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[WIP] repeat op (#143) 

* - write new repeat helper (cpu)

Signed-off-by: Yurii <yurii@skymind.io>

* - update NDArray::cpu

Signed-off-by: Yurii <yurii@skymind.io>

* - update NDArray::repeat cuda

Signed-off-by: Yurii <yurii@skymind.io>
master
raver119 2019-08-21 21:10:29 +03:00 committed by GitHub
parent 3cf72e5e30
commit e604ffe0d2
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GPG Key ID: 4AEE18F83AFDEB23
15 changed files with 298 additions and 328 deletions
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9
libnd4j/blas/NDArray.h
View File

@ -316,10 +316,10 @@ namespace nd4j {
/** /**
* create a new array by replicating current array by repeats times along given dimension * create a new array by replicating current array by repeats times along given dimension
* dimension - dimension along which to repeat elements * axis - axis along which to repeat elements
* repeats - number of repetitions * repeats - number of repetitions
*/ */
NDArray* repeat(int dimension, const std::vector<Nd4jLong>& repeats) const; NDArray* repeat(const int axis, const std::vector<int>& repeats) const;
/** /**
* This method fills this array with zeros * This method fills this array with zeros
@ -344,9 +344,10 @@ namespace nd4j {
/** /**
* fill target array by repeating current array * fill target array by repeating current array
* dimension - dimension along which to repeat elements * axis - axis along which to repeat elements
* repeats - vector containing numbers of repetition for elements at given axis
*/ */
void repeat(int dimension, NDArray& target) const; void repeat(const int axis, const std::vector<int>& repeats, NDArray& target) const;
/** /**
* creates array which points on certain sub-range of this array, sub-range is defined by given indices * creates array which points on certain sub-range of this array, sub-range is defined by given indices

109
libnd4j/blas/cpu/NDArray.cpp
View File

@ -363,79 +363,62 @@ void NDArray::tile(NDArray& target) const {
} }
} }
////////////////////////////////////////////////////////////////////////
template<typename X, typename Z>
static void repeat_(const NDArray& input, NDArray& output, const std::vector<int>& repeats, const int axis) {
const X* x = input.bufferAsT<X>();
Z* z = output.bufferAsT<Z>();
const int rank = input.rankOf(); // xRank = zRank
const int zLen = output.lengthOf(); // xLen <= zLen
const int repSize = repeats.size();
std::vector<Nd4jLong> coords(rank);
// loop through input array
PRAGMA_OMP_PARALLEL_FOR_ARGS(schedule(guided) firstprivate(coords))
for (Nd4jLong i = 0; i < zLen; ++i) {
shape::index2coords(rank, output.shapeOf(), i, zLen, coords.data());
const auto zOffset = shape::getOffset(0, output.shapeOf(), output.stridesOf(), coords.data(), rank);
if(repSize > 1) {
for (uint j = 0; j < repSize; ++j) {
coords[axis] -= repeats[j];
if (coords[axis] < 0) {
coords[axis] = j;
break;
}
}
}
else
coords[axis] /= repeats[0];
z[zOffset] = x[shape::getOffset(0, input.shapeOf(), input.stridesOf(), coords.data(), rank)];
}
}
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
// create new array by repeating it the number of times given by reps // create new array by repeating it the number of times given by repeats
NDArray* NDArray::repeat(int dimension, const std::vector<Nd4jLong>& repeats) const { NDArray* NDArray::repeat(const int axis, const std::vector<int>& repeats) const {
auto outShape = ShapeUtils::evalRepeatShape(dimension, repeats, *this);
// the size of outShape == rank auto output = new NDArray('c', ShapeUtils::evalRepeatShape(axis, repeats, *this), dataType(), getContext());
int rank = rankOf(); // = outShape.size()
auto ret = new NDArray('c', outShape, dataType(), getContext()); BUILD_SINGLE_SELECTOR_TWICE(dataType(), repeat_, (*this, *output, repeats, axis), LIBND4J_TYPES);
auto retArrs = ret->allTensorsAlongDimension({dimension}); return output;
auto thisArrs = this->allTensorsAlongDimension({dimension});
auto repeatDelta = shape::prodLong(outShape.data(), rank) / this->lengthOf();
auto numTads = retArrs->size();
for (int i = 0; i < numTads; i++) {
auto thisTensor = thisArrs->at(i);
auto retTensor = retArrs->at(i);
Nd4jLong retIdx = 0;
for (Nd4jLong k = 0; k < thisTensor->lengthOf(); k++) {
auto s = thisTensor->e(k);
for (Nd4jLong j = 0; j < repeatDelta; j++)
retTensor->p(retIdx++, s);
}
}
delete retArrs;
delete thisArrs;
return ret;
} }
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
// fill array by repeating it the number of times given by reps // fill array by repeating it the number of times given by reps
void NDArray::repeat(int dimension, NDArray& target) const { void NDArray::repeat(const int axis, const std::vector<int>& repeats, NDArray& target) const {
if(dimension < 0) if(!target.isSameShape(ShapeUtils::evalRepeatShape(axis, repeats, *this)))
dimension += rankOf(); throw std::invalid_argument("NDArray::repeat(const int axis, const std::vector<int>& repeats, NDArray& target) method: wrong shape of target array!");
if(rankOf() != target.rankOf()) BUILD_DOUBLE_SELECTOR(dataType(), target.dataType(), repeat_, (*this, target, repeats, axis), LIBND4J_TYPES, LIBND4J_TYPES);
throw std::invalid_argument("NDArray::repeat(int dimension, NDArray& target) method: wrong rank of target array it must be equal to this array rank!");
Nd4jLong repeatDelta = target.sizeAt(dimension) / sizeAt(dimension);
if(repeatDelta == 0)
throw std::invalid_argument("NDArray::repeat(int dimension, NDArray& target) method: wrong shape of target array!");
std::vector<int> dimsToExclude = ShapeUtils::evalDimsToExclude(rankOf(), {dimension});
const Nd4jLong numTads = ShapeUtils::getNumOfSubArrs(getShapeInfo(), dimsToExclude);
for (int i = 0; i < numTads; i++) {
auto thisTensor = (*this)(i, dimsToExclude);
auto retTensor = target(i, dimsToExclude);
int tensorLength = thisTensor.lengthOf();
int retIdx = 0;
if (isR()) {
for (int k = 0; k < tensorLength; k++) {
auto s = thisTensor.e<double>(k);
for (int j = 0; j < repeatDelta; j++) {
retTensor.p<double>(retIdx++, s);
}
}
} else {
for (int k = 0; k < tensorLength; k++) {
auto s = thisTensor.e<Nd4jLong>(k);
for (int j = 0; j < repeatDelta; j++) {
retTensor.p<Nd4jLong>(retIdx++, s);
}
}
}
}
} }
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////

143
libnd4j/blas/cuda/NDArray.cu
View File

@ -392,64 +392,115 @@ void NDArray::tile(NDArray& target) const {
registerSpecialUse({&target}, {this}); registerSpecialUse({&target}, {this});
} }
////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////
// create new array by repeating it the number of times given by reps template<typename X, typename Z>
NDArray* NDArray::repeat(int dimension, const std::vector<Nd4jLong>& repeats) const { __global__ static void repeatCuda(const void* vx, const Nd4jLong* xShapeInfo,
auto outShape = ShapeUtils::evalRepeatShape(dimension, repeats, *this); void* vz, const Nd4jLong* zShapeInfo,
const int* repeats, const int repSize,
const int axis) {
// the size of outShape == rank const X* x = reinterpret_cast<const X*>(vx);
int rank = rankOf(); // = outShape.size() Z* z = reinterpret_cast<Z*>(vz);
std::vector<Nd4jLong> newShape(rank); __shared__ int rank;
for (int i = 0; i < rank; i++) __shared__ Nd4jLong zLen, totalThreads, *sharedMem; // xLen = zLen
newShape[i] = outShape[i];
auto ret = new NDArray('c', outShape, dataType(), getContext()); if (threadIdx.x == 0) {
auto repeatDelta = shape::prodLong(newShape.data(), rank) / this->lengthOf(); extern __shared__ unsigned char shmem[];
std::vector<int> dimsToExclude = ShapeUtils::evalDimsToExclude(rankOf(), {dimension}); sharedMem = reinterpret_cast<Nd4jLong*>(shmem);
const Nd4jLong numTads = ShapeUtils::getNumOfSubArrs(getShapeInfo(), dimsToExclude); //this->tensorsAlongDimension({dimension});
std::vector<int> copy({dimension});
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), copy); rank = shape::rank(zShapeInfo); // xRank = zRank
auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(ret->getShapeInfo(), copy); zLen = shape::length(zShapeInfo); // xLen <= zLen
prepareSpecialUse({ret}, {this}); totalThreads = gridDim.x * blockDim.x;
auto stream = getContext()->getCudaStream(); }
BUILD_SINGLE_SELECTOR(dataType(), repeatKernelH, (getSpecialBuffer(), ret->getSpecialBuffer(), numTads, lengthOf(), ret->lengthOf(), packX.platformShapeInfo(), packX.platformOffsets(), packZ.platformShapeInfo(), packZ.platformOffsets(), *stream), LIBND4J_TYPES);
registerSpecialUse({ret}, {this});
return ret; __syncthreads();
auto coords = sharedMem + threadIdx.x * rank;
const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
for (Nd4jLong i = tid; i < zLen; i += totalThreads) {
shape::index2coords(rank, zShapeInfo + 1, i, zLen, coords);
const auto zOffset = shape::getOffset(0, zShapeInfo + 1, zShapeInfo + rank + 1, coords, rank);
if(repSize > 1) {
for (uint j = 0; j < repSize; ++j) {
coords[axis] -= repeats[j];
if (coords[axis] < 0) {
coords[axis] = j;
break;
}
}
}
else
coords[axis] /= repeats[0];
z[zOffset] = x[shape::getOffset(0, xShapeInfo + 1, xShapeInfo + rank + 1, coords, rank)];
}
} }
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
// fill array by repeating it the number of times given by reps template<typename X, typename Z>
void NDArray::repeat(int dimension, NDArray& target) const { static void repeatCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream,
const void* vx, const Nd4jLong* xShapeInfo,
void* vz, const Nd4jLong* zShapeInfo,
const int* repeats, const int repSize,
const int axis) {
if(dimension < 0) repeatCuda<X,Z><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vz, zShapeInfo, repeats, repSize, axis);
dimension += rankOf();
if(rankOf() != target.rankOf())
throw std::invalid_argument("NDArray::repeat(int dimension, NDArray& target) method: wrong rank of target array it must be equal to this array rank!");
Nd4jLong repeatDelta = target.sizeAt(dimension) / sizeAt(dimension);
if(repeatDelta == 0)
throw std::invalid_argument("NDArray::repeat(int dimension, NDArray& target) method: wrong shape of target array!");
std::vector<int> dimsToExclude = ShapeUtils::evalDimsToExclude(rankOf(), {dimension});
const Nd4jLong numTads = ShapeUtils::getNumOfSubArrs(getShapeInfo(), dimsToExclude);
std::vector<int> copy({dimension});
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), copy);
auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(target.getShapeInfo(), copy);
NDArray::prepareSpecialUse({&target}, {this});
auto stream = getContext()->getCudaStream();
BUILD_SINGLE_SELECTOR_TWICE(target.dataType(), repeatKernelHH, (getSpecialBuffer(), target.getSpecialBuffer(), numTads, lengthOf(), packX.platformShapeInfo(), packX.platformOffsets(), packZ.platformShapeInfo(), packZ.platformOffsets(), *stream), LIBND4J_TYPES);
NDArray::registerSpecialUse({&target}, {this});
} }
BUILD_DOUBLE_TEMPLATE(template void repeatCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const void *vx, const Nd4jLong *xShapeInfo, void *vz, const Nd4jLong *zShapeInfo, const int* repeats, const int repSize, const int axis), LIBND4J_TYPES, LIBND4J_TYPES);
//////////////////////////////////////////////////////////////////////////
// create new array by repeating it the number of times given by repeats
NDArray* NDArray::repeat(const int axis, const std::vector<int>& repeats) const {
auto output = new NDArray('c', ShapeUtils::evalRepeatShape(axis, repeats, *this), dataType(), getContext());
const int threadsPerBlock = MAX_NUM_THREADS / 2;
const int blocksPerGrid = (output->lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
const int sharedMem = output->rankOf() * sizeof(Nd4jLong) * threadsPerBlock + 128;
PointersManager manager(getContext(), "NDArray::repeat(const int axis, const std::vector<int>& repeats)");
const int* reps = reinterpret_cast<int*>(manager.replicatePointer(repeats.data(), repeats.size() * sizeof(int)));
prepareSpecialUse({output}, {this});
BUILD_SINGLE_SELECTOR_TWICE(dataType(), repeatCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, getContext()->getCudaStream(), getSpecialBuffer(), getSpecialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), reps, repeats.size(), axis), LIBND4J_TYPES);
prepareSpecialUse({output}, {this});
manager.synchronize();
return output;
}
//////////////////////////////////////////////////////////////////////////
// fill array by repeating it the number of times given by repeats
void NDArray::repeat(const int axis, const std::vector<int>& repeats, NDArray& target) const {
if(!target.isSameShape(ShapeUtils::evalRepeatShape(axis, repeats, *this)))
throw std::invalid_argument("NDArray::repeat(const int axis, const std::vector<int>& repeats, NDArray& target) method: wrong shape of target array!");
const int threadsPerBlock = MAX_NUM_THREADS / 2;
const int blocksPerGrid = (target.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
const int sharedMem = target.rankOf() * sizeof(Nd4jLong) * threadsPerBlock + 128;
PointersManager manager(getContext(), "NDArray::repeat(const int axis, const std::vector<int>& repeats)");
const int* reps = reinterpret_cast<int*>(manager.replicatePointer(repeats.data(), repeats.size() * sizeof(int)));
prepareSpecialUse({&target}, {this});
BUILD_DOUBLE_SELECTOR(dataType(), target.dataType(), repeatCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, getContext()->getCudaStream(), getSpecialBuffer(), getSpecialShapeInfo(), target.specialBuffer(), target.specialShapeInfo(), reps, repeats.size(), axis), LIBND4J_TYPES, LIBND4J_TYPES);
prepareSpecialUse({&target}, {this});
manager.synchronize();
}
//////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////
void* NDArray::specialBuffer() { void* NDArray::specialBuffer() {

2
libnd4j/include/helpers/ShapeUtils.h
View File

@ -47,7 +47,7 @@ namespace nd4j {
static Nd4jLong* evalReduceShapeInfoEmpty(const char order, std::vector<int>& dimensions, const Nd4jLong *shapeInfo, const nd4j::DataType dataType, const bool keepDims, nd4j::memory::Workspace* workspace); static Nd4jLong* evalReduceShapeInfoEmpty(const char order, std::vector<int>& dimensions, const Nd4jLong *shapeInfo, const nd4j::DataType dataType, const bool keepDims, nd4j::memory::Workspace* workspace);
// evaluate shape for array which is result of repeat operation applied to arr // evaluate shape for array which is result of repeat operation applied to arr
static std::vector<Nd4jLong> evalRepeatShape(int dimension, const std::vector<Nd4jLong>& repeats, const NDArray& arr); static std::vector<Nd4jLong> evalRepeatShape(int axis, const std::vector<int>& repeats, const NDArray& arr);
// evaluate shapeInfo of permuted array // evaluate shapeInfo of permuted array
static Nd4jLong* evalPermShapeInfo(const int* dimensions, const int rank, const NDArray& arr, nd4j::memory::Workspace* workspace); static Nd4jLong* evalPermShapeInfo(const int* dimensions, const int rank, const NDArray& arr, nd4j::memory::Workspace* workspace);

36
libnd4j/include/helpers/impl/ShapeUtils.cpp
View File

@ -281,37 +281,21 @@ Nd4jLong* ShapeUtils::evalReduceShapeInfo(const char order, std::vector<int>& di
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
// evaluate shape for array which is result of repeat operation applied to arr // evaluate shape for array which is result of repeat operation applied to arr
std::vector<Nd4jLong> ShapeUtils::evalRepeatShape(int dimension, const std::vector<Nd4jLong>& repeats, const NDArray& arr) { std::vector<Nd4jLong> ShapeUtils::evalRepeatShape(int axis, const std::vector<int>& repeats, const NDArray& arr) {
int rank = arr.rankOf(); if (axis < 0)
axis += arr.rankOf();
if (dimension < 0) if(repeats.size() != 1 && repeats.size() != arr.sizeAt(axis))
dimension += rank; throw std::invalid_argument("ShapeUtils::evalRepeatShape: size of repeats vector must be 1 or equal to dimension at given axis !");
std::vector<Nd4jLong> reps; std::vector<Nd4jLong> outShape = arr.getShapeAsVector();
if ((int) reps.size() < rank) { if(repeats.size() == 1)
if (dimension > 0) { outShape[axis] *= repeats[0];
for (int e = 0; e < rank - (int) repeats.size(); e++)
reps.push_back(1);
for (auto r: repeats) else
reps.push_back(r); outShape[axis] = std::accumulate(repeats.begin(), repeats.end(), 0);
} else {
for (auto r: repeats)
reps.push_back(r);
for (int e = 0; e < rank - (int) repeats.size(); e++)
reps.push_back(1);
}
}/* else {
for (auto r: repeats)
reps.push_back(r);
}*/
std::vector<Nd4jLong> outShape(rank);
for (int i = 0; i < rank; i++)
outShape[i] = arr.sizeAt(i) * reps.at(i);
return outShape; return outShape;
} }

97
libnd4j/include/loops/cuda/specials/repeatKernel.cu
View File

@ -1,97 +0,0 @@
/*******************************************************************************
* 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
******************************************************************************/
//
// @author GS <sgazeos@gmail.com>, created on 17.01.2019
//
#include <loops/special_kernels.h>
namespace nd4j {
template <typename T>
static __global__ void repeatKernel(void const* inputBuffer, void* outputBuffer, Nd4jLong numTads, Nd4jLong inputLength,
Nd4jLong* tadOnlyInputShapeInfo, Nd4jLong *tadInputOffsets,
Nd4jLong* tadOnlyOutputShapeInfo, Nd4jLong *tadOutputOffsets) {
//auto tid = blockIdx.x * blockDim.x; // + threadIdx.x;
// int totalThreads = gridDim.x * blockDim.x;
int totalThreads = blockDim.x;
//const auto resultLength = shape::length(outputShape);
for (Nd4jLong i = blockIdx.x; i < numTads; i += gridDim.x) {
auto yOffset = tadInputOffsets[i];
auto xOffset = tadOutputOffsets[i];
for (Nd4jLong j = threadIdx.x; j < inputLength; j += totalThreads) {
auto outputOffset = shape::getIndexOrderOffset(j, tadOnlyOutputShapeInfo, inputLength, shape::order(tadOnlyInputShapeInfo));
auto inputOffset = shape::getIndexOrderOffset(j, tadOnlyInputShapeInfo, inputLength, shape::order(tadOnlyInputShapeInfo));
*(reinterpret_cast<T*>(outputBuffer) + xOffset + outputOffset) = *(reinterpret_cast<T const*>(inputBuffer) + yOffset + inputOffset);
}
}
}
BUILD_SINGLE_TEMPLATE(template __global__ void repeatKernel, (void const* inputBuffer, void* outputBuffer,
Nd4jLong numTads, Nd4jLong inputLength, Nd4jLong* tadOnlyInputShapeInfo, Nd4jLong *tadInputOffsets,
Nd4jLong* tadOnlyOutputShapeInfo, Nd4jLong *tadOutputOffsets), LIBND4J_TYPES);
template <typename X, typename Y>
static __global__ void repeatKernelDouble(void const* inputBuffer, void* outputBuffer, Nd4jLong numTads, Nd4jLong inputLength,
Nd4jLong* tadOnlyInputShapeInfo, Nd4jLong *tadInputOffsets,
Nd4jLong* tadOnlyOutputShapeInfo, Nd4jLong *tadOutputOffsets) {
//auto tid = blockIdx.x * blockDim.x; // + threadIdx.x;
int totalThreads = gridDim.x * blockDim.x;
//const auto resultLength = shape::length(outputShape);
for (Nd4jLong i = blockIdx.x; i < numTads; i += gridDim.x) {
auto yOffset = tadInputOffsets[i];
auto xOffset = tadOutputOffsets[i];
for (Nd4jLong j = threadIdx.x; j < inputLength; j += totalThreads) {
auto outputOffset = shape::getIndexOrderOffset(j, tadOnlyOutputShapeInfo, inputLength, shape::order(tadOnlyInputShapeInfo));
auto inputOffset = shape::getIndexOrderOffset(j, tadOnlyInputShapeInfo, inputLength, shape::order(tadOnlyInputShapeInfo));
*(reinterpret_cast<X*>(outputBuffer) + xOffset + outputOffset) = static_cast<X>(*(reinterpret_cast<Y const*>(inputBuffer) + yOffset + inputOffset));
}
}
}
BUILD_SINGLE_TEMPLATE_TWICE(template __global__ void repeatKernelDouble, (void const* inputBuffer, void* outputBuffer,
Nd4jLong numTads, Nd4jLong inputLength, Nd4jLong* tadOnlyInputShapeInfo, Nd4jLong *tadInputOffsets,
Nd4jLong* tadOnlyOutputShapeInfo, Nd4jLong *tadOutputOffsets), LIBND4J_TYPES);
template <typename T>
void repeatKernelH(void const* inputBuffer, void* outputBuffer, Nd4jLong numTads, Nd4jLong inputLength, Nd4jLong outputLength,
Nd4jLong *tadOnlyInputShapeInfo, Nd4jLong *tadInputOffsets,
Nd4jLong *tadOnlyOutputShapeInfo,Nd4jLong *tadOutputOffsets,
cudaStream_t stream) {
dim3 launchDims(256, 512, 8192);
repeatKernel<T><<<launchDims.x, launchDims.y, launchDims.z, stream>>>(inputBuffer, outputBuffer, numTads, inputLength, tadOnlyInputShapeInfo, tadInputOffsets, tadOnlyOutputShapeInfo, tadOutputOffsets);
}
BUILD_SINGLE_TEMPLATE(template void repeatKernelH, (void const* inputBuffer, void* outputBuffer, Nd4jLong numTads, Nd4jLong inputLength, Nd4jLong outputLength,
Nd4jLong* tadOnlyInputShapeInfo, Nd4jLong *tadInputOffsets,
Nd4jLong* tadOnlyOutputShapeInfo, Nd4jLong *tadOutputOffsets,
cudaStream_t stream), LIBND4J_TYPES);
template <typename X, typename Y>
void repeatKernelHH(void const* inputBuffer, void* outputBuffer, Nd4jLong numTads, Nd4jLong inputLength,
Nd4jLong *tadOnlyInputShapeInfo, Nd4jLong *tadInputOffsets,
Nd4jLong *tadOnlyOutputShapeInfo,Nd4jLong *tadOutputOffsets,
cudaStream_t stream) {
dim3 launchDims(256, 512, 8192);
repeatKernelDouble<X,Y><<<launchDims.x, launchDims.y, launchDims.z, stream>>>(inputBuffer, outputBuffer, numTads, inputLength, tadOnlyInputShapeInfo, tadInputOffsets, tadOnlyOutputShapeInfo, tadOutputOffsets);
}
BUILD_SINGLE_TEMPLATE_TWICE(template void repeatKernelHH, (void const* inputBuffer, void* outputBuffer, Nd4jLong numTads, Nd4jLong inputLength,
Nd4jLong* tadOnlyInputShapeInfo, Nd4jLong *tadInputOffsets,
Nd4jLong* tadOnlyOutputShapeInfo, Nd4jLong *tadOutputOffsets,
cudaStream_t stream), LIBND4J_TYPES);
}

11
libnd4j/include/loops/special_kernels.h
View File

@ -89,17 +89,6 @@ namespace nd4j {
template <typename X, typename Y> template <typename X, typename Y>
_CUDA_H void tileKernelHH(void const* inputBuffer, Nd4jLong* inputShape, void* outputBuffer, Nd4jLong* outputShape, Nd4jLong resultLength, Nd4jLong ews, cudaStream_t *stream); _CUDA_H void tileKernelHH(void const* inputBuffer, Nd4jLong* inputShape, void* outputBuffer, Nd4jLong* outputShape, Nd4jLong resultLength, Nd4jLong ews, cudaStream_t *stream);
template <typename T>
_CUDA_H void repeatKernelH(void const* inputBuffer, void* outputBuffer, Nd4jLong numTads, Nd4jLong inputLength, Nd4jLong outputLength,
Nd4jLong *tadOnlyInputShapeInfo, Nd4jLong *tadInputOffsets,
Nd4jLong *tadOnlyOutputShapeInfo,Nd4jLong *tadOutputOffsets,
cudaStream_t stream);
template <typename X, typename Y>
_CUDA_H void repeatKernelHH(void const* inputBuffer, void* outputBuffer, Nd4jLong numTads, Nd4jLong inputLength,
Nd4jLong *tadOnlyInputShapeInfo, Nd4jLong *tadInputOffsets,
Nd4jLong *tadOnlyOutputShapeInfo,Nd4jLong *tadOutputOffsets,
cudaStream_t stream);
class NDArray; class NDArray;
template <typename T> template <typename T>

36
libnd4j/include/ops/declarable/generic/transforms/repeat.cpp
View File

@ -15,7 +15,7 @@
******************************************************************************/ ******************************************************************************/
// //
// @author raver119@gmail.com // @author Yurii Shyrma (iuriish@yahoo.com)
// //
#include <op_boilerplate.h> #include <op_boilerplate.h>
@ -25,14 +25,25 @@
namespace nd4j { namespace nd4j {
namespace ops { namespace ops {
////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////
// here iArgs is int vector of repeats at the beginning and last element in iArgs is dimension // here iArgs is int vector of repeats at the beginning and last element in iArgs is dimension
CUSTOM_OP_IMPL(repeat, 1, 1, true, 0, -1) { CUSTOM_OP_IMPL(repeat, 1, 1, true, 0, -1) {
auto x = INPUT_VARIABLE(0); auto input = INPUT_VARIABLE(0);
auto ret = OUTPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0);
x->repeat(block.getIArguments()->back(), *ret); std::vector<int> repeats = *block.getIArguments();
const int axis = repeats.back() < 0 ? repeats.back() + input->rankOf() : repeats.back();
repeats.pop_back();
REQUIRE_TRUE(0 <= axis && axis < input->rankOf(), 0, "CUSTOM REPEAT OP: wrong axis argument it should be less then input array rank %i, but got %i instead !", input->rankOf(), axis);
REQUIRE_TRUE(repeats.size() == 1 || repeats.size() == input->sizeAt(axis), 0, "CUSTOM REPEAT OP: wrong axis argument, size of repeats vector must be 1 or equal to dimension at given axis, but got repeats.size = %i and axis = %i !", repeats.size(), axis);
input->repeat(axis, repeats, *output);
return Status::OK(); return Status::OK();
} }
@ -45,15 +56,18 @@ namespace nd4j {
DECLARE_SHAPE_FN(repeat) { DECLARE_SHAPE_FN(repeat) {
auto x = INPUT_VARIABLE(0); auto input = INPUT_VARIABLE(0);
auto argumets = block.getIArguments();
int argsSize = argumets->size(); std::vector<int> repeats = *block.getIArguments();
int dimension = (*argumets)[argsSize-1];
auto repeats = *argumets; const int axis = repeats.back() < 0 ? repeats.back() + input->rankOf() : repeats.back();
repeats.pop_back(); repeats.pop_back();
auto outShape = ShapeUtils::evalRepeatShape(dimension, ArrayUtils::toLongVector(repeats), *x); auto outShape = ShapeUtils::evalRepeatShape(axis, repeats, *input);
return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(x->dataType(), x->ordering(), outShape)));
return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(input->dataType(), input->ordering(), outShape)));
} }
} }
} }

4
libnd4j/include/ops/declarable/helpers/cpu/transforms.cpp
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@ -1270,6 +1270,10 @@ static void tileBP_(const NDArray& gradO /*input*/, NDArray& gradI /*output*/, c
BUILD_SINGLE_TEMPLATE(template void tileBP_, (const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector<Nd4jLong> reps), FLOAT_TYPES); BUILD_SINGLE_TEMPLATE(template void tileBP_, (const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector<Nd4jLong> reps), FLOAT_TYPES);
} }
} }
} }

1
libnd4j/include/ops/declarable/helpers/transforms.h
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@ -73,7 +73,6 @@ namespace helpers {
void concat(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output, const int axis); void concat(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output, const int axis);
void tileBP(nd4j::LaunchContext * context, const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector<Nd4jLong> reps); void tileBP(nd4j::LaunchContext * context, const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector<Nd4jLong> reps);
} }
} }
} }

34
libnd4j/tests_cpu/layers_tests/DeclarableOpsTests1.cpp
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@ -1782,40 +1782,6 @@ TEST_F(DeclarableOpsTests1, Reshape7){
} }
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Repeat1) {
float eBuffer[8] = {1.0,2.0,1.0,2.0,3.0,4.0,3.0,4.0};
Nd4jLong eShape[8] = {2, 4, 2, 2, 1, 0, 1, 99};
ArrayOptions::setDataType(eShape, nd4j::DataType::FLOAT32);
auto x = NDArrayFactory::create_<float>('c', {2, 2});
auto exp = new NDArray(eBuffer, eShape);
for (int e = 0; e < x->lengthOf(); e++)
x->p(e, e + 1);
auto variableSpace = new VariableSpace();
variableSpace->putVariable(-1, x);
variableSpace->putVariable(1, new Variable());
auto block = new Context(1, variableSpace, false);
block->fillInputs({-1});
std::vector<int>* arguments = block->getIArguments();
*arguments = {2}; // set repeats
arguments->push_back(0); // set dimension
nd4j::ops::repeat repeat;
Nd4jStatus status = repeat.execute(block);
ASSERT_EQ(ND4J_STATUS_OK, status);
auto result = variableSpace->getVariable(block->getNodeId())->getNDArray();
ASSERT_TRUE(exp->equalsTo(result));
delete exp;
delete block;
delete variableSpace;
}
////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests1, Transpose1) { TEST_F(DeclarableOpsTests1, Transpose1) {

90
libnd4j/tests_cpu/layers_tests/DeclarableOpsTests14.cpp
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@ -450,3 +450,93 @@ TEST_F(DeclarableOpsTests14, test_empty_tanh_5) {
delete result; delete result;
} }
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests14, repeat_1) {
NDArray x('c', {2, 3}, {1, 2, 3, 4, 5, 6});
NDArray e('c', {4, 3}, {1, 2, 3, 1, 2, 3, 4, 5, 6, 4, 5, 6});
nd4j::ops::repeat op;
auto result = op.execute({&x}, {}, {2, 0});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
ASSERT_TRUE(e.isSameShape(z));
ASSERT_TRUE(e.equalsTo(z));
delete result;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests14, repeat_2) {
NDArray x('c', {2, 3}, {1, 2, 3, 4, 5, 6});
NDArray e('c', {2, 6}, {1, 1, 2, 2, 3, 3,4, 4, 5, 5, 6, 6});
nd4j::ops::repeat op;
auto result = op.execute({&x}, {}, {2, 1});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
ASSERT_TRUE(e.isSameShape(z));
ASSERT_TRUE(e.equalsTo(z));
delete result;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests14, repeat_3) {
NDArray x('c', {2, 3}, {1, 2, 3, 4, 5, 6});
NDArray e('c', {2, 6}, {1, 2, 2, 3, 3, 3,4, 5, 5, 6, 6, 6});
nd4j::ops::repeat op;
auto result = op.execute({&x}, {}, {1,2,3, 1});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
ASSERT_TRUE(e.isSameShape(z));
ASSERT_TRUE(e.equalsTo(z));
delete result;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests14, repeat_4) {
NDArray x('c', {2, 3}, {1, 2, 3, 4, 5, 6});
NDArray e('c', {7, 3}, {1, 2, 3, 1, 2, 3, 1, 2, 3, 4, 5, 6, 4, 5, 6, 4, 5, 6, 4, 5, 6});
nd4j::ops::repeat op;
auto result = op.execute({&x}, {}, {3,4, 0});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
ASSERT_TRUE(e.isSameShape(z));
ASSERT_TRUE(e.equalsTo(z));
delete result;
}
//////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests14, repeat_5) {
NDArray x('c', {2, 3, 4}, {1, 2, 3, 4, 5, 6, 7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24});
NDArray e('c', {2, 4, 4}, {1, 2, 3, 4, 5, 6, 7, 8, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 17, 18, 19, 20, 21, 22, 23, 24});
nd4j::ops::repeat op;
auto result = op.execute({&x}, {}, {1,2,1, 1});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
ASSERT_TRUE(e.isSameShape(z));
ASSERT_TRUE(e.equalsTo(z));
delete result;
}

14
libnd4j/tests_cpu/layers_tests/DeclarableOpsTests16.cpp
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@ -39,20 +39,6 @@ public:
} }
}; };
TEST_F(DeclarableOpsTests16, test_repeat_119) {
auto x = NDArrayFactory::create<double>('c', {2, 3}, {1, 2, 3, 4, 5, 6});
auto e = NDArrayFactory::create<double>('c', {4, 3}, {1, 2, 3, 1, 2, 3, 4, 5, 6, 4, 5, 6});
nd4j::ops::repeat op;
auto result = op.execute({&x}, {}, {2, 0});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
ASSERT_EQ(e, *z);
delete result;
}
TEST_F(DeclarableOpsTests16, test_scatter_update_119) { TEST_F(DeclarableOpsTests16, test_scatter_update_119) {
auto x = NDArrayFactory::create<float>('c', {3}, {1, 1, 1}); auto x = NDArrayFactory::create<float>('c', {3}, {1, 1, 1});
auto y = NDArrayFactory::create<int>(0); auto y = NDArrayFactory::create<int>(0);

2
libnd4j/tests_cpu/layers_tests/MultiDataTypeTests.cpp
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@ -196,7 +196,7 @@ TEST_F(MultiDataTypeTests, ndarray_repeat_test1) {
NDArray y('c', {2, 4}, nd4j::DataType::HALF); NDArray y('c', {2, 4}, nd4j::DataType::HALF);
NDArray exp('c', {2, 4}, {0.5, 0.5, 1.5, 1.5, 2.5, 2.5, 3.5, 3.5}, nd4j::DataType::HALF); NDArray exp('c', {2, 4}, {0.5, 0.5, 1.5, 1.5, 2.5, 2.5, 3.5, 3.5}, nd4j::DataType::HALF);
x.repeat(1, y); x.repeat(1, {2}, y);
ASSERT_EQ(y, exp); ASSERT_EQ(y, exp);
} }

2
libnd4j/tests_cpu/layers_tests/NDArrayTests.cpp
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@ -324,7 +324,7 @@ TEST_F(NDArrayTest, TestRepeat2) {
auto rep = exp->dup(); auto rep = exp->dup();
rep->assign(0.); rep->assign(0.);
array->repeat(0, *rep); array->repeat(0, {2}, *rep);
//rep->printIndexedBuffer("Repeated"); //rep->printIndexedBuffer("Repeated");
ASSERT_EQ(4, rep->sizeAt(0)); ASSERT_EQ(4, rep->sizeAt(0));