89 lines
4.0 KiB
Plaintext
89 lines
4.0 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/helpers.h>
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#include <ops/declarable/helpers/hamming.h>
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namespace sd {
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namespace ops {
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namespace helpers {
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template <typename X, typename Z>
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static _CUDA_G void _hammingKernel(const void *vx, const Nd4jLong *xShapeInfo, const void *vy, const Nd4jLong *yShapeInfo, void *vz, void *reductionBuffer, Nd4jLong length) {
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auto x = reinterpret_cast<const X*>(vx);
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auto y = reinterpret_cast<const X*>(vy);
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auto z = reinterpret_cast<Z*>(vz);
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__shared__ Nd4jLong shared[CUDA_BLOCK_SIZE];
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// we want to nullify temporary memory before accumulating intermediate results
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shared[threadIdx.x] = 0;
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auto tid = threadIdx.x + blockIdx.x * blockDim.x;
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for (Nd4jLong e = tid; e < length; e += blockDim.x * gridDim.x) {
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auto _x = static_cast<unsigned long long>(x[shape::getIndexOffset(e, xShapeInfo)]);
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auto _y = static_cast<unsigned long long>(y[shape::getIndexOffset(e, yShapeInfo)]);
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// we save intermediate result into shared memory
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shared[threadIdx.x] += __popcll(_x ^ _y);
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}
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__syncthreads();
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// now we accumulate values
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auto numItems = sd::math::nd4j_min<Nd4jLong>(blockDim.x, length);
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auto floorPow2 = numItems;
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if (floorPow2 & (floorPow2 - 1)) {
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while (floorPow2 & (floorPow2 - 1))
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floorPow2 &= floorPow2 - 1;
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if (threadIdx.x >= floorPow2)
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shared[threadIdx.x - floorPow2] = shared[threadIdx.x - floorPow2] + shared[threadIdx.x];
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__syncthreads();
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}
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__syncthreads();
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for (Nd4jLong activeThreads = floorPow2 >> 1; activeThreads; activeThreads >>= 1) {
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if (threadIdx.x < activeThreads && threadIdx.x + activeThreads < numItems)
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shared[threadIdx.x] = shared[threadIdx.x] + shared[threadIdx.x + activeThreads];
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__syncthreads();
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}
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__syncthreads();
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// FIXME: do we really want atomicAdd on global memory here
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// and store them to output
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if (threadIdx.x == 0 && shared[0] > 0)
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sd::math::atomics::nd4j_atomicAdd<Z>(&z[0], static_cast<Z>(shared[threadIdx.x]));
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}
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template <typename X, typename Z>
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static void _hamming(LaunchContext *context, NDArray &x, NDArray &y, NDArray &z) {
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_hammingKernel<X, Z><<<256, CUDA_BLOCK_SIZE, 1024, *context->getCudaStream()>>>(x.specialBuffer(), x.specialShapeInfo(), y.specialBuffer(), y.specialShapeInfo(), z.specialBuffer(), nullptr, x.lengthOf());
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}
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void hamming(LaunchContext *context, NDArray &x, NDArray &y, NDArray &output) {
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NDArray::prepareSpecialUse({&output}, {&x, &y});
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BUILD_DOUBLE_SELECTOR(x.dataType(), output.dataType(), _hamming, (context, x, y, output), INTEGER_TYPES, INDEXING_TYPES);
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NDArray::registerSpecialUse({&output}, {&x, &y});
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}
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}
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}
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} |