279 lines
13 KiB
Plaintext
279 lines
13 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 George A. Shulinok <sgazeos@gmail.com>, created on 4/18/2019
|
|
//
|
|
|
|
#include <ops/declarable/helpers/BarnesHutTsne.h>
|
|
|
|
namespace nd4j {
|
|
namespace ops {
|
|
namespace helpers {
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
// count rows kernel - count input pRows and pCols and put result onto pRowCounts
|
|
// pRowCounts - array of ints, with length N
|
|
// pRows - array of ints with length N, vals from 0 to N-1
|
|
// pCols - array of ints with length < N and vals between 0 and max(pRows)
|
|
//
|
|
static __global__ void countRowsKernel(int* pRowCounts, int const* pRows, int const* pCols, Nd4jLong N) {
|
|
auto start = blockIdx.x * blockDim.x;
|
|
auto step = blockDim.x * gridDim.x;
|
|
for (int n = threadIdx.x + start; n < N; n += step) {
|
|
int begin = pRows[n];//->e<int>(n);
|
|
int end = pRows[n + 1];//rowP->e<int>(n + 1);
|
|
for (int i = begin; i < end; i++) {
|
|
bool present = false;
|
|
// loop between near pRows
|
|
for (int m = pRows[pCols[i]]; m < pRows[pCols[i] + 1]; m++)
|
|
if (pCols[m] == n) { // mark index as existed with columns array
|
|
present = true;
|
|
break;
|
|
}
|
|
|
|
atomicAdd(&pRowCounts[n], 1);
|
|
|
|
if (!present) // increment row counter for given index
|
|
atomicAdd(&pRowCounts[pCols[i]], 1);
|
|
}
|
|
}
|
|
}
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
// row counter caller
|
|
Nd4jLong barnes_row_count(const NDArray* rowP, const NDArray* colP, Nd4jLong N, NDArray& rowCounts) {
|
|
|
|
int* pRowCounts = reinterpret_cast<int*>(rowCounts.specialBuffer());
|
|
int const* pRows = reinterpret_cast<int const*>(rowP->getSpecialBuffer());
|
|
int const* pCols = reinterpret_cast<int const*>(colP->getSpecialBuffer());
|
|
auto stream = rowCounts.getContext()->getCudaStream();
|
|
countRowsKernel<<<1, 1, 128, *stream>>>(pRowCounts, pRows, pCols, N);
|
|
NDArray numElementsArr = rowCounts.sumNumber(); //reduceAlongDimension(reduce::Sum, {});
|
|
//rowCounts.printBuffer("Row counts");
|
|
auto numElements = numElementsArr.e<Nd4jLong>(0);
|
|
return numElements;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
// extend symRowP with pRowCounts array vals
|
|
// pRowCounts - int array with length N
|
|
// symRowP - int array with length N+1
|
|
// N - given array length
|
|
//
|
|
static __global__ void fillUpsymRow(int const* pRowCounts, int* symRowP, int N) {
|
|
|
|
auto start = blockIdx.x * blockDim.x + threadIdx.x;
|
|
auto step = blockDim.x * gridDim.x;
|
|
|
|
for (int n = start; n < N + 1; n += step) { // to avoid race condition use shift only for given index
|
|
symRowP[n] = 0;
|
|
for (int i = 0; i < n; i++)
|
|
atomicAdd(&symRowP[n], pRowCounts[i]);
|
|
}
|
|
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
// symmetrize routine kernel
|
|
// pRows - rows buffer (ints)
|
|
// pCols - column buffer (ints) with vals between 0 and max(pRows)
|
|
// pVals - values vector (floats)
|
|
// symRowP - ints, shifted pRows
|
|
// symColP - ints, shifted pCols,
|
|
// offset - ints, shitfs
|
|
// pOutput - result matrix (floats)
|
|
// N - pRows length
|
|
//
|
|
template <typename T>
|
|
static __global__ void symmetrizeKernel(int const* pRows, int const* pCols, T const* pVals, int* symRowP, int* symColP, int* offset, T* pOutput, int N) {
|
|
auto start = blockIdx.x * blockDim.x + threadIdx.x;
|
|
auto step = blockDim.x * gridDim.x;
|
|
|
|
for (int n = start; n < N; n += step) {
|
|
int begin = pRows[n];
|
|
int bound = pRows[n + 1];
|
|
|
|
for (int i = begin; i < bound; i++) {
|
|
bool present = false;
|
|
int colPI = pCols[i];
|
|
int start = pRows[colPI];
|
|
int end = pRows[colPI + 1];
|
|
|
|
for (int m = start; m < end; m++) {
|
|
if (pCols[m] == n) {
|
|
present = true;
|
|
if (n <= colPI) {
|
|
symColP[symRowP[n] + offset[n]] = colPI;
|
|
symColP[symRowP[colPI] + offset[colPI]] = n;
|
|
pOutput[symRowP[n] + offset[n]] = pVals[i] + pVals[m];
|
|
pOutput[symRowP[colPI] + offset[colPI]] = pVals[i] + pVals[m];
|
|
}
|
|
}
|
|
}
|
|
|
|
// If (colP[i], n) is not present, there is no addition involved
|
|
if (!present) {
|
|
symColP[symRowP[n] + offset[n]] = colPI;
|
|
symColP[symRowP[pCols[i]] + offset[colPI]] = n;
|
|
pOutput[symRowP[n] + offset[n]] = pVals[i];
|
|
pOutput[symRowP[colPI] + offset[colPI]] = pVals[i];
|
|
}
|
|
// Update offsets
|
|
if (!present || (present && n <= colPI)) {
|
|
atomicAdd(&offset[n], 1);
|
|
|
|
if (colPI != n)
|
|
atomicAdd(&offset[colPI], 1);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
// symmetrize algorithm itself
|
|
//
|
|
template <typename T>
|
|
static void barnes_symmetrize_(const NDArray* rowP, const NDArray* colP, const NDArray* valP, Nd4jLong N, NDArray* outputRows, NDArray* outputCols, NDArray* outputVals, NDArray* rowCounts) {
|
|
int const* pRows = reinterpret_cast<int const*>(rowP->getSpecialBuffer());
|
|
int* symRowP = reinterpret_cast<int*>(outputRows->specialBuffer());
|
|
int* pRowCounts = reinterpret_cast<int*>(rowCounts->specialBuffer());
|
|
auto stream = outputCols->getContext()->getCudaStream();
|
|
// fill up syRowP array
|
|
fillUpsymRow<<<1, N, 128, *stream>>>(pRowCounts, symRowP, N);
|
|
outputRows->syncToHost();
|
|
// outputRows->printBuffer("output rows");
|
|
int* symColP = reinterpret_cast<int*>(outputCols->specialBuffer());
|
|
// outputRows->printBuffer("SymRows are");
|
|
int const* pCols = reinterpret_cast<int const*>(colP->getSpecialBuffer());
|
|
T const* pVals = reinterpret_cast<T const*>(valP->getSpecialBuffer());
|
|
T* pOutput = reinterpret_cast<T*>(outputVals->specialBuffer());
|
|
//std::vector<int> rowCountsV = rowCounts->getBufferAsVector<int>();
|
|
auto offsetArr = NDArrayFactory::create<int>('c', {N});
|
|
int* offset = reinterpret_cast<int*>(offsetArr.specialBuffer());
|
|
// symmetrize itself
|
|
symmetrizeKernel<T><<<1, 1, 1024, *stream>>>(pRows, pCols, pVals, symRowP, symColP, offset, pOutput, N);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
// symmetrize caller and adoption
|
|
//
|
|
void barnes_symmetrize(const NDArray* rowP, const NDArray* colP, const NDArray* valP, Nd4jLong N, NDArray* outputRows, NDArray* outputCols, NDArray* outputVals, NDArray* rowCounts) {
|
|
BUILD_SINGLE_SELECTOR(valP->dataType(), barnes_symmetrize_, (rowP, colP, valP, N, outputRows, outputCols, outputVals, rowCounts), NUMERIC_TYPES);
|
|
|
|
*outputVals /= 2.0;
|
|
}
|
|
BUILD_SINGLE_TEMPLATE(template void barnes_symmetrize_, (const NDArray* rowP, const NDArray* colP, const NDArray* valP, Nd4jLong N, NDArray* outputRows, NDArray* outputCols, NDArray* outputVals, NDArray* rowCounts), NUMERIC_TYPES);
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
// edge forces implementation
|
|
//
|
|
template <typename T>
|
|
static __global__ void edgeForcesKernel(int const* pRows, int const* pCols, T const* dataP, T const* vals, T* outputP, int N, int colCount, int rowSize) {
|
|
// std::vector<T> buffer(colCount);
|
|
|
|
auto start = blockIdx.x * blockDim.x + threadIdx.x;
|
|
auto step = blockDim.x * gridDim.x;
|
|
|
|
for (int n = start; n < N; n += step) {
|
|
int start = pRows[n];
|
|
int end = pRows[n + 1];
|
|
int shift = n * colCount;
|
|
for (int i = start; i < end; i++) {
|
|
T const* thisSlice = dataP + pCols[i] * colCount;
|
|
T res = 1;
|
|
|
|
for (int k = 0; k < colCount; k++) {
|
|
auto valTemp = dataP[shift + k] - thisSlice[k];//thisSlice[k];
|
|
res += valTemp * valTemp; // (dataP[shift + k] * dataP[shift + k] - 2 * dataP[shift + k] * thisSlice[k] + thisSlice[k] * thisSlice[k])
|
|
}
|
|
res = vals[i] / res;
|
|
for (int k = 0; k < colCount; k++)
|
|
math::atomics::nd4j_atomicAdd(&outputP[shift + k], T((dataP[shift + k] - thisSlice[k]) * res));
|
|
}
|
|
}
|
|
}
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
// edge forces algorithm
|
|
//
|
|
|
|
template <typename T>
|
|
static void barnes_edge_forces_(const NDArray* rowP, NDArray const* colP, NDArray const* valP, int N, NDArray const* data, NDArray* output) {
|
|
NDArray::prepareSpecialUse({output}, {data, rowP, colP, valP, valP});
|
|
T const* dataP = reinterpret_cast<T const*>(data->getSpecialBuffer());
|
|
T const* vals = reinterpret_cast<T const*>(valP->getSpecialBuffer());
|
|
T* outputP = reinterpret_cast<T*>(output->specialBuffer());
|
|
int const* pRows = reinterpret_cast<int const*>(rowP->getSpecialBuffer());
|
|
int const* pCols = reinterpret_cast<int const*>(colP->getSpecialBuffer());
|
|
int colCount = data->columns();
|
|
//auto shift = 0;
|
|
auto rowSize = sizeof(T) * colCount;
|
|
auto stream = output->getContext()->getCudaStream();
|
|
edgeForcesKernel<T><<<1, 128, 1024, *stream>>>(pRows, pCols, dataP, vals, outputP, N, colCount, rowSize);
|
|
NDArray::registerSpecialUse({output}, {rowP, colP, valP, data});
|
|
}
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
// edge forces caller
|
|
//
|
|
void barnes_edge_forces(const NDArray* rowP, NDArray const* colP, NDArray const* valP, int N, NDArray* output, NDArray const& data) {
|
|
// Loop over all edges in the graph
|
|
BUILD_SINGLE_SELECTOR(output->dataType(), barnes_edge_forces_, (rowP, colP, valP, N, &data, output), FLOAT_TYPES);
|
|
}
|
|
BUILD_SINGLE_TEMPLATE(template void barnes_edge_forces_, (const NDArray* rowP, NDArray const* colP, NDArray const* valP, int N, NDArray const* data, NDArray* output), FLOAT_TYPES);
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
// gains - run a function T((x + 2.) * nd4j::math::nd4j_sign<T,T>(grad) != nd4j::math::nd4j_sign<T,T>(eps)) + T(x * 0.8 * nd4j::math::nd4j_sign<T,T>(grad) != nd4j::math::nd4j_sign<T,T>(eps));
|
|
// for all members in input and put all in output
|
|
//
|
|
template <typename T>
|
|
void barnes_gains_(NDArray* input, NDArray* gradX, NDArray* epsilon, NDArray* output) {
|
|
auto gainsInternal = LAMBDA_TTT(x, grad, eps) {
|
|
T res = nd4j::math::nd4j_sign<T,T>(grad) != nd4j::math::nd4j_sign<T,T>(eps) ? x + T(.2) : x * T(.8);
|
|
if(res < .01) res = .01;
|
|
return res;
|
|
};
|
|
|
|
input->applyTriplewiseLambda(*gradX, *epsilon, gainsInternal, *output);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
// gains caller
|
|
void barnes_gains(NDArray* input, NDArray* gradX, NDArray* epsilon, NDArray* output) {
|
|
BUILD_SINGLE_SELECTOR(input->dataType(), barnes_gains_, (input, gradX, epsilon, output), NUMERIC_TYPES);
|
|
}
|
|
BUILD_SINGLE_TEMPLATE(template void barnes_gains_, (NDArray* input, NDArray* gradX, NDArray* epsilon, NDArray* output), NUMERIC_TYPES);
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
// cell contains - check cells for given point
|
|
//
|
|
bool cell_contains(NDArray* corner, NDArray* width, NDArray* point, Nd4jLong dimension) {
|
|
auto cornerMinusWidth = *corner - *width;
|
|
auto cornerPlusWidth = *corner + *width;
|
|
// executes on host side, so sync all to host memory
|
|
cornerMinusWidth.syncToHost();
|
|
cornerPlusWidth.syncToHost();
|
|
for (Nd4jLong i = 0; i < dimension; i++) {
|
|
if (cornerMinusWidth.e<double>(i) > point->e<double>(i))
|
|
return false;
|
|
if (cornerPlusWidth.e<double>(i) < point->e<double>(i))
|
|
return false;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|