wehub-resource-sync
152 lines
6.6 KiB
Plaintext
152 lines
6.6 KiB
Plaintext
/* ******************************************************************************
|
|
*
|
|
*
|
|
* 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 <helpers/ConstantTadHelper.h>
|
|
#include <helpers/OmpLaunchHelper.h>
|
|
#include <helpers/PointersManager.h>
|
|
#include <helpers/ShapeUtils.h>
|
|
#include <ops/declarable/helpers/batchnorm.h>
|
|
|
|
#include "execution/cuda/LaunchDims.h"
|
|
|
|
|
|
namespace sd {
|
|
namespace ops {
|
|
namespace helpers {
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
SD_KERNEL static void batchnormCuda2(const void* vx, const LongType* xShapeInfo, const void* vMean,
|
|
const LongType* meanShapeInfo, const void* vVariance,
|
|
const LongType* varianceShapeInfo, const void* vGamma,
|
|
const LongType* gammaShapeInfo, const void* vBeta,
|
|
const LongType* betaShapeInfo, void* vz, const LongType* zShapeInfo,
|
|
const int numDims, const LongType* 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__ LongType 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();
|
|
|
|
LongType coords[SD_MAX_RANK];
|
|
|
|
const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
|
|
|
|
for (LongType i = tid; i < xLen; i += totalThreads) {
|
|
INDEX2COORDS(i, xRank, shape::shapeOf(xShapeInfo), coords);
|
|
|
|
LongType xOffset, zOffset;
|
|
COORDS2INDEX(xRank, shape::stride(xShapeInfo), coords, xOffset);
|
|
COORDS2INDEX(xRank, shape::stride(zShapeInfo), coords, zOffset);
|
|
|
|
if (minRank == xRank) {
|
|
for (LongType 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]];
|
|
|
|
LongType meanOffset, varianceOffset;
|
|
COORDS2INDEX(minRank, shape::stride(meanShapeInfo), coords, meanOffset);
|
|
COORDS2INDEX(minRank, shape::stride(varianceShapeInfo), coords, varianceOffset);
|
|
|
|
T sigmaInvGam = 1. / math::sd_sqrt<T, T>(variance[varianceOffset] + epsilon);
|
|
|
|
if (gamma != nullptr) {
|
|
LongType gammaOffset;
|
|
COORDS2INDEX(minRank, shape::stride(gammaShapeInfo), coords, gammaOffset);
|
|
sigmaInvGam *= gamma[gammaOffset];
|
|
}
|
|
|
|
z[zOffset] = (x[xOffset] - mean[meanOffset]) * sigmaInvGam;
|
|
|
|
if (beta != nullptr) {
|
|
LongType betaOffset;
|
|
COORDS2INDEX(minRank, shape::stride(betaShapeInfo), coords, betaOffset);
|
|
z[zOffset] += beta[betaOffset];
|
|
}
|
|
}
|
|
}
|
|
///////////////////////////////////////////////////////////////////
|
|
|
|
///////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
SD_HOST static void batchnormCudaLauncher2(const int blocksPerGrid, const int threadsPerBlock,
|
|
const cudaStream_t* stream, const void* vx, const LongType* xShapeInfo,
|
|
const void* vMean, const LongType* meanShapeInfo, const void* vVariance,
|
|
const LongType* varianceShapeInfo, const void* vGamma,
|
|
const LongType* gammaShapeInfo, const void* vBeta,
|
|
const LongType* betaShapeInfo, void* vz, const LongType* zShapeInfo,
|
|
const int numDims, const LongType* 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));
|
|
sd::DebugHelper::checkGlobalErrorCode("batchNormCuda2 failed");
|
|
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void batchnorm(NDArray* input, NDArray* mean, NDArray* variance, NDArray* gamma,
|
|
NDArray* beta, NDArray* output, const std::vector<LongType>& axes, const double epsilon) {
|
|
|
|
dim3 batchNormDims = getBatchNormDims(input->lengthOf());
|
|
PointersManager manager(input->getContext(), "batchnorm");
|
|
|
|
const LongType* dims = reinterpret_cast<LongType*>(manager.replicatePointer(axes.data(), axes.size() * sizeof(LongType)));
|
|
|
|
NDArray::prepareSpecialUse({output}, {input, mean, variance, gamma, beta});
|
|
BUILD_SINGLE_SELECTOR(input->dataType(), batchnormCudaLauncher2,
|
|
(batchNormDims.x, batchNormDims.y, 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),
|
|
SD_FLOAT_TYPES);
|
|
NDArray::registerSpecialUse({output}, {input, mean, variance, gamma, beta});
|
|
|
|
manager.synchronize();
|
|
}
|
|
|
|
} // namespace helpers
|
|
} // namespace ops
|
|
} // namespace sd
|