312 lines
13 KiB
Plaintext
312 lines
13 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 Yurii Shyrma (iuriish@yahoo.com)
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//
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// #include <exceptions/cuda_exception.h>
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#include <TrueBroadcastHelper.h>
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#include <PointersManager.h>
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#include <execution/LaunchContext.h>
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#include <specials.h>
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#include <logger.h>
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#include <ops/ops.h>
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// #include <cuda_runtime.h>
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// #include <cuda.h>
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using namespace simdOps;
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namespace nd4j {
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namespace helpers {
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////////////////////////////////////////////////////////////////////////
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template <typename X, typename Y, typename Z, typename OpType>
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__global__ static void trueBroadcastCuda(const void* vx, const Nd4jLong* xShapeInfo, const void* vy, const Nd4jLong* yShapeInfo, void* vz, const Nd4jLong* zShapeInfo) {
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const auto x = reinterpret_cast<const X*>(vx);
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const auto y = reinterpret_cast<const Y*>(vy);
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auto z = reinterpret_cast<Z*>(vz);
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__shared__ int xRank, yRank, zRank;
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__shared__ Nd4jLong zLen, totalThreads, *sharedMem; // xLen == zLen, except when xRank = 1, in this case zLen = 2*xLen
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if (threadIdx.x == 0) {
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extern __shared__ unsigned char shmem[];
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sharedMem = reinterpret_cast<Nd4jLong*>(shmem);
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xRank = shape::rank(xShapeInfo);
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yRank = shape::rank(yShapeInfo);
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zRank = shape::rank(zShapeInfo);
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zLen = shape::length(zShapeInfo);
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totalThreads = gridDim.x * blockDim.x;
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}
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__syncthreads();
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auto xCoords = sharedMem + threadIdx.x * (xRank + yRank + zRank);
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auto yCoords = xCoords + xRank;
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auto zCoords = yCoords + yRank;
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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for (Nd4jLong i = tid; i < zLen; i += totalThreads) {
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shape::index2coords(i, zShapeInfo, zCoords);
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for(int ix = xRank - 1, iy = yRank - 1, iz = zRank - 1; iz >= 0; --iz) {
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if(ix >= 0)
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if(xShapeInfo[ix + 1] == zShapeInfo[iz + 1])
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xCoords[ix--] = zCoords[iz];
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else
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xCoords[ix--] = 0;
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if(iy >= 0)
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if(yShapeInfo[iy + 1] == zShapeInfo[iz + 1])
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yCoords[iy--] = zCoords[iz];
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else
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yCoords[iy--] = 0;
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}
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const auto xOffset = shape::getOffset(xShapeInfo, xCoords);
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const auto zOffset = shape::getOffset(zShapeInfo, zCoords);
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const auto yOffset = shape::getOffset(yShapeInfo, yCoords);
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z[zOffset] = OpType::op(x[xOffset], y[yOffset], nullptr);
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}
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}
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////////////////////////////////////////////////////////////////////////
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template<typename X, typename Y, typename Z>
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template <typename OpType>
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void TrueBroadcastHelper<X,Y,Z>::execLauncher(dim3 launchDims, cudaStream_t *stream, const void *vx, const Nd4jLong *xShapeInfo, const void *vy, const Nd4jLong *yShapeInfo, void *vz, const Nd4jLong *zShapeInfo) {
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trueBroadcastCuda<X, Y, Z, OpType><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(vx, xShapeInfo, vy, yShapeInfo, vz, zShapeInfo);
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename X, typename Y, typename Z>
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void TrueBroadcastHelper<X,Y,Z>::exec(const nd4j::broadcast::Ops opNum, const NDArray& xArr, const NDArray& yArr, NDArray& zArr) {
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dim3 launchDims;
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launchDims.y = MAX_NUM_THREADS / 8; // threadsPerBlock
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launchDims.x = (zArr.lengthOf() + launchDims.y - 1) / launchDims.y; // blocksPerGrid
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launchDims.z = sizeof(Nd4jLong) * launchDims.y * (xArr.rankOf() + yArr.rankOf() + zArr.rankOf()) + 128; // sharedMe
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PointersManager manager(xArr.getContext(), "TrueBroadcastHelper<X,Y,Z>::exec");
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NDArray::prepareSpecialUse({&zArr}, {&xArr, &yArr});
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DISPATCH_BY_OPNUM_TTT(execLauncher, PARAMS(launchDims, xArr.getContext()->getCudaStream(), xArr.getSpecialBuffer(), xArr.getSpecialShapeInfo(), yArr.getSpecialBuffer(), yArr.getSpecialShapeInfo(), zArr.specialBuffer(), zArr.specialShapeInfo()), OPS_A(BROADCAST_OPS));
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NDArray::registerSpecialUse({&zArr}, {&xArr, &yArr});
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manager.synchronize();
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}
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////////////////////////////////////////////////////////////////////////
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template <typename X, typename Z, typename OpType>
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__global__ static void trueBroadcastBoolCuda(const void* vx, const Nd4jLong* xShapeInfo, const void* vy, const Nd4jLong* yShapeInfo, void* vz, const Nd4jLong* zShapeInfo) {
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const auto x = reinterpret_cast<const X*>(vx);
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const auto y = reinterpret_cast<const X*>(vy);
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auto z = reinterpret_cast<Z*>(vz);
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__shared__ int xRank, yRank, zRank;
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__shared__ Nd4jLong zLen, totalThreads, *sharedMem; // xLen == zLen, except when xRank = 1, in this case zLen = 2*xLen
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if (threadIdx.x == 0) {
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extern __shared__ unsigned char shmem[];
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sharedMem = reinterpret_cast<Nd4jLong*>(shmem);
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xRank = shape::rank(xShapeInfo);
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yRank = shape::rank(yShapeInfo);
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zRank = shape::rank(zShapeInfo);
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zLen = shape::length(zShapeInfo);
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totalThreads = gridDim.x * blockDim.x;
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}
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__syncthreads();
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auto xCoords = sharedMem + threadIdx.x * (xRank + yRank + zRank);
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auto yCoords = xCoords + xRank;
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auto zCoords = yCoords + yRank;
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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for (Nd4jLong i = tid; i < zLen; i += totalThreads) {
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shape::index2coords(i, zShapeInfo, zCoords);
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for(int ix = xRank - 1, iy = yRank - 1, iz = zRank - 1; iz >= 0; --iz) {
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if(ix >= 0)
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if(xShapeInfo[ix + 1] == zShapeInfo[iz + 1])
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xCoords[ix--] = zCoords[iz];
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else
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xCoords[ix--] = 0;
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if(iy >= 0)
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if(yShapeInfo[iy + 1] == zShapeInfo[iz + 1])
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yCoords[iy--] = zCoords[iz];
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else
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yCoords[iy--] = 0;
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}
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const auto xOffset = shape::getOffset(xShapeInfo, xCoords);
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const auto zOffset = shape::getOffset(zShapeInfo, zCoords);
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const auto yOffset = shape::getOffset(yShapeInfo, yCoords);
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z[zOffset] = OpType::op(x[xOffset], y[yOffset], nullptr);
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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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void TrueBroadcastBoolHelper<X,Z>::execLauncher(dim3 launchDims, cudaStream_t *stream, const void *vx, const Nd4jLong *xShapeInfo, const void *vy, const Nd4jLong *yShapeInfo, void *vz, const Nd4jLong *zShapeInfo) {
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trueBroadcastBoolCuda<X,Z,OpType><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(vx, xShapeInfo, vy, yShapeInfo, vz, zShapeInfo);
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename X, typename Y>
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void TrueBroadcastBoolHelper<X,Y>::exec(const nd4j::broadcast::BoolOps opNum, const NDArray& xArr, const NDArray& yArr, NDArray& zArr) {
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dim3 launchDims;
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launchDims.y = MAX_NUM_THREADS / 8; // threadsPerBlock
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launchDims.x = (zArr.lengthOf() + launchDims.y - 1) / launchDims.y; // blocksPerGrid
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launchDims.z = sizeof(Nd4jLong) * launchDims.y * (xArr.rankOf() + yArr.rankOf() + zArr.rankOf()) + 128; // sharedMe
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PointersManager manager(xArr.getContext(), "TrueBroadcastBoolHelper<X,Y>::exec");
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NDArray::prepareSpecialUse({&zArr}, {&xArr, &yArr});
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DISPATCH_BY_OPNUM_TT(execLauncher, PARAMS(launchDims, xArr.getContext()->getCudaStream(), xArr.getSpecialBuffer(), xArr.getSpecialShapeInfo(), yArr.getSpecialBuffer(), yArr.getSpecialShapeInfo(), zArr.specialBuffer(), zArr.specialShapeInfo()), OPS_A(BROADCAST_BOOL_OPS));
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NDArray::registerSpecialUse({&zArr}, {&xArr, &yArr});
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manager.synchronize();
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}
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////////////////////////////////////////////////////////////////////////
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template <typename X, typename OpType>
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__global__ static void trueBroadcastIntCuda(const void* vx, const Nd4jLong* xShapeInfo, const void* vy, const Nd4jLong* yShapeInfo, void* vz, const Nd4jLong* zShapeInfo) {
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const auto x = reinterpret_cast<const X*>(vx);
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const auto y = reinterpret_cast<const X*>(vy);
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auto z = reinterpret_cast<X*>(vz);
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__shared__ int xRank, yRank, zRank;
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__shared__ Nd4jLong zLen, totalThreads, *sharedMem; // xLen == zLen, except when xRank = 1, in this case zLen = 2*xLen
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if (threadIdx.x == 0) {
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extern __shared__ unsigned char shmem[];
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sharedMem = reinterpret_cast<Nd4jLong*>(shmem);
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xRank = shape::rank(xShapeInfo);
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yRank = shape::rank(yShapeInfo);
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zRank = shape::rank(zShapeInfo);
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zLen = shape::length(zShapeInfo);
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totalThreads = gridDim.x * blockDim.x;
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}
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__syncthreads();
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auto xCoords = sharedMem + threadIdx.x * (xRank + yRank + zRank);
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auto yCoords = xCoords + xRank;
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auto zCoords = yCoords + yRank;
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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for (Nd4jLong i = tid; i < zLen; i += totalThreads) {
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shape::index2coords(i, zShapeInfo, zCoords);
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for(int ix = xRank - 1, iy = yRank - 1, iz = zRank - 1; iz >= 0; --iz) {
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if(ix >= 0)
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if(xShapeInfo[ix + 1] == zShapeInfo[iz + 1])
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xCoords[ix--] = zCoords[iz];
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else
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xCoords[ix--] = 0;
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if(iy >= 0)
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if(yShapeInfo[iy + 1] == zShapeInfo[iz + 1])
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yCoords[iy--] = zCoords[iz];
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else
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yCoords[iy--] = 0;
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}
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const auto xOffset = shape::getOffset(xShapeInfo, xCoords);
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const auto zOffset = shape::getOffset(zShapeInfo, zCoords);
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const auto yOffset = shape::getOffset(yShapeInfo, yCoords);
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z[zOffset] = OpType::op(x[xOffset], y[yOffset]);
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}
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}
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////////////////////////////////////////////////////////////////////////
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template<typename X>
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template <typename OpType>
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void TrueBroadcastIntHelper<X>::execLauncher(dim3 launchDims, cudaStream_t *stream, const void *vx, const Nd4jLong *xShapeInfo, const void *vy, const Nd4jLong *yShapeInfo, void *vz, const Nd4jLong *zShapeInfo) {
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trueBroadcastIntCuda<X,OpType><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(vx, xShapeInfo, vy, yShapeInfo, vz, zShapeInfo);
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename X>
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void TrueBroadcastIntHelper<X>::exec(const nd4j::broadcast::IntOps opNum, const NDArray& xArr, const NDArray& yArr, NDArray& zArr) {
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dim3 launchDims;
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launchDims.y = MAX_NUM_THREADS / 8; // threadsPerBlock
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launchDims.x = (zArr.lengthOf() + launchDims.y - 1) / launchDims.y; // blocksPerGrid
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launchDims.z = sizeof(Nd4jLong) * launchDims.y * (xArr.rankOf() + yArr.rankOf() + zArr.rankOf()) + 128; // sharedMe
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PointersManager manager(xArr.getContext(), "TrueBroadcastIntHelper<X>::exec");
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NDArray::prepareSpecialUse({&zArr}, {&xArr, &yArr});
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DISPATCH_BY_OPNUM_T(execLauncher, PARAMS(launchDims, xArr.getContext()->getCudaStream(), xArr.getSpecialBuffer(), xArr.getSpecialShapeInfo(), yArr.getSpecialBuffer(), yArr.getSpecialShapeInfo(), zArr.specialBuffer(), zArr.specialShapeInfo()), OPS_A(BROADCAST_INT_OPS));
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NDArray::registerSpecialUse({&zArr}, {&xArr, &yArr});
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manager.synchronize();
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}
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BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_0);
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BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_1);
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BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_2);
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BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_3);
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BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_4);
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BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_5);
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BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_6);
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BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_7);
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BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_8);
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BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_9);
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BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastBoolHelper, , LIBND4J_TYPES, BOOL_TYPES);
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BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastIntHelper, , INTEGER_TYPES);
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}
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} |