562 lines
21 KiB
Plaintext
562 lines
21 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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// @author raver119@gmail.com
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// @author Yurii Shyrma (iuriish@yahoo.com), created on 19.11.2018
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#include <system/op_boilerplate.h>
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#include <loops/reduce3.h>
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#include <loops/legacy_ops.h>
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#include <types/types.h>
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#include <ops/specials_cuda.h>
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using namespace simdOps;
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namespace functions {
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namespace reduce3 {
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////////////////////////////////////////////////////////////////////////
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template <typename X, typename Z>
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__global__ void execScalarGeneric(const int opNum,
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void *vx, Nd4jLong *xShapeInfo,
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void *vy, Nd4jLong *yShapeInfo,
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void *extraParams,
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void *vz, Nd4jLong *zShapeInfo,
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int* allocationPointer,
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void *reductionBuffer,
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Nd4jLong *tadOnlyShapeInfo) {
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Reduce3<X,Z>::execScalarCuda(opNum, vx, xShapeInfo, vy, yShapeInfo, extraParams, vz, zShapeInfo, allocationPointer, reductionBuffer, tadOnlyShapeInfo);
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}
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template <typename X, typename Z>
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__global__ void execAllGeneric(const int opNum,
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void *vx, Nd4jLong *xShapeInfo,
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void *vy, Nd4jLong *yShapeInfo,
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void *extraParams,
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void *vz, Nd4jLong *zShapeInfo,
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int *dimension, int dimensionLength,
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int postProcessOrNot,
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int *allocationPointer,
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Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets,
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Nd4jLong *yTadOnlyShapeInfo, Nd4jLong *yTadOffsets) {
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Reduce3<X,Z>::execAllCuda(opNum, vx, xShapeInfo, vy, yShapeInfo, extraParams, vz, zShapeInfo, dimension, dimensionLength, postProcessOrNot, allocationPointer, tadOnlyShapeInfo, tadOffsets, yTadOnlyShapeInfo, yTadOffsets);
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}
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////////////////////////////////////////////////////////////////////////
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template <typename X, typename Z>
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__global__ void execGeneric(const int opNum,
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void *vx, Nd4jLong *xShapeInfo,
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void *vy, Nd4jLong *yShapeInfo,
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void *extraParams,
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void *vz, Nd4jLong *zShapeInfo,
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int *dimension, int dimensionLength,
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int postProcessOrNot,
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int *allocationPointer,
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Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets,
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Nd4jLong *yTadOnlyShapeInfo, Nd4jLong *yTadOffsets) {
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Reduce3<X,Z>::execCuda(opNum, vx, xShapeInfo, vy, yShapeInfo, extraParams, vz, zShapeInfo, dimension, dimensionLength, postProcessOrNot, allocationPointer, tadOnlyShapeInfo, tadOffsets, yTadOnlyShapeInfo, yTadOffsets);
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename X, typename Z>
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template <typename OpType>
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__device__ void Reduce3<X,Z>::aggregatePartials(void* vsPartials, Nd4jLong tid, Nd4jLong numItems, void *vextraParams) {
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// start the shared memory loop on the next power of 2 less
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// than the block size. If block size is not a power of 2,
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// accumulate the intermediate sums in the remainder range.
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auto sPartials = reinterpret_cast<Z*>(vsPartials);
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auto extraParams = reinterpret_cast<Z *>(vextraParams);
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Nd4jLong 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 (tid >= floorPow2)
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sPartials[tid - floorPow2] = OpType::update(sPartials[tid - floorPow2], sPartials[tid], extraParams);
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__syncthreads();
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}
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for (Nd4jLong activeThreads = floorPow2 >> 1; activeThreads; activeThreads >>= 1) {
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if (tid < activeThreads)
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sPartials[tid] = OpType::update(sPartials[tid], sPartials[tid + activeThreads], extraParams);
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__syncthreads();
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}
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename X, typename Z>
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template<typename OpType>
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__device__ void Reduce3<X,Z>::execScalarCuda( void *vx, Nd4jLong *xShapeInfo,
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void *vy, Nd4jLong *yShapeInfo,
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void *extraParams,
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void *vz, Nd4jLong *zShapeInfo,
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int *allocationPointer, void *reductionBuffer, Nd4jLong *tadOnlyShapeInfo) {
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auto x = reinterpret_cast<X*>(vx);
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auto y = reinterpret_cast<X*>(vy);
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auto z = reinterpret_cast<Z*>(vz);
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__shared__ Z extraZ[3];
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__shared__ Z* sPartials;
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if (threadIdx.x == 0) {
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extern __shared__ unsigned char shmem[];
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sPartials = reinterpret_cast<Z*>(shmem);
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extraZ[0] = (Z) 0.0f;
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extraZ[1] = (Z) 0.0f;
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if (extraParams != nullptr)
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extraZ[2] = static_cast<Z*>(extraParams)[2];
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else
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extraZ[2] = (Z) 0.0f;
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}
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__syncthreads();
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sPartials[threadIdx.x] = OpType::startingValue(x);
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Nd4jLong length = shape::length(xShapeInfo);
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int xEws = shape::elementWiseStride(xShapeInfo);
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int yEws = shape::elementWiseStride(yShapeInfo);
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int tid = blockIdx.x * blockDim.x + threadIdx.x;
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char xOrder = shape::order(xShapeInfo);
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char yOrder = shape::order(yShapeInfo);
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if(xOrder == yOrder && (xEws > 0 && yEws > 0) && shape::strideDescendingCAscendingF(xShapeInfo) && shape::strideDescendingCAscendingF(yShapeInfo)) {
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if (xEws == 1 && yEws == 1) {
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for(Nd4jLong i = tid; i < length; i+= gridDim.x * blockDim.x)
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sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::opAtomic(x[i], y[i], extraZ), extraZ);
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}
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else {
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for(Nd4jLong i = tid; i < length; i+= gridDim.x * blockDim.x)
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sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::opAtomic(x[i * xEws], y[i * yEws], extraZ), extraZ);
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}
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}
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else {
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sPartials[threadIdx.x] = OpType::startingValue(x);
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auto threadCount = gridDim.x * blockDim.x;
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for(Nd4jLong i = tid; i < length; i += threadCount) {
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auto xOffset = shape::getIndexOffset(i, xShapeInfo);
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auto yOffset = shape::getIndexOffset(i, yShapeInfo);
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sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::opAtomic(x[xOffset], y[yOffset], extraZ), extraZ);
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}
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}
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__syncthreads();
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aggregatePartials<OpType>(reinterpret_cast<void*>(sPartials), threadIdx.x, sd::math::nd4j_min<int>(blockDim.x, length), extraZ);
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__syncthreads();
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if (gridDim.x > 1) {
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auto tc = reinterpret_cast<unsigned int *>(reductionBuffer);
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__shared__ bool amLast;
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int rank = shape::rank(xShapeInfo);
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tid = threadIdx.x;
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Z *extraBuffer = (Z *) allocationPointer;
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if (threadIdx.x == 0) {
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reinterpret_cast<Z*>(reductionBuffer)[blockIdx.x] = sPartials[0];
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extraBuffer[blockIdx.x] = extraZ[0];
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extraBuffer[gridDim.x + blockIdx.x] = extraZ[1];
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}
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__threadfence();
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__syncthreads();
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if (threadIdx.x == 0) {
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unsigned int ticket = atomicInc(&tc[16384], gridDim.x);
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amLast = (ticket == gridDim.x - 1);
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}
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sPartials[tid] = OpType::startingValue(x);
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__syncthreads();
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if (amLast) {
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tc[16384] = 0;
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sPartials[threadIdx.x] = OpType::startingValue(x);
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// TODO: later probably replace this. Right now we need extraZ sync for CosineSimilarity ONLY
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if (tid == 0 && extraZ[0] != static_cast<Z>(0) && extraZ[1] != static_cast<Z>(0)) {
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extraZ[0] = 0.0;
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extraZ[1] = 0.0;
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for (int i = 0; i < gridDim.x; i++) {
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extraZ[0] += extraBuffer[i];
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extraZ[1] += extraBuffer[gridDim.x + i];
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}
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}
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for (Nd4jLong i = threadIdx.x; i < gridDim.x; i += blockDim.x)
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sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], static_cast<Z*>(reductionBuffer)[i], extraZ);
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__syncthreads();
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aggregatePartials<OpType>(reinterpret_cast<void*>(sPartials), threadIdx.x, sd::math::nd4j_min<int>(gridDim.x, blockDim.x), extraZ);
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__syncthreads();
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if (threadIdx.x == 0)
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z[0] = OpType::postProcess(sPartials[0], length, extraZ);
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}
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}
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else {
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if (tid == 0) {
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auto tc = reinterpret_cast<unsigned int*>(reductionBuffer);
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tc[16384] = 0;
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z[0] = OpType::postProcess(sPartials[0], length, extraZ);
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//printf("Z: [%f]\n", (float) z[0]);
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}
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}
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename X, typename Z>
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template<typename OpType>
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__device__ void Reduce3<X,Z>::transformAll( void *vx, Nd4jLong *xShapeInfo,
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void *vy, Nd4jLong *yShapeInfo,
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void *extraParams,
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void *vz, Nd4jLong *zShapeInfo,
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int *dimension, int dimensionLength,
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int postProcessOrNot,
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int *allocationPointer,
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Nd4jLong *xTadShapeInfo, Nd4jLong *xOffsets,
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Nd4jLong *yTadShapeInfo,Nd4jLong *yOffsets) {
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auto dx = reinterpret_cast<X*>(vx);
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auto dy = reinterpret_cast<X*>(vy);
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auto z = reinterpret_cast<Z*>(vz);
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// initialize partials first
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__shared__ Z* sPartials;
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if(threadIdx.x == 0) {
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extern __shared__ unsigned char shmem[];
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sPartials = reinterpret_cast<Z*>(shmem);
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}
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__syncthreads();
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Z startingVal = OpType::startingValue(dx);
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sPartials[threadIdx.x] = startingVal;
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X *tempX = reinterpret_cast<X*>(sPartials) + blockDim.x;
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const int maxBlock = blockDim.x;
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__shared__ Z extraZ[OpType::extraParamsLen > 0 ? OpType::extraParamsLen : 1];
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__shared__ int xTadLength;
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__shared__ int yTadLength;
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__shared__ int xTads;
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__shared__ int yTads;
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//reading initial data
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if (threadIdx.x == 0) {
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xTadLength = shape::length(xTadShapeInfo);
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yTadLength = shape::length(yTadShapeInfo);
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xTads = shape::length(xShapeInfo) / xTadLength;
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yTads = shape::length(yShapeInfo) / yTadLength;
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}
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__syncthreads();
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int limit = xTadLength / maxBlock;
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if (xTadLength % maxBlock > 0)
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limit++;
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for (int r = blockIdx.x; r < xTads; r += blockDim.x * gridDim.x) {
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X *x = dx + xOffsets[r];
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if (threadIdx.x < xTadLength && threadIdx.x < maxBlock) {
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auto x0 = shape::getIndexOffset(threadIdx.x, xTadShapeInfo);
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tempX[threadIdx.x] = x[x0];
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}
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__syncthreads();
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for (int g = 0; g < yTads; g++) {
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X *y = dy + yOffsets[g];
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int ri = (r * yTads) + g;
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sPartials[threadIdx.x] = startingVal;
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if (OpType::extraParamsLen > 0 && threadIdx.x < OpType::extraParamsLen)
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extraZ[threadIdx.x] = startingVal;
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__syncthreads();
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// we might have data too large for single cache block, rendering cache useless though :(
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for (int t = 0; t < limit; t++) {
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// we reset tempX IF we have >1 tiles
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if (t >= 1 || (limit > 1 && g > 0))
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if (threadIdx.x + (t * maxBlock) < xTadLength) {
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auto x0 = shape::getIndexOffset(threadIdx.x + (t * maxBlock), xTadShapeInfo);
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tempX[threadIdx.x] = x[x0];
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}
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for (int f = threadIdx.x + (t * maxBlock); f < xTadLength && f < threadIdx.x + ((t + 1) * maxBlock); f += blockDim.x * gridDim.x) {
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auto y0 = shape::getIndexOffset(f, yTadShapeInfo);
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sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::opAtomic(tempX[threadIdx.x], y[y0], extraZ), extraZ);
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}
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// we MUST step through this block altogether
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__syncthreads();
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}
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aggregatePartials<OpType>(reinterpret_cast<void*>(sPartials), threadIdx.x, sd::math::nd4j_min<int>(blockDim.x, xTadLength), extraZ);
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__syncthreads();
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if (threadIdx.x == 0) {
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z[ri] = OpType::postProcess(sPartials[threadIdx.x], xTadLength, extraZ);
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}
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__syncthreads();
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}
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}
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename X, typename Z>
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template<typename OpType>
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__device__ void Reduce3<X,Z>::transform(void *vx, Nd4jLong *xShapeInfo,
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void *vy, Nd4jLong *yShapeInfo,
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void *extraParams,
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void *vz, Nd4jLong *zShapeInfo,
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int *dimension, int dimensionLength,
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int postProcessOrNot,
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int *allocationPointer,
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Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets,
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Nd4jLong *yTadOnlyShapeInfo, Nd4jLong *yTadOffsets) {
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// FIXME
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if(shape::isScalar(zShapeInfo))
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return;
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if (yTadOnlyShapeInfo == nullptr) {
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yTadOnlyShapeInfo = yShapeInfo; // execReduce3TAD case
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}
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auto x = reinterpret_cast<X*>(vx);
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auto y = reinterpret_cast<X*>(vy);
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auto z = reinterpret_cast<Z*>(vz);
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Z startingVal = OpType::startingValue(x);
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__shared__ Z extraZ[OpType::extraParamsLen > 0 ? OpType::extraParamsLen : 1];
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__shared__ Z* sPartials;
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__shared__ int tadLen;
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__shared__ Nd4jLong zLen;
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__shared__ Nd4jLong xTadEws;
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__shared__ Nd4jLong yTadEws;
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__shared__ Nd4jLong yTadNum;
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__shared__ char xTadOrder;
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__shared__ char yTadOrder;
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if(threadIdx.x == 0) {
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extern __shared__ unsigned char shmem[];
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sPartials = reinterpret_cast<Z*>(shmem);
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tadLen = shape::length(tadOnlyShapeInfo);
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zLen = shape::length(zShapeInfo);
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xTadEws = shape::elementWiseStride(tadOnlyShapeInfo);
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yTadEws = shape::elementWiseStride(yTadOnlyShapeInfo);
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yTadNum = shape::length(yShapeInfo) / tadLen;
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xTadOrder = shape::order(tadOnlyShapeInfo);
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yTadOrder = shape::order(yTadOnlyShapeInfo);
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}
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__syncthreads();
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sPartials[threadIdx.x] = startingVal;
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if(xTadEws >= 1 && yTadEws >= 1 && xTadOrder == yTadOrder) {
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for(int i = blockIdx.x; i < zLen; i+= gridDim.x) {
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Nd4jLong xOffset = tadOffsets[i];
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Nd4jLong yOffset = yTadNum == 1 ? 0 : yTadOffsets[i];
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if (OpType::extraParamsLen > 0 && threadIdx.x < OpType::extraParamsLen)
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extraZ[threadIdx.x] = startingVal;
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__syncthreads();
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for (int j = threadIdx.x; j < tadLen; j += blockDim.x) {
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Nd4jLong xOffset2 = xOffset + j*xTadEws;
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Nd4jLong yOffset2 = yOffset + j*yTadEws;
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sPartials[threadIdx.x] = j < blockDim.x ? OpType::opAtomic(x[xOffset2], y[yOffset2], extraZ) : OpType::update(sPartials[threadIdx.x], OpType::opAtomic(x[xOffset2], y[yOffset2], extraZ), extraZ);
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}
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__syncthreads();
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aggregatePartials<OpType>(reinterpret_cast<void*>(sPartials), threadIdx.x, sd::math::nd4j_min<int>(blockDim.x, tadLen), extraZ);
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__syncthreads();
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if (threadIdx.x == 0)
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z[i] = OpType::postProcess(sPartials[threadIdx.x], tadLen, extraZ);
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__syncthreads();
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}
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}
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else {
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for(int i = blockIdx.x; i < zLen; i += gridDim.x) {
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Nd4jLong xOffset = tadOffsets[i];
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Nd4jLong yOffset = yTadNum == 1 ? 0 : yTadOffsets[i];
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if (OpType::extraParamsLen > 0 && threadIdx.x < OpType::extraParamsLen)
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extraZ[threadIdx.x] = startingVal;
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__syncthreads();
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for (int j = threadIdx.x; j < tadLen; j += blockDim.x) {
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Nd4jLong xOffset2 = xOffset + shape::getIndexOffset(j, tadOnlyShapeInfo);
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Nd4jLong yOffset2 = yOffset + shape::getIndexOffset(j, yTadOnlyShapeInfo);
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sPartials[threadIdx.x] = j < blockDim.x ? OpType::opAtomic(x[xOffset2], y[yOffset2], extraZ) : OpType::update(sPartials[threadIdx.x], OpType::opAtomic(x[xOffset2], y[yOffset2], extraZ), extraZ);
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}
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__syncthreads();
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aggregatePartials<OpType>(reinterpret_cast<void*>(sPartials), threadIdx.x, sd::math::nd4j_min<int>(blockDim.x, tadLen), extraZ);
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__syncthreads();
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if (threadIdx.x == 0)
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z[i] = OpType::postProcess(sPartials[threadIdx.x], tadLen, extraZ);
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__syncthreads();
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}
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}
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename X, typename Y>
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__device__ void Reduce3<X,Y>::execCuda(const int opNum,
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void *vx, Nd4jLong *xShapeInfo,
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void *vy, Nd4jLong *yShapeInfo,
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void *extraParams,
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void *vz, Nd4jLong *zShapeInfo,
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int *dimension, int dimensionLength,
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int postProcessOrNot,
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int *allocationPointer,
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Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets,
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Nd4jLong *yTadOnlyShapeInfo, Nd4jLong *yTadOffsets) {
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|
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DISPATCH_BY_OPNUM_TT(transform, PARAMS(vx, xShapeInfo, vy, yShapeInfo, extraParams, vz, zShapeInfo, dimension, dimensionLength, postProcessOrNot, allocationPointer, tadOnlyShapeInfo, tadOffsets, yTadOnlyShapeInfo, yTadOffsets), REDUCE3_OPS);
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename X, typename Y>
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__device__ void Reduce3<X,Y>::execAllCuda( const int opNum,
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void *vx, Nd4jLong *xShapeInfo,
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void *vy, Nd4jLong *yShapeInfo,
|
|
void *extraParams,
|
|
void *vz, Nd4jLong *zShapeInfo,
|
|
int *dimension, int dimensionLength,
|
|
int postProcessOrNot,
|
|
int *allocationPointer,
|
|
Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets,
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|
Nd4jLong *yTadOnlyShapeInfo, Nd4jLong *yTadOffsets) {
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|
|
|
DISPATCH_BY_OPNUM_TT(transformAll, PARAMS(vx, xShapeInfo, vy, yShapeInfo, extraParams, vz, zShapeInfo, dimension, dimensionLength, postProcessOrNot, allocationPointer, tadOnlyShapeInfo, tadOffsets, yTadOnlyShapeInfo, yTadOffsets), REDUCE3_OPS);
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}
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|
|
|
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|
//////////////////////////////////////////////////////////////////////////
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|
template <typename X, typename Y>
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|
__device__ void Reduce3<X,Y>::execScalarCuda(const int opNum,
|
|
void *vx, Nd4jLong *xShapeInfo,
|
|
void *vy, Nd4jLong *yShapeInfo,
|
|
void *extraParams,
|
|
void *vz, Nd4jLong *zShapeInfo,
|
|
int * allocationPointer, void *reductionBuffer,
|
|
Nd4jLong *tadOnlyShapeInfo) {
|
|
|
|
DISPATCH_BY_OPNUM_TT(execScalarCuda, PARAMS(vx, xShapeInfo, vy, yShapeInfo, extraParams, vz, zShapeInfo, allocationPointer, reductionBuffer, tadOnlyShapeInfo), REDUCE3_OPS);
|
|
}
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template <typename X, typename Z>
|
|
__host__ void Reduce3<X,Z>::exec(dim3 launchDims, cudaStream_t *stream,
|
|
int opNum,
|
|
void *vx, Nd4jLong *xShapeInfo,
|
|
void *vy, Nd4jLong *yShapeInfo,
|
|
void *extraParams,
|
|
void *vz, Nd4jLong *zShapeInfo,
|
|
int *dimension, int dimensionLength,
|
|
int postProcessOrNot,
|
|
int *allocationPointer,
|
|
Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets,
|
|
Nd4jLong *yTadOnlyShapeInfo, Nd4jLong *yTadOffsets) {
|
|
|
|
execGeneric<X, Z><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(opNum, vx, xShapeInfo, vy, yShapeInfo, extraParams, vz, zShapeInfo, dimension, dimensionLength, postProcessOrNot, allocationPointer, tadOnlyShapeInfo, tadOffsets, yTadOnlyShapeInfo, yTadOffsets);
|
|
sd::DebugHelper::checkErrorCode(stream, "reduce3exec(...) failed");
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template <typename X, typename Z>
|
|
__host__ void Reduce3<X,Z>::execAll(dim3 launchDims, cudaStream_t *stream,
|
|
int opNum,
|
|
void *vx, Nd4jLong *xShapeInfo,
|
|
void *vy, Nd4jLong *yShapeInfo,
|
|
void *extraParams,
|
|
void *vz, Nd4jLong *zShapeInfo,
|
|
int *dimension, int dimensionLength,
|
|
int postProcessOrNot,
|
|
int *allocationPointer,
|
|
Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets,
|
|
Nd4jLong *yTadOnlyShapeInfo, Nd4jLong *yTadOffsets) {
|
|
|
|
execAllGeneric<X, Z><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(opNum, vx, xShapeInfo, vy, yShapeInfo, extraParams, vz, zShapeInfo, dimension, dimensionLength, postProcessOrNot, allocationPointer, tadOnlyShapeInfo, tadOffsets, yTadOnlyShapeInfo, yTadOffsets);
|
|
sd::DebugHelper::checkErrorCode(stream, "execAllGeneric(...) failed");
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template <typename X, typename Z>
|
|
__host__ void Reduce3<X,Z>::execScalar(dim3 launchDims, cudaStream_t *stream,
|
|
int opNum,
|
|
void *vx, Nd4jLong *xShapeInfo,
|
|
void *vy, Nd4jLong *yShapeInfo,
|
|
void *extraParams,
|
|
void *vz, Nd4jLong *zShapeInfo,
|
|
int* allocationPointer,
|
|
void *reductionBuffer,
|
|
Nd4jLong *tadOnlyShapeInfo) {
|
|
|
|
execScalarGeneric<X,Z><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(opNum, vx, xShapeInfo, vy, yShapeInfo, extraParams, vz, zShapeInfo, allocationPointer, reductionBuffer, tadOnlyShapeInfo);
|
|
sd::DebugHelper::checkErrorCode(stream, "execScalarGeneric(...) failed");
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
//BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT Reduce3, , LIBND4J_TYPES, FLOAT_TYPES);
|
|
|
|
}
|
|
} |