345 lines
15 KiB
Plaintext
345 lines
15 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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// @author Yurii Shyrma (iuriish@yahoo.com)
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//
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#include <system/op_boilerplate.h>
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#include <loops/reduce_long.h>
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#include <loops/legacy_ops.h>
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#include <helpers/DebugHelper.h>
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#include <types/types.h>
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#include <execution/LaunchContext.h>
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#include <exceptions/cuda_exception.h>
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#include <loops/scalar.h>
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using namespace simdOps;
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////////////////////////////////////////////////////////////////////////
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template <typename X, typename Z, typename OpType>
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__global__ void simpleReduce(const void *x, const Nd4jLong *outerXTadShapeInfo, const Nd4jLong *innerXTadShapeInfo,
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void *extraParams, void *vreductionBuffer, void *z, const Nd4jLong *zShapeInfo) {
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functions::reduce::ReduceLongFunction<X,Z>::template transformCudaXD<OpType>(x, outerXTadShapeInfo, innerXTadShapeInfo, extraParams, vreductionBuffer, z, zShapeInfo);
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}
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////////////////////////////////////////////////////////////////////////
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template <typename X, typename Z, typename OpType>
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__device__ void reduceScalarGeneric(const void *x, const Nd4jLong *xShapeInfo,
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void *extraParams,
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void *z, const Nd4jLong *zShapeInfo,
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int *dimension, int dimensionLength,
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void *reductionBuffer,
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const Nd4jLong *tadOnlyShapeInfo) {
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functions::reduce::ReduceLongFunction<X, Z>::template execScalarCuda<OpType>(x, xShapeInfo, extraParams, z, zShapeInfo, reductionBuffer, tadOnlyShapeInfo);
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}
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////////////////////////////////////////////////////////////////////////
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template <typename X, typename Z, typename OpType>
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__global__ void simpleScalar(const void *x, const Nd4jLong *xShapeInfo,
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void *extraParams,
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void *z, const Nd4jLong *zShapeInfo,
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int *dimension, int dimensionLength,
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void *reductionBuffer,
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const Nd4jLong *tadOnlyShapeInfo) {
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reduceScalarGeneric<X, Z, OpType>(x, xShapeInfo, extraParams, z, zShapeInfo, dimension, dimensionLength, reductionBuffer, tadOnlyShapeInfo);
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}
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namespace functions {
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namespace reduce {
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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 ReduceLongFunction<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<X*>(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 && tid + activeThreads < numItems)
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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 ReduceLongFunction<X,Z>::transformCudaXD(const void *vx, const Nd4jLong *outerXTadShapeInfo, const Nd4jLong *innerXTadShapeInfo,
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void *vextraParams, void *vreductionBuffer,
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void *vz, const Nd4jLong *zShapeInfo) {
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auto x = reinterpret_cast<const X*>(vx);
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auto z = reinterpret_cast<Z*>(vz);
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auto extraParams = reinterpret_cast<X*>(vextraParams);
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//shared memory space for storing intermediate results
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__shared__ Z sPartials[CUDA_BLOCK_SIZE];
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__shared__ int tadLen, numTads;
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__shared__ bool sameOffsets;
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if (threadIdx.x == 0) {
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sameOffsets = shape::haveSameShapeAndStrides(zShapeInfo, outerXTadShapeInfo);
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tadLen = shape::length(innerXTadShapeInfo);
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numTads = shape::length(outerXTadShapeInfo);
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}
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__syncthreads();
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int coords[MAX_RANK];
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for (int r = blockIdx.x; r < numTads; r += gridDim.x) {
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shape::index2coords(r, outerXTadShapeInfo, coords);
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const auto outerOffset = shape::getOffset(outerXTadShapeInfo, coords);
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const auto zOffset = sameOffsets ? outerOffset : shape::getOffset(zShapeInfo, coords);
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const X* xTad = x + outerOffset;
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sPartials[threadIdx.x] = OpType::startingValue(xTad);
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for (int i = threadIdx.x; i < tadLen; i += blockDim.x)
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sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(xTad[shape::getIndexOffset(i, innerXTadShapeInfo)], extraParams), extraParams);
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__syncthreads();
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// aggregate. do NOT reduce for elements > tadLen
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aggregatePartials<OpType>(sPartials, threadIdx.x, sd::math::nd4j_min<int>(blockDim.x, tadLen), extraParams);
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__syncthreads();
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if (threadIdx.x == 0)
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z[zOffset] = OpType::postProcess(sPartials[threadIdx.x], tadLen, extraParams);
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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 ReduceLongFunction<X,Z>::execScalarCuda(const void *vx, const Nd4jLong *xShapeInfo,
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void *vextraParams,
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void *vz, const Nd4jLong *zShapeInfo,
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void *vreductionBuffer,
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const Nd4jLong *tadOnlyShapeInfo) {
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auto x = reinterpret_cast<const X*>(vx);
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auto z = reinterpret_cast<Z*>(vz);
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auto extraParams = reinterpret_cast<X*>(vextraParams);
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auto reductionBuffer = reinterpret_cast<Z*>(vreductionBuffer);
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auto tid = blockDim.x * blockIdx.x + threadIdx.x;
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//shared memory space for storing intermediate results
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__shared__ Z sPartials[CUDA_BLOCK_SIZE];
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__shared__ Nd4jLong xEws;
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__shared__ Nd4jLong len;
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if(threadIdx.x == 0) {
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xEws = shape::elementWiseStride(xShapeInfo);
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len = shape::length(xShapeInfo);
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}
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__syncthreads();
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sPartials[threadIdx.x] = OpType::startingValue(x);
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if (xEws > 0)
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for (int i = tid; i < len; i += (blockDim.x * gridDim.x))
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sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[i * xEws], extraParams), extraParams);
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else
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for (int i = tid; i < len; i += blockDim.x * gridDim.x)
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sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[shape::getIndexOffset(i, xShapeInfo)], extraParams), extraParams);
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__syncthreads();
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aggregatePartials<OpType>(sPartials, threadIdx.x, sd::math::nd4j_min<int>(blockDim.x, len), extraParams);
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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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tid = threadIdx.x;
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if (threadIdx.x == 0)
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reductionBuffer[blockIdx.x] = sPartials[0];//this->postProcess(sPartials[0],len,extraParams);
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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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__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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for (int i = threadIdx.x; i < gridDim.x; i += blockDim.x)
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sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], reductionBuffer[i], extraParams);
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__syncthreads();
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aggregatePartials<OpType>(sPartials, threadIdx.x, sd::math::nd4j_min<int>(gridDim.x, blockDim.x), extraParams);
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__syncthreads();
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if (threadIdx.x == 0) {
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z[0] = OpType::postProcess(sPartials[0], len, extraParams);
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}
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}
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}
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else {
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if (threadIdx.x == 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], len, extraParams);
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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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__host__ void ReduceLongFunction<X,Z>::intermediateXD(dim3 launchDims, cudaStream_t *stream,
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const void *x, const Nd4jLong *dXShapeInfo, const Nd4jLong *hXShapeInfo,
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void *extraParams, void *vreductionBuffer,
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void *z, const Nd4jLong *dZShapeInfo, const Nd4jLong *hZShapeInfo, const int* dims) {
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if(shape::isEmpty(hXShapeInfo)) {
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if(shape::isEmpty(hZShapeInfo))
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return;
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const auto startingVal = static_cast<Z>(OpType::startingValue(reinterpret_cast<const X*>(x)));
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auto res = cudaMemcpyAsync(sd::LaunchContext::defaultContext()->getScalarPointer(), &startingVal, sizeof(Z), cudaMemcpyHostToDevice, *stream);
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if (res != 0)
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throw sd::cuda_exception::build("ReduceLongFunction<X,Z>::intermediateXD: failed to copy temporary scalar", res);
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auto ptr = sd::LaunchContext::defaultContext()->getScalarPointer();
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// scalar assign
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functions::scalar::ScalarTransform<Z, Z, Z>::executeCudaShaped(launchDims, stream, 14, z, dZShapeInfo, hXShapeInfo, z, dZShapeInfo, hZShapeInfo, ptr, nullptr);
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}
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else {
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const int zRank = shape::rank(hZShapeInfo);
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const int tadRank = shape::rank(hXShapeInfo) - zRank;
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auto outerPack = sd::ConstantShapeHelper::getInstance().createSubArrShapeInfo(hXShapeInfo, dims, zRank);
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auto innerPack = sd::ConstantShapeHelper::getInstance().createSubArrShapeInfo(hXShapeInfo, dims+zRank, tadRank);
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simpleReduce<X, Z, OpType><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(x, reinterpret_cast<Nd4jLong const*>(outerPack.special()), reinterpret_cast<Nd4jLong const*>(innerPack.special()), extraParams, vreductionBuffer, z, dZShapeInfo);
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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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__host__ void ReduceLongFunction<X,Z>::intermediateScalar(dim3 launchDims, cudaStream_t *stream,
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const void *x, const Nd4jLong *xShapeInfo, const Nd4jLong *hXShapeInfo,
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void *extraParams,
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void *z, const Nd4jLong *zShapeInfo, const Nd4jLong *hZShapeInfo,
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int *dimension, int dimensionLength,
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void *reductionBuffer,
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const Nd4jLong *tadOnlyShapeInfo) {
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if (shape::isEmpty(hXShapeInfo)) {
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if (shape::isEmpty(hZShapeInfo))
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return;
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const auto startingVal = static_cast<Z>(OpType::startingValue(reinterpret_cast<const X*>(x)));
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auto res = cudaMemcpyAsync(z, &startingVal, sizeof(Z), cudaMemcpyHostToDevice, *stream);
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if (res != 0)
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throw sd::cuda_exception::build("ReduceLongFunction<X,Z>::intermediateScalar: failed to copy resulting scalar", res);
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}
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else {
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simpleScalar<X, Z, OpType><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(x, xShapeInfo, extraParams, z, zShapeInfo, dimension, dimensionLength, reductionBuffer, tadOnlyShapeInfo);
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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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_CUDA_H void ReduceLongFunction<X,Y>::execReduceScalar(dim3 launchDims, cudaStream_t *stream,
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const int opNum,
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const void *x, const Nd4jLong *xShapeInfo, const Nd4jLong* hXShapeInfo,
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void *extraParams,
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void *z, const Nd4jLong *zShapeInfo, const Nd4jLong* hZShapeInfo,
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int *dimension, int dimensionLength,
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void *reductionBuffer,
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const Nd4jLong *tadOnlyShapeInfo) {
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DISPATCH_BY_OPNUM_TT(intermediateScalar, PARAMS(launchDims, stream, x, xShapeInfo, hXShapeInfo, extraParams, z, zShapeInfo, hZShapeInfo, dimension, dimensionLength, reductionBuffer, tadOnlyShapeInfo), OPS_A(REDUCE_LONG_OPS));
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sd::DebugHelper::checkErrorCode(stream, "execReduceScalarFloat(...) failed");
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}
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////////////////////////////////////////////////////////////////////////
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template <typename X, typename Y>
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_CUDA_H void ReduceLongFunction<X,Y>::execReduceXD(dim3 launchDims, cudaStream_t *stream, const int opNum,
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const void *x, const Nd4jLong *dXShapeInfo, const Nd4jLong *hXShapeInfo,
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void *extraParams, void *vreductionBuffer,
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void *z, const Nd4jLong *dZShapeInfo, const Nd4jLong *hZShapeInfo, const int *dims) {
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if(shape::length(hZShapeInfo) == 1) {
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ReduceLongFunction<X,Y>::execReduceScalar(launchDims, stream, opNum, x, dXShapeInfo, hXShapeInfo, extraParams, z, dZShapeInfo, hZShapeInfo, nullptr, 0, vreductionBuffer, nullptr);
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}
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else {
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DISPATCH_BY_OPNUM_TT(intermediateXD, PARAMS(launchDims, stream, x, dXShapeInfo, hXShapeInfo, extraParams, vreductionBuffer, z, dZShapeInfo, hZShapeInfo, dims), OPS_A(REDUCE_LONG_OPS));
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}
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DEBUG_KERNEL(stream, opNum);
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}
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////////////////////////////////////////////////////////////////////////
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template <typename X>
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__device__ void initializeShared(X *extraParams, X **sPartials, int sMemSize) {
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int sPartialsLength = sMemSize / sizeof(X);
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X *sPartialsDeref = (X *) *sPartials;
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for (int i = 0; i < sPartialsLength; i++)
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sPartialsDeref[i] = extraParams[0];
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}
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BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT ReduceLongFunction, , LIBND4J_TYPES, LONG_TYPES);
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}
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}
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