* initial commit Signed-off-by: raver119 <raver119@gmail.com> * Implementation of hashcode cuda helper. Working edition. * Fixed parallel test input arangements. * Fixed tests for hashcode op. * Fixed shape calculation for image:crop_and_resize op and test. * NativeOps tests. Initial test suite. * Added tests for indexReduce methods. * Added test on execBroadcast with NDArray as dimensions. * Added test on execBroadcastBool with NDArray as dimensions. * Added tests on execPairwiseTransform and execPairwiseTransofrmBool. * Added tests for execReduce with scalar results. * Added reduce tests for non-empty dims array. * Added tests for reduce3. * Added tests for execScalar. * Added tests for execSummaryStats. * - provide cpu/cuda code for batch_to_space - testing it Signed-off-by: Yurii <yurii@skymind.io> * - remove old test for batch_to_space (had wrong format and numbers were not checked) Signed-off-by: Yurii <yurii@skymind.io> * Fixed complilation errors with test. * Added test for execTransformFloat. * Added test for execTransformSame. * Added test for execTransformBool. * Added test for execTransformStrict. * Added tests for execScalar/execScalarBool with TADs. * Added test for flatten. * - provide cpu/cuda code for space_to_Batch operaion Signed-off-by: Yurii <yurii@skymind.io> * Added test for concat. * comment unnecessary stuff in s_t_b Signed-off-by: Yurii <yurii@skymind.io> * Added test for specialConcat. * Added tests for memcpy/set routines. * Fixed pullRow cuda test. * Added pullRow test. * Added average test. * - correct typo in NDArray::applyPairwiseTransform(nd4j::pairwise::BoolOps op...) Signed-off-by: Yurii <yurii@skymind.io> * - debugging and fixing cuda tests in JavaInteropTests file Signed-off-by: Yurii <yurii@skymind.io> * - correct some tests Signed-off-by: Yurii <yurii@skymind.io> * Added test for shuffle. * Fixed ops declarations. * Restored omp and added shuffle test. * Added convertTypes test. * Added tests for execRandom. Eliminated usage of RandomBuffer with NativeOps. * Added sort tests. * Added tests for execCustomOp. * - further debuging and fixing tests terminated with crash Signed-off-by: Yurii <yurii@skymind.io> * Added tests for calculateOutputShapes. * Addded Benchmarks test. * Commented benchmark tests. * change assertion Signed-off-by: raver119 <raver119@gmail.com> * Added tests for apply_sgd op. Added cpu helper for that op. * Implement cuda helper for aplly_sgd op. Fixed tests for NativeOps. * Added test for assign broadcastable. * Added tests for assign_bp op. * Added tests for axpy op. * - assign/execScalar/execTransformAny signature change - minor test fix Signed-off-by: raver119 <raver119@gmail.com> * Fixed axpy op. * meh Signed-off-by: raver119 <raver119@gmail.com> * - fix tests for nativeOps::concat Signed-off-by: Yurii <yurii@skymind.io> * sequential transform/scalar Signed-off-by: raver119 <raver119@gmail.com> * allow nested parallelism Signed-off-by: raver119 <raver119@gmail.com> * assign_bp leak fix Signed-off-by: raver119 <raver119@gmail.com> * block setRNG fix Signed-off-by: raver119 <raver119@gmail.com> * enable parallelism by default Signed-off-by: raver119 <raver119@gmail.com> * enable nested parallelism by default Signed-off-by: raver119 <raver119@gmail.com> * Added cuda implementation for row_count helper. * Added implementation for tnse gains op helper. * - take into account possible situations when input arrays are empty in reduce_ cuda stuff Signed-off-by: Yurii <yurii@skymind.io> * Implemented tsne/edge_forces op cuda-based helper. Parallelized cpu-based helper for edge_forces. * Added kernel for tsne/symmetrized op heleper. * Implementation of tsne/symmetrized op cuda helper. Working edition. * Eliminated waste printfs. * Added test for broadcastgradientargs op. * host-only fallback for empty reduce float Signed-off-by: raver119 <raver119@gmail.com> * - some tests fixes Signed-off-by: Yurii <yurii@skymind.io> * - correct the rest of reduce_ stuff Signed-off-by: Yurii <yurii@skymind.io> * - further correction of reduce_ stuff Signed-off-by: Yurii <yurii@skymind.io> * Added test for Cbow op. Also added cuda implementation for cbow helpers. * - improve code of stack operation for scalar case Signed-off-by: Yurii <yurii@skymind.io> * - provide cuda kernel for gatherND operation Signed-off-by: Yurii <yurii@skymind.io> * Implementation of cbow helpers with cuda kernels. * minor tests tweaks Signed-off-by: raver119 <raver119@gmail.com> * minor tests tweaks Signed-off-by: raver119 <raver119@gmail.com> * - further correction of cuda stuff Signed-off-by: Yurii <yurii@skymind.io> * Implementatation of cbow op helper with cuda kernels. Working edition. * Skip random testing for cudablas case. * lstmBlockCell context fix Signed-off-by: raver119 <raver119@gmail.com> * Added tests for ELU and ELU_BP ops. * Added tests for eq_scalar, gt_scalar, gte_scalar and lte_scalar ops. * Added tests for neq_scalar. * Added test for noop. * - further work on clipbynorm_bp Signed-off-by: Yurii <yurii@skymind.io> * - get rid of concat op call, use instead direct concat helper call Signed-off-by: Yurii <yurii@skymind.io> * lstmBlockCell context fix Signed-off-by: raver119 <raver119@gmail.com> * Added tests for lrelu and lrelu_bp. * Added tests for selu and selu_bp. * Fixed lrelu derivative helpers. * - some corrections in lstm Signed-off-by: Yurii <yurii@skymind.io> * operator * result shape fix Signed-off-by: raver119 <raver119@gmail.com> * - correct typo in lstmCell Signed-off-by: Yurii <yurii@skymind.io> * few tests fixed Signed-off-by: raver119 <raver119@gmail.com> * CUDA inverse broadcast bool fix Signed-off-by: raver119 <raver119@gmail.com> * disable MMAP test for CUDA Signed-off-by: raver119 <raver119@gmail.com> * BooleanOp syncToDevice Signed-off-by: raver119 <raver119@gmail.com> * meh Signed-off-by: raver119 <raver119@gmail.com> * additional data types for im2col/col2im Signed-off-by: raver119 <raver119@gmail.com> * Added test for firas_sparse op. * one more RandomBuffer test excluded Signed-off-by: raver119 <raver119@gmail.com> * Added tests for flatten op. * Added test for Floor op. * bunch of tests fixed Signed-off-by: raver119 <raver119@gmail.com> * mmulDot tests fixed Signed-off-by: raver119 <raver119@gmail.com> * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * Implemented floordiv_bp op and tests. * Fixed scalar case with cuda implementation for bds. * - work on cuda kernel for clip_by_norm backprop op is completed Signed-off-by: Yurii <yurii@skymind.io> * Eliminate cbow crach. * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * Eliminated abortion with batched nlp test. * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * Fixed shared flag initializing. * disabled bunch of cpu workspaces tests Signed-off-by: raver119 <raver119@gmail.com> * scalar operators fix: missing registerSpecialUse call Signed-off-by: raver119 <raver119@gmail.com> * Fixed logdet for cuda and tests. * - correct clipBynorm_bp Signed-off-by: Yurii <yurii@skymind.io> * Fixed crop_and_resize shape datatype. * - correct some mmul tests Signed-off-by: Yurii <yurii@skymind.io>
247 lines
10 KiB
C++
247 lines
10 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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// Created by raver119 on 08.10.2017.
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//
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#include "../scalar.h"
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#include <op_boilerplate.h>
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#include <types/types.h>
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#include <LoopKind.h>
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#include "../legacy_ops.h"
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using namespace simdOps;
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namespace functions {
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namespace scalar {
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////////////////////////////////////////////////////////////////////////
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template<typename X, typename Y, typename Z>
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template<typename OpType>
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void ScalarTransform<X, Y, Z>::transform(void *vx, Nd4jLong *xShapeInfo,
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void *vextraParams,
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void *vz, Nd4jLong *zShapeInfo,
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void *vscalars,
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int *dimension, int dimensionLength,
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Nd4jLong *xTadShapeInfo, Nd4jLong *xTadOffsets,
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Nd4jLong *zTadShapeInfo, Nd4jLong *zTadOffsets) {
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auto x = reinterpret_cast<X *>(vx);
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auto z = reinterpret_cast<Z *>(vz);
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auto scalars = reinterpret_cast<Y *>(vscalars);
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auto extraParams = reinterpret_cast<Z *>(vextraParams);
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if (zTadShapeInfo == nullptr) {
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zTadShapeInfo = xTadShapeInfo;
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zTadOffsets = xTadOffsets;
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}
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const int xTadEws = shape::elementWiseStride(xTadShapeInfo);
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const int zTadEws = shape::elementWiseStride(zTadShapeInfo);
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const int tadLength = shape::tadLength(xShapeInfo, dimension, dimensionLength);
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const int numTads = shape::length(xShapeInfo) / tadLength;
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nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopXZ(xTadShapeInfo, zTadShapeInfo);
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if (kindOfLoop != nd4j::LoopKind::EWS1 && kindOfLoop != nd4j::LoopKind::EWSNONZERO) {
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printf("ScalarTransform<X, Z>::transform: super-bad loop visited. Shouldn't ever happen\n");
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return;
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}
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int num_threads = nd4j::math::nd4j_min<int>(numTads, omp_get_max_threads());
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if (kindOfLoop == nd4j::LoopKind::EWS1) {
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PRAGMA_OMP_PARALLEL_FOR_THREADS(num_threads)
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for (unsigned int r = 0; r < numTads; r++) {
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auto oZ = z + zTadOffsets[r];
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auto oX = x + xTadOffsets[r];
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PRAGMA_OMP_SIMD
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for (unsigned int f = 0; f < tadLength; f++)
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oZ[f] = OpType::op(oX[f], scalars[r], extraParams);
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}
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}
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else {
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PRAGMA_OMP_PARALLEL_FOR_THREADS(num_threads)
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for (unsigned int r = 0; r < numTads; r++) {
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auto oZ = z + zTadOffsets[r];
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auto oX = x + xTadOffsets[r];
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PRAGMA_OMP_SIMD
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for (unsigned int f = 0; f < tadLength; f++)
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oZ[f * zTadEws] = OpType::op(oX[f * xTadEws], scalars[r], extraParams);
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}
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}
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}
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////////////////////////////////////////////////////////////////////////
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template<typename X, typename Y, typename Z>
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void ScalarTransform<X,Y,Z>::transform(int opNum,
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void *x, Nd4jLong *xShapeInfo,
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void *extraParams,
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void *z, Nd4jLong *zShapeInfo,
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void *scalars,
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int *dimension, int dimensionLength,
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Nd4jLong *xTadShapeInfo, Nd4jLong *xTadOffsets,
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Nd4jLong *zTadShapeInfo, Nd4jLong *zTadOffsets) {
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DISPATCH_BY_OPNUM_TTT(transform, PARAMS(x, xShapeInfo, extraParams, z, zShapeInfo, scalars, dimension, dimensionLength, xTadShapeInfo, xTadOffsets, zTadShapeInfo, zTadOffsets), SCALAR_OPS);
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}
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////////////////////////////////////////////////////////////////////////
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template<typename X, typename Y, typename Z>
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void ScalarTransform<X, Y, Z>::transform(const int opNum,
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void *x, Nd4jLong xStride,
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void *z, Nd4jLong zStride,
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void *scalar,
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void *extraParams,
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const Nd4jLong n, bool allowParallelism) {
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DISPATCH_BY_OPNUM_TTT(transform, PARAMS(x, xStride, z, zStride, scalar, extraParams, n, allowParallelism), SCALAR_OPS);
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}
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////////////////////////////////////////////////////////////////////////
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template<typename X, typename Y, typename Z>
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void ScalarTransform<X, Y, Z>::transform(const int opNum,
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void *x, Nd4jLong *xShapeInfo,
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void *z, Nd4jLong *zShapeInfo,
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void *scalar,
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void *extraParams, bool allowParallelism) {
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DISPATCH_BY_OPNUM_TTT(transform, PARAMS(x, xShapeInfo, z, zShapeInfo, scalar, extraParams, allowParallelism), SCALAR_OPS);
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}
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////////////////////////////////////////////////////////////////////////
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template<typename X, typename Y, typename Z>
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template<typename OpType>
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void ScalarTransform<X, Y, Z>::transform(void *vx, Nd4jLong *xShapeInfo,
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void *vz, Nd4jLong *zShapeInfo,
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void *vscalar,
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void *vextraParams, bool allowParallelism) {
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auto x = reinterpret_cast<X *>(vx);
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auto z = reinterpret_cast<Z *>(vz);
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auto scalar = reinterpret_cast<Y *>(vscalar)[0];
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auto extraParams = reinterpret_cast<Z *>(vextraParams);
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const auto len = shape::length(xShapeInfo);
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const auto xEws = shape::elementWiseStride(xShapeInfo);
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const auto zEws = shape::elementWiseStride(zShapeInfo);
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nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopXZ(xShapeInfo, zShapeInfo);
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if (kindOfLoop == nd4j::LoopKind::EWS1 || kindOfLoop == nd4j::LoopKind::EWSNONZERO) {
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transform<OpType>(x, xEws, z, zEws, vscalar, extraParams, len, allowParallelism);
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}
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else {
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uint xShapeInfoCast[MAX_RANK];
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const bool canCastX = nd4j::DataTypeUtils::castShapeInfo<uint>(xShapeInfo, xShapeInfoCast);
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nd4j::OmpLaunchHelper info(len, allowParallelism ? -1 : 1);
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if(shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) {
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PRAGMA_OMP_PARALLEL_THREADS_IF(info._numThreads, allowParallelism)
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{
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auto threadNum = omp_get_thread_num();
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auto threadOffset = info.getThreadOffset(threadNum);
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auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
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PRAGMA_OMP_SIMD
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for (unsigned int i = 0; i < ulen; i++) {
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auto offset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, len, canCastX);
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z[offset] = OpType::op(x[offset], scalar, extraParams);
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}
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}
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}
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else {
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uint zShapeInfoCast[MAX_RANK];
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const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo<uint>(zShapeInfo, zShapeInfoCast);
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PRAGMA_OMP_PARALLEL_THREADS_IF(info._numThreads, allowParallelism)
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{
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auto threadNum = omp_get_thread_num();
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auto threadOffset = info.getThreadOffset(threadNum);
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auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
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PRAGMA_OMP_SIMD
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for (unsigned int i = 0; i < ulen; i++) {
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auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, len, canCastX);
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auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, len, canCastZ);
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z[zOffset] = OpType::op(x[xOffset], scalar, extraParams);
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}
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}
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}
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}
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}
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////////////////////////////////////////////////////////////////////////
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template<typename X, typename Y, typename Z>
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template<typename OpType>
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void ScalarTransform<X, Y, Z>::transform(void *vx, Nd4jLong xEws,
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void *vz, Nd4jLong zEws,
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void *vscalar,
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void *vextraParams,
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const Nd4jLong len, bool allowParallelism) {
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auto x = reinterpret_cast<X *>(vx);
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auto z = reinterpret_cast<Z *>(vz);
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auto scalar = reinterpret_cast<Y *>(vscalar)[0];
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auto extraParams = reinterpret_cast<Z *>(vextraParams);
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nd4j::OmpLaunchHelper info(len, allowParallelism ? -1 : 1);
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if (xEws == 1 && zEws == 1) {
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PRAGMA_OMP_PARALLEL_THREADS_IF(info._numThreads, allowParallelism)
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{
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auto threadNum = omp_get_thread_num();
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auto threadOffset = info.getThreadOffset(threadNum);
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auto xi = x + threadOffset;
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auto zi = z + threadOffset;
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auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
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PRAGMA_OMP_SIMD
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for (unsigned int i = 0; i < ulen; i++)
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zi[i] = OpType::op(xi[i], scalar, extraParams);
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}
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}
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else {
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PRAGMA_OMP_PARALLEL_THREADS_IF(info._numThreads, allowParallelism)
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{
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auto threadNum = omp_get_thread_num();
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auto threadOffset = info.getThreadOffset(threadNum);
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auto xi = x + xEws * threadOffset;
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auto zi = z + zEws * threadOffset;
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auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
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PRAGMA_OMP_SIMD
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for (unsigned int i = 0; i < ulen; i++)
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zi[i * zEws] = OpType::op(xi[i * xEws], scalar, extraParams);
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}
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}
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}
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}
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}
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