- fix wrong calculation of elements offsets in batchnorm op when input arrays have unusual (#169)
Signed-off-by: Yurii <iuriish@yahoo.com>master
Yurii Shyrma
raver119
parent
c84307a6fe
commit
bbf88b53dd
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@ -15,7 +15,7 @@
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******************************************************************************/
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//
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// @author Yurii Shyrma, created on 25.02.2018
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// @author Yurii Shyrma (iuriish@yahoo.com)
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//
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@ -31,112 +31,160 @@ namespace helpers {
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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static 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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static void batchnorm_(const NDArray* input, const NDArray* mean, const NDArray* variance, const NDArray* gamma, const NDArray* beta,
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NDArray* output,
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const std::vector<int>& axes, const double epsilon) {
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// formula: output = gamma * ((input - mean) / sqrt(variance + epsilon)) + beta
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NDArray sigmaInvGam(mean); // do not copy mean's buffer, take only its shapeInfo
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T eps = epsilon;
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const T* x = input->bufferAsT<T>();
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T* z = output->bufferAsT<T>();
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const T* m = mean->bufferAsT<T>();
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const T* v = variance->bufferAsT<T>();
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const T* g = gamma == nullptr ? nullptr : gamma->bufferAsT<T>();
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const T* b = beta == nullptr ? nullptr : beta->bufferAsT<T>();
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if(gamma != nullptr) {
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auto lambda = LAMBDA_TT(x, y, eps) {return x / nd4j::math::nd4j_sqrt<T, T>(y + eps);};
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const_cast<NDArray*>(gamma)->applyPairwiseLambda<T>(*variance, lambda, sigmaInvGam);
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}
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else {
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auto lambda = LAMBDA_T(x, eps) { return 1. / nd4j::math::nd4j_sqrt<T, T>(x + eps); };
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const_cast<NDArray*>(variance)->applyLambda<T>(lambda, sigmaInvGam);
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}
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const bool xzSameOffset = shape::haveSameShapeAndStrides(input->getShapeInfo(), output->getShapeInfo());
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// auto sigmaInvGam = (*variance + epsilon).transform(transform::RSqrt); // sigmaInvGam = 1 / sqrt(variance + epsilon)
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// if(gamma != nullptr) sigmaInvGam *= *gamma;
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const T* sigmaBuff = sigmaInvGam.bufferAsT<T>();
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const T* meanBuff = mean->bufferAsT<T>();
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const T* inBuff = input->bufferAsT<T>();
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T* outBuff = output->bufferAsT<T>();
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bool paramSameOffset = shape::haveSameShapeAndStrides(mean->getShapeInfo(), variance->getShapeInfo());
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if(paramSameOffset && gamma != nullptr)
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paramSameOffset &= shape::haveSameShapeAndStrides(mean->getShapeInfo(), gamma->getShapeInfo());
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if(paramSameOffset && beta != nullptr)
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paramSameOffset &= shape::haveSameShapeAndStrides(mean->getShapeInfo(), beta->getShapeInfo());
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const Nd4jLong lenBig = input->lengthOf();
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const Nd4jLong lenSmall = mean->lengthOf();
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const Nd4jLong* inShapeInfo = input->getShapeInfo();
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const Nd4jLong* meanShapeInfo = mean->getShapeInfo();
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uint inShapeInfoCast[MAX_RANK];
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uint meanShapeInfoCast[MAX_RANK];
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bool canCastIn = nd4j::DataTypeUtils::castShapeInfo(inShapeInfo, inShapeInfoCast);
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bool canCastMean = nd4j::DataTypeUtils::castShapeInfo(meanShapeInfo, meanShapeInfoCast);
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const Nd4jLong step = lenBig / lenSmall;
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const Nd4jLong steps = lenBig / lenSmall;
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std::vector<int> dimsToExclude = ShapeUtils::evalDimsToExclude(input->rankOf(), axes);
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OmpLaunchHelper info(lenBig, lenSmall);
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if(beta != nullptr) {
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const T* betaBuff = beta->bufferAsT<T>();
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auto func = PRAGMA_THREADS_DO {
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const auto threadNum = thread_id;
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Nd4jLong* inOffsets = new Nd4jLong[step];
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Nd4jLong* memBuff = new Nd4jLong[2 * inShapeInfo[0]];
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auto func = PRAGMA_THREADS_DO {
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for (int j = 0; j < lenSmall; ++j) {
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Nd4jLong* xOffsets = new Nd4jLong[steps];
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Nd4jLong* zOffsets = xzSameOffset ? xOffsets : new Nd4jLong[steps];
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Nd4jLong* auxBuff = new Nd4jLong[2 * input->rankOf()];
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const bool isOwner = j < info._numThreads ? threadNum == j : threadNum == j % info._numThreads;
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if (!isOwner) continue;
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for (int j = 0; j < lenSmall; ++j) {
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const Nd4jLong start = j * step;
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const Nd4jLong end = start + step;
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const bool isOwner = (j < info._numThreads) ? thread_id == j : thread_id == (j % info._numThreads);
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// calculate offset for mean, variance, gamma, beta (all of them have the same shape)
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auto offsetSmall = shape::indexOffset(j, meanShapeInfo, meanShapeInfoCast, canCastMean);
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// calculate offset for input and output (all of them have the same shape)
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shape::outerArrayOffsets(inOffsets, j, inShapeInfo, meanShapeInfo, memBuff, dimsToExclude.data());
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if(!isOwner)
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continue;
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PRAGMA_OMP_SIMD
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for (Nd4jLong i = 0; i < step; ++i) {
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auto offsetBig = inOffsets[i];
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outBuff[offsetBig] = (inBuff[offsetBig] - meanBuff[offsetSmall]) * sigmaBuff[offsetSmall] + betaBuff[offsetSmall];
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const auto meanOffset = shape::getIndexOffset(j, mean->getShapeInfo());
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const auto varOffset = paramSameOffset ? meanOffset : shape::getIndexOffset(j, variance->getShapeInfo());
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const auto meanVal = m[meanOffset];
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auto sigmaInvGam = static_cast<T>(1) / nd4j::math::nd4j_sqrt<T, T>(v[varOffset] + epsilon);
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if(g != nullptr) {
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const auto gammaOffset = paramSameOffset ? meanOffset : shape::getIndexOffset(j, gamma->getShapeInfo());
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sigmaInvGam *= g[gammaOffset];
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}
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T betaVal = static_cast<T>(0);
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if(b != nullptr) {
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const auto betaOffset = paramSameOffset ? meanOffset : shape::getIndexOffset(j, beta->getShapeInfo());
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betaVal = b[betaOffset];
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}
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// calculate offsets for input and output
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shape::outerArrayOffsets(xOffsets, j, input->getShapeInfo(), mean->getShapeInfo(), auxBuff, dimsToExclude.data());
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if(!xzSameOffset)
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shape::outerArrayOffsets(zOffsets, j, output->getShapeInfo(), mean->getShapeInfo(), auxBuff, dimsToExclude.data());
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PRAGMA_OMP_SIMD
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for (uint i = 0; i < steps; ++i)
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z[zOffsets[i]] = (x[xOffsets[i]] - meanVal) * sigmaInvGam + betaVal;
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}
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delete []auxBuff;
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delete []xOffsets;
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if(!xzSameOffset)
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delete []zOffsets;
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};
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samediff::Threads::parallel_do(func, info._numThreads);
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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static void batchnorm2_(const NDArray* input, const NDArray* mean, const NDArray* variance, const NDArray* gamma, const NDArray* beta,
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NDArray* output,
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const std::vector<int>& axes, const double epsilon) {
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// formula: output = gamma * ((input - mean) / sqrt(variance + epsilon)) + beta
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const auto x = input->bufferAsT<T>();
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auto z = output->bufferAsT<T>();
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const auto m = mean->bufferAsT<T>();
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const auto v = variance->bufferAsT<T>();
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const auto g = gamma == nullptr ? nullptr : gamma->bufferAsT<T>();
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const auto b = beta == nullptr ? nullptr : beta->bufferAsT<T>();
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// xRank == zRank, minRank = meanRank = varianceRank = gammaRank = betaRank
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const uint xRank = input->rankOf();
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const uint minRank = mean->rankOf();
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const uint numAxes = axes.size();
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const bool xzSameOffset = shape::haveSameShapeAndStrides(input->getShapeInfo(), output->getShapeInfo());
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bool paramSameOffset = shape::haveSameShapeAndStrides(mean->getShapeInfo(), variance->getShapeInfo());
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if(paramSameOffset && gamma != nullptr)
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paramSameOffset &= shape::haveSameShapeAndStrides(mean->getShapeInfo(), gamma->getShapeInfo());
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if(paramSameOffset && beta != nullptr)
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paramSameOffset &= shape::haveSameShapeAndStrides(mean->getShapeInfo(), beta->getShapeInfo());
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auto func = PRAGMA_THREADS_FOR {
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Nd4jLong coords[MAX_RANK];
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for (auto i = start; i < stop; i += increment) {
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shape::index2coords(i, input->getShapeInfo(), coords);
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const auto xOffset = shape::getOffset(input->getShapeInfo(), coords);
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const auto zOffset = xzSameOffset ? xOffset : shape::getOffset(output->getShapeInfo(), 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 < numAxes && i != axes[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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delete []inOffsets;
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delete []memBuff;
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};
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else // minRank = numAxes = 1 in this case
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coords[0] = coords[axes[0]];
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samediff::Threads::parallel_do(func, info._numThreads);
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}
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else {
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auto func = PRAGMA_THREADS_DO {
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const auto threadNum = thread_id;
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Nd4jLong* inOffsets = new Nd4jLong[step];
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Nd4jLong* memBuff = new Nd4jLong[2 * inShapeInfo[0]];
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const auto meanOffset = shape::getOffset(mean->getShapeInfo(), coords);
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const auto varianceOffset = paramSameOffset ? meanOffset : shape::getOffset(variance->getShapeInfo(), coords);
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for (int j = 0; j < lenSmall; ++j) {
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const bool isOwner = j < info._numThreads ? threadNum == j : threadNum == j % info._numThreads;
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if (!isOwner) continue;
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T sigmaInvGam = 1. / nd4j::math::nd4j_sqrt<T, T>(v[varianceOffset] + epsilon);
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const Nd4jLong start = j * step;
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const Nd4jLong end = start + step;
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// calculate offset for mean, variance, gamma, beta (all of them have the same shape)
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auto offsetSmall = shape::indexOffset(j, meanShapeInfo, meanShapeInfoCast, canCastMean);
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// calculate offset for input and output (all of them have the same shape)
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shape::outerArrayOffsets(inOffsets, j, inShapeInfo, meanShapeInfo, memBuff, dimsToExclude.data());
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PRAGMA_OMP_SIMD
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for (Nd4jLong i = 0; i < step; ++i) {
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auto offsetBig = inOffsets[i];
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outBuff[offsetBig] = (inBuff[offsetBig] - meanBuff[offsetSmall]) * sigmaBuff[offsetSmall];
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}
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if(g != nullptr) {
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const auto gammaOffset = paramSameOffset ? meanOffset : shape::getOffset(gamma->getShapeInfo(), coords);
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sigmaInvGam *= g[gammaOffset];
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}
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delete []inOffsets;
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delete []memBuff;
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};
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samediff::Threads::parallel_do(func, info._numThreads);
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}
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z[zOffset] = (x[xOffset] - m[meanOffset]) * sigmaInvGam;
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if(b != nullptr) {
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const auto betaOffset = paramSameOffset ? meanOffset : shape::getOffset(beta->getShapeInfo(), coords);
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z[zOffset] += b[betaOffset];
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}
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}
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};
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samediff::Threads::parallel_for(func, 0, input->lengthOf());
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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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// batchnorm2_ is slower
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BUILD_SINGLE_SELECTOR(input->dataType(), batchnorm_, (input, mean, variance, gamma, beta, output, axes, epsilon), FLOAT_TYPES);
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}
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@ -31,66 +31,66 @@ 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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// 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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// 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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// // 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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// 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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// 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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// 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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// 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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// 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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// 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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// 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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// 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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// 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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// 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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// 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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// 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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// 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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@ -110,13 +110,12 @@ __global__ static void batchnormCuda2(const void* vx, const Nd4jLong* xShapeInfo
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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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__shared__ int xRank, minRank; // xRank == zRank, minRank = meanRank = varianceRank = gammaRank = betaRank
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__shared__ Nd4jLong xLen, totalThreads; // 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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@ -125,7 +124,8 @@ __global__ static void batchnormCuda2(const void* vx, const Nd4jLong* xShapeInfo
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}
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__syncthreads();
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auto coords = sharedMem + threadIdx.x * xRank;
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Nd4jLong coords[MAX_RANK];
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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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@ -166,24 +166,24 @@ __global__ static void batchnormCuda2(const void* vx, const Nd4jLong* xShapeInfo
|
|||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
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) {
|
||||
// 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));
|
||||
}
|
||||
// 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 int sharedMem, const cudaStream_t *stream,
|
||||
__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,
|
||||
|
|
@ -193,42 +193,41 @@ __host__ static void batchnormCudaLauncher2(const int blocksPerGrid, const int t
|
|||
const int numDims, const int* dims,
|
||||
const double epsilon) {
|
||||
|
||||
batchnormCuda2<T><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vMean, meanShapeInfo, vVariance, varianceShapeInfo, vGamma, gammaShapeInfo, vBeta, betaShapeInfo, vz, zShapeInfo, numDims, dims, static_cast<T>(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);
|
||||
// std::vector<int> dimsToExclude = ShapeUtils::evalDimsToExclude(input->rankOf(), axes);
|
||||
|
||||
// auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimsToExclude);
|
||||
// auto packZ = nd4j::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->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);
|
||||
// NDArray::registerSpecialUse({output}, {input, mean, variance, gamma, beta});
|
||||
|
||||
// manager.synchronize();
|
||||
|
||||
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimsToExclude);
|
||||
auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->shapeInfo(), dimsToExclude);
|
||||
|
||||
const int threadsPerBlock = MAX_NUM_THREADS / 2;
|
||||
const int blocksPerGrid = (mean->lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
|
||||
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(), 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);
|
||||
BUILD_SINGLE_SELECTOR(input->dataType(), batchnormCudaLauncher2, (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(), axes.size(), dims, epsilon), FLOAT_TYPES);
|
||||
NDArray::registerSpecialUse({output}, {input, mean, variance, gamma, beta});
|
||||
|
||||
manager.synchronize();
|
||||
|
||||
|
||||
// const int threadsPerBlock = MAX_NUM_THREADS / 4;
|
||||
// const int blocksPerGrid = (input->lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
|
||||
// const int sharedMem = sizeof(Nd4jLong) * threadsPerBlock * input->rankOf() + 128;
|
||||
|
||||
// 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, 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);
|
||||
// NDArray::registerSpecialUse({output}, {input, mean, variance, gamma, beta});
|
||||
|
||||
// manager.synchronize();
|
||||
}
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -3431,6 +3431,35 @@ TEST_F(DeclarableOpsTests10, batchnorm_test6) {
|
|||
delete results;
|
||||
}
|
||||
|
||||
////////////////////////////////////////////////////////////////////
|
||||
TEST_F(DeclarableOpsTests10, batchnorm_test7) {
|
||||
|
||||
NDArray input1('c', {3,3,15,15}, nd4j::DataType::FLOAT32);
|
||||
NDArray input2('c', {3,15,15,3}, nd4j::DataType::FLOAT32);
|
||||
input2.permutei({0,3,1,2});
|
||||
|
||||
NDArray mean ('c', {3}, {0, 0, 0}, nd4j::DataType::FLOAT32);
|
||||
NDArray variance('c', {3}, {1, 1, 1}, nd4j::DataType::FLOAT32);
|
||||
NDArray gamma ('c', {3}, {1, 1, 1}, nd4j::DataType::FLOAT32);
|
||||
NDArray beta ('c', {3}, {0, 0, 0}, nd4j::DataType::FLOAT32);
|
||||
|
||||
NDArray out1('c', {3,3,15,15}, nd4j::DataType::FLOAT32);
|
||||
NDArray out2('c', {3,3,15,15}, nd4j::DataType::FLOAT32);
|
||||
|
||||
input1.linspace(-1012, 1);
|
||||
input2.assign(input1);
|
||||
|
||||
nd4j::ops::batchnorm op;
|
||||
|
||||
auto res1 = op.execute({&input1, &mean, &variance, &gamma, &beta}, {&out1}, {1e-5}, {1,1,1}, {});
|
||||
ASSERT_EQ(ND4J_STATUS_OK, res1);
|
||||
|
||||
auto res2 = op.execute({&input2, &mean, &variance, &gamma, &beta}, {&out2}, {1e-5}, {1,1,1}, {});
|
||||
ASSERT_EQ(ND4J_STATUS_OK, res2);
|
||||
|
||||
ASSERT_TRUE(out1.equalsTo(out2));
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
TEST_F(DeclarableOpsTests10, bool_broadcast_test_1) {
|
||||
|
||||
|
|
|
|||
|
|
@ -422,13 +422,50 @@ TEST_F(PlaygroundTests, my) {
|
|||
delete variableSpace;
|
||||
}
|
||||
|
||||
*/
|
||||
|
||||
#include<ops/declarable/helpers/batchnorm.h>
|
||||
|
||||
TEST_F(PlaygroundTests, my) {
|
||||
|
||||
NDArray a('c',{2,3,4}, nd4j::DataType::DOUBLE);
|
||||
a({0,0, 0,1, 0,1}).printShapeInfo();
|
||||
a({0,1, 0,0, 0,1}).printShapeInfo();
|
||||
a({0,0, 0,1, 0,1}).printShapeInfo();
|
||||
const int N = 10000;
|
||||
const Nd4jLong dim0(128), dim1(128), dim2(128);
|
||||
|
||||
NDArray input('c', {dim0,dim1,dim2}, nd4j::DataType::DOUBLE);
|
||||
NDArray mean('c', {dim1}, nd4j::DataType::DOUBLE);
|
||||
NDArray variance('c', {dim1}, nd4j::DataType::DOUBLE);
|
||||
NDArray gamma('c', {dim1}, nd4j::DataType::DOUBLE);
|
||||
NDArray beta ('c', {dim1}, nd4j::DataType::DOUBLE);
|
||||
|
||||
NDArray output('c', {dim0,dim1,dim2}, nd4j::DataType::DOUBLE);
|
||||
|
||||
input.linspace(-100, 0.1);
|
||||
mean.linspace(-50, 0.15);
|
||||
variance.linspace(-5, 0.2);
|
||||
gamma = 1.5;
|
||||
beta = -2.5;
|
||||
|
||||
// warm up
|
||||
ops::helpers::batchnorm(&input, &mean, &variance, &gamma, &beta, &output, {1}, 1e-5);
|
||||
|
||||
auto timeStart = std::chrono::system_clock::now();
|
||||
for (int i = 0; i < N; ++i)
|
||||
ops::helpers::batchnorm(&input, &mean, &variance, &gamma, &beta, &output, {1}, 1e-5);
|
||||
|
||||
auto timeEnd = std::chrono::system_clock::now();
|
||||
auto time = std::chrono::duration_cast<std::chrono::microseconds> ((timeEnd - timeStart)/N).count();
|
||||
|
||||
printf("time: %li \n", time);
|
||||
|
||||
}
|
||||
|
||||
|
||||
*/
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
}
|
||||
Loading…
Reference in New Issue