936 lines
46 KiB
Plaintext
936 lines
46 KiB
Plaintext
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author raver119@gmail.com
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//
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#include <ops/declarable/helpers/top_k.h>
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#include <MmulHelper.h>
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#include <NDArrayFactory.h>
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#include <Status.h>
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#include <ConstantTadHelper.h>
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#include <ShapeUtils.h>
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#include <cusolverDn.h>
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#include <cuda_exception.h>
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namespace nd4j {
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namespace ops {
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namespace helpers {
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// template <typename T>
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// static __device__ void swapRows_(T* matrix, Nd4jLong* shape, int theFirst, int theSecond, Nd4jLong N) {
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// if (theFirst != theSecond) {
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// auto start = threadIdx.x + blockIdx.x * blockDim.x;
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// auto step = blockDim.x * gridDim.x;
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// for (auto i = start; i < N; i += step) {
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// Nd4jLong iCoord1[] = {theFirst, i};
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// Nd4jLong iCoord2[] = {theSecond, i};
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// auto iIndex1 = shape::getOffset(0, shape::shapeOf(shape), shape::stride(shape), iCoord1, 2);
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// auto iIndex2 = shape::getOffset(0, shape::shapeOf(shape), shape::stride(shape), iCoord2, 2);
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// //atomicExch(&matrix[iIndex1], matrix[iIndex2]);
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// T e0 = matrix[iIndex1];
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// T e1 = matrix[iIndex2];
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// matrix[iIndex1] = e0;
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// matrix[iIndex2] = e1;
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// }
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// }
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// }
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// BUILD_SINGLE_TEMPLATE(template void swapRows_, (NDArray* matrix, int theFirst, int theSecond), FLOAT_TYPES);
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//
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// void swapRows(NDArray* matrix, int theFirst, int theSecond) {
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// BUILD_SINGLE_SELECTOR(matrix->dataType(), swapRows_, (matrix, theFirst, theSecond), FLOAT_TYPES);
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// }
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template<typename T>
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static __global__ void
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invertKernelLow(void *invertedBuf, Nd4jLong *invertedShape, void *inputBuf, Nd4jLong *inputShape, Nd4jLong n) {
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T *inverted = reinterpret_cast<T *>(invertedBuf);
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T *input = reinterpret_cast<T *>(inputBuf);
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auto start = threadIdx.x + blockIdx.x * blockDim.x;
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auto step = blockDim.x * gridDim.x;
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for (int i = start + 1; i < n; i += step) {
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Nd4jLong pos[] = {i, i - 1};
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Nd4jLong posX[] = {i, i};
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Nd4jLong posY[] = {i - 1, i - 1};
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auto xIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), pos, 2);
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auto dxIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), posX, 2);
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auto dyIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), posY, 2);
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auto zIndex = shape::getOffset(0, shape::shapeOf(invertedShape), shape::stride(invertedShape), pos, 2);
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inverted[zIndex] = -input[xIndex] / (input[dxIndex] * input[dyIndex]);
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// math::atomics::nd4j_atomicAdd(&inverted[zIndex], - input[xIndex] * inverted[iIndex] / input[dIndex]);
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}
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}
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template<typename T>
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static __global__ void
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upvertKernel(void *invertedBuf, Nd4jLong *invertedShape, void *inputBuf, Nd4jLong *inputShape, Nd4jLong n) {
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T *inverted = reinterpret_cast<T *>(invertedBuf);
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T *input = reinterpret_cast<T *>(inputBuf);
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auto start = threadIdx.x + blockIdx.x * blockDim.x;
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auto step = blockDim.x * gridDim.x;
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for (int i = start; i < n; i += step) {
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Nd4jLong pos[] = {i, i};
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auto xIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), pos, 2);
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auto zIndex = shape::getOffset(0, shape::shapeOf(invertedShape), shape::stride(invertedShape), pos, 2);
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// math::atomics::nd4j_atomicDiv(&inverted[zIndex], input[xIndex]);
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inverted[zIndex] /= input[xIndex];
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}
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}
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template<typename T>
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static __global__ void
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upvertKernelUp(void *invertedBuf, Nd4jLong *invertedShape, void *inputBuf, Nd4jLong *inputShape, Nd4jLong n) {
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__shared__ T* inverted;
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__shared__ T* input;
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__shared__ Nd4jLong* inputStride;
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__shared__ Nd4jLong* invertedStride;
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__shared__ Nd4jLong* invertedShapeOf;
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__shared__ Nd4jLong* inputShapeOf;
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if (threadIdx.x == 0) {
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inverted = reinterpret_cast<T *>(invertedBuf);
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input = reinterpret_cast<T *>(inputBuf);
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inputStride = shape::stride(inputShape);
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invertedStride = shape::stride(invertedShape);
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invertedShapeOf = shape::shapeOf(invertedShape);
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inputShapeOf = shape::shapeOf(inputShape);
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}
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__syncthreads();
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auto start = threadIdx.x + blockIdx.x * blockDim.x;
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auto step = blockDim.x * gridDim.x;
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for (int i = start; i < n - 1; i += step) {
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Nd4jLong pos[] = {i, i + 1};
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//Nd4jLong posY[] = {i, i};
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Nd4jLong posX[] = {i + 1, i + 1};
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auto xIndex = shape::getOffset(0, inputShapeOf, shape::stride(inputShape), pos, 2);
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// auto yIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), posY, 2);
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// auto yIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), pos, 2);
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auto iIndex = shape::getOffset(0, invertedShapeOf, invertedStride, posX, 2);
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auto zIndex = shape::getOffset(0, invertedShapeOf, invertedStride, pos, 2);
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math::atomics::nd4j_atomicAdd(&inverted[zIndex], -input[xIndex] * inverted[iIndex]); // / input[yIndex]);
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//inputMatrix->t<T>(i, i + 1) * invertedMatrix->t<T>(i + 1, i + 1) / inputMatrix->t<T>(i, i)
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}
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}
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template<typename T>
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static __global__ void
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invertLowKernel(void *invertedBuf, Nd4jLong *invertedShape, void *inputBuf, Nd4jLong *inputShape, Nd4jLong n) {
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T *inverted = reinterpret_cast<T *>(invertedBuf);
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T *input = reinterpret_cast<T *>(inputBuf);
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for (int i = blockIdx.x + 2; i < n; i += gridDim.x) {
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for (int j = i - 2; j >= 0; --j)
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for (int k = threadIdx.x; k < i; k += blockDim.x) {
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Nd4jLong posZ[] = {i, j};
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Nd4jLong posY[] = {k, j};
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Nd4jLong posX[] = {i, k};
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Nd4jLong posD[] = {i, i};
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auto xIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), posX, 2);
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auto yIndex = shape::getOffset(0, shape::shapeOf(invertedShape), shape::stride(invertedShape), posY,
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2);
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auto dIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), posD, 2);
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auto zIndex = shape::getOffset(0, shape::shapeOf(invertedShape), shape::stride(invertedShape), posZ,
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2);
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math::atomics::nd4j_atomicAdd(&inverted[zIndex], -inverted[yIndex] * input[xIndex] / input[dIndex]);
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}
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}
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}
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template<typename T>
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static __global__ void
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invertUpKernel(void *invertedBuf, Nd4jLong *invertedShape, void *inputBuf, Nd4jLong *inputShape, Nd4jLong n) {
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__shared__ T* inverted;
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__shared__ T* input;
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__shared__ Nd4jLong* inputShapeOf;
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__shared__ Nd4jLong* invertedShapeOf;
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__shared__ Nd4jLong* invertedStrideOf;
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__shared__ Nd4jLong* inputStrideOf;
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if (threadIdx.x == 0) {
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inverted = reinterpret_cast<T *>(invertedBuf);;
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input = reinterpret_cast<T *>(inputBuf);
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inputShapeOf = shape::shapeOf(inputShape);
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invertedShapeOf = shape::shapeOf(invertedShape);
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inputStrideOf = shape::stride(inputShape);
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invertedStrideOf = shape::stride(invertedShape);
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}
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__syncthreads();
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for (int i = (int)n - blockIdx.x - 2; i >= 0; i -= gridDim.x) {
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for (int j = i + 2; j < (int)n; j++)
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for (int k = i + threadIdx.x; k < (int)n; k += blockDim.x) {
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Nd4jLong posZ[] = {i, j};
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Nd4jLong posY[] = {k, j};
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Nd4jLong posX[] = {i, k};
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// Nd4jLong posD[] = {i, i};
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auto xIndex = shape::getOffset(0, inputShapeOf, inputStrideOf, posX, 2);
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auto yIndex = shape::getOffset(0, invertedShapeOf, invertedStrideOf, posY, 2);
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// auto dIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), posD, 2);
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auto zIndex = shape::getOffset(0, invertedShapeOf, invertedStrideOf, posZ, 2);
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math::atomics::nd4j_atomicAdd(&inverted[zIndex], -inverted[yIndex] * input[xIndex]);// / input[dIndex]);
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// printf("(%d, %d) inverted[%lld] = %lf (-inverted[%lld] * input[%lld]\n", blockIdx.x, threadIdx.x, zIndex, inverted[zIndex], yIndex, xIndex);
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}
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}
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}
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template<typename T>
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static void invertLowerMatrix_(LaunchContext *context, NDArray *inputMatrix, NDArray *invertedMatrix) {
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int n = inputMatrix->rows();
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invertedMatrix->setIdentity();
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if (inputMatrix->isIdentityMatrix()) return;
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auto stream = context->getCudaStream();
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// invert main diagonal
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upvertKernel<T><<<1, n, 512, *stream>>>(invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(), inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
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// invert the second diagonal
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invertKernelLow<T><<<1, n, 512, *stream>>>(invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(), inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
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// invertKernelLow<T><<<1, n, 128, *stream>>>(invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(), inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
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invertLowKernel<T><<<n, n, 512, *stream>>>(invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(), inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
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}
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void invertLowerMatrix(LaunchContext *context, NDArray *inputMatrix, NDArray *invertedMatrix) {
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NDArray::prepareSpecialUse({invertedMatrix}, {inputMatrix});
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BUILD_SINGLE_SELECTOR(inputMatrix->dataType(), invertLowerMatrix_, (context, inputMatrix, invertedMatrix), FLOAT_NATIVE);
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NDArray::registerSpecialUse({invertedMatrix}, {inputMatrix});
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}
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template<typename T>
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static void invertUpperMatrix_(LaunchContext *context, NDArray* inputMatrix, NDArray* invertedMatrix) {
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int n = inputMatrix->rows();
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invertedMatrix->setIdentity();
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auto stream = context->getCudaStream();
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if (inputMatrix->isIdentityMatrix()) { // the inverse for I is I
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return;
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}
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//upvertKernel<T><<<1, n, 128, *stream>>>(invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(), inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
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upvertKernelUp<T><<<1, n, 512, *stream >>>(invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(),
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inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
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invertedMatrix->tickWriteDevice();
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invertedMatrix->printIndexedBuffer("Step1 UP inversion");
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invertUpKernel<T><<<n, n, 512, *stream >>>(invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(),inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
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}
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void invertUpperMatrix(LaunchContext *context, NDArray *inputMatrix, NDArray *invertedMatrix) {
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NDArray::prepareSpecialUse({invertedMatrix}, {inputMatrix});
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BUILD_SINGLE_SELECTOR(invertedMatrix->dataType(), invertUpperMatrix_, (context, inputMatrix, invertedMatrix), FLOAT_NATIVE);
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NDArray::prepareSpecialUse({invertedMatrix}, {inputMatrix});
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}
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// template <typename T>
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// static __global__ void lupKernel(T* compound, Nd4jLong* compoundShape, T* permutation, Nd4jLong* permutationShape, Nd4jLong rowNum) {
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// int swapCount = 0;
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// for(int i = blockIdx.x; i < rowNum; i += gridDim.x ) {
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// auto pivotValue = T(0.0);
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// auto pivot = -1;
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//
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// for(int rowCounter = i; rowCounter < rowNum; rowCounter++ ) {
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// Nd4jLong rowCoord[] = {rowCounter, i};
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// auto rowPos = shape::getOffset(0, shape::shapeOf(compoundShape), shape::stride(compoundShape), rowCoord, 2);
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// if(nd4j::math::nd4j_abs(compound[rowPos]) > pivotValue ) {
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// pivotValue = nd4j::math::nd4j_abs(compound[rowPos]);
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// pivot = rowCounter;
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// }
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// }
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//
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// if( pivotValue != T(0.0) ) {
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// swapRows_<T>(compound, compoundShape, pivot, i, rowNum);
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// swapRows_<T>(permutation, permutationShape, pivot, i, rowNum);
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// if (pivot != i)
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// swapCount++;
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//
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// for( int j = i + 1; j < rowNum; j++ ) {
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// Nd4jLong posJIbuf[] = {j, i};
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// Nd4jLong posIIbuf[] = {i, i};
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// auto posJI = shape::getOffset(0, shape::shapeOf(compoundShape), shape::stride(compoundShape), posJIbuf, 2);
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// auto posII = shape::getOffset(0, shape::shapeOf(compoundShape), shape::stride(compoundShape), posIIbuf, 2);
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//
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// compound[posJI] /= compound[posII];
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// for( int k = i + 1; k < rowNum; k++ ) {
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// Nd4jLong posJKbuf[] = {j, k};
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// Nd4jLong posIKbuf[] = {i, k};
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// auto posJK = shape::getOffset(0, shape::shapeOf(compoundShape), shape::stride(compoundShape), posJKbuf, 2);
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// auto posIK = shape::getOffset(0, shape::shapeOf(compoundShape), shape::stride(compoundShape), posIKbuf, 2);
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// T arg = compound[posJI] * compound[posIK];
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// compound[posJK] -= arg;
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// }
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// }
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// }
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// }
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// }
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// template <typename T, typename F>
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template<typename T>
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static __global__ void determinantKernel(T *compound, T *result, Nd4jLong len) {
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//F tempRes = result[0];
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auto start = blockIdx.x * blockDim.x + threadIdx.x;
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auto step = blockDim.x * gridDim.x;
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for (auto i = start; i < len; i += step) {
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auto pos = i * len + i; //shape::getOffset(0, shape::shapeOf(shape), shape::stride(shape), di, 2);
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math::atomics::nd4j_atomicMul(&result[0], compound[pos]);
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}
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}
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template<typename T>
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static __global__ void determinantLogKernel(T *compound, T *result, Nd4jLong len) {
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// F tempRes = (F)result[0];
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auto start = blockIdx.x * blockDim.x + threadIdx.x;
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auto step = blockDim.x * gridDim.x;
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for (auto i = start; i < len; i += step) {
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auto pos = i * len + i; //shape::getOffset(0, shape::shapeOf(shape), shape::stride(shape), di, 2);
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math::atomics::nd4j_atomicAdd(result, math::nd4j_log<T,T>(math::nd4j_abs(compound[pos])));
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}
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// __syncthreads();
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//
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// if (threadIdx.x == 0) {
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// result[0] = (T)math::nd4j_log<F,F>(math::nd4j_abs(tempRes));
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// }
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}
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template<typename T, typename F>
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static __global__ void
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fillMatrix(void *output, Nd4jLong *outShape, void *input, Nd4jLong *inputShape, Nd4jLong pos, Nd4jLong rowLen) {
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__shared__
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F *matrix;
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__shared__
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T *inputBuf;
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__shared__
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Nd4jLong inputLen;
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__shared__
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Nd4jLong n2;
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if (threadIdx.x == 0) {
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matrix = reinterpret_cast<F *>(output);
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inputBuf = reinterpret_cast<T *>(input);
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inputLen = shape::length(inputShape);
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n2 = rowLen * rowLen;
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}
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__syncthreads();
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auto start = blockIdx.x * blockDim.x + threadIdx.x;
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auto step = blockDim.x * gridDim.x;
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for (int k = pos + start, j = start; j < n2; k += step, j += step) {
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auto xIndex = shape::getIndexOffset(k, inputShape, inputLen);
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matrix[j] = (F) inputBuf[xIndex];
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}
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}
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template<typename T>
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static __global__ void
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returnMatrix(void *output, Nd4jLong *outputShape, void *input, Nd4jLong *inputShape, Nd4jLong pos,
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Nd4jLong rowLen) {
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__shared__ T *matrix;
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__shared__ T *outputBuf;
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__shared__ Nd4jLong outputLen;
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__shared__ Nd4jLong n2;
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if (threadIdx.x == 0) {
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matrix = reinterpret_cast<T *>(input);
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outputBuf = reinterpret_cast<T *>(output);
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outputLen = shape::length(inputShape);
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n2 = rowLen * rowLen;
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}
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__syncthreads();
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auto start = blockIdx.x * blockDim.x + threadIdx.x;
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auto step = blockDim.x * gridDim.x;
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for (int k = pos + start, j = start; j < n2; k += step, j += step) {
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auto zIndex = shape::getIndexOffset(k, outputShape, outputLen);
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outputBuf[zIndex] = (T) matrix[j];
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}
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}
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template<typename F>
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static __global__ void fillUpPermutation(void *output, Nd4jLong *shape, int *source, int rowNum) {
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F *permutation = reinterpret_cast<F *>(output);
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auto start = blockIdx.x * blockDim.x + threadIdx.x;
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auto step = blockDim.x * gridDim.x;
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for (auto i = start; i < rowNum; i += step) {
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int val = source[i] - 1;
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Nd4jLong posF[] = {i, val};
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auto pos = shape::getOffset(0, shape::shapeOf(shape), shape::stride(shape), posF, 2);
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permutation[pos] = F(1.f);
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}
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}
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template<typename T>
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static void lup_(LaunchContext *context, NDArray *input, NDArray *compound, NDArray *permutation) {
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auto stream = context->getCudaStream();
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auto n = input->rows();
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cusolverDnHandle_t cusolverH = nullptr;
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cusolverStatus_t status = cusolverDnCreate(&cusolverH);
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if (CUSOLVER_STATUS_SUCCESS != status) {
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throw cuda_exception::build("Cannot create cuSolver handle", status);
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}
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status = cusolverDnSetStream(cusolverH, *stream);
|
|
if (CUSOLVER_STATUS_SUCCESS != status) {
|
|
throw cuda_exception::build("Cannot set up stream for cuda solver", status);
|
|
}
|
|
int lwork = 0;
|
|
int *d_info = nullptr;
|
|
|
|
auto err = cudaMalloc((void **) &d_info, sizeof(int));
|
|
if (err) {
|
|
throw cuda_exception::build("helpers::lup_: Cannot allocate memory for solver info buffer", err);
|
|
}
|
|
|
|
DataType dtype = input->dataType();
|
|
switch (dtype) {
|
|
|
|
case DataType::DOUBLE: {
|
|
double *d_work = nullptr;
|
|
err = cudaMalloc((void **) &d_work, sizeof(float) * lwork);
|
|
if (err) {
|
|
throw cuda_exception::build("helpers::lup_: Cannot allocate memory for solver data buffer",
|
|
err);
|
|
}
|
|
double *matrix = reinterpret_cast<double *>(input->specialBuffer());
|
|
status = cusolverDnDgetrf_bufferSize(
|
|
cusolverH,
|
|
n,
|
|
n,
|
|
matrix,
|
|
n,
|
|
&lwork);
|
|
if (CUSOLVER_STATUS_SUCCESS != status) {
|
|
throw cuda_exception::build("helpers::lup_: Cannot create cuSolver handle", status);
|
|
}
|
|
if (permutation == nullptr)
|
|
status = cusolverDnDgetrf(
|
|
cusolverH,
|
|
n,
|
|
n,
|
|
matrix,
|
|
n,
|
|
d_work,
|
|
nullptr,
|
|
d_info);
|
|
else {
|
|
NDArray permutVector('c', {n}, nd4j::DataType::INT32, context);
|
|
int *permutationBuf = reinterpret_cast<int *>(permutVector.specialBuffer());
|
|
status = cusolverDnDgetrf(
|
|
cusolverH,
|
|
n,
|
|
n,
|
|
matrix,
|
|
n,
|
|
d_work,
|
|
permutationBuf,
|
|
d_info);
|
|
fillUpPermutation<double> << < n, n, 1024, *stream >> >
|
|
(permutation->specialBuffer(), permutation->specialShapeInfo(), permutationBuf, n);
|
|
permutation->tickWriteDevice();
|
|
}
|
|
err = cudaFree(d_work);
|
|
if (err) {
|
|
throw cuda_exception::build("helpers::lup_: Cannot deallocate memory for solver data buffer",
|
|
err);
|
|
}
|
|
}
|
|
break;
|
|
case DataType::FLOAT32: {
|
|
float *matrix = reinterpret_cast<float *>(input->specialBuffer());
|
|
float *d_work = nullptr;
|
|
err = cudaMalloc((void **) &d_work, sizeof(float) * lwork);
|
|
if (err) {
|
|
throw cuda_exception::build("helpers::lup_: Cannot allocate memory for solver data buffer",
|
|
err);
|
|
}
|
|
|
|
status = cusolverDnSgetrf_bufferSize(
|
|
cusolverH,
|
|
n,
|
|
n,
|
|
matrix,
|
|
n,
|
|
&lwork);
|
|
if (CUSOLVER_STATUS_SUCCESS != status) {
|
|
throw cuda_exception::build("helpers::lup_: Cannot create cuSolver handle", status);
|
|
}
|
|
|
|
if (permutation == nullptr)
|
|
status = cusolverDnSgetrf(
|
|
cusolverH,
|
|
n,
|
|
n,
|
|
matrix,
|
|
n,
|
|
d_work,
|
|
nullptr,
|
|
d_info);
|
|
else {
|
|
NDArray permutVector('c', {n}, nd4j::DataType::INT32, context);
|
|
int *permutationBuf = reinterpret_cast<int *>(permutVector.specialBuffer());
|
|
status = cusolverDnSgetrf(
|
|
cusolverH,
|
|
n,
|
|
n,
|
|
matrix,
|
|
n,
|
|
d_work,
|
|
permutationBuf,
|
|
d_info);
|
|
fillUpPermutation<T> <<< n, n, 128, *stream >> >
|
|
(permutation->specialBuffer(), permutation->specialShapeInfo(), permutationBuf, n);
|
|
permutation->tickWriteDevice();
|
|
}
|
|
err = cudaFree(d_work);
|
|
if (err) {
|
|
throw cuda_exception::build("helpers::lup_: Cannot deallocate memory for solver data buffer",
|
|
err);
|
|
}
|
|
|
|
}
|
|
}
|
|
if (CUSOLVER_STATUS_SUCCESS != status) {
|
|
throw cuda_exception::build("helpers::lup_: Cannot make LU decomposition", status);
|
|
}
|
|
err = cudaFree(d_info);
|
|
if (err) {
|
|
throw cuda_exception::build("helpers::lup_: Cannot deallocate memory for solver info buffer", err);
|
|
}
|
|
cusolverDnDestroy(cusolverH);
|
|
// NDArray::registerSpecialUse({input}, {input});
|
|
input->tickWriteDevice();
|
|
}
|
|
|
|
BUILD_SINGLE_TEMPLATE(template void lup_,(LaunchContext * context, NDArray * input, NDArray * output, NDArray * permutation), FLOAT_NATIVE);
|
|
|
|
template<typename T>
|
|
static int determinant_(nd4j::LaunchContext *context, NDArray *input, NDArray *output) {
|
|
Nd4jLong n = input->sizeAt(-1);
|
|
Nd4jLong n2 = n * n;
|
|
std::vector<int> dims();
|
|
auto packX = ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), {input->rankOf() - 2, input->rankOf() - 1});
|
|
//auto packZ = ConstantTadHelper::getInstance()->tadForDimensions(output->shapeInfo(), {output->rankOf() - 1});
|
|
// DataType dtype = input->dataType();
|
|
// if (dtype != DataType::DOUBLE)
|
|
// dtype = DataType::FLOAT32;
|
|
auto matrix = NDArrayFactory::create(input->ordering(), {n, n}, DataTypeUtils::fromT<T>(), context); //, block.getWorkspace());
|
|
auto det = NDArrayFactory::create<T>(1);
|
|
auto stream = context->getCudaStream();
|
|
NDArray::prepareSpecialUse({output}, {input});
|
|
dim3 launchDims(256, 256, 1024);
|
|
output->assign(1.f);
|
|
for (int e = 0; e < output->lengthOf(); e++) {
|
|
Nd4jLong pos = e * n2;
|
|
// if (matrix.dataType() == input->dataType())
|
|
fillMatrix<T, T><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(matrix.specialBuffer(), matrix.specialShapeInfo(), input->specialBuffer(), input->specialShapeInfo(), pos, n);
|
|
// else
|
|
// fillMatrix<T, float><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(matrix.specialBuffer(), matrix.specialShapeInfo(), input->specialBuffer(), input->specialShapeInfo(), pos, n);
|
|
|
|
// if (matrix.dataType() == input->dataType())
|
|
lup_<T>(context, &matrix, nullptr, nullptr);
|
|
// else
|
|
// lup_<float>(context, &matrix, nullptr, nullptr);
|
|
auto offset = shape::getIndexOffset(e, output->shapeInfo(), output->lengthOf());
|
|
auto inputBuf = reinterpret_cast<T *>(matrix.specialBuffer());
|
|
auto outputBuf = reinterpret_cast<T *>(output->specialBuffer()) + offset;
|
|
// if (matrix.dataType() == input->dataType())
|
|
determinantKernel<T> << < launchDims.x, launchDims.y, launchDims.z, *stream >> >
|
|
(inputBuf, outputBuf, n);
|
|
// else
|
|
// determinantKernel<T, float><<<launchDims.x, launchDims.y, launchDims.z, *stream >>> (inputBuf, outputBuf, n);
|
|
}
|
|
NDArray::registerSpecialUse({output}, {input});
|
|
|
|
return Status::OK();
|
|
}
|
|
|
|
int determinant(nd4j::LaunchContext *context, NDArray *input, NDArray *output) {
|
|
NDArray::prepareSpecialUse({output}, {input});
|
|
BUILD_SINGLE_SELECTOR(input->dataType(), return determinant_, (context, input, output), FLOAT_NATIVE);
|
|
NDArray::registerSpecialUse({output}, {input});
|
|
}
|
|
|
|
template<typename T>
|
|
int logAbsDeterminant_(LaunchContext *context, NDArray *input, NDArray *output) {
|
|
Nd4jLong n = input->sizeAt(-1);
|
|
Nd4jLong n2 = n * n;
|
|
std::vector<int> dims();
|
|
auto packX = ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), {input->rankOf() - 2, input->rankOf() - 1});
|
|
//auto packZ = ConstantTadHelper::getInstance()->tadForDimensions(output->shapeInfo(), {output->rankOf() - 1});
|
|
DataType dtype = input->dataType();
|
|
if (dtype != DataType::DOUBLE)
|
|
dtype = DataType::FLOAT32;
|
|
|
|
auto matrix = NDArrayFactory::create(input->ordering(), {n, n}, dtype, context); //, block.getWorkspace());
|
|
auto det = NDArrayFactory::create<T>(1);
|
|
auto stream = context->getCudaStream();
|
|
NDArray::prepareSpecialUse({output}, {input});
|
|
dim3 launchDims(256, 256, 1024);
|
|
output->assign(0.f);
|
|
for (int e = 0; e < output->lengthOf(); e++) {
|
|
Nd4jLong pos = e * n2;
|
|
// if (matrix.dataType() == input->dataType())
|
|
fillMatrix<T, T><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(matrix.specialBuffer(), matrix.specialShapeInfo(), input->specialBuffer(), input->specialShapeInfo(), pos, n);
|
|
// else
|
|
// fillMatrix<T, float><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(matrix.specialBuffer(), matrix.specialShapeInfo(), input->specialBuffer(), input->specialShapeInfo(), pos, n);
|
|
|
|
// if (matrix.dataType() == input->dataType())
|
|
lup_<T>(context, &matrix, nullptr, nullptr);
|
|
// else
|
|
// lup_<float>(context, &matrix, nullptr, nullptr);
|
|
auto offset = shape::getIndexOffset(e, output->shapeInfo(), output->lengthOf());
|
|
auto inputBuf = reinterpret_cast<T *>(matrix.specialBuffer());
|
|
auto outputBuf = reinterpret_cast<T *>(output->specialBuffer()) + offset;
|
|
// if (matrix.dataType() == input->dataType())
|
|
determinantLogKernel<T><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(inputBuf, outputBuf, n);
|
|
// else
|
|
// determinantLogKernel<T, float><<<launchDims.x, launchDims.y, launchDims.z, *stream >>> (inputBuf, outputBuf, n);
|
|
}
|
|
NDArray::registerSpecialUse({output}, {input});
|
|
|
|
return Status::OK();
|
|
|
|
return ND4J_STATUS_OK;
|
|
}
|
|
|
|
int logAbsDeterminant(nd4j::LaunchContext *context, NDArray *input, NDArray *output) {
|
|
NDArray::prepareSpecialUse({output}, {input});
|
|
BUILD_SINGLE_SELECTOR(input->dataType(), return logAbsDeterminant_, (context, input, output), FLOAT_NATIVE);
|
|
NDArray::registerSpecialUse({output}, {input});
|
|
}
|
|
|
|
template<typename T>
|
|
static __global__ void
|
|
fillLowerUpperKernel(void *lowerBuf, Nd4jLong *lowerShape, void *upperBuf, Nd4jLong *upperShape,
|
|
void *matrixBuf, Nd4jLong *matrixShape, Nd4jLong n) {
|
|
|
|
__shared__
|
|
Nd4jLong *xShapeOf;
|
|
__shared__
|
|
Nd4jLong *yShapeOf;
|
|
__shared__
|
|
Nd4jLong *zShapeOf;
|
|
__shared__
|
|
Nd4jLong *xStrideOf;
|
|
__shared__
|
|
Nd4jLong *yStrideOf;
|
|
__shared__
|
|
Nd4jLong *zStrideOf;
|
|
__shared__
|
|
T *lowerMatrix;
|
|
__shared__
|
|
T *upperMatrix;
|
|
__shared__
|
|
T *matrix;
|
|
|
|
if (threadIdx.x == 0) {
|
|
xShapeOf = shape::shapeOf(lowerShape);
|
|
xStrideOf = shape::stride(lowerShape);
|
|
|
|
yShapeOf = shape::shapeOf(upperShape);
|
|
yStrideOf = shape::stride(upperShape);
|
|
|
|
zShapeOf = shape::shapeOf(matrixShape);
|
|
zStrideOf = shape::stride(matrixShape);
|
|
lowerMatrix = reinterpret_cast<T *>(lowerBuf);
|
|
upperMatrix = reinterpret_cast<T *>(upperBuf);
|
|
matrix = reinterpret_cast<T *>(matrixBuf);
|
|
}
|
|
__syncthreads();
|
|
|
|
for (int k = blockIdx.x; k < n; k += gridDim.x) { // and then put all values under main diagonal on to it
|
|
for (int j = threadIdx.x; j < n; j += blockDim.x) {
|
|
Nd4jLong posX[] = {k, j};
|
|
Nd4jLong posD[] = {j, j};
|
|
auto xPos = shape::getOffset(0, xShapeOf, xStrideOf, posX, 2);
|
|
auto yPos = shape::getOffset(0, yShapeOf, yStrideOf, posX, 2);
|
|
auto iPos = shape::getOffset(0, zShapeOf, zStrideOf, posX, 2);
|
|
auto dPos = shape::getOffset(0, zShapeOf, zStrideOf, posD, 2);
|
|
if (k >= j)
|
|
lowerMatrix[xPos] = matrix[iPos];//(k, j);
|
|
else
|
|
upperMatrix[yPos] = matrix[iPos]; //k, j);
|
|
}
|
|
}
|
|
}
|
|
|
|
template<typename T>
|
|
static int inverse_(nd4j::LaunchContext *context, NDArray *input, NDArray *output) {
|
|
auto n = input->sizeAt(-1);
|
|
auto n2 = n * n;
|
|
auto dtype = DataTypeUtils::fromT<T>(); //input->dataType();
|
|
// if (dtype != DataType::DOUBLE)
|
|
// dtype = DataType::FLOAT32;
|
|
NDArray matrix = NDArrayFactory::create('c', {n, n}, dtype, context);
|
|
NDArray upper = NDArrayFactory::create('c', {n, n}, dtype, context);
|
|
NDArray lower = NDArrayFactory::create('c', {n, n}, dtype, context);
|
|
NDArray compound = NDArrayFactory::create('c', {n, n}, dtype, context);
|
|
NDArray permutation = NDArrayFactory::create('c', {n, n}, dtype, context);
|
|
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(),
|
|
{input->rankOf() - 2,
|
|
input->rankOf() - 1});
|
|
auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(),
|
|
{output->rankOf() - 2,
|
|
output->rankOf() - 1});
|
|
auto stream = context->getCudaStream();
|
|
|
|
for (auto i = 0LL; i < packX.numberOfTads(); i++) {
|
|
fillMatrix<T, T><<<1, n2, 1024, *stream>>>(matrix.specialBuffer(), matrix.specialShapeInfo(), input->specialBuffer(), input->specialShapeInfo(), i * n2, n);
|
|
matrix.tickWriteDevice();
|
|
compound.assign(matrix);
|
|
lup_<T>(context, &compound, nullptr, nullptr);
|
|
fillLowerUpperKernel<T><<<n, n, 1024, *stream>>>(lower.specialBuffer(), lower.specialShapeInfo(), upper.specialBuffer(), upper.specialShapeInfo(), compound.specialBuffer(), compound.specialShapeInfo(), n);
|
|
matrix.assign(0);
|
|
invertUpperMatrix(context, &upper, &matrix); // U^{-1}
|
|
matrix.tickWriteDevice();
|
|
// matrix.printIndexedBuffer("Upper Inverted");
|
|
compound.assign(0);
|
|
invertLowerMatrix(context, &lower, &compound); // L{-1}
|
|
compound.tickWriteDevice();
|
|
// compound.printIndexedBuffer("Lower Inverted");
|
|
// matrix.tickWriteDevice();
|
|
// compound.tickWriteDevice();
|
|
nd4j::MmulHelper::mmul(&matrix, &compound, &upper, 1.0, 0.0);
|
|
upper.tickWriteDevice();
|
|
// upper.printIndexedBuffer("Full inverted");
|
|
returnMatrix<T> <<<1, n2, 1024, *stream>>>(output->specialBuffer(), output->specialShapeInfo(), upper.specialBuffer(), upper.specialShapeInfo(), i * n2, n);
|
|
}
|
|
return Status::OK();
|
|
}
|
|
|
|
int inverse(nd4j::LaunchContext *context, NDArray *input, NDArray *output) {
|
|
NDArray::prepareSpecialUse({output}, {input});
|
|
BUILD_SINGLE_SELECTOR(input->dataType(), return inverse_, (context, input, output), FLOAT_NATIVE);
|
|
NDArray::registerSpecialUse({output}, {input});
|
|
}
|
|
|
|
bool checkCholeskyInput(nd4j::LaunchContext *context, NDArray const *input) {
|
|
return true;
|
|
}
|
|
|
|
template<typename F>
|
|
__global__ void fillBatchKernel(F **dArrayBatch, F *buf, Nd4jLong *offsets, Nd4jLong batchSize) {
|
|
auto start = blockIdx.x * blockDim.x + threadIdx.x;
|
|
auto step = blockDim.x * gridDim.x;
|
|
|
|
for (auto i = start; i < batchSize; i += step) {
|
|
dArrayBatch[i] = buf + offsets[i];
|
|
}
|
|
}
|
|
|
|
template<typename F>
|
|
__global__ void
|
|
adjustResultsKernel(F *dArray, Nd4jLong *shape, Nd4jLong *offsets, Nd4jLong batchSize, Nd4jLong n) {
|
|
//auto i = blockIdx.x * blockDim.x + threadIdx.x;
|
|
Nd4jLong *shapeOf = shape::shapeOf(shape);
|
|
Nd4jLong *strideOf = shape::stride(shape);
|
|
|
|
for (auto i = blockIdx.x; i < batchSize; i += gridDim.x) {
|
|
auto current = dArray + offsets[i];
|
|
for (auto r = threadIdx.x; r < n; r += blockDim.x) {
|
|
for (auto c = r + 1; c < n; c++) {
|
|
Nd4jLong posRC[] = {r, c};
|
|
auto pos = r * n + c; //shape::getOffset(0, shapeOf, strideOf, posRC, 2);
|
|
current[pos] = 0.;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
template<typename F>
|
|
int cholesky__(LaunchContext *context, NDArray *input, NDArray *output, bool inplace) {
|
|
if (!inplace)
|
|
output->assign(input);
|
|
std::unique_ptr<NDArray> tempOutput(output->dup());
|
|
cusolverDnHandle_t handle = nullptr;
|
|
auto n = input->sizeAt(-1);
|
|
auto n2 = n * n;
|
|
NDArray::prepareSpecialUse({output}, {input});
|
|
auto status = cusolverDnCreate(&handle);
|
|
if (CUSOLVER_STATUS_SUCCESS != status) {
|
|
throw cuda_exception::build("helpers::cholesky_: Cannot create solver handle", status);
|
|
}
|
|
F **dArrayBatch = nullptr;
|
|
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(tempOutput->getShapeInfo(),
|
|
{tempOutput->rankOf() - 2,
|
|
tempOutput->rankOf() - 1});
|
|
const Nd4jLong batchSize = packX.numberOfTads();
|
|
int *dInfoArray = nullptr;
|
|
auto err = cudaMalloc((void **) &dArrayBatch, sizeof(F *) * batchSize);
|
|
if (err) {
|
|
throw cuda_exception::build("helpers::cholesky_: Cannot allocate memory for solver batch data buffer",
|
|
err);
|
|
}
|
|
err = cudaMalloc((void **) &dInfoArray, sizeof(int) * batchSize);
|
|
if (err) {
|
|
throw cuda_exception::build("helpers::cholesky_: Cannot allocate memory for solver errors buffer", err);
|
|
}
|
|
auto stream = context->getCudaStream();
|
|
fillBatchKernel<F> << < 1, batchSize, 128, *stream >> >
|
|
(dArrayBatch, reinterpret_cast<F *>(tempOutput->specialBuffer()), packX.specialOffsets(), batchSize);
|
|
|
|
status = cusolverDnSetStream(handle, *stream);
|
|
if (CUSOLVER_STATUS_SUCCESS != status) {
|
|
throw cuda_exception::build("helpers::cholesky_: Cannot set stream to solver handle", status);
|
|
}
|
|
const cublasFillMode_t uplo = CUBLAS_FILL_MODE_UPPER;
|
|
if (input->dataType() == DataType::DOUBLE)
|
|
status = cusolverDnDpotrfBatched(
|
|
handle,
|
|
uplo,
|
|
n,
|
|
(double **) dArrayBatch,
|
|
n,
|
|
dInfoArray,
|
|
batchSize);
|
|
else
|
|
status = cusolverDnSpotrfBatched(
|
|
handle,
|
|
uplo,
|
|
n,
|
|
(float **) dArrayBatch,
|
|
n,
|
|
dInfoArray,
|
|
batchSize);
|
|
|
|
if (CUSOLVER_STATUS_SUCCESS != status) {
|
|
throw cuda_exception::build("helpers::cholesky_: Cholesky factorization failed for batch", status);
|
|
}
|
|
adjustResultsKernel<F> << < batchSize, n2, 128, *stream >> >
|
|
(reinterpret_cast<F *>(tempOutput->specialBuffer()), packX.specialShapeInfo(), packX.specialOffsets(), batchSize, n);
|
|
|
|
err = cudaFree(dArrayBatch);
|
|
if (err) {
|
|
throw cuda_exception::build("helpers::cholesky_: Cannot deallocate memory for solver batch data buffer",
|
|
err);
|
|
}
|
|
err = cudaFree(dInfoArray);
|
|
if (err) {
|
|
throw cuda_exception::build("helpers::cholesky_: Cannot allocate memory for solver errors buffer", err);
|
|
}
|
|
|
|
if (!inplace)
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|
output->assign(tempOutput.get());
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|
else
|
|
input->assign(tempOutput.get());
|
|
|
|
NDArray::registerSpecialUse({output}, {input});
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|
return Status::OK();
|
|
}
|
|
|
|
// template <typename T>
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|
int cholesky_(LaunchContext *context, NDArray *input, NDArray *output, bool inplace) {
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|
NDArray::prepareSpecialUse({output}, {input});
|
|
if (input->dataType() == DataType::DOUBLE)
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|
cholesky__<double>(context, input, output, inplace);
|
|
else if (input->dataType() == DataType::FLOAT32)
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|
cholesky__<float>(context, input, output, inplace);
|
|
else {
|
|
std::unique_ptr<NDArray> tempOutput(
|
|
NDArrayFactory::create_('c', input->getShapeAsVector(), DataType::FLOAT32, context));
|
|
tempOutput->assign(input);
|
|
cholesky__<float>(context, tempOutput.get(), tempOutput.get(), true);
|
|
output->assign(tempOutput.get());
|
|
}
|
|
NDArray::registerSpecialUse({output}, {input});
|
|
return Status::OK();
|
|
}
|
|
|
|
int cholesky(nd4j::LaunchContext *context, NDArray *input, NDArray *output, bool inplace) {
|
|
// BUILD_SINGLE_SELECTOR(input->dataType(), return cholesky_, (context, input, output, inplace), FLOAT_TYPES);
|
|
return cholesky_(context, input, output, inplace);
|
|
}
|
|
// BUILD_SINGLE_TEMPLATE(template int cholesky_, (LaunchContext* context, NDArray* input, NDArray* output, bool inplace), FLOAT_TYPES);
|
|
BUILD_SINGLE_TEMPLATE(template int inverse_, (nd4j::LaunchContext * context, NDArray * input, NDArray * output),
|
|
FLOAT_NATIVE);
|
|
|
|
template<typename T>
|
|
__global__ void
|
|
logDetKernel(T *inputBuf, Nd4jLong *inputShape, Nd4jLong batchNum, Nd4jLong *tadShape, Nd4jLong *tadOffsets,
|
|
T *outputBuf, Nd4jLong *outputShape) {
|
|
|
|
__shared__ int n;
|
|
if (threadIdx.x == 0) {
|
|
n = shape::sizeAt(inputShape, -1); // * shape::sizeAt(inputShape, -1);
|
|
}
|
|
__syncthreads();
|
|
|
|
T *output = outputBuf;
|
|
T *input = inputBuf;
|
|
|
|
Nd4jLong *shapeOf = shape::shapeOf(tadShape);
|
|
Nd4jLong *strideOf = shape::stride(tadShape);
|
|
|
|
for (auto i = blockIdx.x; i < batchNum; i += gridDim.x) {
|
|
T *current = input + tadOffsets[i];
|
|
|
|
auto zIndex = shape::getIndexOffset(i, outputShape, batchNum);
|
|
for (auto e = threadIdx.x; e < n; e += blockDim.x) {
|
|
Nd4jLong diag[] = {e, e};
|
|
auto xIndex = shape::getOffset(0, shapeOf, strideOf, diag, 2);
|
|
math::atomics::nd4j_atomicAdd(&output[zIndex],
|
|
math::nd4j_log<T, T>(current[xIndex] * current[xIndex]));
|
|
}
|
|
}
|
|
}
|
|
|
|
template<typename T>
|
|
int logdetFunctor_(nd4j::LaunchContext *context, NDArray *input, NDArray *output) {
|
|
NDArray::prepareSpecialUse({output}, {input});
|
|
auto n2 = input->sizeAt(-1) * input->sizeAt(-2);
|
|
auto stream = context->getCudaStream();
|
|
std::unique_ptr<NDArray> tempOutput(input->dup());
|
|
// auto inputs = tempOutput->allTensorsAlongDimension({input->rankOf() - 2, input->rankOf() - 1});
|
|
// for (Nd4jLong e = 0; e < packX.numberOfTads(); e++) {
|
|
// auto subArray = inputs->at(e);
|
|
// cholesky(context, subArray, subArray, true);
|
|
// }
|
|
// delete inputs;
|
|
cholesky(context, input, tempOutput.get(), false);
|
|
tempOutput->syncToHost();
|
|
tempOutput->printIndexedBuffer("Cholesky res!!!");
|
|
auto outputBuf = reinterpret_cast<T*>(output->specialBuffer()); // + e * n2; // + e * n2;
|
|
auto inputBuf = reinterpret_cast<T*>(tempOutput->specialBuffer());
|
|
output->assign(0);
|
|
output->syncToDevice();
|
|
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(tempOutput->getShapeInfo(),
|
|
{input->rankOf() - 2,
|
|
input->rankOf() - 1});
|
|
logDetKernel<T> << < packX.numberOfTads(), n2, 128, *stream >> >
|
|
(inputBuf, tempOutput->specialShapeInfo(), packX.numberOfTads(), packX.specialShapeInfo(), packX.specialOffsets(), outputBuf, output->specialShapeInfo());
|
|
// }
|
|
NDArray::registerSpecialUse({output}, {input});
|
|
//delete tempOutput;
|
|
return Status::OK();
|
|
}
|
|
|
|
int logdetFunctor(nd4j::LaunchContext *context, NDArray *input, NDArray *output) {
|
|
BUILD_SINGLE_SELECTOR(output->dataType(), logdetFunctor_, (context, input, output), FLOAT_NATIVE);
|
|
}
|
|
|
|
BUILD_SINGLE_TEMPLATE(template int logdetFunctor_,
|
|
(nd4j::LaunchContext * context, NDArray * input, NDArray * output), FLOAT_NATIVE);
|
|
}
|
|
}
|
|
}
|