cavis/libnd4j/include/ops/declarable/helpers/cuda/histogramFixedWidth.cu

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/*******************************************************************************
* 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>
namespace nd4j {
namespace ops {
namespace helpers {
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, bufferLength)];
}
}
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, bufferLength)] = 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, inputLength)];
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);
}
}
}