* correct logsoftmax looss (#2) * Small SameDiff listener fix (#4) * Various fixes (#6) * #7839 Fix for asXMatrix and tests * #7866 EmbeddingSequenceLayer dtype fix + test * #7856 SameDiff save/load stream methods * #7859 RegressionEvaluation rank 4 fix + tests + axis configuration * EvaluationBinary 3d/4d * More evaluation 3d/4d tests * #7847 Evaluation empty checks * Small test ifx * #7848 Fix median edge case * Improve DL4J samediff layer tests * [WIP] FastText wrapper implemented (#8) * FastText implemented * Some fixes * Fix shapes for wordsNearest * Validation of input vectors * Fixes * Fixed test * Thread tagged * Some tweaks * setContextClassLoader for DeallocatorServiceThread * Numpy format tests (#1) * Various fixes (#11) * #7852 SameDiff gather fix * #7892 SameDiff placeholder to constant conversion * #7890 validate input rank for MLN/CG init methods * Fix broken permute shape calculation * Permute and gather fixes * Tests * #7850 LogSumExp fix + test * Handful of test fixes * Empty arrays with non-scalar shapes (#10) * minor rearrangements for lambdas * empty tensors with non-scalar shapes * numpy empty tensors with non-scalar shapes * few more empty tweaks * Small fixes * conv3d signature update * micro fix in batchnorm mkldnn * Import fixes * Fix * MKL-DNN update * Small fill fix * fill with empty input + test * Fixes * Small error improvement * Fix * one special test * couple of fixes for lstm * Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone * Fixes * FP16 * Unsigned * BFloat16 * Fill op - empty tweaks * - couple of fixes for empty arrays construction - stack updated * strided slice fix * one transform test * provide method for reducing shapeInfo in case of input array is empty * Fixed reduceAlongDimensions to use empty input properly. * couple of broadcast tests * couple of tests broadcast tests + tweak to make them pass * add check of non-empty to methods producing sub-arrays * Fixed reshapeC with zeros in shape. * complete empty check in reduce_... legacy ops * Concat and cumsum/prod * Tweak to empty shape inference on import * add empty check to the rest of reduce legacy ops * one more test * correct typo in evalReduceShapeInfoEmpty * Added tests for reduce_* ops to tests with zero shapes. * few more tests for empty reductions * Fixed strided_slice op with empty case and tests. * one more empty reduction test * Fixed strided_slice test. * add empty check to NDArray::reshapei * infOrMax * empty min/max with infinity tests * made unstack working correctly with empty arrays * few IndexReduce tests + tweaks for empty shapes * add test for empty concat * few tests fixed * Validation fix for reductions on empty shapes * Reverse fix * Reduction shape calc fixes * SameDiff.generateOutputVariable: don't use shape function to determine number of outputs * Range fix * - NDArray constructor updated for scalars/empty arrays - few tests fixed * More fixes * Empty creator fixes * concat fix * concat fix * TF import tests: allow 'both all NaN' and 'both all inf' to pass * Slice, zero fraction, and reshape fixes * transpose, gather * Zero fraction * scalar cast fix * Empty reduction axis support * few more tests fixed * Fixed input checks conforming with TF for concat op and tests. * few tests fixed * matmul scalar shape fix * Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats. * broadcast bool fix * few more tests * few more tests * correct evalReduceShapeInfoEmpty * argmax/argmin + tests * one more empty edge case + one more test * argmax/argmin/realdiv_bp tweaks * empty reshape test + fix * Helper fixes * Small fixes * Gather test fix * Gather test fix * Small fixes * reduce scalar zero values * scalar mean workaround * Remove debug code * along dim mean workaround * one more test * - equalsTo() tweak for empty arrays - one more test * broadcast tweaks
252 lines
11 KiB
C++
252 lines
11 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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// @author raver119@gmail.com
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// @author Yurii Shyrma (iuriish@yahoo.com), created on 19.11.2018
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#include <types/types.h>
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#include <op_boilerplate.h>
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#include <loops/reduce3.h>
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#include <loops/legacy_ops.h>
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#include <helpers/ConstantTadHelper.h>
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#include <Loops.h>
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using namespace simdOps;
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namespace functions {
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namespace reduce3 {
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//////////////////////////////////////////////////////////////////////////
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template <typename X, typename Z>
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template<typename OpType>
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void Reduce3<X,Z>::execScalar(void *vx, Nd4jLong *xShapeInfo,
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void *vextraParams,
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void *vy, Nd4jLong *yShapeInfo,
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void *vz, Nd4jLong *zShapeInfo) {
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auto x = reinterpret_cast<X *>(vx);
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auto y = reinterpret_cast<X *>(vy);
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auto z = reinterpret_cast<Z *>(vz);
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auto extraParams = reinterpret_cast<Z *>(vextraParams);
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auto length = shape::length(xShapeInfo);
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auto xEws = shape::elementWiseStride(xShapeInfo);
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auto yEws = shape::elementWiseStride(yShapeInfo);
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if(nd4j::ArrayOptions::arrayType(xShapeInfo) == nd4j::ArrayType::EMPTY || nd4j::ArrayOptions::arrayType(yShapeInfo) == nd4j::ArrayType::EMPTY) {
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if(nd4j::ArrayOptions::arrayType(zShapeInfo) == nd4j::ArrayType::EMPTY)
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return;
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const auto startingVal = OpType::startingValue(x);
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PRAGMA_OMP_PARALLEL_FOR_IF(length > nd4j::Environment::getInstance()->elementwiseThreshold())
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for (uint i = 0; i < length; i++)
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z[i] = startingVal;
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return;
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}
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Z extraParamsVals[3] = {(Z) 0.0f, (Z) 0.0f, (Z) 0.0f};
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// it's possible case for EqualsWithEps op
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if (extraParams != nullptr)
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extraParamsVals[2] = extraParams[0];
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uint xShapeInfoCast[MAX_RANK];
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const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
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Z startingVal = OpType::startingValue(x);
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const int maxThreads = nd4j::math::nd4j_min<int>(256, omp_get_max_threads());
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nd4j::OmpLaunchHelper t(length, maxThreads);
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Z intermediate[256];
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Z extraParamsLocal[3 * 256];
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PRAGMA_OMP_SIMD
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for (int e = 0; e < maxThreads; e++)
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intermediate[e] = startingVal;
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memset(extraParamsLocal, 0, 3 * 256 * sizeof(Z));
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if (extraParams != nullptr)
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PRAGMA_OMP_SIMD
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for (int e = 0; e < maxThreads; e++)
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extraParamsLocal[3 * e + 2] = extraParams[0];
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nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopXZ(xShapeInfo, yShapeInfo);
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if (kindOfLoop == nd4j::LoopKind::EWS1) {
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PRAGMA_OMP_PARALLEL_FOR_SIMD_THREADS(t._numThreads)
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for(unsigned int i = 0; i < length; i++) {
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const auto threadNum = omp_get_thread_num();
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intermediate[threadNum] = OpType::update(intermediate[threadNum], OpType::op(x[i], y[i], extraParamsLocal + 3 * threadNum), extraParamsLocal + 3 * threadNum);
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}
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} else if(shape::haveSameShapeAndStrides(xShapeInfo, yShapeInfo)) {
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PRAGMA_OMP_PARALLEL_FOR_SIMD_THREADS(t._numThreads)
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for(unsigned int i = 0; i < length; i++) {
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const auto threadNum = omp_get_thread_num();
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auto offset = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, length, canCastX);
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intermediate[threadNum] = OpType::update(intermediate[threadNum], OpType::op(x[offset], y[offset], extraParamsLocal + 3 * threadNum), extraParamsLocal + 3 * threadNum);
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}
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} else {
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uint yShapeInfoCast[MAX_RANK];
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const bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast);
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PRAGMA_OMP_PARALLEL_FOR_SIMD_THREADS(t._numThreads)
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for(unsigned int i = 0; i < length; i++) {
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const auto threadNum = omp_get_thread_num();
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auto xOffset = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, length, canCastX);
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auto yOffset = shape::indexOffset(i, yShapeInfo, yShapeInfoCast, length, canCastY);
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intermediate[threadNum] = OpType::update(intermediate[threadNum], OpType::op(x[xOffset], y[yOffset], extraParamsLocal + 3 * threadNum), extraParamsLocal + 3 * threadNum);
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}
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}
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// merge step
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for (int e = 0; e < maxThreads; e++)
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OpType::aggregateExtraParams(extraParamsVals, extraParamsLocal + 3 * e);
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for (int e = 0; e < maxThreads; e++)
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startingVal = OpType::update(startingVal, intermediate[e], extraParamsVals);
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z[0] = OpType::postProcess(startingVal, length, extraParamsVals);
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename X, typename Y>
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void Reduce3<X,Y>::execScalar(const int opNum,
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void *vx, Nd4jLong *xShapeInfo,
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void *extraParamsVals,
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void *vy, Nd4jLong *yShapeInfo,
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void *vz, Nd4jLong *zShapeInfo) {
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DISPATCH_BY_OPNUM_TT(execScalar, PARAMS(vx, xShapeInfo, extraParamsVals, vy, yShapeInfo, vz, zShapeInfo), REDUCE3_OPS);
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename X, typename Z>
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template<typename OpType>
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void Reduce3<X,Z>::exec(void *vx, Nd4jLong *xShapeInfo,
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void *vextraParams,
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void *vy, Nd4jLong *yShapeInfo,
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void *vz, Nd4jLong *zShapeInfo,
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int *dimension, int dimensionLength) {
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auto x = reinterpret_cast<X*>(vx);
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auto y = reinterpret_cast<X*>(vy);
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auto z = reinterpret_cast<Z*>(vz);
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auto extraParams = reinterpret_cast<Z*>(vextraParams);
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if(shape::isScalar(zShapeInfo)) {
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execScalar<OpType>(vx, xShapeInfo, vextraParams, vy, yShapeInfo, vz, zShapeInfo);
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return;
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}
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#ifdef INLINE_LOOPS
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nd4j::Reduction3Loops<X,Z>::template loopReduce3<OpType>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, extraParams);
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#else
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nd4j::Reduction3Loops<X,Z>::template innerloopReduce3<OpType>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, extraParams);
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#endif
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename X, typename Z>
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template<typename OpType>
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void Reduce3<X,Z>::exec(void *vx, Nd4jLong *xShapeInfo,
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void *vextraParams,
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void *vy, Nd4jLong *yShapeInfo,
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void *vz, Nd4jLong *zShapeInfo,
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int *dimension, int dimensionLength,
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Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
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auto x = reinterpret_cast<X *>(vx);
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auto y = reinterpret_cast<X *>(vy);
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auto z = reinterpret_cast<Z *>(vz);
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auto extraParams = reinterpret_cast<Z *>(vextraParams);
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#ifdef INLINE_LOOPS
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nd4j::Reduction3Loops<X,Z>::template loopReduce3<OpType>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, extraParams);
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#else
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nd4j::Reduction3Loops<X,Z>::template innerloopReduce3<OpType>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, extraParams);
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#endif
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename X, typename Z>
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template<typename OpType>
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void Reduce3<X,Z>:: execAll(void *vx, Nd4jLong *xShapeInfo,
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void *vextraParams,
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void *vy, Nd4jLong *yShapeInfo,
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void *vz, Nd4jLong *zShapeInfo,
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int *dimension, int dimensionLength,
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Nd4jLong *xTadShapeInfo, Nd4jLong *xOffsets,
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Nd4jLong *yTadShapeInfo, Nd4jLong *yOffsets) {
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auto x = reinterpret_cast<X *>(vx);
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auto y = reinterpret_cast<X *>(vy);
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auto z = reinterpret_cast<Z *>(vz);
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auto extraParams = reinterpret_cast<Z*>(vextraParams);
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#ifdef INLINE_LOOPS
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nd4j::Reduction3Loops<X,Z>::template loopReduce3All<OpType>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, xTadShapeInfo, xOffsets, yTadShapeInfo, yOffsets, extraParams);
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#else
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nd4j::Reduction3Loops<X,Z>::template innerloopReduce3All<OpType>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, xTadShapeInfo, xOffsets, yTadShapeInfo, yOffsets, extraParams);
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#endif
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename X, typename Y>
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void Reduce3<X,Y>::exec( const int opNum,
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void *vx, Nd4jLong *xShapeInfo,
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void *extraParamsVals,
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void *vy, Nd4jLong *yShapeInfo,
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void *vz, Nd4jLong *zShapeInfo,
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int *dimension, int dimensionLength) {
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DISPATCH_BY_OPNUM_TT(exec, PARAMS(vx, xShapeInfo, extraParamsVals, vy, yShapeInfo, vz, zShapeInfo, dimension, dimensionLength), REDUCE3_OPS);
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename X, typename Y>
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void Reduce3<X,Y>::exec( const int opNum,
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void *vx, Nd4jLong *xShapeInfo,
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void *extraParamsVals,
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void *vy, Nd4jLong *yShapeInfo,
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void *vz, Nd4jLong *zShapeInfo,
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int *dimension, int dimensionLength,
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Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
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DISPATCH_BY_OPNUM_TT(exec, PARAMS(vx,xShapeInfo,extraParamsVals,vy, yShapeInfo,vz,zShapeInfo, dimension, dimensionLength, tadShapeInfo, tadOffsets), REDUCE3_OPS);
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename X, typename Y>
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void Reduce3<X,Y>::execAll(const int opNum,
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void *vx, Nd4jLong *xShapeInfo,
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void *extraParamsVals,
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void *vy, Nd4jLong *yShapeInfo,
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void *vz, Nd4jLong *zShapeInfo,
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int *dimension, int dimensionLength,
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Nd4jLong *xTadShapeInfo, Nd4jLong *xOffsets,
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Nd4jLong *yTadShapeInfo, Nd4jLong *yOffsets) {
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DISPATCH_BY_OPNUM_TT(execAll, PARAMS(vx, xShapeInfo, extraParamsVals, vy, yShapeInfo, vz, zShapeInfo, dimension, dimensionLength, xTadShapeInfo, xOffsets, yTadShapeInfo, yOffsets), REDUCE3_OPS);
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}
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BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT Reduce3, , LIBND4J_TYPES, FLOAT_TYPES);
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}
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} |