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

289 lines
12 KiB
C++

/*******************************************************************************
* 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 raver119@gmail.com, created on 29/10/17.
// @author Yurii Shyrma (iuriish@yahoo.com)
//
#include <op_boilerplate.h>
#if NOT_EXCLUDED(OP_batchnorm)
#include <ops/declarable/CustomOperations.h>
#include<ops/declarable/helpers/batchnorm.h>
namespace nd4j {
namespace ops {
//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(batchnorm, 3, 1, false, 1, 2) {
auto input = INPUT_VARIABLE(0);
auto mean = INPUT_VARIABLE(1);
auto variance = INPUT_VARIABLE(2);
NDArray* gamma = nullptr;
NDArray* beta = nullptr;
auto output = OUTPUT_VARIABLE(0);
const bool applyScale = (bool)INT_ARG(0);
const bool applyOffset = (bool)INT_ARG(1);
const double epsilon = T_ARG(0);
if(applyScale)
gamma = INPUT_VARIABLE(3);
if(applyOffset)
beta = INPUT_VARIABLE(3 + (int)applyScale);
const int numOfIntArgs = block.getIArguments()->size();
const int inRank = input->rankOf();
// get axes args to normalize input array over
std::vector<int> axes;
if(numOfIntArgs > 2)
for(int i = 2; i < numOfIntArgs; ++i)
axes.push_back(INT_ARG(i));
else
axes.push_back(inRank-1); // default dimension to reduce along is last dimension
const int numOfAxes = axes.size();
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);
// evaluate expected shape for mean, variance and gamma. These 3 arrays should have identical shapes
// 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}
std::vector<Nd4jLong> expShape;
if(numOfAxes == 1)
expShape.push_back(input->sizeAt(axes[0]));
else { // get, for example, something like {1, inputDim1, 1, inputDim3, 1} if axes = {1, 3}
expShape = std::vector<Nd4jLong>(inRank, 1);
for(uint i = 0; i < numOfAxes; ++i)
expShape[axes[i]] = input->sizeAt(axes[i]);
}
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());
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());
if(gamma)
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());
if(beta)
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());
// types of all input arrays should be the same
for(int i = 1; i < block.width(); ++i)
REQUIRE_TRUE(INPUT_VARIABLE(0)->dataType() == INPUT_VARIABLE(i)->dataType(), 0, "BATCHNORM op: types of all input arrays should be the same !");
nd4j_debug("MKL-DNN is not used for batchnorm!\n", 0);
// formula: output = gamma * ((input - mean) / sqrt(variance + epsilon)) + beta
helpers::batchnorm(input, mean, variance, gamma, beta, output, axes, epsilon);
return Status::OK();
}
DECLARE_TYPES(batchnorm) {
getOpDescriptor()->setAllowedInputTypes({ALL_FLOATS})->setSameMode(true);
}
DECLARE_SHAPE_FN(batchnorm) {
auto inShapeInfo = inputShape->at(0);
DataType outType = DataTypeUtils::pickFloatingType(ArrayOptions::dataType(inShapeInfo));
auto outShapeInfo = ShapeBuilders::copyShapeInfoAndType(inShapeInfo, outType, false, block.getWorkspace()); // output shape is identical to input shape
return SHAPELIST(CONSTANT(outShapeInfo));
}
//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(batchnorm_bp, 4, 3, false, 1, 2) {
NDArray* input = INPUT_VARIABLE(0);
NDArray* mean = INPUT_VARIABLE(1);
NDArray* variance = INPUT_VARIABLE(2);
NDArray* dLdO = INPUT_VARIABLE(3); // next epsilon
NDArray* gamma = nullptr;
NDArray* beta = nullptr;
NDArray* dLdI = OUTPUT_VARIABLE(0);
NDArray* dLdM = OUTPUT_VARIABLE(1);
NDArray* dLdV = OUTPUT_VARIABLE(2);
NDArray* dLdG = nullptr;
NDArray* dLdB = nullptr;
const bool applyScale = (bool)INT_ARG(0);
const bool applyOffset = (bool)INT_ARG(1);
const float epsilon = T_ARG(0);
if(applyScale) {
gamma = INPUT_VARIABLE(4);
dLdG = OUTPUT_VARIABLE(3);
}
if(applyOffset) {
beta = INPUT_VARIABLE(4 + (int)applyScale);
dLdB = OUTPUT_VARIABLE(3 + (int)applyScale);
}
const int numOfIntArgs = block.getIArguments()->size();
const int inRank = input->rankOf();
// get axes args to normalize input array over
std::vector<int> axes;
if(numOfIntArgs > 2)
for(int i = 2; i < numOfIntArgs; ++i)
axes.push_back(INT_ARG(i));
else
axes.push_back(inRank-1); // default dimension to reduce along is last dimension
const int numOfAxes = axes.size();
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);
// evaluate expected shape for mean, variance and gamma. These 3 arrays should have identical shapes
// 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}
std::vector<Nd4jLong> expShape;
if(numOfAxes == 1)
expShape.push_back(input->sizeAt(axes[0]));
else { // get, for example, something like {1, inputDim1, 1, inputDim3, 1} if axes = {1, 3}
expShape = std::vector<Nd4jLong>(inRank, 1);
for(uint i = 0; i < numOfAxes; ++i)
expShape[axes[i]] = input->sizeAt(axes[i]);
}
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());
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());
if(gamma)
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());
if(beta)
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());
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());
// types of all input arrays should be the same (except dLdO)
for(int i = 1; i < block.width() - 1; ++i)
if(i != 3)
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 !");
// ***** calculations ***** //
// formula for forward step: output = gamma * ((input - mean) / sqrt(variance + epsilon)) + beta
// consider mean and variance as constants (since we get them as inputs and don't calculate them)
// dLdI = (dLdO * gamma) / (variance + epsilon)^0.5
// dLdV = (-0.5 * gamma * (dLdO * (x - mean))_sum) / (variance + epsilon)^1.5
// dLdM = - (dLdO_sum * gamma) / (variance + epsilon)^0.5
// dLdG = (dLdO * (x - mean))_sum / (variance + epsilon)^0.5
// dLdB = dLdO_sum
const auto excludedAxes = ShapeUtils::evalDimsToExclude(inRank, axes);
NDArray temp1 = *variance + epsilon;
temp1.applyTransform(transform::Reciprocal); // 1 / (variance + epsilon)
auto temp2 = temp1.transform(transform::Sqrt); // 1 / (variance + epsilon)^0.5
if(applyScale)
temp2 *= *gamma; // gamma / (variance + epsilon)^0.5
NDArray temp3(input); // empty array with same shape as input
input->applyBroadcast(nd4j::broadcast::Subtract, axes, mean, &temp3); // input - mean
temp3 *= *dLdO; // (input - mean) * dLdO
const bool keepUnitiesInShape = inRank == mean->rankOf();
// dLdI
dLdO->applyBroadcast(nd4j::broadcast::Multiply, axes, &temp2, dLdI);
// dLdM
dLdO->reduceAlongDimension(reduce::Sum, dLdM, excludedAxes, keepUnitiesInShape); // dLdO sum over excluded axes
// dLdB
if(applyOffset)
dLdB->assign(dLdM);
// dLdM
// dLdM->applyPairwiseTransform(nd4j::pairwise::Multiply, temp2);
// dLdM->applyTransform(nd4j::transform::Neg);
*dLdM = 0; // put zeros so far
//dLdV
temp3.reduceAlongDimension(reduce::Sum, dLdV, excludedAxes, keepUnitiesInShape); // ((input - mean) * dLdO)_sum
// dLdG
if(applyScale) {
dLdV->applyPairwiseTransform(nd4j::pairwise::Multiply, &temp2, dLdG);
// dLdV->assign(dLdG);
dLdG->applyPairwiseTransform(nd4j::pairwise::Divide, *gamma);
}
else
// dLdV->applyPairwiseTransform(nd4j::pairwise::Multiply, temp2);
// dLdV
// dLdV->applyPairwiseTransform(nd4j::pairwise::Multiply, temp1);
// *dLdV *= -0.5;
*dLdV = 0; // put zeros so far
return Status::OK();
}
DECLARE_TYPES(batchnorm_bp) {
getOpDescriptor()
->setAllowedInputTypes(0, nd4j::DataType::ANY)
->setAllowedInputTypes(1, nd4j::DataType::ANY)
->setAllowedInputTypes(2, nd4j::DataType::ANY)
->setAllowedInputTypes(3, {ALL_FLOATS})
->setAllowedInputTypes(4, nd4j::DataType::ANY)
->setAllowedInputTypes(5, nd4j::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
//////////////////////////////////////////////////////////////////////////
DECLARE_SHAPE_FN(batchnorm_bp) {
Nd4jLong* inShapeInfo = inputShape->at(0);
Nd4jLong* meanShapeInfo = inputShape->at(1);
const bool applyScale = (bool)INT_ARG(0);
const bool applyOffset = (bool)INT_ARG(1);
DataType outType = DataTypeUtils::pickFloatingType(ArrayOptions::dataType(inShapeInfo));
auto shapes = SHAPELIST();
// dLdI shapeInfo
shapes->push_back(ConstantShapeHelper::getInstance()->createShapeInfo(outType, inShapeInfo));
// dLdM shapeInfo
shapes->push_back(ConstantShapeHelper::getInstance()->createShapeInfo(outType, meanShapeInfo));
// dLdV shapeInfo (same as dLdM)
shapes->push_back(shapes->at(shapes->size()-1));
// dLdG shapeInfo (same as dLdM)
if(applyScale)
shapes->push_back(shapes->at(shapes->size()-1));
// dLdB shapeInfo (same as dLdM)
if(applyOffset)
shapes->push_back(shapes->at(shapes->size()-1));
return shapes;
}
}
}
#endif