239 lines
11 KiB
Plaintext
239 lines
11 KiB
Plaintext
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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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 Yurii Shyrma, created on 25.02.2018
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//
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#include<ops/declarable/helpers/batchnorm.h>
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#include <helpers/ShapeUtils.h>
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#include <OmpLaunchHelper.h>
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#include <ConstantTadHelper.h>
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#include <PointersManager.h>
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namespace nd4j {
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namespace ops {
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namespace helpers {
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//////////////////////////////////////////////////////////////////////////
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template<typename T>
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__global__ static void batchnormCuda(const void* vx, const Nd4jLong* xShapeInfo,
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const void* vMean, const Nd4jLong* meanShapeInfo,
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const void* vVariance, const Nd4jLong* varianceShapeInfo,
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const void* vGamma, const Nd4jLong* gammaShapeInfo,
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const void* vBeta, const Nd4jLong* betaShapeInfo,
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void* vz, const Nd4jLong* zShapeInfo,
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const Nd4jLong* xTadShapeInfo, const Nd4jLong* xTadOffsets,
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const Nd4jLong* zTadShapeInfo, const Nd4jLong* zTadOffsets,
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const T epsilon) {
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const auto x = reinterpret_cast<const T*>(vx);
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auto z = reinterpret_cast<T*>(vz);
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const auto mean = reinterpret_cast<const T*>(vMean);
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const auto variance = reinterpret_cast<const T*>(vVariance);
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const auto gamma = reinterpret_cast<const T*>(vGamma);
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const auto beta = reinterpret_cast<const T*>(vBeta);
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// maxRank = xRank = zRank, minRank = meanRank = varianceRank = gammaRank = betaRank
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__shared__ Nd4jLong minLen, tadLen, totalThreads;
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if (threadIdx.x == 0) {
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totalThreads = gridDim.x * blockDim.x;
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minLen = shape::length(meanShapeInfo);
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tadLen = shape::length(xShapeInfo) / minLen;
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}
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__syncthreads();
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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for (uint i = tid; i < minLen; i += totalThreads) {
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const auto meanOffset = shape::getIndexOffset(i, meanShapeInfo);
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const auto varianceOffset = shape::getIndexOffset(i, varianceShapeInfo);
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T sigmaInvGam = 1. / nd4j::math::nd4j_sqrt<T, T>(variance[varianceOffset] + epsilon);
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if(gamma != nullptr)
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sigmaInvGam *= gamma[shape::getIndexOffset(i, gammaShapeInfo)];
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auto betaOffset = 0;
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if(beta != nullptr)
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betaOffset = shape::getIndexOffset(i, betaShapeInfo);
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const auto xTad = x + xTadOffsets[i];
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auto zTad = z + zTadOffsets[i];
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for (uint j = 0; j < tadLen; ++j) {
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const auto xTadOffset = shape::getIndexOffset(j, xTadShapeInfo);
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const auto zTadOffset = shape::getIndexOffset(j, zTadShapeInfo);
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zTad[zTadOffset] = (xTad[xTadOffset] - mean[meanOffset]) * sigmaInvGam;
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if(beta != nullptr)
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zTad[zTadOffset] += beta[betaOffset];
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}
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}
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename T>
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__global__ static void batchnormCuda2(const void* vx, const Nd4jLong* xShapeInfo,
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const void* vMean, const Nd4jLong* meanShapeInfo,
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const void* vVariance, const Nd4jLong* varianceShapeInfo,
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const void* vGamma, const Nd4jLong* gammaShapeInfo,
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const void* vBeta, const Nd4jLong* betaShapeInfo,
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void* vz, const Nd4jLong* zShapeInfo,
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const int numDims, const int* dims,
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const T epsilon) {
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const auto x = reinterpret_cast<const T*>(vx);
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auto z = reinterpret_cast<T*>(vz);
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const auto mean = reinterpret_cast<const T*>(vMean);
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const auto variance = reinterpret_cast<const T*>(vVariance);
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const auto gamma = reinterpret_cast<const T*>(vGamma);
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const auto beta = reinterpret_cast<const T*>(vBeta);
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__shared__ int xRank, minRank; // xRank == zRank. minRank = meanRank = varianceRank = gammaRank = betaRank
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__shared__ Nd4jLong xLen, totalThreads, *sharedMem; // xLen = zLen
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if (threadIdx.x == 0) {
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extern __shared__ unsigned char shmem[];
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sharedMem = reinterpret_cast<Nd4jLong*>(shmem);
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totalThreads = gridDim.x * blockDim.x;
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xLen = shape::length(xShapeInfo);
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xRank = shape::rank(xShapeInfo);
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minRank = shape::rank(meanShapeInfo);
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}
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__syncthreads();
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auto coords = sharedMem + threadIdx.x * xRank;
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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for (uint i = tid; i < xLen; i += totalThreads) {
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shape::index2coords(i, xShapeInfo, coords);
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const auto xOffset = shape::getOffset(xShapeInfo, coords);
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const auto zOffset = shape::getOffset(zShapeInfo, coords);
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if(minRank == xRank) {
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for (uint i = 0, j = 0; i < xRank; ++i) {
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if(j < numDims && i != dims[j])
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coords[i] = 0;
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else
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++j;
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}
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}
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else // minRank = numDims = 1 in this case
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coords[0] = coords[dims[0]];
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const auto meanOffset = shape::getOffset(meanShapeInfo, coords);
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const auto varianceOffset = shape::getOffset(varianceShapeInfo, coords);
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T sigmaInvGam = 1. / nd4j::math::nd4j_sqrt<T, T>(variance[varianceOffset] + epsilon);
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if(gamma != nullptr) {
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const auto gammaOffset = shape::getOffset(gammaShapeInfo, coords);
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sigmaInvGam *= gamma[gammaOffset];
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}
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z[zOffset] = (x[xOffset] - mean[meanOffset]) * sigmaInvGam;
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if(beta != nullptr) {
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const auto betaOffset = shape::getOffset(betaShapeInfo, coords);
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z[zOffset] += beta[betaOffset];
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}
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}
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}
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///////////////////////////////////////////////////////////////////
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template<typename T>
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__host__ static void batchnormCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream,
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const void* vx, const Nd4jLong* xShapeInfo,
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const void* vMean, const Nd4jLong* meanShapeInfo,
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const void* vVariance, const Nd4jLong* varianceShapeInfo,
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const void* vGamma, const Nd4jLong* gammaShapeInfo,
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const void* vBeta, const Nd4jLong* betaShapeInfo,
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void* vz, const Nd4jLong* zShapeInfo,
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const Nd4jLong* xTadShapeInfo, const Nd4jLong* xTadOffsets,
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const Nd4jLong* zTadShapeInfo, const Nd4jLong* zTadOffsets,
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const double epsilon) {
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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));
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}
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///////////////////////////////////////////////////////////////////
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template<typename T>
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__host__ static void batchnormCudaLauncher2(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream,
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const void* vx, const Nd4jLong* xShapeInfo,
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const void* vMean, const Nd4jLong* meanShapeInfo,
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const void* vVariance, const Nd4jLong* varianceShapeInfo,
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const void* vGamma, const Nd4jLong* gammaShapeInfo,
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const void* vBeta, const Nd4jLong* betaShapeInfo,
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void* vz, const Nd4jLong* zShapeInfo,
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const int numDims, const int* dims,
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const double epsilon) {
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batchnormCuda2<T><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vMean, meanShapeInfo, vVariance, varianceShapeInfo, vGamma, gammaShapeInfo, vBeta, betaShapeInfo, vz, zShapeInfo, numDims, dims, static_cast<T>(epsilon));
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}
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//////////////////////////////////////////////////////////////////////////
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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) {
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std::vector<int> dimsToExclude = ShapeUtils::evalDimsToExclude(input->rankOf(), axes);
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auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimsToExclude);
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auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->shapeInfo(), dimsToExclude);
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const int threadsPerBlock = MAX_NUM_THREADS / 2;
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const int blocksPerGrid = (mean->lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
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PointersManager manager(input->getContext(), "batchnorm");
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NDArray::prepareSpecialUse({output}, {input, mean, variance, gamma, beta});
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BUILD_SINGLE_SELECTOR(input->dataType(), batchnormCudaLauncher, (blocksPerGrid, threadsPerBlock, input->getContext()->getCudaStream(), input->getSpecialBuffer(), input->getSpecialShapeInfo(), mean->getSpecialBuffer(), mean->getSpecialShapeInfo(), variance->getSpecialBuffer(), variance->getSpecialShapeInfo(), gamma ? gamma->getSpecialBuffer() : nullptr, gamma ? gamma->getSpecialShapeInfo() : nullptr, beta ? beta->getSpecialBuffer() : nullptr, beta ? beta->getSpecialShapeInfo() : nullptr, output->specialBuffer(), output->specialShapeInfo(), packX.platformShapeInfo(), packX.platformOffsets(), packZ.platformShapeInfo(), packZ.platformOffsets(), epsilon), FLOAT_TYPES);
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NDArray::registerSpecialUse({output}, {input, mean, variance, gamma, beta});
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manager.synchronize();
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// const int threadsPerBlock = MAX_NUM_THREADS / 4;
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// const int blocksPerGrid = (input->lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
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// const int sharedMem = sizeof(Nd4jLong) * threadsPerBlock * input->rankOf() + 128;
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// PointersManager manager(input->getContext(), "batchnorm");
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// const int* dims = reinterpret_cast<int*>(manager.replicatePointer(axes.data(), axes.size() * sizeof(int)));
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// NDArray::prepareSpecialUse({output}, {input, mean, variance, gamma, beta});
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// BUILD_SINGLE_SELECTOR(input->dataType(), batchnormCudaLauncher2, (blocksPerGrid, threadsPerBlock, sharedMem, input->getContext()->getCudaStream(), input->getSpecialBuffer(), input->getSpecialShapeInfo(), mean->getSpecialBuffer(), mean->getSpecialShapeInfo(), variance->getSpecialBuffer(), variance->getSpecialShapeInfo(), gamma ? gamma->getSpecialBuffer() : nullptr, gamma ? gamma->getSpecialShapeInfo() : nullptr, beta ? beta->getSpecialBuffer() : nullptr, beta ? beta->getSpecialShapeInfo() : nullptr, output->specialBuffer(), output->specialShapeInfo(), axes.size(), dims, epsilon), FLOAT_TYPES);
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// NDArray::registerSpecialUse({output}, {input, mean, variance, gamma, beta});
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// manager.synchronize();
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}
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}
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}
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}
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