360 lines
15 KiB
C++
360 lines
15 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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* Copyright (c) 2019 Konduit K.K.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author raver119@gmail.com, created on 29/10/17.
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// @author Yurii Shyrma (iuriish@yahoo.com)
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//
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#include <system/op_boilerplate.h>
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#if NOT_EXCLUDED(OP_batchnorm)
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#include <ops/declarable/CustomOperations.h>
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#include<ops/declarable/helpers/batchnorm.h>
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namespace sd {
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namespace ops {
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(batchnorm, 3, 1, false, 1, 2) {
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auto input = INPUT_VARIABLE(0);
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auto mean = INPUT_VARIABLE(1);
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auto variance = INPUT_VARIABLE(2);
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NDArray* gamma = nullptr;
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NDArray* beta = nullptr;
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auto output = OUTPUT_VARIABLE(0);
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const bool applyScale = (bool)INT_ARG(0);
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const bool applyOffset = (bool)INT_ARG(1);
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const double epsilon = T_ARG(0);
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if(applyScale)
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gamma = INPUT_VARIABLE(3);
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if(applyOffset)
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beta = INPUT_VARIABLE(3 + (int)applyScale);
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const int numOfIntArgs = block.getIArguments()->size();
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const int inRank = input->rankOf();
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// get axes args to normalize input array over
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std::vector<int> axes;
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if(numOfIntArgs > 2)
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for(int i = 2; i < numOfIntArgs; ++i)
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axes.push_back(INT_ARG(i));
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else
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axes.push_back(inRank-1); // default dimension to reduce along is last dimension
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const uint numOfAxes = axes.size();
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REQUIRE_TRUE(numOfAxes <= inRank, 0, "BATCHNORM op: too big number of input axes to normalize over, expected number should be less or equal to rank of input array, but got %i and %i correspondingly !", numOfAxes, inRank);
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// evaluate expected shape for mean, variance and gamma. These 3 arrays should have identical shapes
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// for example if input shape is {2,3,4,5,6} and axes = {1,3}, then expected shape would be {1,3,1,5,1}, and if axes = {3}, then expected shape would be {5}
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std::vector<Nd4jLong> expShape;
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if(numOfAxes == 1)
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expShape.push_back(input->sizeAt(axes[0]));
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else { // get, for example, something like {1, inputDim1, 1, inputDim3, 1} if axes = {1, 3}
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expShape = std::vector<Nd4jLong>(inRank, 1);
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for(uint i = 0; i < numOfAxes; ++i)
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expShape[axes[i]] = input->sizeAt(axes[i]);
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}
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REQUIRE_TRUE(mean->isSameShape(expShape) , 0, "BATCHNORM op: wrong shape of mean array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expShape).c_str(), ShapeUtils::shapeAsString(mean).c_str());
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REQUIRE_TRUE(variance->isSameShape(expShape), 0, "BATCHNORM op: wrong shape of variance array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expShape).c_str(), ShapeUtils::shapeAsString(variance).c_str());
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if(gamma)
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REQUIRE_TRUE(gamma->isSameShape(expShape), 0, "BATCHNORM op: wrong shape of gamma array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expShape).c_str(), ShapeUtils::shapeAsString(gamma).c_str());
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if(beta)
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REQUIRE_TRUE(beta->isSameShape(expShape), 0, "BATCHNORM op: wrong shape of beta array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expShape).c_str(), ShapeUtils::shapeAsString(beta).c_str());
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// types of all input arrays should be the same
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for(unsigned long i = 1; i < block.width(); ++i)
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REQUIRE_TRUE(INPUT_VARIABLE(0)->dataType() == INPUT_VARIABLE(i)->dataType(), 0, "BATCHNORM op: types of all input arrays should be the same !");
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nd4j_debug("MKL-DNN is not used for batchnorm!\n", 0);
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// formula: output = gamma * ((input - mean) / sqrt(variance + epsilon)) + beta
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// auto v = input->varianceAlongDimension(variance::SummaryStatsVariance, false, ShapeUtils::evalDimsToExclude(input->rankOf(), axes));
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// auto m = input->reduceAlongDimension(sd::reduce::Mean, ShapeUtils::evalDimsToExclude(input->rankOf(), axes));
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helpers::batchnorm(input, mean, variance, gamma, beta, output, axes, epsilon);
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// NDArray stdInv = *v + epsilon;
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// stdInv.applyTransform(transform::Reciprocal); // 1 / (variance + epsilon)
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// stdInv.applyTransform(transform::Sqrt); // 1 / (variance + epsilon)^0.5
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// if(applyScale)
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// stdInv *= *gamma;
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// // empty array with same shape as input
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// input->applyBroadcast(sd::broadcast::Subtract, axes, m, output);
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// output->applyBroadcast(sd::broadcast::Multiply, axes, &stdInv);
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// if(applyOffset)
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// output->applyBroadcast(sd::broadcast::Add, axes, beta);
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// delete v;
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// delete m;
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return Status::OK();
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}
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DECLARE_TYPES(batchnorm) {
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getOpDescriptor()->setAllowedInputTypes({ALL_FLOATS})->setSameMode(true);
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}
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DECLARE_SHAPE_FN(batchnorm) {
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auto inShapeInfo = inputShape->at(0);
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DataType outType = DataTypeUtils::pickFloatingType(ArrayOptions::dataType(inShapeInfo));
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auto outShapeInfo = ShapeBuilders::copyShapeInfoAndType(inShapeInfo, outType, false, block.getWorkspace()); // output shape is identical to input shape
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return SHAPELIST(CONSTANT(outShapeInfo));
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}
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(batchnorm_bp, 4, 3, false, 1, 2) {
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NDArray* input = INPUT_VARIABLE(0);
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NDArray* mean = INPUT_VARIABLE(1);
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NDArray* variance = INPUT_VARIABLE(2);
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NDArray* gamma = nullptr;
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NDArray* beta = nullptr;
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NDArray* dLdO = INPUT_VARIABLE(block.width() - 1); // next epsilon
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NDArray* dLdI = OUTPUT_VARIABLE(0);
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NDArray* dLdM = OUTPUT_VARIABLE(1);
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NDArray* dLdV = OUTPUT_VARIABLE(2);
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NDArray* dLdG = nullptr;
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NDArray* dLdB = nullptr;
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const bool applyScale = (bool)INT_ARG(0);
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const bool applyOffset = (bool)INT_ARG(1);
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const float epsilon = T_ARG(0);
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if(applyScale) {
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gamma = INPUT_VARIABLE(3);
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dLdG = OUTPUT_VARIABLE(3);
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}
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if(applyOffset) {
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beta = INPUT_VARIABLE(3 + (int)applyScale);
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dLdB = OUTPUT_VARIABLE(3 + (int)applyScale);
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}
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const int numOfIntArgs = block.getIArguments()->size();
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const int inRank = input->rankOf();
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// get axes args to normalize input array over
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std::vector<int> axes;
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if(numOfIntArgs > 2)
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for(int i = 2; i < numOfIntArgs; ++i)
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axes.push_back(INT_ARG(i));
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else
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axes.push_back(inRank-1); // default dimension to reduce along is last dimension
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const uint numOfAxes = axes.size();
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REQUIRE_TRUE(numOfAxes <= inRank, 0, "BATCHNORM_BP op: too big number of input axes to normalize over, expected number should be less or equal to rank of input array, but got %i and %i correspondingly !", numOfAxes, inRank);
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// evaluate expected shape for mean, variance and gamma. These 3 arrays should have identical shapes
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// for example if input shape is {2,3,4,5,6} and axes = {1,3}, then expected shape would be {1,3,1,5,1}, and if axes = {3}, then expected shape would be {5}
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std::vector<Nd4jLong> expShape;
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if(numOfAxes == 1)
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expShape.push_back(input->sizeAt(axes[0]));
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else { // get, for example, something like {1, inputDim1, 1, inputDim3, 1} if axes = {1, 3}
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expShape = std::vector<Nd4jLong>(inRank, 1);
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for(uint i = 0; i < numOfAxes; ++i)
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expShape[axes[i]] = input->sizeAt(axes[i]);
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}
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REQUIRE_TRUE(mean->isSameShape(expShape), 0, "BATCHNORM_BP op: wrong shape of mean array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expShape).c_str(), ShapeUtils::shapeAsString(mean).c_str());
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REQUIRE_TRUE(variance->isSameShape(expShape), 0, "BATCHNORM_BP op: wrong shape of variance array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expShape).c_str(), ShapeUtils::shapeAsString(variance).c_str());
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if(gamma)
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REQUIRE_TRUE(gamma->isSameShape(expShape), 0, "BATCHNORM_BP op: wrong shape of gamma array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expShape).c_str(), ShapeUtils::shapeAsString(gamma).c_str());
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if(beta)
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REQUIRE_TRUE(beta->isSameShape(expShape), 0, "BATCHNORM_BP op: wrong shape of beta array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expShape).c_str(), ShapeUtils::shapeAsString(beta).c_str());
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REQUIRE_TRUE(input->isSameShape(dLdO), 0, "BATCHNORM_BP op: wrong shape of output gradients array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(input).c_str(), ShapeUtils::shapeAsString(dLdO).c_str());
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// types of all input arrays should be the same (except dLdO)
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for(unsigned long i = 1; i < block.width() - 2; ++i)
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REQUIRE_TRUE(INPUT_VARIABLE(0)->dataType() == INPUT_VARIABLE(i)->dataType(), 0, "BATCHNORM_BP op: types of arrays (input, mean, variance, gamma, beta) should be the same !");
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// ***** calculations ***** //
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// notations:
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// f = g * (gamma * ((x - m) / (v + eps)^0.5) + beta) -> means dLdO * ff_output, g = dLdO
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// stdInv = 1 / (v + eps)^0.5
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// N - batch size (product of spatial dimensions)
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// derivatives:
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// dLdI = dfdx + dfdm*dmdx + dfdv*(dvdm*dmdx + dvdx)
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// dfdx = gamma*stdInv*g;
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// dfdm = -gamma*stdInv*g_sum;
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// dmdx = 1/N;
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// dvdx = 2 * (x - m) / N
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// dvdm = -2 * [(x - m)]_sum / N
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// dfdv = -0.5 * [g*(x - m)]_sum * stdInv^3, drop gamma here for calc convenience
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// finally:
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// dLdI = gamma * ( stdInv * (g - g_sum/N) + (2/N) * dfdv * (dvdm/2 + (x - m)) )
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// dLdG = (g * (x - m))_sum * stdInv
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// dLdB = g_sum
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// variance = input->varianceAlongDimension(variance::SummaryStatsVariance, false, ShapeUtils::evalDimsToExclude(input->rankOf(), axes));
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// mean = input->reduceAlongDimension(sd::reduce::Mean, ShapeUtils::evalDimsToExclude(input->rankOf(), axes));
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const auto excludedAxes = ShapeUtils::evalDimsToExclude(inRank, axes);
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const bool keepUnitiesInShape = inRank == mean->rankOf();
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// inverse batch size 1/N
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const float Ninv = 1.f * shape::tadLength(input->shapeInfo(), axes.data(), axes.size()) / input->lengthOf();
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// input - mean
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NDArray xMinusMean(input); // empty array with same shape as input
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input->applyBroadcast(sd::broadcast::Subtract, axes, *mean, xMinusMean);
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// stdInv
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NDArray stdInv = *variance + epsilon;
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stdInv.applyTransform(transform::Reciprocal, stdInv); // 1 / (variance + epsilon)
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stdInv.applyTransform(transform::Sqrt, stdInv); // 1 / (variance + epsilon)^0.5
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// dvdm (use dLdM as storage for dvdm)
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xMinusMean.reduceAlongDimension(sd::reduce::Sum, *dLdM, excludedAxes, keepUnitiesInShape);
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*dLdM *= -Ninv;
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// g_sum
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auto gSum = dLdO->reduceAlongDimension(sd::reduce::Sum, excludedAxes, keepUnitiesInShape);
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// dLdB
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if(applyOffset)
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dLdB->assign(gSum);
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// stdInv * (g - g_sum/N) (use dLdI as storage for this expression)
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gSum *= Ninv;
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dLdO->applyBroadcast(sd::broadcast::Subtract, axes, gSum, *dLdI);
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dLdI->applyBroadcast(sd::broadcast::Multiply, axes, stdInv, *dLdI);
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// dLdV <- [g*(x - m)]_sum
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(xMinusMean * *dLdO).reduceAlongDimension(sd::reduce::Sum, *dLdV, excludedAxes, keepUnitiesInShape);
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// dLdG
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*dLdV *= stdInv;
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if(applyScale)
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dLdG->assign(dLdV);
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// (2 / N) * dfdv (use dLdV as storage for dfdv)
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*dLdV *= stdInv*stdInv; // dLdV*stdInv * stdInv^2
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*dLdV *= -Ninv; // -0.5f * (2 / N);
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// dfdv * (dvdm + (x - m)) (use xMinusMean as storage for this expression)
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xMinusMean.applyBroadcast(sd::broadcast::Add, axes, *dLdM, xMinusMean);
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xMinusMean.applyBroadcast(sd::broadcast::Multiply, axes, *dLdV, xMinusMean);
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// dLdI
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*dLdI += xMinusMean;
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if(applyScale)
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dLdI->applyBroadcast(sd::broadcast::Multiply, axes, *gamma, *dLdI);
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*dLdM = 0; // put zeros so far
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*dLdV = 0; // put zeros so far
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// java code
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// NDArray std = *variance + epsilon;
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// std.applyTransform(transform::Reciprocal); // 1 / (variance + epsilon)
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// std.applyTransform(transform::Sqrt); // 1 / (variance + epsilon)^0.5
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// NDArray xMu(input);
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// input->applyBroadcast(sd::broadcast::Subtract, axes, mean, &xMu);
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// NDArray xHat(input);
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// xMu.applyBroadcast(sd::broadcast::Multiply, axes, &std, &xHat);
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// NDArray dxhat(input);
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// dLdO->applyBroadcast(sd::broadcast::Multiply, axes, gamma, &dxhat);
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// NDArray temp = dxhat*xMu;
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// temp.reduceAlongDimension(reduce::Sum, dLdV, excludedAxes, keepUnitiesInShape);
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// *dLdV *= -0.5f * std*std*std;
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// NDArray* dxmu1 = dxhat.reduceAlongDimension(reduce::Sum, excludedAxes, keepUnitiesInShape);
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// *dxmu1 *= -std;
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// NDArray* dxmu2 = xMu.reduceAlongDimension(reduce::Sum, excludedAxes, keepUnitiesInShape);
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// *dxmu2 *= *dLdV * (-2.f/N);
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// NDArray dLdmu = *dxmu1 + *dxmu2;
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// dLdmu *= (1.f /N);
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// *dLdV *= (2.f/N);
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// dxhat.applyBroadcast(sd::broadcast::Multiply, axes, &std);
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// xMu.applyBroadcast(sd::broadcast::Multiply, axes, dLdV);
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// dxhat += xMu;
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// dxhat.applyBroadcast(sd::broadcast::Add, axes, &dLdmu, dLdI);
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// delete dxmu1;
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// delete dxmu2;
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// xHat *= *dLdO;
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// xHat.reduceAlongDimension(reduce::Sum, dLdG, excludedAxes, keepUnitiesInShape);
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return Status::OK();
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}
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DECLARE_TYPES(batchnorm_bp) {
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getOpDescriptor()
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->setAllowedInputTypes(0, sd::DataType::ANY)
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->setAllowedInputTypes(1, sd::DataType::ANY)
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->setAllowedInputTypes(2, sd::DataType::ANY)
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->setAllowedInputTypes(3, {ALL_FLOATS})
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->setAllowedInputTypes(4, sd::DataType::ANY)
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->setAllowedInputTypes(5, sd::DataType::ANY)
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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//////////////////////////////////////////////////////////////////////////
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DECLARE_SHAPE_FN(batchnorm_bp) {
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Nd4jLong const* inShapeInfo = inputShape->at(0);
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Nd4jLong const* meanShapeInfo = inputShape->at(1);
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const bool applyScale = (bool)INT_ARG(0);
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const bool applyOffset = (bool)INT_ARG(1);
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DataType outType = DataTypeUtils::pickFloatingType(ArrayOptions::dataType(inShapeInfo));
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auto shapes = SHAPELIST();
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// dLdI shapeInfo
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shapes->push_back(ConstantShapeHelper::getInstance().createShapeInfo(outType, inShapeInfo));
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// dLdM shapeInfo
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shapes->push_back(ConstantShapeHelper::getInstance().createShapeInfo(outType, meanShapeInfo));
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// dLdV shapeInfo (same as dLdM)
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shapes->push_back(shapes->at(shapes->size()-1));
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// dLdG shapeInfo (same as dLdM)
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if(applyScale)
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shapes->push_back(shapes->at(shapes->size()-1));
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// dLdB shapeInfo (same as dLdM)
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if(applyOffset)
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shapes->push_back(shapes->at(shapes->size()-1));
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return shapes;
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}
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}
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}
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#endif
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