,
                                           &conv_src_md, nullptr, &conv_weights_md, nullptr,
                                           &conv_bias_md, &conv_dst_md,
                                           &user_src_md, nullptr, &user_weights_md, nullptr,
                                           &user_bias_md, &user_dst_md,
                                           conv_strides, conv_padding, conv_padding_r, conv_dilation);
    auto conv_desc = bias != nullptr
                     ? convolution_forward::desc(prop_kind::forward,
                                                 algorithm::convolution_auto, conv_src_md,
                                                 conv_weights_md, conv_bias_md,
                                                 conv_dst_md, conv_strides, conv_dilation, conv_padding,
                                                 conv_padding_r)
                     : convolution_forward::desc(prop_kind::forward,
                                                 algorithm::convolution_auto, conv_src_md,
                                                 conv_weights_md,
                                                 conv_dst_md, conv_strides, conv_dilation, conv_padding,
                                                 conv_padding_r);
    auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
    dnnl::stream stream(engine);
    auto conv_prim_desc = convolution_forward::primitive_desc(conv_desc, engine);
    auto user_src_memory = dnnl::memory(user_src_md, engine, const_cast<NDArray *>(input)->buffer());
    auto user_weights_memory = dnnl::memory(user_weights_md, engine,
                                              const_cast<NDArray *>(weights)->buffer());
    auto user_dst_memory = dnnl::memory(user_dst_md, engine, output->buffer());
    auto conv_src_memory = user_src_memory;
    if (conv_prim_desc.src_desc() != user_src_memory.get_desc()) {
        conv_src_memory = dnnl::memory(conv_prim_desc.src_desc(), engine);
        reorder(user_src_memory, conv_src_memory).execute(stream, user_src_memory, conv_src_memory);
    }
    auto conv_weights_memory = user_weights_memory;
    if (conv_prim_desc.weights_desc() != user_weights_memory.get_desc()) {
        conv_weights_memory = dnnl::memory(conv_prim_desc.weights_desc(), engine);
        reorder(user_weights_memory, conv_weights_memory).execute(stream, user_weights_memory,
                                                                  conv_weights_memory);
    }
    auto conv_dst_memory = user_dst_memory;
    if (conv_prim_desc.dst_desc() != user_dst_memory.get_desc()) {
        conv_dst_memory = dnnl::memory(conv_prim_desc.dst_desc(), engine);
    }
    if (bias != nullptr) {
        auto conv_bias_memory = dnnl::memory(conv_prim_desc.bias_desc(), engine, bias->buffer());
        convolution_forward(conv_prim_desc).execute(stream, {{DNNL_ARG_SRC,     conv_src_memory},
                                                             {DNNL_ARG_WEIGHTS, conv_weights_memory},
                                                             {DNNL_ARG_BIAS,    conv_bias_memory},
                                                             {DNNL_ARG_DST,     conv_dst_memory}});
    } else {
        convolution_forward(conv_prim_desc).execute(stream, {{DNNL_ARG_SRC,     conv_src_memory},
                                                             {DNNL_ARG_WEIGHTS, conv_weights_memory},
                                                             {DNNL_ARG_DST,     conv_dst_memory}});
    }
    if (conv_prim_desc.dst_desc() != user_dst_memory.get_desc()) {
        reorder(conv_dst_memory, user_dst_memory).execute(stream, conv_dst_memory, user_dst_memory);
    }
    stream.wait();

    return Status::OK();
}

PLATFORM_CHECK(conv3dnew) {
    // we don't want to use mkldnn if cpu doesn't support avx/avx2
    if (::optimalLevel() < 2)
        return false;

    auto input = INPUT_VARIABLE(
            0);                                    // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
    auto weights = INPUT_VARIABLE(1);                                    // [kD, kH, kW, iC, oC] always
    auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr;      // [oC]
    auto output = OUTPUT_VARIABLE(
            0);                                   // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW)

    return block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, weights, bias, output});
}


//////////////////////////////////////////////////////////////////////
PLATFORM_IMPL(conv3dnew_bp) {
    auto input = INPUT_VARIABLE(
            0);                                                // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
    auto weights = INPUT_VARIABLE(
            1);                                                // [kD, kH, kW, iC, oC] always
    auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr;                  // [oC]
    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

    auto gradI = OUTPUT_VARIABLE(
            0);                                                 // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW), epsilon
    auto gradW = OUTPUT_VARIABLE(
            1);                                                 // [kD, kH, kW, iC, oC] always
    auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr;                   // [oC]

    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());
    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());
    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());

    int kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) depth
    int kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1));// filter(kernel) height
    int kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<int>(weights->sizeAt(2));// filter(kernel) width
    int sD = INT_ARG(3);                                                        // strides depth
    int sH = INT_ARG(4);                                                        // strides height
    int sW = INT_ARG(5);                                                        // strides width
    int pD = INT_ARG(6);                                                        // paddings depth
    int pH = INT_ARG(7);                                                        // paddings height
    int pW = INT_ARG(8);                                                        // paddings width
    int dD = INT_ARG(9);                                                        // dilations depth
    int dH = INT_ARG(10);                                                       // dilations height
    int dW = INT_ARG(11);                                                       // dilations width
    int isSameMode = INT_ARG(12);                                               // 1-SAME,  0-VALID
    int isNDHWC =
            block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1;       // INT_ARG(13): 1-NDHWC, 0-NCDHW

    int bS, iC, iD, iH, iW, oC, oD, oH, oW;                     // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
    int indIOioC, indIOioD, indWoC, indWiC, indWkD;             // corresponding indexes
    ConvolutionUtils::getSizesAndIndexesConv3d(isNDHWC, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW,
                                               indIOioC, indIOioD, indWiC, indWoC, indWkD);

    if(isSameMode)                       // SAME
        ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW);

    int trueoD, trueoH, trueoW;          // true output depth/height/width
    ConvolutionUtils::calcOutSizePool3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH,
                                        dW, iD, iH, iW, isSameMode);

    std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx(
            {bS, oC, trueoD, trueoH, trueoW, 0, indIOioC, indIOioD, indIOioD + 1, indIOioD + 2}));
    std::string expectedWeightsShape = ShapeUtils::shapeAsString({kD, kH, kW, iC, oC});
    REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0,
                 "CUSTOM CONV3D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !",
                 expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradO).c_str());
    REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0,
                 "CUSTOM CONV3D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !",
                 expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str());
    if (bias)
        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());


    dnnl_memory_desc_t empty;
    dnnl::memory::desc conv_src_md(empty), conv_diff_src_md(empty), conv_weights_md(empty),
            conv_diff_weights_md(empty), conv_bias_md(empty), conv_dst_md(empty);
    dnnl::memory::desc user_src_md(empty), user_diff_src_md(empty), user_weights_md(empty),
            user_diff_weights_md(empty), user_bias_md(empty), user_dst_md(empty);
    dnnl::memory::dims conv_strides, conv_padding, conv_padding_r, conv_dilation;
    mkldnnUtils::getMKLDNNMemoryDescConv3d(kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, isSameMode,
                                           isNDHWC,
                                           bS, iC, iD, iH, iW, oC, oD, oH, oW, input, gradI, weights,
                                           gradW, gradB, gradO,
                                           &conv_src_md, &conv_diff_src_md, &conv_weights_md,
                                           &conv_diff_weights_md, &conv_bias_md, &conv_dst_md,
                                           &user_src_md, &user_diff_src_md, &user_weights_md,
                                           &user_diff_weights_md, &user_bias_md, &user_dst_md,
                                           conv_strides, conv_padding, conv_padding_r, conv_dilation);
    auto conv_desc = gradB != nullptr
                     ? convolution_forward::desc(prop_kind::forward,
                                                 algorithm::convolution_auto, conv_src_md,
                                                 conv_weights_md, conv_bias_md,
                                                 conv_dst_md, conv_strides, conv_dilation, conv_padding,
                                                 conv_padding_r)
                     : convolution_forward::desc(prop_kind::forward,
                                                 algorithm::convolution_auto, conv_src_md,
                                                 conv_weights_md,
                                                 conv_dst_md, conv_strides, conv_dilation, conv_padding,
                                                 conv_padding_r);
    auto conv_prim_desc = convolution_forward::primitive_desc(conv_desc, mkldnnUtils::getEngine(
            LaunchContext::defaultContext()->engine()));
    if (gradW != nullptr) {
        auto convW_desc = gradB != nullptr
                          ? convolution_backward_weights::desc(
                        algorithm::convolution_auto, conv_src_md, conv_diff_weights_md, conv_bias_md,
                        conv_dst_md, conv_strides, conv_dilation, conv_padding, conv_padding_r)
                          : convolution_backward_weights::desc(
                        algorithm::convolution_auto, conv_src_md, conv_diff_weights_md,
                        conv_dst_md, conv_strides, conv_dilation, conv_padding, conv_padding_r);

        auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
        dnnl::stream stream(engine);
        auto convW_prim_desc = convolution_backward_weights::primitive_desc(convW_desc, engine,
                                                                            conv_prim_desc);
        auto userW_src_memory = dnnl::memory(user_src_md, engine,
                                               const_cast<NDArray *>(input)->buffer());
        auto userW_weights_memory = dnnl::memory(user_diff_weights_md, engine, gradW->buffer());
        auto userW_dst_memory = dnnl::memory(user_dst_md, engine,
                                               const_cast<NDArray *>(gradO)->buffer());

        auto convW_src_memory = userW_src_memory;
        if (convW_prim_desc.src_desc() != userW_src_memory.get_desc()) {
            convW_src_memory = dnnl::memory(convW_prim_desc.src_desc(), engine);
            reorder(userW_src_memory, convW_src_memory).execute(stream, userW_src_memory,
                                                                convW_src_memory);
        }

        auto convW_weights_memory = userW_weights_memory;
        if (convW_prim_desc.diff_weights_desc() != userW_weights_memory.get_desc()) {
            convW_weights_memory = dnnl::memory(convW_prim_desc.diff_weights_desc(), engine);
        }

        auto convW_dst_memory = userW_dst_memory;
        if (convW_prim_desc.diff_dst_desc() != userW_dst_memory.get_desc()) {
            convW_dst_memory = dnnl::memory(convW_prim_desc.diff_dst_desc(), engine);
            reorder(userW_dst_memory, convW_dst_memory).execute(stream, userW_dst_memory,
                                                                convW_dst_memory);
        }

        if (gradB != nullptr) {
            auto convW_bias_memory = dnnl::memory(convW_prim_desc.diff_bias_desc(), engine,
                                                    gradB->buffer());
            convolution_backward_weights(convW_prim_desc).execute(stream,
                                                                  {{DNNL_ARG_SRC,          convW_src_memory},
                                                                   {DNNL_ARG_DIFF_DST,     convW_dst_memory},
                                                                   {DNNL_ARG_DIFF_WEIGHTS, convW_weights_memory},
                                                                   {DNNL_ARG_DIFF_BIAS,    convW_bias_memory}});
        } else {
            convolution_backward_weights(convW_prim_desc).execute(stream,
                                                                  {{DNNL_ARG_SRC,          convW_src_memory},
                                                                   {DNNL_ARG_DIFF_DST,     convW_dst_memory},
                                                                   {DNNL_ARG_DIFF_WEIGHTS, convW_weights_memory}});
        }

        if (convW_prim_desc.diff_weights_desc() != userW_weights_memory.get_desc()) {
            reorder(convW_weights_memory, userW_weights_memory).execute(stream, convW_weights_memory,
                                                                        userW_weights_memory);
        }

        stream.wait();
    }
    if (gradI != nullptr) {
        auto convI_desc = convolution_backward_data::desc(algorithm::convolution_auto,
                                                          conv_diff_src_md, conv_weights_md,
                                                          conv_dst_md, conv_strides, conv_dilation, conv_padding,
                                                          conv_padding_r);

        auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
        dnnl::stream stream(engine);
        auto convI_prim_desc = convolution_backward_data::primitive_desc(convI_desc, engine,
                                                                         conv_prim_desc);
        auto userI_src_memory = dnnl::memory(user_diff_src_md, engine, gradI->buffer());
        auto userI_weights_memory = dnnl::memory(user_weights_md, engine,
                                                   const_cast<NDArray *>(weights)->buffer());
        auto userI_dst_memory = dnnl::memory(user_dst_md, engine,
                                               const_cast<NDArray *>(gradO)->buffer());

        auto convI_src_memory = userI_src_memory;
        if (convI_prim_desc.diff_src_desc() != userI_src_memory.get_desc()) {
            convI_src_memory = dnnl::memory(convI_prim_desc.diff_src_desc(), engine);
        }

        auto convI_weights_memory = userI_weights_memory;
        if (convI_prim_desc.weights_desc() != userI_weights_memory.get_desc()) {
            convI_weights_memory = dnnl::memory(convI_prim_desc.weights_desc(), engine);
            reorder(userI_weights_memory, convI_weights_memory).execute(stream, userI_weights_memory,
                                                                        convI_weights_memory);
        }

        auto convI_dst_memory = userI_dst_memory;
        if (convI_prim_desc.diff_dst_desc() != userI_dst_memory.get_desc()) {
            convI_dst_memory = dnnl::memory(convI_prim_desc.diff_dst_desc(), engine);
            reorder(userI_dst_memory, convI_dst_memory).execute(stream, userI_dst_memory,
                                                                convI_dst_memory);
        }

        convolution_backward_data(convI_prim_desc).execute(stream,
                                                           {{DNNL_ARG_DIFF_DST, convI_dst_memory},
                                                            {DNNL_ARG_WEIGHTS,  convI_weights_memory},
                                                            {DNNL_ARG_DIFF_SRC, convI_src_memory}});

        if (convI_prim_desc.diff_src_desc() != userI_src_memory.get_desc()) {
            reorder(convI_src_memory, userI_src_memory).execute(stream, convI_src_memory,
                                                                userI_src_memory);
        }

        stream.wait();
    }

    return Status::OK();
}

PLATFORM_CHECK(conv3dnew_bp) {
    auto input = INPUT_VARIABLE(
            0);                                                // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
    auto weights = INPUT_VARIABLE(
            1);                                                // [kD, kH, kW, iC, oC] always
    auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr;                  // [oC]
    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

    auto gradI = OUTPUT_VARIABLE(
            0);                                                 // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW), epsilon
    auto gradW = OUTPUT_VARIABLE(
            1);                                                 // [kD, kH, kW, iC, oC] always
    auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr;                   // [oC]

    return block.isUseMKLDNN() &&
           nd4j::MKLDNNStream::isSupported({input, weights, bias, gradO, gradI, gradW, gradB});
}



}
}
}