raver119 3c4e959e21 [WIP] More of CUDA (#95)
* 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>
2019-08-05 11:27:05 +10:00

247 lines
10 KiB
C++

/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// Created by raver119 on 08.10.2017.
//
#include "../scalar.h"
#include <op_boilerplate.h>
#include <types/types.h>
#include <LoopKind.h>
#include "../legacy_ops.h"
using namespace simdOps;
namespace functions {
namespace scalar {
////////////////////////////////////////////////////////////////////////
template<typename X, typename Y, typename Z>
template<typename OpType>
void ScalarTransform<X, Y, Z>::transform(void *vx, Nd4jLong *xShapeInfo,
void *vextraParams,
void *vz, Nd4jLong *zShapeInfo,
void *vscalars,
int *dimension, int dimensionLength,
Nd4jLong *xTadShapeInfo, Nd4jLong *xTadOffsets,
Nd4jLong *zTadShapeInfo, Nd4jLong *zTadOffsets) {
auto x = reinterpret_cast<X *>(vx);
auto z = reinterpret_cast<Z *>(vz);
auto scalars = reinterpret_cast<Y *>(vscalars);
auto extraParams = reinterpret_cast<Z *>(vextraParams);
if (zTadShapeInfo == nullptr) {
zTadShapeInfo = xTadShapeInfo;
zTadOffsets = xTadOffsets;
}
const int xTadEws = shape::elementWiseStride(xTadShapeInfo);
const int zTadEws = shape::elementWiseStride(zTadShapeInfo);
const int tadLength = shape::tadLength(xShapeInfo, dimension, dimensionLength);
const int numTads = shape::length(xShapeInfo) / tadLength;
nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopXZ(xTadShapeInfo, zTadShapeInfo);
if (kindOfLoop != nd4j::LoopKind::EWS1 && kindOfLoop != nd4j::LoopKind::EWSNONZERO) {
printf("ScalarTransform<X, Z>::transform: super-bad loop visited. Shouldn't ever happen\n");
return;
}
int num_threads = nd4j::math::nd4j_min<int>(numTads, omp_get_max_threads());
if (kindOfLoop == nd4j::LoopKind::EWS1) {
PRAGMA_OMP_PARALLEL_FOR_THREADS(num_threads)
for (unsigned int r = 0; r < numTads; r++) {
auto oZ = z + zTadOffsets[r];
auto oX = x + xTadOffsets[r];
PRAGMA_OMP_SIMD
for (unsigned int f = 0; f < tadLength; f++)
oZ[f] = OpType::op(oX[f], scalars[r], extraParams);
}
}
else {
PRAGMA_OMP_PARALLEL_FOR_THREADS(num_threads)
for (unsigned int r = 0; r < numTads; r++) {
auto oZ = z + zTadOffsets[r];
auto oX = x + xTadOffsets[r];
PRAGMA_OMP_SIMD
for (unsigned int f = 0; f < tadLength; f++)
oZ[f * zTadEws] = OpType::op(oX[f * xTadEws], scalars[r], extraParams);
}
}
}
////////////////////////////////////////////////////////////////////////
template<typename X, typename Y, typename Z>
void ScalarTransform<X,Y,Z>::transform(int opNum,
void *x, Nd4jLong *xShapeInfo,
void *extraParams,
void *z, Nd4jLong *zShapeInfo,
void *scalars,
int *dimension, int dimensionLength,
Nd4jLong *xTadShapeInfo, Nd4jLong *xTadOffsets,
Nd4jLong *zTadShapeInfo, Nd4jLong *zTadOffsets) {
DISPATCH_BY_OPNUM_TTT(transform, PARAMS(x, xShapeInfo, extraParams, z, zShapeInfo, scalars, dimension, dimensionLength, xTadShapeInfo, xTadOffsets, zTadShapeInfo, zTadOffsets), SCALAR_OPS);
}
////////////////////////////////////////////////////////////////////////
template<typename X, typename Y, typename Z>
void ScalarTransform<X, Y, Z>::transform(const int opNum,
void *x, Nd4jLong xStride,
void *z, Nd4jLong zStride,
void *scalar,
void *extraParams,
const Nd4jLong n, bool allowParallelism) {
DISPATCH_BY_OPNUM_TTT(transform, PARAMS(x, xStride, z, zStride, scalar, extraParams, n, allowParallelism), SCALAR_OPS);
}
////////////////////////////////////////////////////////////////////////
template<typename X, typename Y, typename Z>
void ScalarTransform<X, Y, Z>::transform(const int opNum,
void *x, Nd4jLong *xShapeInfo,
void *z, Nd4jLong *zShapeInfo,
void *scalar,
void *extraParams, bool allowParallelism) {
DISPATCH_BY_OPNUM_TTT(transform, PARAMS(x, xShapeInfo, z, zShapeInfo, scalar, extraParams, allowParallelism), SCALAR_OPS);
}
////////////////////////////////////////////////////////////////////////
template<typename X, typename Y, typename Z>
template<typename OpType>
void ScalarTransform<X, Y, Z>::transform(void *vx, Nd4jLong *xShapeInfo,
void *vz, Nd4jLong *zShapeInfo,
void *vscalar,
void *vextraParams, bool allowParallelism) {
auto x = reinterpret_cast<X *>(vx);
auto z = reinterpret_cast<Z *>(vz);
auto scalar = reinterpret_cast<Y *>(vscalar)[0];
auto extraParams = reinterpret_cast<Z *>(vextraParams);
const auto len = shape::length(xShapeInfo);
const auto xEws = shape::elementWiseStride(xShapeInfo);
const auto zEws = shape::elementWiseStride(zShapeInfo);
nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopXZ(xShapeInfo, zShapeInfo);
if (kindOfLoop == nd4j::LoopKind::EWS1 || kindOfLoop == nd4j::LoopKind::EWSNONZERO) {
transform<OpType>(x, xEws, z, zEws, vscalar, extraParams, len, allowParallelism);
}
else {
uint xShapeInfoCast[MAX_RANK];
const bool canCastX = nd4j::DataTypeUtils::castShapeInfo<uint>(xShapeInfo, xShapeInfoCast);
nd4j::OmpLaunchHelper info(len, allowParallelism ? -1 : 1);
if(shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) {
PRAGMA_OMP_PARALLEL_THREADS_IF(info._numThreads, allowParallelism)
{
auto threadNum = omp_get_thread_num();
auto threadOffset = info.getThreadOffset(threadNum);
auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
PRAGMA_OMP_SIMD
for (unsigned int i = 0; i < ulen; i++) {
auto offset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, len, canCastX);
z[offset] = OpType::op(x[offset], scalar, extraParams);
}
}
}
else {
uint zShapeInfoCast[MAX_RANK];
const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo<uint>(zShapeInfo, zShapeInfoCast);
PRAGMA_OMP_PARALLEL_THREADS_IF(info._numThreads, allowParallelism)
{
auto threadNum = omp_get_thread_num();
auto threadOffset = info.getThreadOffset(threadNum);
auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
PRAGMA_OMP_SIMD
for (unsigned int i = 0; i < ulen; i++) {
auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, len, canCastX);
auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, len, canCastZ);
z[zOffset] = OpType::op(x[xOffset], scalar, extraParams);
}
}
}
}
}
////////////////////////////////////////////////////////////////////////
template<typename X, typename Y, typename Z>
template<typename OpType>
void ScalarTransform<X, Y, Z>::transform(void *vx, Nd4jLong xEws,
void *vz, Nd4jLong zEws,
void *vscalar,
void *vextraParams,
const Nd4jLong len, bool allowParallelism) {
auto x = reinterpret_cast<X *>(vx);
auto z = reinterpret_cast<Z *>(vz);
auto scalar = reinterpret_cast<Y *>(vscalar)[0];
auto extraParams = reinterpret_cast<Z *>(vextraParams);
nd4j::OmpLaunchHelper info(len, allowParallelism ? -1 : 1);
if (xEws == 1 && zEws == 1) {
PRAGMA_OMP_PARALLEL_THREADS_IF(info._numThreads, allowParallelism)
{
auto threadNum = omp_get_thread_num();
auto threadOffset = info.getThreadOffset(threadNum);
auto xi = x + threadOffset;
auto zi = z + threadOffset;
auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
PRAGMA_OMP_SIMD
for (unsigned int i = 0; i < ulen; i++)
zi[i] = OpType::op(xi[i], scalar, extraParams);
}
}
else {
PRAGMA_OMP_PARALLEL_THREADS_IF(info._numThreads, allowParallelism)
{
auto threadNum = omp_get_thread_num();
auto threadOffset = info.getThreadOffset(threadNum);
auto xi = x + xEws * threadOffset;
auto zi = z + zEws * threadOffset;
auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
PRAGMA_OMP_SIMD
for (unsigned int i = 0; i < ulen; i++)
zi[i * zEws] = OpType::op(xi[i * xEws], scalar, extraParams);
}
}
}
}
}