/* ******************************************************************************
*
*
* 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
******************************************************************************/
//
// @author Yurii Shyrma, created on 25.02.2018
//
#include<ops/declarable/helpers/batchnorm.h>
#include <helpers/ShapeUtils.h>
#include <helpers/OmpLaunchHelper.h>
#include <helpers/ConstantTadHelper.h>
#include <helpers/PointersManager.h>
namespace sd {
namespace ops {
namespace helpers {
//////////////////////////////////////////////////////////////////////////
// template<typename T>
// __global__ static void batchnormCuda(const void* vx, const Nd4jLong* xShapeInfo,
// const void* vMean, const Nd4jLong* meanShapeInfo,
// const void* vVariance, const Nd4jLong* varianceShapeInfo,
// const void* vGamma, const Nd4jLong* gammaShapeInfo,
// const void* vBeta, const Nd4jLong* betaShapeInfo,
// void* vz, const Nd4jLong* zShapeInfo,
// const Nd4jLong* xTadShapeInfo, const Nd4jLong* xTadOffsets,
// const Nd4jLong* zTadShapeInfo, const Nd4jLong* zTadOffsets,
// const T epsilon) {
// const auto x = reinterpret_cast<const T*>(vx);
// auto z = reinterpret_cast<T*>(vz);
// const auto mean = reinterpret_cast<const T*>(vMean);
// const auto variance = reinterpret_cast<const T*>(vVariance);
// const auto gamma = reinterpret_cast<const T*>(vGamma);
// const auto beta = reinterpret_cast<const T*>(vBeta);
// // maxRank = xRank = zRank, minRank = meanRank = varianceRank = gammaRank = betaRank
// __shared__ Nd4jLong minLen, tadLen, totalThreads;
// if (threadIdx.x == 0) {
// totalThreads = gridDim.x * blockDim.x;
// minLen = shape::length(meanShapeInfo);
// tadLen = shape::length(xShapeInfo) / minLen;
// }
// __syncthreads();
// const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
// for (uint i = tid; i < minLen; i += totalThreads) {
// const auto meanOffset = shape::getIndexOffset(i, meanShapeInfo);
// const auto varianceOffset = shape::getIndexOffset(i, varianceShapeInfo);
// T sigmaInvGam = 1. / sd::math::nd4j_sqrt<T, T>(variance[varianceOffset] + epsilon);
// if(gamma != nullptr)
// sigmaInvGam *= gamma[shape::getIndexOffset(i, gammaShapeInfo)];
// auto betaOffset = 0;
// if(beta != nullptr)
// betaOffset = shape::getIndexOffset(i, betaShapeInfo);
// const auto xTad = x + xTadOffsets[i];
// auto zTad = z + zTadOffsets[i];
// for (uint j = 0; j < tadLen; ++j) {
// const auto xTadOffset = shape::getIndexOffset(j, xTadShapeInfo);
// const auto zTadOffset = shape::getIndexOffset(j, zTadShapeInfo);
// zTad[zTadOffset] = (xTad[xTadOffset] - mean[meanOffset]) * sigmaInvGam;
// if(beta != nullptr)
// zTad[zTadOffset] += beta[betaOffset];
// }
// }
// }
//////////////////////////////////////////////////////////////////////////
template<typename T>
__global__ static void batchnormCuda2(const void* vx, const Nd4jLong* xShapeInfo,
const void* vMean, const Nd4jLong* meanShapeInfo,
const void* vVariance, const Nd4jLong* varianceShapeInfo,
const void* vGamma, const Nd4jLong* gammaShapeInfo,
const void* vBeta, const Nd4jLong* betaShapeInfo,
void* vz, const Nd4jLong* zShapeInfo,
const int numDims, const int* dims,
const T epsilon) {
const auto x = reinterpret_cast<const T*>(vx);
auto z = reinterpret_cast<T*>(vz);
const auto mean = reinterpret_cast<const T*>(vMean);
const auto variance = reinterpret_cast<const T*>(vVariance);
const auto gamma = reinterpret_cast<const T*>(vGamma);
const auto beta = reinterpret_cast<const T*>(vBeta);
__shared__ int xRank, minRank; // xRank == zRank, minRank = meanRank = varianceRank = gammaRank = betaRank
__shared__ Nd4jLong xLen, totalThreads; // xLen = zLen
if (threadIdx.x == 0) {
totalThreads = gridDim.x * blockDim.x;
xLen = shape::length(xShapeInfo);
xRank = shape::rank(xShapeInfo);
minRank = shape::rank(meanShapeInfo);
}
__syncthreads();
int coords[MAX_RANK];
const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
for (uint i = tid; i < xLen; i += totalThreads) {
shape::index2coords(i, xShapeInfo, coords);
const auto xOffset = shape::getOffset(xShapeInfo, coords);
const auto zOffset = shape::getOffset(zShapeInfo, coords);
if(minRank == xRank) {
for (uint i = 0, j = 0; i < xRank; ++i) {
if(j < numDims && i != dims[j])
coords[i] = 0;
else
++j;
}
}
else // minRank = numDims = 1 in this case
coords[0] = coords[dims[0]];
const auto meanOffset = shape::getOffset(meanShapeInfo, coords);
const auto varianceOffset = shape::getOffset(varianceShapeInfo, coords);
T sigmaInvGam = 1. / sd::math::nd4j_sqrt<T, T>(variance[varianceOffset] + epsilon);
if(gamma != nullptr) {
const auto gammaOffset = shape::getOffset(gammaShapeInfo, coords);
sigmaInvGam *= gamma[gammaOffset];
}
z[zOffset] = (x[xOffset] - mean[meanOffset]) * sigmaInvGam;
if(beta != nullptr) {
const auto betaOffset = shape::getOffset(betaShapeInfo, coords);
z[zOffset] += beta[betaOffset];
}
}
}
///////////////////////////////////////////////////////////////////
// template<typename T>
// __host__ static void batchnormCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream,
// const void* vx, const Nd4jLong* xShapeInfo,
// const void* vMean, const Nd4jLong* meanShapeInfo,
// const void* vVariance, const Nd4jLong* varianceShapeInfo,
// const void* vGamma, const Nd4jLong* gammaShapeInfo,
// const void* vBeta, const Nd4jLong* betaShapeInfo,
// void* vz, const Nd4jLong* zShapeInfo,
// const Nd4jLong* xTadShapeInfo, const Nd4jLong* xTadOffsets,
// const Nd4jLong* zTadShapeInfo, const Nd4jLong* zTadOffsets,
// const double epsilon) {
// batchnormCuda<T><<<blocksPerGrid, threadsPerBlock, 1024, *stream>>>(vx, xShapeInfo, vMean, meanShapeInfo, vVariance, varianceShapeInfo, vGamma, gammaShapeInfo, vBeta, betaShapeInfo, vz, zShapeInfo, xTadShapeInfo, xTadOffsets, zTadShapeInfo, zTadOffsets, static_cast<T>(epsilon));
// }
///////////////////////////////////////////////////////////////////
template<typename T>
__host__ static void batchnormCudaLauncher2(const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream,
const void* vx, const Nd4jLong* xShapeInfo,
const void* vMean, const Nd4jLong* meanShapeInfo,
const void* vVariance, const Nd4jLong* varianceShapeInfo,
const void* vGamma, const Nd4jLong* gammaShapeInfo,
const void* vBeta, const Nd4jLong* betaShapeInfo,
void* vz, const Nd4jLong* zShapeInfo,
const int numDims, const int* dims,
const double epsilon) {
batchnormCuda2<T><<<blocksPerGrid, threadsPerBlock, 512, *stream>>>(vx, xShapeInfo, vMean, meanShapeInfo, vVariance, varianceShapeInfo, vGamma, gammaShapeInfo, vBeta, betaShapeInfo, vz, zShapeInfo, numDims, dims, static_cast<T>(epsilon));
}
//////////////////////////////////////////////////////////////////////////
void batchnorm(const NDArray* input, const NDArray* mean, const NDArray* variance, const NDArray* gamma, const NDArray* beta, NDArray* output, const std::vector<int>& axes, const double epsilon) {
// std::vector<int> dimsToExclude = ShapeUtils::evalDimsToExclude(input->rankOf(), axes);
// auto packX = sd::ConstantTadHelper::getInstance().tadForDimensions(input->shapeInfo(), dimsToExclude);
// auto packZ = sd::ConstantTadHelper::getInstance().tadForDimensions(output->shapeInfo(), dimsToExclude);
// const int threadsPerBlock = MAX_NUM_THREADS / 2;
// const int blocksPerGrid = (mean->lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
// PointersManager manager(input->getContext(), "batchnorm");
// NDArray::prepareSpecialUse({output}, {input, mean, variance, gamma, beta});
// BUILD_SINGLE_SELECTOR(input->dataType(), batchnormCudaLauncher, (blocksPerGrid, threadsPerBlock, input->getContext()->getCudaStream(), input->specialBuffer(), input->specialShapeInfo(), mean->specialBuffer(), mean->specialShapeInfo(), variance->specialBuffer(), variance->specialShapeInfo(), gamma ? gamma->specialBuffer() : nullptr, gamma ? gamma->specialShapeInfo() : nullptr, beta ? beta->specialBuffer() : nullptr, beta ? beta->specialShapeInfo() : nullptr, output->specialBuffer(), output->special(), packX.platformShapeInfo(), packX.platformOffsets(), packZ.platform(), packZ.platform(), epsilon), FLOAT_TYPES);
// NDArray::registerSpecialUse({output}, {input, mean, variance, gamma, beta});
// manager.synchronize();
const int threadsPerBlock = MAX_NUM_THREADS / 2;
const int blocksPerGrid = (input->lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
PointersManager manager(input->getContext(), "batchnorm");
const int* dims = reinterpret_cast<int*>(manager.replicatePointer(axes.data(), axes.size() * sizeof(int)));
NDArray::prepareSpecialUse({output}, {input, mean, variance, gamma, beta});
BUILD_SINGLE_SELECTOR(input->dataType(), batchnormCudaLauncher2, (blocksPerGrid, threadsPerBlock, input->getContext()->getCudaStream(), input->specialBuffer(), input->specialShapeInfo(), mean->specialBuffer(), mean->specialShapeInfo(), variance->specialBuffer(), variance->specialShapeInfo(), gamma ? gamma->specialBuffer() : nullptr, gamma ? gamma->specialShapeInfo() : nullptr, beta ? beta->specialBuffer() : nullptr, beta ? beta->specialShapeInfo() : nullptr, output->specialBuffer(), output->specialShapeInfo(), axes.size(), dims, epsilon), FLOAT_TYPES);
NDArray::registerSpecialUse({output}, {input, mean, variance, gamma, beta});
manager.synchronize();
}
}
}
}