cavis/libnd4j/include/ops/declarable/generic/nn/fusedBatchNorm.cpp

/* ******************************************************************************
 *
 *
 * 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.
 *
 *  See the NOTICE file distributed with this work for additional
 *  information regarding copyright ownership.
 * 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
 ******************************************************************************/

//
// Created by raver119 on 29/10/17.
//

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

#include <ops/declarable/CustomOperations.h>

namespace sd {
    namespace ops {

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

        CUSTOM_OP_IMPL(fused_batch_norm, 3, 3, false, 0, 2) {
            auto x      = INPUT_VARIABLE(0);                 // [bS,iH,iW,iD] (NHWC) or [bS,iD,iH,iW] (NCHW)
            auto scale  = INPUT_VARIABLE(1);                 // [iD]
            auto offset = INPUT_VARIABLE(2);                 // [iD]

            auto y = OUTPUT_VARIABLE(0);                     // [bS,iH,iW,iD] (NHWC) or [bS,iD,iH,iW] (NCHW)
            auto batchMean = OUTPUT_VARIABLE(1);             // [iD]
            auto batchVar  = OUTPUT_VARIABLE(2);             // [iD]

            const bool dataFormat = (bool)INT_ARG(0);               // 0->NHWC, 1->NCHW
            const bool isTraining = (bool)INT_ARG(1);
            nd4j_debug("CUSTOM_OP fused_batch_norm: data format, is NCHW: %d, isTraining: %d\n",dataFormat,isTraining);

            REQUIRE_TRUE(x->rankOf() == 4, 0, "CUSTOM_OP fused_batch_norm: the rank of input x array must be equal to 4, but got %i instead !", x->rankOf());

            int bS = x->sizeAt(0);              // batch size
            int iH, iW, iD;                     // input height, input width, input depth(number of channels)
            if(dataFormat) {
                iD = x->sizeAt(1);
                iH = x->sizeAt(2);
                iW = x->sizeAt(3);
            }
            else {
                iD = x->sizeAt(3);
                iH = x->sizeAt(1);
                iW = x->sizeAt(2);
            }

            auto xCast = x->cast(sd::DataType::FLOAT32);
            //move to NWHC
            /**
             * TODO: TF has a permute to NWHC here:
             * https://github.com/tensorflow/tensorflow/blob/ce34a83e03394492b1c4e5bb92fbd56da2ba7ce5/tensorflow/core/kernels/fused_batch_norm_op.cc#L137
             *
             * This should be done as well for us, but results are still off.
             * Figure out differences.
             */
            if(dataFormat) {
                xCast.printShapeInfo("x cast shape info pre permute");
                xCast = xCast.permute({0, 2, 3, 1});
                xCast.printShapeInfo("x cast shape info post permute");
            }
            REQUIRE_TRUE(scale->rankOf() == 1  && scale->sizeAt(0)  == iD, 0, "CUSTOM_OP fused_batch_norm: wrong shape of input scale array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(scale).c_str());
            REQUIRE_TRUE(offset->rankOf() == 1 && offset->sizeAt(0) == iD, 0, "CUSTOM_OP fused_batch_norm: wrong shape of input offset array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(offset).c_str());

            NDArray *mean(nullptr), *variance(nullptr);
            if(!isTraining) {
                mean     = INPUT_VARIABLE(3);
                variance = INPUT_VARIABLE(4);
                REQUIRE_TRUE(mean->rankOf() == 1     && mean->sizeAt(0) == iD,     0, "CUSTOM_OP fused_batch_norm: wrong shape of input mean array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(mean).c_str());
                REQUIRE_TRUE(variance->rankOf() == 1 && variance->sizeAt(0) == iD, 0, "CUSTOM_OP fused_batch_norm: wrong shape of input variance array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(variance).c_str());
            }
            else {
                //REQUIRE_TRUE(block.width() == 3, 0, "CUSTOM_OP fused_batch_norm: when isTraining=true then number of input arrays must be equal to 3, but got %i instead !", block.width());
                std::vector<Nd4jLong> shape = {iD};
                mean = NDArrayFactory::create_(scale->ordering(), shape, sd::DataType::FLOAT32, block.launchContext());
                variance = NDArrayFactory::create_(scale->ordering(), shape, sd::DataType::FLOAT32, block.launchContext());
            }


            float epsilon;
            if(block.getTArguments()->size() > 0) {
                epsilon = (float) (T_ARG(0) > 1.001e-5 ? T_ARG(0) : 1.001e-5);
            }
            else {
                epsilon = 0.001f;
            }

            const int restSize = x->lengthOf() / iD;

            auto xAffected = NDArrayFactory::create(x->ordering(), {restSize, iD}, sd::DataType::FLOAT32, block.launchContext());
            xAffected.assign(xCast);

            const int restSizeMinusOne = (restSize > 1) ? (restSize - 1) : 1;
            const float restSizeInv = 1.0f / restSize;
            const float restSizeAdjust = (float)restSize / restSizeMinusOne;

            if(isTraining) {
                auto sum = xAffected.reduceAlongDimension(reduce::Sum, {0});
                sum *=  restSizeInv;
                mean->assign(sum);
                *batchMean = *mean;
            }
            else
                *batchMean = 0.;

            auto xCentered = xAffected - *mean;
            xAffected -= *mean;

            if(isTraining) {
                int power = 2;
                xAffected.applyScalar(scalar::Pow, power, xAffected);
                auto sum = xAffected.reduceAlongDimension(reduce::Sum, {0});
                sum *=  restSizeInv;
                variance->assign(sum);
                auto varOutput   = (*variance) * restSizeAdjust;
                batchVar->assign(varOutput);
            }
            else
                *batchVar  = 0.;

            auto scaledVariance =  ((*variance + epsilon).transform(transform::RSqrt) * (*scale)).cast(xAffected.dataType());
            auto xScaled1 = xCentered * scaledVariance;
            auto xShifted1 = xScaled1 + *offset;
            if(dataFormat) {
                //need to reshape from matrix to 4d then permute the ordering due to NWHC  ordering
                auto reshaped = xShifted1.reshape(xCast.ordering(),xCast.getShapeAsVector());
                reshaped.permutei({0,3,1,2});
                y->assign(reshaped);

            }
            else //NWHC case
                y->assign(xShifted1);


            if(isTraining) {
                delete mean;
                delete variance;
            }

            return Status::OK();
        }



        DECLARE_SHAPE_FN(fused_batch_norm) {
            auto xShapeInfo     = inputShape->at(0);
            auto scaleShapeInfo = inputShape->at(1);

            const bool dataFormat = (bool)INT_ARG(0);               // 0->NHWC, 1->NCHW
            const int iD = dataFormat ? xShapeInfo[2] : xShapeInfo[4];

            REQUIRE_TRUE(scaleShapeInfo[0] == 1  && scaleShapeInfo[1] == iD, 0, "CUSTOM_OP fused_batch_norm: wrong shape of input scale array, expected is [%i], but got %s instead", iD, ShapeUtils::shapeAsString(scaleShapeInfo).c_str());

            Nd4jLong* outShapeInfo(nullptr), *batchMeanShapeInfo(nullptr), *batchVarShapeInfo(nullptr);

            COPY_SHAPE(xShapeInfo, outShapeInfo);
            COPY_SHAPE(scaleShapeInfo, batchMeanShapeInfo);
            COPY_SHAPE(scaleShapeInfo, batchVarShapeInfo);

            return SHAPELIST(CONSTANT(outShapeInfo), CONSTANT(batchMeanShapeInfo), CONSTANT(batchVarShapeInfo));
        }

    }
}

#endif