Brutex
/
cavis
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
cavis/libnd4j/include/ops/declarable/helpers/cuda/histogramFixedWidth.cu

201 lines
8.6 KiB
Plaintext
Raw Blame History

/*******************************************************************************
* 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 Yurii Shyrma (iuriish@yahoo.com), created on 31.08.2018
//
#include <ops/declarable/helpers/histogramFixedWidth.h>
#include <cuda_exception.h>
#include <PointersManager.h>
namespace nd4j {
namespace ops {
namespace helpers {
///////////////////////////////////////////////////////////////////
template<typename X, typename Z>
__global__ static void histogramFixedWidthCuda( const void* vx, const Nd4jLong* xShapeInfo,
void* vz, const Nd4jLong* zShapeInfo,
const X leftEdge, const X rightEdge) {
const auto x = reinterpret_cast<const X*>(vx);
auto z = reinterpret_cast<Z*>(vz);
__shared__ Nd4jLong xLen, zLen, totalThreads, nbins;
__shared__ X binWidth, secondEdge, lastButOneEdge;
if (threadIdx.x == 0) {
xLen = shape::length(xShapeInfo);
nbins = shape::length(zShapeInfo); // nbins = zLen
totalThreads = gridDim.x * blockDim.x;
binWidth = (rightEdge - leftEdge ) / nbins;
secondEdge = leftEdge + binWidth;
lastButOneEdge = rightEdge - binWidth;
}
__syncthreads();
const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
for (Nd4jLong i = tid; i < xLen; i += totalThreads) {
const X value = x[shape::getIndexOffset(i, xShapeInfo)];
Nd4jLong zIndex;
if(value < secondEdge)
zIndex = 0;
else if(value >= lastButOneEdge)
zIndex = nbins - 1;
else
zIndex = static_cast<Nd4jLong>((value - leftEdge) / binWidth);
nd4j::math::atomics::nd4j_atomicAdd<Z>(&z[shape::getIndexOffset(zIndex, zShapeInfo)], 1);
}
}
///////////////////////////////////////////////////////////////////
template<typename X, typename Z>
__host__ static void histogramFixedWidthCudaLauncher(const cudaStream_t *stream, const NDArray& input, const NDArray& range, NDArray& output) {
const X leftEdge = range.e<X>(0);
const X rightEdge = range.e<X>(1);
histogramFixedWidthCuda<X, Z><<<256, 256, 1024, *stream>>>(input.getSpecialBuffer(), input.getSpecialShapeInfo(), output.specialBuffer(), output.specialShapeInfo(), leftEdge, rightEdge);
}
////////////////////////////////////////////////////////////////////////
void histogramFixedWidth(nd4j::LaunchContext* context, const NDArray& input, const NDArray& range, NDArray& output) {
// firstly initialize output with zeros
output.nullify();
PointersManager manager(context, "histogramFixedWidth");
NDArray::prepareSpecialUse({&output}, {&input});
BUILD_DOUBLE_SELECTOR(input.dataType(), output.dataType(), histogramFixedWidthCudaLauncher, (context->getCudaStream(), input, range, output), LIBND4J_TYPES, INDEXING_TYPES);
NDArray::registerSpecialUse({&output}, {&input});
manager.synchronize();
}
// template <typename T>
// __global__ static void copyBuffers(Nd4jLong* destination, void const* source, Nd4jLong* sourceShape, Nd4jLong bufferLength) {
// const auto tid = blockIdx.x * gridDim.x + threadIdx.x;
// const auto step = gridDim.x * blockDim.x;
// for (int t = tid; t < bufferLength; t += step) {
// destination[t] = reinterpret_cast<T const*>(source)[shape::getIndexOffset(t, sourceShape)];
// }
// }
// template <typename T>
// __global__ static void returnBuffers(void* destination, Nd4jLong const* source, Nd4jLong* destinationShape, Nd4jLong bufferLength) {
// const auto tid = blockIdx.x * gridDim.x + threadIdx.x;
// const auto step = gridDim.x * blockDim.x;
// for (int t = tid; t < bufferLength; t += step) {
// reinterpret_cast<T*>(destination)[shape::getIndexOffset(t, destinationShape)] = source[t];
// }
// }
// template <typename T>
// static __global__ void histogramFixedWidthKernel(void* outputBuffer, Nd4jLong outputLength, void const* inputBuffer, Nd4jLong* inputShape, Nd4jLong inputLength, double const leftEdge, double binWidth, double secondEdge, double lastButOneEdge) {
// __shared__ T const* x;
// __shared__ Nd4jLong* z; // output buffer
// if (threadIdx.x == 0) {
// z = reinterpret_cast<Nd4jLong*>(outputBuffer);
// x = reinterpret_cast<T const*>(inputBuffer);
// }
// __syncthreads();
// auto tid = blockIdx.x * gridDim.x + threadIdx.x;
// auto step = blockDim.x * gridDim.x;
// for(auto i = tid; i < inputLength; i += step) {
// const T value = x[shape::getIndexOffset(i, inputShape)];
// Nd4jLong currInd = static_cast<Nd4jLong>((value - leftEdge) / binWidth);
// if(value < secondEdge)
// currInd = 0;
// else if(value >= lastButOneEdge)
// currInd = outputLength - 1;
// nd4j::math::atomics::nd4j_atomicAdd(&z[currInd], 1LL);
// }
// }
// template <typename T>
// void histogramFixedWidth_(nd4j::LaunchContext * context, const NDArray& input, const NDArray& range, NDArray& output) {
// const int nbins = output.lengthOf();
// auto stream = context->getCudaStream();
// // firstly initialize output with zeros
// //if(output.ews() == 1)
// // memset(output.buffer(), 0, nbins * output.sizeOfT());
// //else
// output.assign(0);
// if (!input.isActualOnDeviceSide())
// input.syncToDevice();
// const double leftEdge = range.e<double>(0);
// const double rightEdge = range.e<double>(1);
// const double binWidth = (rightEdge - leftEdge ) / nbins;
// const double secondEdge = leftEdge + binWidth;
// double lastButOneEdge = rightEdge - binWidth;
// Nd4jLong* outputBuffer;
// cudaError_t err = cudaMalloc(&outputBuffer, output.lengthOf() * sizeof(Nd4jLong));
// if (err != 0)
// throw cuda_exception::build("helpers::histogramFixedWidth: Cannot allocate memory for output", err);
// copyBuffers<Nd4jLong ><<<256, 512, 8192, *stream>>>(outputBuffer, output.getSpecialBuffer(), output.getSpecialShapeInfo(), output.lengthOf());
// histogramFixedWidthKernel<T><<<256, 512, 8192, *stream>>>(outputBuffer, output.lengthOf(), input.getSpecialBuffer(), input.getSpecialShapeInfo(), input.lengthOf(), leftEdge, binWidth, secondEdge, lastButOneEdge);
// returnBuffers<Nd4jLong><<<256, 512, 8192, *stream>>>(output.specialBuffer(), outputBuffer, output.specialShapeInfo(), output.lengthOf());
// //cudaSyncStream(*stream);
// err = cudaFree(outputBuffer);
// if (err != 0)
// throw cuda_exception::build("helpers::histogramFixedWidth: Cannot deallocate memory for output buffer", err);
// output.tickWriteDevice();
// //#pragma omp parallel for schedule(guided)
// // for(Nd4jLong i = 0; i < input.lengthOf(); ++i) {
// //
// // const T value = input.e<T>(i);
// //
// // if(value < secondEdge)
// //#pragma omp critical
// // output.p<Nd4jLong>(0, output.e<Nd4jLong>(0) + 1);
// // else if(value >= lastButOneEdge)
// //#pragma omp critical
// // output.p<Nd4jLong>(nbins-1, output.e<Nd4jLong>(nbins-1) + 1);
// // else {
// // Nd4jLong currInd = static_cast<Nd4jLong>((value - leftEdge) / binWidth);
// //#pragma omp critical
// // output.p<Nd4jLong>(currInd, output.e<Nd4jLong>(currInd) + 1);
// // }
// // }
// }
// void histogramFixedWidth(nd4j::LaunchContext * context, const NDArray& input, const NDArray& range, NDArray& output) {
// BUILD_SINGLE_SELECTOR(input.dataType(), histogramFixedWidth_, (context, input, range, output), LIBND4J_TYPES);
// }
// BUILD_SINGLE_TEMPLATE(template void histogramFixedWidth_, (nd4j::LaunchContext * context, const NDArray& input, const NDArray& range, NDArray& output), LIBND4J_TYPES);
}
}
}
Reference in New Issue View Git Blame Copy Permalink