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indice.cu.h
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indice.cu.h
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// Copyright 2019 Yan Yan
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://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.
#ifndef INDICE_CU_H_
#define INDICE_CU_H_
#include <cuhash/hash_table.cuh>
#include <spconv/geometry.h>
#include <tensorview/kernel_utils.h>
#include <tensorview/tensorview.h>
namespace spconv {
template <typename Index, unsigned NDim, int KernelMaxVolume = 256,
typename Index1D = int>
__global__ void prepareIndicePairsKernel(
tv::TensorView<const Index> indicesIn, tv::TensorView<Index> indicePairs,
tv::TensorView<Index> indiceNum, tv::TensorView<Index1D> indicePairUnique,
const tv::SimpleVector<Index, NDim> kernelSize,
const tv::SimpleVector<Index, NDim> stride,
const tv::SimpleVector<Index, NDim> padding,
const tv::SimpleVector<Index, NDim> dilation,
const tv::SimpleVector<Index, NDim> outSpatialShape) {
auto numActIn = indicesIn.dim(0);
Index spatialVolume = 1;
#pragma unroll
for (int i = 0; i < NDim; ++i) {
spatialVolume *= outSpatialShape[i];
}
Index kernelVolume = 1;
#pragma unroll
for (int i = 0; i < NDim; ++i) {
kernelVolume *= kernelSize[i];
}
Index numValidPoints = 0;
Index validPoints[KernelMaxVolume * (NDim + 1)];
Index *pointPtr = nullptr;
auto indicePairsDim2 = indicePairs.dim(2);
Index index;
for (int ix : tv::KernelLoopX<int>(numActIn)) {
numValidPoints = getValidOutPos<Index, NDim>(
indicesIn.data() + ix * (NDim + 1) + 1, kernelSize.data(),
stride.data(), padding.data(), dilation.data(), outSpatialShape.data(),
validPoints);
for (Index i = 0; i < numValidPoints; ++i) {
pointPtr = validPoints + i * (NDim + 1);
auto offset = pointPtr[NDim];
Index oldNum = atomicAdd(indiceNum.data() + offset, Index(1));
indicePairs(0, offset, oldNum) = ix;
index = tv::ArrayIndexRowMajor<NDim, NDim>::runPtrs(
pointPtr, outSpatialShape.data(), 0) +
spatialVolume * indicesIn(ix, 0);
indicePairs(1, offset, oldNum) = index;
indicePairUnique[offset * indicePairsDim2 + oldNum] = index;
}
}
}
template <typename Index, unsigned NDim, int KernelMaxVolume = 256>
__global__ void prepareDeConvIndicePairsKernel(
tv::TensorView<const Index> indicesIn, tv::TensorView<Index> indicePairs,
tv::TensorView<Index> indiceNum, tv::TensorView<Index> indicePairUnique,
const tv::SimpleVector<Index, NDim> kernelSize,
const tv::SimpleVector<Index, NDim> stride,
const tv::SimpleVector<Index, NDim> padding,
const tv::SimpleVector<Index, NDim> dilation,
const tv::SimpleVector<Index, NDim> outSpatialShape) {
auto numActIn = indicesIn.dim(0);
Index spatialVolume = 1;
#pragma unroll
for (int i = 0; i < NDim; ++i) {
spatialVolume *= outSpatialShape[i];
}
Index kernelVolume = 1;
#pragma unroll
for (int i = 0; i < NDim; ++i) {
kernelVolume *= kernelSize[i];
}
Index numValidPoints = 0;
Index validPoints[KernelMaxVolume * (NDim + 1)];
Index *pointPtr = nullptr;
auto indicePairsDim2 = indicePairs.dim(2);
Index index;
for (int ix : tv::KernelLoopX<int>(numActIn)) {
numValidPoints = getValidOutPosTranspose<Index, NDim>(
indicesIn.data() + ix * (NDim + 1) + 1, kernelSize.data(),
stride.data(), padding.data(), dilation.data(), outSpatialShape.data(),
validPoints);
for (Index i = 0; i < numValidPoints; ++i) {
pointPtr = validPoints + i * (NDim + 1);
auto offset = pointPtr[NDim];
Index oldNum = atomicAdd(indiceNum.data() + offset, Index(1));
indicePairs(0, offset, oldNum) = ix;
index = tv::ArrayIndexRowMajor<NDim, NDim>::runPtrs(
pointPtr, outSpatialShape.data(), 0) +
spatialVolume * indicesIn(ix, 0);
indicePairs(1, offset, oldNum) = index;
indicePairUnique[offset * indicePairsDim2 + oldNum] = index;
}
}
}
template <typename Index, typename IndexGrid, unsigned NDim>
__global__ void assignGridAndIndiceOutKernel(
tv::TensorView<Index> indicesOut, tv::TensorView<IndexGrid> gridsOut,
int numAct, tv::TensorView<Index> indicePairs,
tv::TensorView<Index> indicePairUnique,
const tv::SimpleVector<Index, NDim> outSpatialShape, int batchSize) {
Index index;
auto indicesOutPtr = indicesOut.data();
for (int ix : tv::KernelLoopX<int>(numAct)) {
index = indicePairUnique[ix];
gridsOut[index] = ix;
index = tv::rowArrayIdxInv<Index, NDim>(
index, indicesOutPtr + ix * (NDim + 1) + 1, outSpatialShape.data());
indicesOut[ix * (NDim + 1)] = index % batchSize;
}
}
template <typename Index, unsigned NDim, unsigned kNumHashFunctions = 4>
__global__ void
assignIndiceOutKernel(tv::TensorView<Index> indicesOut, int numAct,
tv::TensorView<Index> indicePairUnique,
const tv::SimpleVector<Index, NDim> outSpatialShape,
int batchSize) {
Index index;
auto indicesOutPtr = indicesOut.data();
for (unsigned ix : tv::KernelLoopX<unsigned>(numAct)) {
index = indicePairUnique[ix];
index = tv::rowArrayIdxInv<Index, NDim>(
index, indicesOutPtr + ix * (NDim + 1) + 1, outSpatialShape.data());
indicesOut[ix * (NDim + 1)] = index % batchSize;
}
}
template <typename Index, unsigned NDim, unsigned kNumHashFunctions = 4>
__global__ void
assignIndicePairsHashKernel(tv::TensorView<Index> indicesOut, int numActIn,
tv::TensorView<Index> indicePairs,
tv::TensorView<Index> indicePairUnique,
unsigned table_size, const cuhash::Entry *table,
cuhash::Functions<kNumHashFunctions> constants,
uint2 stash_constants, unsigned stash_count) {
Index index;
int kernelVolume = indicePairs.dim(1);
auto indicePairsOut = indicePairs.subview(1);
for (int ix : tv::KernelLoopX<int>(numActIn)) {
for (int i = 0; i < kernelVolume; ++i) {
index = indicePairsOut(i, ix);
if (index > -1) {
auto val = cuhash::retrieve((unsigned)(index), table_size, table,
constants, stash_constants, stash_count);
assert(val != cuhash::kNotFound);
indicePairsOut(i, ix) = (unsigned)val;
}
}
}
}
template <typename Index, typename IndexGrid, unsigned NDim>
__global__ void
assignIndicePairsKernel(tv::TensorView<Index> indicesOut,
tv::TensorView<IndexGrid> gridsOut, int numActIn,
tv::TensorView<Index> indicePairs,
tv::TensorView<Index> indicePairUnique,
const tv::SimpleVector<Index, NDim> outSpatialShape) {
Index index;
int kernelVolume = indicePairs.dim(1);
auto indicePairsOut = indicePairs.subview(1);
for (int ix : tv::KernelLoopX<int>(numActIn)) {
for (int i = 0; i < kernelVolume; ++i) {
index = indicePairsOut(i, ix);
if (index > -1) {
indicePairsOut(i, ix) = gridsOut[index];
}
}
}
}
template <typename Index, typename IndexGrid, unsigned NDim>
__global__ void prepareSubMGridKernel(
tv::TensorView<const Index> indicesIn, tv::TensorView<IndexGrid> gridsOut,
const tv::SimpleVector<Index, NDim> outSpatialShape, Index spatialVolume) {
auto numActIn = indicesIn.dim(0);
Index index = 0;
for (int ix : tv::KernelLoopX<int>(numActIn)) {
index =
tv::ArrayIndexRowMajor<NDim, NDim>::runPtrs(
indicesIn.data() + ix * (NDim + 1) + 1, outSpatialShape.data(), 0) +
spatialVolume * indicesIn(ix, 0);
gridsOut[index] = ix;
}
}
template <typename Index, unsigned NDim>
__global__ void
prepareSubMHashKernel(tv::TensorView<const Index> indicesIn, unsigned *keys,
unsigned *values,
const tv::SimpleVector<Index, NDim> outSpatialShape) {
auto numActIn = indicesIn.dim(0);
Index spatialVolume = 1;
#pragma unroll
for (int i = 0; i < NDim; ++i) {
spatialVolume *= outSpatialShape[i];
}
Index index = 0;
for (int ix : tv::KernelLoopX<int>(numActIn)) {
index = tv::rowArrayIdx<Index, NDim>(indicesIn.data() + ix * (NDim + 1) + 1,
outSpatialShape.data()) +
spatialVolume * indicesIn(ix, 0);
keys[ix] = index;
values[ix] = ix;
}
}
template <typename Index, typename IndexGrid, unsigned NDim,
int KernelMaxVolume = 256>
__global__ void getSubMIndicePairsKernel(
tv::TensorView<const Index> indicesIn, tv::TensorView<IndexGrid> gridsOut,
tv::TensorView<Index> indicePairs, tv::TensorView<Index> indiceNum,
const tv::SimpleVector<Index, NDim> kernelSize,
const tv::SimpleVector<Index, NDim> stride,
const tv::SimpleVector<Index, NDim> padding,
const tv::SimpleVector<Index, NDim> dilation,
const tv::SimpleVector<Index, NDim> outSpatialShape) {
auto numActIn = indicesIn.dim(0);
Index spatialVolume = 1;
#pragma unroll
for (int i = 0; i < NDim; ++i) {
spatialVolume *= outSpatialShape[i];
}
Index numValidPoints = 0;
Index validPoints[KernelMaxVolume * (NDim + 1)];
Index *pointPtr = nullptr;
Index index = 0;
for (int ix : tv::KernelLoopX<int>(numActIn)) {
numValidPoints = getValidOutPos<Index, NDim>(
indicesIn.data() + ix * (NDim + 1) + 1, kernelSize.data(),
stride.data(), padding.data(), dilation.data(), outSpatialShape.data(),
validPoints);
for (int i = 0; i < numValidPoints; ++i) {
pointPtr = validPoints + i * (NDim + 1);
auto offset = pointPtr[NDim];
index = tv::ArrayIndexRowMajor<NDim, NDim>::runPtrs(
pointPtr, outSpatialShape.data(), 0) +
spatialVolume * indicesIn(ix, 0);
if (gridsOut[index] > -1) {
Index oldNum = atomicAdd(indiceNum.data() + offset, Index(1));
indicePairs(1, offset, oldNum) = gridsOut[index];
indicePairs(0, offset, oldNum) = ix;
}
}
}
}
template <typename Index, typename IndexGrid, unsigned K0, unsigned K1,
unsigned K2>
__global__ void getSubMIndicePairsKernel3(
tv::TensorView<const Index> indicesIn, tv::TensorView<IndexGrid> gridsOut,
tv::TensorView<Index> indicePairs, tv::TensorView<Index> indiceNum,
const tv::SimpleVector<Index, 3> outSpatialShape, Index spatialVolume) {
auto numActIn = indicesIn.dim(0);
Index point[3];
Index index = 0;
Index offset;
constexpr unsigned KV = K0 * K1 * K2;
constexpr unsigned center = KV / 2;
*(indiceNum.data() + center) = numActIn;
for (int ix : tv::KernelLoopX<int>(numActIn)) {
const Index *indice_data = indicesIn.data() + ix * (3 + 1);
#pragma unroll
for (int i = 0; i < K0; ++i) {
#pragma unroll
for (int j = 0; j < K1; ++j) {
#pragma unroll
for (int k = 0; k < K2; ++k) {
offset = i * K1 * K2 + j * K2 + k;
if (offset > center){
continue;
}
if (center == offset){
// center of subm indice pairs dont need atomicadd
indicePairs(1, offset, ix) = ix;
indicePairs(0, offset, ix) = ix;
}else{
point[2] = indice_data[3] - k + K2 / 2;
point[1] = indice_data[2] - j + K1 / 2;
point[0] = indice_data[1] - i + K0 / 2;
if (point[1] >= 0 && point[1] < outSpatialShape[1] && point[2] >= 0 &&
point[2] < outSpatialShape[2] && point[0] >= 0 &&
point[0] < outSpatialShape[0]) {
index = tv::ArrayIndexRowMajor<3, 3>::runPtrs(
point, outSpatialShape.data(), 0) +
spatialVolume * indice_data[0];
if (gridsOut[index] != -1) {
// for subm: indicePairs[0, i] = indicePairs[1, kernelVolume - i - 1]
Index oldNum = atomicAdd(indiceNum.data() + offset, Index(1));
atomicAdd(indiceNum.data() + KV - offset - 1, Index(1));
indicePairs(1, offset, oldNum) = gridsOut[index];
indicePairs(0, offset, oldNum) = ix;
indicePairs(1, KV - offset - 1, oldNum) = ix;
indicePairs(0, KV - offset - 1, oldNum) = gridsOut[index];
}
}
}
}
}
}
}
}
template <typename Index, typename IndexGrid, unsigned K0, unsigned K1>
__global__ void getSubMIndicePairsKernel2(
tv::TensorView<const Index> indicesIn, tv::TensorView<IndexGrid> gridsOut,
tv::TensorView<Index> indicePairs, tv::TensorView<Index> indiceNum,
const tv::SimpleVector<Index, 2> outSpatialShape, Index spatialVolume) {
auto numActIn = indicesIn.dim(0);
Index point[2];
Index index = 0;
Index offset;
constexpr unsigned KV = K0 * K1;
constexpr unsigned center = KV / 2;
*(indiceNum.data() + center) = numActIn;
for (int ix : tv::KernelLoopX<int>(numActIn)) {
const Index *indice_data = indicesIn.data() + ix * (2 + 1);
#pragma unroll
for (int i = 0; i < K0; ++i) {
#pragma unroll
for (int j = 0; j < K1; ++j) {
offset = i * K1 + j;
if (offset > center){
continue;
}
if (center == offset){
// center of subm indice pairs dont need atomicadd
indicePairs(1, offset, ix) = ix;
indicePairs(0, offset, ix) = ix;
}else{
point[1] = indice_data[2] - j + K1 / 2;
point[0] = indice_data[1] - i + K0 / 2;
if (point[1] >= 0 && point[1] < outSpatialShape[1] && point[0] >= 0 &&
point[0] < outSpatialShape[0]) {
index = tv::ArrayIndexRowMajor<2, 2>::runPtrs(
point, outSpatialShape.data(), 0) +
spatialVolume * indice_data[0];
if (gridsOut[index] > -1) {
Index oldNum = atomicAdd(indiceNum.data() + offset, Index(1));
atomicAdd(indiceNum.data() + KV - offset - 1, Index(1));
indicePairs(1, offset, oldNum) = gridsOut[index];
indicePairs(0, offset, oldNum) = ix;
indicePairs(1, KV - offset - 1, oldNum) = ix;
indicePairs(0, KV - offset - 1, oldNum) = gridsOut[index];
}
}
}
}
}
}
}
template <typename Index, unsigned NDim, int KernelMaxVolume = 256,
unsigned kNumHashFunctions = 4>
__global__ void getSubMIndicePairsHashKernel(
tv::TensorView<const Index> indicesIn, tv::TensorView<Index> indicePairs,
tv::TensorView<Index> indiceNum,
const tv::SimpleVector<Index, NDim> kernelSize,
const tv::SimpleVector<Index, NDim> stride,
const tv::SimpleVector<Index, NDim> padding,
const tv::SimpleVector<Index, NDim> dilation,
const tv::SimpleVector<Index, NDim> outSpatialShape, unsigned table_size,
const cuhash::Entry *table, cuhash::Functions<kNumHashFunctions> constants,
uint2 stash_constants, unsigned stash_count) {
auto numActIn = indicesIn.dim(0);
Index spatialVolume = 1;
#pragma unroll
for (int i = 0; i < NDim; ++i) {
spatialVolume *= outSpatialShape[i];
}
Index numValidPoints = 0;
Index validPoints[KernelMaxVolume * (NDim + 1)];
Index *pointPtr = nullptr;
Index index = 0;
for (int ix : tv::KernelLoopX<int>(numActIn)) {
numValidPoints = getValidOutPos<Index, NDim>(
indicesIn.data() + ix * (NDim + 1) + 1, kernelSize.data(),
stride.data(), padding.data(), dilation.data(), outSpatialShape.data(),
validPoints);
for (int i = 0; i < numValidPoints; ++i) {
pointPtr = validPoints + i * (NDim + 1);
auto offset = pointPtr[NDim];
index = tv::ArrayIndexRowMajor<NDim, NDim>::runPtrs(
pointPtr, outSpatialShape.data(), 0) +
spatialVolume * indicesIn(ix, 0);
auto val = cuhash::retrieve((unsigned)(index), table_size, table,
constants, stash_constants, stash_count);
if (val != cuhash::kNotFound) {
Index oldNum = atomicAdd(indiceNum.data() + offset, Index(1));
indicePairs(1, offset, oldNum) = val;
indicePairs(0, offset, oldNum) = ix;
}
}
}
}
template <typename Index, typename IndexGrid, unsigned NDim>
__global__ void resetGridKernel(const Index *indicePairUnique,
tv::TensorView<IndexGrid> gridsOut,
int numAct) {
for (int ix : tv::KernelLoopX<int>(numAct)) {
gridsOut[indicePairUnique[ix]] = -1;
}
}
template <typename T> __global__ void arangeKernel(T *data, int size) {
for (int ix : tv::KernelLoopX<int>(size)) {
data[ix] = ix;
}
}
template <typename Index, typename IndexGrid, unsigned NDim>
__global__ void
resetGridSubMKernel(const Index *indices, tv::TensorView<IndexGrid> gridsOut,
const tv::SimpleVector<Index, NDim> outSpatialShape,
int numAct) {
Index outSpatialShapeReg[NDim];
for (int i = 0; i < NDim; ++i) {
outSpatialShapeReg[i] = outSpatialShape[i];
}
Index spatialVolume = 1;
auto indsPtr = indices;
#pragma unroll
for (int i = 0; i < NDim; ++i) {
spatialVolume *= outSpatialShape[i];
}
Index index;
for (int ix : tv::KernelLoopX<int>(numAct)) {
indsPtr = indices + ix * (NDim + 1);
index = tv::ArrayIndexRowMajor<NDim, NDim>::runPtrs(indsPtr + 1,
outSpatialShapeReg, 0);
gridsOut[index + spatialVolume * indsPtr[0]] = -1;
}
}
} // namespace spconv
#undef atomicAdd
#endif