cavis/libnd4j/include/ops/declarable/helpers/cuda/segment_prod.cu

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/*******************************************************************************
* 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>
//
#include <ops/declarable/helpers/segment.h>
#include <ops/declarable/helpers/segment_common.h>
#include <NDArrayFactory.h>
#include <helpers/ShapeUtils.h>
#include <helpers/TAD.h>
#include <exceptions/cuda_exception.h>
#include <PointersManager.h>
#include <ConstantTadHelper.h>
namespace nd4j {
namespace ops {
namespace helpers {
// -------------------------------------------------------------------------------------------------------------- //
// Segment Prod ops linear kernels
// -------------------------------------------------------------------------------------------------------------- //
template <typename T, typename I>
static __global__ void segmentProdLinearKernel(void* input, Nd4jLong* inputShape, int* starts, int* lengths, Nd4jLong numOfClasses, void* output, Nd4jLong* outputShape) {
__shared__ T* val;
__shared__ Nd4jLong xLen, zLen, segment, zIndex;
__shared__ T* x;
__shared__ T* z;
__shared__ int threadsPerSegment, start, finish;
if (threadIdx.x == 0) {
threadsPerSegment = (gridDim.x + numOfClasses - 1) / numOfClasses;
segment = blockIdx.x / threadsPerSegment;
x = reinterpret_cast<T*>(input);
z = reinterpret_cast<T*>(output);
extern __shared__ unsigned char shmem[];
val = reinterpret_cast<T*>(shmem);
xLen = shape::length(inputShape);
zLen = shape::length(outputShape);
if (segment < numOfClasses) {
zIndex = shape::getIndexOffset(segment, outputShape);
start = starts[segment];
finish = start + lengths[segment];
//val[segment] = ;
z[zIndex] = x[shape::getIndexOffset(start, inputShape)];
val[segment] = z[zIndex];
}
}
__syncthreads();
// auto tid = threadIdx.x + blockIdx.x * blockDim.x;
// auto step = blockDim.x * gridDim.x;
for (auto e = start + threadIdx.x + 1; e < finish; e += blockDim.x) {
auto xIndex = shape::getIndexOffset(e, inputShape);
nd4j::math::atomics::nd4j_atomicMul(&val[segment], x[xIndex]);
}
__syncthreads();
if (threadIdx.x == 0) {
z[zIndex] = val[segment];
}
__syncthreads();
}
// -------------------------------------------------------------------------------------------------------------- //
template <typename T, typename I>
static __global__ void unsortedSegmentProdLinearKernel(void* input, Nd4jLong* inputShape, void* indices, Nd4jLong* indicesShape, int* starts, int* lengths, Nd4jLong numOfClasses, void* output, Nd4jLong* outputShape) {
__shared__ T* val;
__shared__ Nd4jLong xLen, zLen, segment, zIndex;
__shared__ T* x;
__shared__ T* z;
__shared__ I* y; //int threadsPerSegment, start, finish;
if (threadIdx.x == 0) {
// threadsPerSegment = (gridDim.x + numOfClasses - 1) / numOfClasses;
segment = blockIdx.x;// / threadsPerSegment;
x = reinterpret_cast<T*>(input);
z = reinterpret_cast<T*>(output);
y = reinterpret_cast<I*>(indices);
// extern __shared__ unsigned char shmem[];
// val = reinterpret_cast<T*>(shmem);
xLen = shape::length(inputShape);
zLen = shape::length(outputShape);
// if (segment < numOfClasses) {
zIndex = shape::getIndexOffset(segment, outputShape);
//start = starts[segment];
//finish = start + lengths[segment];
if (lengths[segment] > 0)
z[zIndex] = x[shape::getIndexOffset(starts[segment], inputShape)];
else
z[zIndex] = 0; //DataTypeUtils::max<T>();
// val[segment] = z[zIndex];
// }
}
__syncthreads();
if (lengths[segment] > 0)
for (auto e = threadIdx.x; e < xLen; e += blockDim.x) {
auto xIndex = shape::getIndexOffset(e, inputShape);
auto yIndex = shape::getIndexOffset(e, indicesShape);
if (y[yIndex] == segment && e != starts[segment]) {
nd4j::math::atomics::nd4j_atomicMul(&z[zIndex], x[xIndex]);
}
}
}
// -------------------------------------------------------------------------------------------------------------- //
// SegmentProd kernel
template <typename T, typename I>
static __global__ void segmentProdTadKernel(void* inputBuf, Nd4jLong* inputShape, Nd4jLong* inputTads, Nd4jLong* inputTadOffsets, I* indices, int* starts, int* lengths, Nd4jLong numOfClasses, void* outputBuf, Nd4jLong* outputShape, Nd4jLong* outputTads, Nd4jLong* outputTadOffsets) {
__shared__ T* val;
__shared__ Nd4jLong len, segment, zIndex, total;
__shared__ T* z;
__shared__ int threadsPerSegment, start, finish;
if (threadIdx.x == 0) {
segment = indices[blockIdx.x]; // / threadsPerSegment;
z = reinterpret_cast<T*>(outputBuf) + outputTadOffsets[segment];
len = shape::length(inputTads);
start = starts[segment];
finish = start + lengths[segment];
total = shape::sizeAt(inputShape, 0);
}
__syncthreads();
auto idx = blockIdx.x;
if (blockIdx.x <= total) {
auto x = reinterpret_cast<T *>(inputBuf) + inputTadOffsets[idx];
if (blockIdx.x == start) {
for (auto e = threadIdx.x; e < len; e += blockDim.x) {
auto xIndex = shape::getIndexOffset(e, inputTads);
auto zIndex = shape::getIndexOffset(e, outputTads);
nd4j::math::atomics::nd4j_atomicMul(&z[zIndex], x[xIndex]);
}
}
else {
for (auto e = threadIdx.x; e < len; e += blockDim.x) {
auto xIndex = shape::getIndexOffset(e, inputTads);
auto zIndex = shape::getIndexOffset(e, outputTads);
if (lengths[segment] > 0)
nd4j::math::atomics::nd4j_atomicMul(&z[zIndex], x[xIndex]);
}
}
}
}
// -------------------------------------------------------------------------------------------------------------- //
template <typename T, typename I>
static void segmentProdFunctor_(nd4j::LaunchContext* context, NDArray* input, NDArray* indices, NDArray* output) {
auto stream = context->getCudaStream();
Nd4jLong numClasses = indices->e<Nd4jLong>(indices->lengthOf() - 1) + 1;
NDArray classesRangesLens = NDArrayFactory::create<int>('c', {numClasses});
NDArray classesRangesBegs = NDArrayFactory::create<int>('c', {numClasses});
output->assign(1);
classesRangesBegs.assign(indices->lengthOf());
classesRangesLens.assign(0);
dim3 dims(numClasses, indices->lengthOf(), numClasses * 32 + 32);
fillUpSegments(indices, numClasses, classesRangesBegs, classesRangesLens);
int* begins = reinterpret_cast<int*>(classesRangesBegs.specialBuffer());
int* lengths = reinterpret_cast<int*>(classesRangesLens.specialBuffer());
if (input->isVector()) {
segmentProdLinearKernel<T,I><<<numClasses, input->lengthOf(), numClasses * 32 + 32, *stream>>>(input->specialBuffer(), input->specialShapeInfo(), begins, lengths, numClasses, output->specialBuffer(), output->specialShapeInfo());
}
else {
std::vector<int> dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0});
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions);
auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions);
Nd4jLong* inputTads = packX.specialShapeInfo();
Nd4jLong* inputTadOffsets = packX.specialOffsets();
Nd4jLong* outputTads = packZ.specialShapeInfo();
Nd4jLong* outputTadOffsets = packZ.specialOffsets();
segmentProdTadKernel<T,I><<<input->sizeAt(0), 512, 2048, *stream>>>(input->specialBuffer(), input->specialShapeInfo(), inputTads, inputTadOffsets, reinterpret_cast<I*>(indices->specialBuffer()), begins, lengths, numClasses, output->specialBuffer(), output->specialShapeInfo(), outputTads, outputTadOffsets);
}
}
// -------------------------------------------------------------------------------------------------------------- //
void segmentProdFunctor(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* output) {
NDArray::prepareSpecialUse({output}, {input, indices});
BUILD_DOUBLE_SELECTOR(output->dataType(), indices->dataType(), segmentProdFunctor_, (context, input, indices, output), NUMERIC_TYPES, INDEXING_TYPES);
NDArray::registerSpecialUse({output}, {input, indices});
}
// -------------------------------------------------------------------------------------------------------------- //
template <typename T, typename I>
static void unsortedSegmentProdFunctor_(nd4j::LaunchContext* context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) {
auto stream = context->getCudaStream();
// NDArray classes = NDArrayFactory::create<int>('c', {numOfClasses, 2});
NDArray classesRangesBegs = NDArrayFactory::create<int>('c', {numOfClasses});
NDArray classesRangesLens = NDArrayFactory::create<int>('c', {numOfClasses});
// NDArray row = NDArrayFactory::create<int>('c', {1, 2}, {(int)indices->lengthOf(), (int)0});
// classes.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Assign(), &row, &classes);
classesRangesBegs.assign(indices->lengthOf());
classesRangesLens.assign(0);
dim3 dims(numOfClasses, indices->lengthOf(), numOfClasses * 32 + 32);
// int* classesBuf = reinterpret_cast<int*>(classes.specialBuffer());
fillUpSegments(indices, numOfClasses, classesRangesBegs, classesRangesLens);
int* begins = reinterpret_cast<int*>(classesRangesBegs.specialBuffer());
int* lengths = reinterpret_cast<int*>(classesRangesLens.specialBuffer());
if (input->isVector()) {
unsortedSegmentProdLinearKernel<T,I><<<dims.x, dims.y, dims.z, *stream>>>(input->specialBuffer(), input->specialShapeInfo(), indices->specialBuffer(), indices->specialShapeInfo(), begins, lengths, numOfClasses, output->specialBuffer(), output->specialShapeInfo());
}
else {
output->assign(1);
std::vector<int> dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0});
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions);
auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions);
Nd4jLong* inputTads = packX.specialShapeInfo();
Nd4jLong* inputTadOffsets = packX.specialOffsets();
Nd4jLong* outputTads = packZ.specialShapeInfo();
Nd4jLong* outputTadOffsets = packZ.specialOffsets();
dims.x = input->sizeAt(0);
segmentProdTadKernel<T,I><<<dims.x, dims.y, dims.z, *stream>>>(input->specialBuffer(), input->specialShapeInfo(), inputTads, inputTadOffsets, reinterpret_cast<I*>(indices->specialBuffer()), begins, lengths, numOfClasses, output->specialBuffer(), output->specialShapeInfo(), outputTads, outputTadOffsets);
}
}
// -------------------------------------------------------------------------------------------------------------- //
void unsortedSegmentProdFunctor(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) {
NDArray::prepareSpecialUse({output}, {input, indices});
BUILD_DOUBLE_SELECTOR(input->dataType(), indices->dataType(), unsortedSegmentProdFunctor_, (context, input, indices, numOfClasses, output),
NUMERIC_TYPES, INDEXING_TYPES);
NDArray::registerSpecialUse({output}, {input, indices});
}
// -------------------------------------------------------------------------------------------------------------- //
template <typename T, typename I>
static __global__ void segmentProdBPLinearKernel(void* inputBuf, Nd4jLong* inputShape, void* forwardOutput,
Nd4jLong* forwardShape, void* eps, Nd4jLong* epsShape, void* indicesBuf, Nd4jLong* indicesShape,
void* outputBuf, Nd4jLong* outputShape) {
__shared__ T* x;
__shared__ T* gradIn;
__shared__ T* gradOut;
__shared__ I* y;
__shared__ T* z;
__shared__ Nd4jLong xLen, gradLen;
if (threadIdx.x == 0) {
xLen = shape::length(inputShape);
x = reinterpret_cast<T*>(inputBuf);
y = reinterpret_cast<I*>(indicesBuf);
z = reinterpret_cast<T*>(outputBuf);
gradIn = reinterpret_cast<T*>(forwardOutput);
gradOut = reinterpret_cast<T*>(eps);
gradLen = shape::length(epsShape);
}
__syncthreads();
auto start = blockIdx.x * blockDim.x + threadIdx.x;
auto step = gridDim.x * blockDim.x;
for (auto e = start; e < xLen; e += step) {
auto zOffset = shape::getIndexOffset(e, outputShape);
auto xOffset = shape::getIndexOffset(e, inputShape);
auto yOffset = shape::getIndexOffset(e, indicesShape);
auto classIndex = y[yOffset];
auto gradOffsetI = shape::getIndexOffset(classIndex, forwardShape);
auto gradOffsetO = shape::getIndexOffset(classIndex, epsShape);
z[zOffset] = gradOut[gradOffsetO] * gradIn[gradOffsetI] / x[xOffset];
}
}
// -------------------------------------------------------------------------------------------------------------- //
template <typename T, typename I>
static __global__ void segmentProdBPTadKernel(void* inputBuf, Nd4jLong* inputShape, void* forwardOutput,
Nd4jLong* forwardShape, void* eps, Nd4jLong* epsShape, void* indicesBuf, Nd4jLong* indicesShape,
void* outputBuf, Nd4jLong* outputShape,Nd4jLong* inputTad,
Nd4jLong* inputOffsets, Nd4jLong* gradInTad, Nd4jLong* gradInOffsets,
Nd4jLong* gradOutTad, Nd4jLong* gradOutOffsets, Nd4jLong* outTad,
Nd4jLong* outOffsets) {
__shared__ T* x;
__shared__ T* gradIn;
__shared__ T* gradOut;
__shared__ I* y;
__shared__ T* z;
__shared__ Nd4jLong xLen, yLen, gradLen, currentLen;
if (threadIdx.x == 0) {
xLen = shape::length(inputShape);
x = reinterpret_cast<T*>(inputBuf);
y = reinterpret_cast<I*>(indicesBuf);
z = reinterpret_cast<T*>(outputBuf);
yLen = shape::length(indicesShape);
gradOut = reinterpret_cast<T*>(eps);
gradIn = reinterpret_cast<T*>(forwardOutput);
gradLen = shape::length(epsShape);
currentLen = shape::length(outTad);
}
__syncthreads();
for (auto i = blockIdx.x; i < yLen; i += gridDim.x) {
auto yIndex = shape::getIndexOffset(i, indicesShape);
auto segment = y[yIndex];
T* current = x + inputOffsets[i];
T* currentOut = z + outOffsets[i];
T* in = gradIn + gradInOffsets[segment];
T* outGrad = gradOut + gradOutOffsets[segment];
for (auto e = threadIdx.x; e < currentLen; e += blockDim.x) {
currentOut[e] = outGrad[e] * in[e] / current[e];
}
}
}
// -------------------------------------------------------------------------------------------------------------- //
template <typename T, typename I>
int segmentProdFunctorBP_(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) {
auto stream = context->getCudaStream();
NDArray tempRes(gradOut->ordering(), gradOut->getShapeAsVector(), DataTypeUtils::fromT<T>(), context);//->shapeInfo(), context);
segmentProdFunctor_<T, I>(context, input, indices, &tempRes);
NDArray::prepareSpecialUse({output}, {input, indices, gradOut});
if (input->isVector()) {
Nd4jLong loopSize = input->lengthOf();
auto numOfClasses = gradOut->lengthOf(); //indices->e<Nd4jLong>(loop_size - 1);
segmentProdBPLinearKernel<T,I><<<gradOut->lengthOf(), loopSize, 256, *stream>>>(input->specialBuffer(), input->specialShapeInfo(),
tempRes.specialBuffer(), tempRes.specialShapeInfo(), gradOut->specialBuffer(), gradOut->specialShapeInfo(),
indices->specialBuffer(), indices->specialShapeInfo(), output->specialBuffer(), output->specialShapeInfo());
}
else {
std::vector<int> dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0});
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions);
auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions);
auto packGradIn = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(tempRes.getShapeInfo(), dimensions);
auto packGradOut = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(gradOut->getShapeInfo(), dimensions);
Nd4jLong* inputTads = packX.specialShapeInfo();
Nd4jLong* inputTadOffsets = packX.specialOffsets();
Nd4jLong* outputTads = packZ.specialShapeInfo();
Nd4jLong* outputTadOffsets = packZ.specialOffsets();
Nd4jLong* gradInTads = packGradIn.specialShapeInfo();
Nd4jLong* gradInTadOffsets = packGradIn.specialOffsets();
Nd4jLong* gradOutTads = packGradOut.specialShapeInfo();
Nd4jLong* gradOutTadOffsets = packGradOut.specialOffsets();
segmentProdBPTadKernel<T,I><<<gradOut->lengthOf(), input->lengthOf(), 256, *stream>>>(input->specialBuffer(), input->specialShapeInfo(),
tempRes.specialBuffer(), tempRes.specialShapeInfo(), gradOut->specialBuffer(), gradOut->specialShapeInfo(),
indices->specialBuffer(), indices->specialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(),
inputTads, inputTadOffsets, gradInTads, gradInTadOffsets, gradOutTads, gradOutTadOffsets,
outputTads, outputTadOffsets);
}
NDArray::registerSpecialUse({output}, {input, indices, gradOut});
return Status::OK();
}
// -------------------------------------------------------------------------------------------------------------- //
int segmentProdFunctorBP(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) {
NDArray::prepareSpecialUse({output}, {input, indices, gradOut});
BUILD_DOUBLE_SELECTOR(output->dataType(), indices->dataType(), return segmentProdFunctorBP_, (context, input,
indices, gradOut, output), FLOAT_TYPES, INDEXING_TYPES);
NDArray::registerSpecialUse({output}, {input, indices, gradOut});
}
// -------------------------------------------------------------------------------------------------------------- //
template <typename T, typename I>
static int unsortedSegmentProdFunctorBP_(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) {
auto stream = context->getCudaStream();
NDArray tempRes(gradOut->ordering(), gradOut->getShapeAsVector(), DataTypeUtils::fromT<T>(), context);//->shapeInfo(), context);
unsortedSegmentProdFunctor_<T, I>(context, input, indices, numOfClasses, &tempRes);
NDArray::prepareSpecialUse({output}, {input, indices, gradOut});
if (input->isVector()) {
Nd4jLong loopSize = input->lengthOf();
auto numOfClasses = gradOut->lengthOf(); //indices->e<Nd4jLong>(loop_size - 1);
segmentProdBPLinearKernel<T,I><<<gradOut->lengthOf(), loopSize, 256, *stream>>>(input->specialBuffer(), input->specialShapeInfo(),
tempRes.specialBuffer(), tempRes.specialShapeInfo(), gradOut->specialBuffer(), gradOut->specialShapeInfo(),
indices->specialBuffer(), indices->specialShapeInfo(), output->specialBuffer(), output->specialShapeInfo());
}
else {
std::vector<int> dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0});
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions);
auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions);
auto packGradIn = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(tempRes.getShapeInfo(), dimensions);
auto packGradOut = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(gradOut->getShapeInfo(), dimensions);
Nd4jLong* inputTads = packX.specialShapeInfo();
Nd4jLong* inputTadOffsets = packX.specialOffsets();
Nd4jLong* outputTads = packZ.specialShapeInfo();
Nd4jLong* outputTadOffsets = packZ.specialOffsets();
Nd4jLong* gradInTads = packGradIn.specialShapeInfo();
Nd4jLong* gradInTadOffsets = packGradIn.specialOffsets();
Nd4jLong* gradOutTads = packGradOut.specialShapeInfo();
Nd4jLong* gradOutTadOffsets = packGradOut.specialOffsets();
segmentProdBPTadKernel<T,I><<<indices->lengthOf(), input->lengthOf(), 256, *stream>>>(input->specialBuffer(), input->specialShapeInfo(),
tempRes.specialBuffer(), tempRes.specialShapeInfo(), gradOut->specialBuffer(), gradOut->specialShapeInfo(),
indices->specialBuffer(), indices->specialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(),
inputTads, inputTadOffsets, gradInTads, gradInTadOffsets, gradOutTads, gradOutTadOffsets,
outputTads, outputTadOffsets);
}
NDArray::registerSpecialUse({output}, {input, indices, gradOut});
return Status::OK();
}
// -------------------------------------------------------------------------------------------------------------- //
int unsortedSegmentProdFunctorBP(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) {
NDArray::prepareSpecialUse({output}, {input, indices, gradOut});
BUILD_DOUBLE_SELECTOR(output->dataType(), indices->dataType(), return unsortedSegmentProdFunctorBP_, (context, input, indices, gradOut, numOfClasses, output), FLOAT_TYPES, INDEXING_TYPES);
NDArray::registerSpecialUse({output}, {input, indices, gradOut});
}
// -------------------------------------------------------------------------------------------------------------- //
}
}
}