* - specifying template instantiation for certain types in float16 and bloat16 Signed-off-by: Yurii <iuriish@yahoo.com> * - polishing bfloat16 and float16 member functions template specialization Signed-off-by: Yurii <iuriish@yahoo.com> * - rewrite and overload array +-*/ scalar and scalar +-*/ arr in NDAray class Signed-off-by: Yurii <iuriish@yahoo.com> * - make corrections which have to do with and rvalue lvalue conversions Signed-off-by: Yurii <iuriish@yahoo.com> * - provide move semantic in NDArray operators array +-/* array Signed-off-by: Yurii <iuriish@yahoo.com> * float16/bfloat16 tweaks Signed-off-by: raver119 <raver119@gmail.com> * one more tweak Signed-off-by: raver119 <raver119@gmail.com> * - make float16 and bfloat16 to compile successfully on cuda Signed-off-by: Yurii <iuriish@yahoo.com> * - do not use resources of view-like arrays when move semantics is applied Signed-off-by: Yurii <iuriish@yahoo.com> * - get rid of pointers in signatures NDArray methods 1 Signed-off-by: Yurii <iuriish@yahoo.com> * - correction of signature of NDArray::dup method Signed-off-by: Yurii <iuriish@yahoo.com> * - correction of signature of NDArray::reduceAlongDimension method Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::applyIndexReduce and applyTrueBroadcast methods Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::applyReduce3 and varianceAlongDimension methods Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::tensorsAlongDimension and diagonal methods Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::allTensorsAlongDimension Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::reduceAlongDimension 2 Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::applyTransform 2 Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::applyPairwiseTransform 2 Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::applyBroadcast 2 Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::applyTrueBroadcast 2 Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::applyScalar and applyScalarArr Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::lambda methods Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::reduce3 methods 2 Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of following NDArray methods: add/sub/mul/div row/column and fillAsTriangular Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::tileToShape methods Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::isShapeSameStrict method Signed-off-by: Yurii <iuriish@yahoo.com> * minor corrections in tests Signed-off-by: Yurii <iuriish@yahoo.com> * - replace reduce op in batchnorm mkldnn Signed-off-by: Yurii <iuriish@yahoo.com> * - add explicit templates instantiations for operator+(NDArray&&. const scalar) Signed-off-by: Yurii <iuriish@yahoo.com> * - corrections of casts in float16/bfloat16 Signed-off-by: Yurii <iuriish@yahoo.com> * - provide move semantics in following NDArray methods: transform, applyTrueBroadcast, transpose, reshape, permute Signed-off-by: Yurii <iuriish@yahoo.com> * - get rid of input array A duplicate in svd cuda op Signed-off-by: Yurii <iuriish@yahoo.com> * - avoid available bug in svd cuda API Signed-off-by: Yurii <iuriish@yahoo.com> * - add temporary global memory buffer in svd cuda when calcUV = false and m != n Signed-off-by: Yurii <iuriish@yahoo.com> * - remove test with blfoat16 type for betainC Signed-off-by: Yurii <iuriish@yahoo.com> * - resolve conflicts after master has been merged in Signed-off-by: Yurii <iuriish@yahoo.com> * - changed type of affected input array in fused_batch_norm Signed-off-by: Yurii <iuriish@yahoo.com> * - add several explicit type castings Signed-off-by: Yurii <iuriish@yahoo.com> * - add ND4J_EXPORT to operators Signed-off-by: Yurii <iuriish@yahoo.com> * - add explicit template types in instantiations of template arithm operators of NDArray class Signed-off-by: Yurii <iuriish@yahoo.com> * - one more test fix Signed-off-by: Yurii <iuriish@yahoo.com> Co-authored-by: raver119 <raver119@gmail.com>
151 lines
6.3 KiB
C++
151 lines
6.3 KiB
C++
/*******************************************************************************
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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 <execution/Threads.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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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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// 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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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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// 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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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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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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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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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] + betaBuff[offsetSmall];
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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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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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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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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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}
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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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}
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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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BUILD_SINGLE_SELECTOR(input->dataType(), batchnorm_, (input, mean, variance, gamma, beta, output, axes, epsilon), FLOAT_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template 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), FLOAT_TYPES);
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}
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}
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}
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