257 lines
14 KiB
Plaintext
257 lines
14 KiB
Plaintext
/*******************************************************************************
|
|
* 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 {
|
|
// -------------------------------------------------------------------------------------------------------------- //
|
|
template <typename T, typename I>
|
|
static __global__ void unsortedSegmentSqrtNLinearKernel(T* input, Nd4jLong* inputShape, I* indices, Nd4jLong* indicesShape, int* starts, int* lengths, Nd4jLong numOfClasses, T* output, Nd4jLong* outputShape) {
|
|
__shared__ Nd4jLong xLen, zLen;
|
|
|
|
if (threadIdx.x == 0) {
|
|
xLen = shape::length(inputShape);
|
|
zLen = shape::length(outputShape);
|
|
}
|
|
__syncthreads();
|
|
|
|
auto start = threadIdx.x + blockIdx.x * blockDim.x;
|
|
auto step = blockDim.x * gridDim.x;
|
|
|
|
for (auto idx = start; idx < xLen; idx += step) {
|
|
auto yIndex = shape::getIndexOffset(idx, indicesShape);
|
|
auto segment = indices[yIndex];
|
|
auto zIndex = shape::getIndexOffset(segment, outputShape);
|
|
if (lengths[segment] == 0) continue;
|
|
auto xIndex = shape::getIndexOffset(idx, inputShape);
|
|
|
|
nd4j::math::atomics::nd4j_atomicAdd(&output[zIndex], input[xIndex] / nd4j::math::nd4j_sqrt<int, T>(lengths[segment]));
|
|
}
|
|
}
|
|
// -------------------------------------------------------------------------------------------------------------- //
|
|
// SegmentSqrtN kernel
|
|
template <typename T, typename I>
|
|
static __global__ void segmentSqrtNTadKernel(T* inputBuf, Nd4jLong* inputShape, Nd4jLong* inputTads, Nd4jLong* inputTadOffsets, I* indices, int* starts, int* lengths, Nd4jLong numOfClasses, void* outputBuf, Nd4jLong* outputShape, Nd4jLong* outputTads, Nd4jLong* outputTadOffsets) {
|
|
|
|
__shared__ Nd4jLong len, total;
|
|
|
|
if (threadIdx.x == 0) {
|
|
total = shape::sizeAt(inputShape, 0);
|
|
len = shape::length(inputTads);
|
|
}
|
|
__syncthreads();
|
|
|
|
for (auto idx = blockIdx.x; idx < total; idx += gridDim.x) {
|
|
auto segment = indices[idx];
|
|
auto x = inputBuf + inputTadOffsets[idx];
|
|
auto z = reinterpret_cast<T *>(outputBuf) + outputTadOffsets[segment];
|
|
auto start = starts[segment];
|
|
auto finish = start + lengths[segment];
|
|
|
|
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_atomicAdd(&z[zIndex], x[xIndex] / nd4j::math::nd4j_sqrt<int, T>(lengths[segment]));
|
|
}
|
|
}
|
|
}
|
|
// -------------------------------------------------------------------------------------------------------------- //
|
|
template <typename T, typename I>
|
|
static void unsortedSegmentSqrtNFunctor_(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);
|
|
dim3 dims(128, 256, 256);
|
|
// 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());
|
|
output->nullify();
|
|
if (input->isVector()) {
|
|
unsortedSegmentSqrtNLinearKernel<T,I><<<dims.x, dims.y, dims.z, *stream>>>(
|
|
input->dataBuffer()->specialAsT<T>(), input->specialShapeInfo(),
|
|
indices->dataBuffer()->specialAsT<I>(), indices->specialShapeInfo(), begins, lengths, numOfClasses,
|
|
output->dataBuffer()->specialAsT<T>(), output->specialShapeInfo());
|
|
}
|
|
else {
|
|
output->nullify();
|
|
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);
|
|
segmentSqrtNTadKernel<T,I><<<dims.x, dims.y, dims.z, *stream>>>(
|
|
input->dataBuffer()->specialAsT<T>(), input->specialShapeInfo(), inputTads, inputTadOffsets, indices->dataBuffer()->specialAsT<I>(),
|
|
begins, lengths, numOfClasses, output->specialBuffer(), output->specialShapeInfo(), outputTads, outputTadOffsets);
|
|
}
|
|
}
|
|
// -------------------------------------------------------------------------------------------------------------- //
|
|
void unsortedSegmentSqrtNFunctor(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) {
|
|
NDArray::prepareSpecialUse({output}, {input, indices});
|
|
BUILD_DOUBLE_SELECTOR(input->dataType(), indices->dataType(), unsortedSegmentSqrtNFunctor_, (context, input, indices, numOfClasses, output),
|
|
FLOAT_TYPES, INDEXING_TYPES);
|
|
NDArray::registerSpecialUse({output}, {input, indices});
|
|
}
|
|
// -------------------------------------------------------------------------------------------------------------- //
|
|
template <typename T, typename I>
|
|
static __global__ void segmentSqrtNBPLinearKernel(void* inputBuf, Nd4jLong* inputShape, void* eps, Nd4jLong* epsShape, void* indicesBuf, Nd4jLong* indicesShape,
|
|
int* lengths, 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);
|
|
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 gradOffsetO = shape::getIndexOffset(classIndex, epsShape);
|
|
|
|
z[zOffset] = T(gradOut[gradOffsetO] / math::nd4j_sqrt<int, float>(lengths[classIndex]));
|
|
}
|
|
}
|
|
// -------------------------------------------------------------------------------------------------------------- //
|
|
|
|
template <typename T, typename I>
|
|
static __global__ void segmentSqrtNBPTadKernel(void* inputBuf, Nd4jLong* inputShape, void* eps, Nd4jLong* epsShape,
|
|
void* indicesBuf, Nd4jLong* indicesShape, int* lengths, void* outputBuf, Nd4jLong* outputShape,Nd4jLong* inputTad,
|
|
Nd4jLong* inputOffsets, Nd4jLong* gradOutTad, Nd4jLong* gradOutOffsets, Nd4jLong* outTad, Nd4jLong* outOffsets) {
|
|
__shared__ T* x;
|
|
__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);
|
|
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[i]; //yIndex];
|
|
T* currentOut = z + outOffsets[i];
|
|
T* outGrad = gradOut + gradOutOffsets[segment];
|
|
|
|
for (auto e = threadIdx.x; e < currentLen; e += blockDim.x) {
|
|
auto zIndex = shape::getIndexOffset(e, outTad);
|
|
auto gradIndex = shape::getIndexOffset(e, gradOutTad);
|
|
if (lengths[segment] > 0)
|
|
currentOut[zIndex] = T(outGrad[gradIndex] / math::nd4j_sqrt<int, float>(lengths[segment]));
|
|
}
|
|
}
|
|
}
|
|
// -------------------------------------------------------------------------------------------------------------- //
|
|
|
|
template <typename T, typename I>
|
|
static int unsortedSegmentSqrtNFunctorBP_(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) {
|
|
auto stream = context->getCudaStream();
|
|
NDArray::prepareSpecialUse({output}, {input, indices, gradOut});
|
|
auto numClasses = indices->e<int>(indices->lengthOf() - 1) + 1;
|
|
NDArray classesRangesLens = NDArrayFactory::create<int>('c', {numClasses});
|
|
NDArray classesRangesBegs = NDArrayFactory::create<int>('c', {numClasses});
|
|
|
|
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()) {
|
|
Nd4jLong loop_size = input->lengthOf();
|
|
auto numOfClasses = gradOut->lengthOf(); //indices->e<Nd4jLong>(loop_size - 1);
|
|
segmentSqrtNBPLinearKernel<T,I><<<gradOut->lengthOf(), input->lengthOf(), 256, *stream>>>(input->specialBuffer(),
|
|
input->specialShapeInfo(), gradOut->specialBuffer(), gradOut->specialShapeInfo(),
|
|
indices->specialBuffer(), indices->specialShapeInfo(), lengths, 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* gradOutTads = packGradOut.specialShapeInfo();
|
|
Nd4jLong* gradOutTadOffsets = packGradOut.specialOffsets();
|
|
|
|
segmentSqrtNBPTadKernel<T,I><<<indices->lengthOf(), input->lengthOf(), 256, *stream>>>(input->specialBuffer(), input->specialShapeInfo(),
|
|
gradOut->specialBuffer(), gradOut->specialShapeInfo(), indices->specialBuffer(), indices->specialShapeInfo(), lengths,
|
|
output->specialBuffer(), output->specialShapeInfo(), inputTads, inputTadOffsets, gradOutTads, gradOutTadOffsets,
|
|
outputTads, outputTadOffsets);
|
|
}
|
|
NDArray::registerSpecialUse({output}, {input, indices, gradOut});
|
|
|
|
return Status::OK();
|
|
}
|
|
// -------------------------------------------------------------------------------------------------------------- //
|
|
int unsortedSegmentSqrtNFunctorBP(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 unsortedSegmentSqrtNFunctorBP_, (context, input, indices, gradOut, numOfClasses, output), FLOAT_TYPES, INDEXING_TYPES);
|
|
NDArray::registerSpecialUse({output}, {input, indices, gradOut});
|
|
}
|
|
}
|
|
}
|
|
} |