/******************************************************************************* * Copyright (c) 2015-2018 Skymind, Inc. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at * https://www.apache.org/licenses/LICENSE-2.0. * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the * License for the specific language governing permissions and limitations * under the License. * * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ // // @author GS <sgazeos@gmail.com>, created on 17.01.2019 // #include <loops/special_kernels.h> namespace nd4j { template <typename T> static __global__ void repeatKernel(void const* inputBuffer, void* outputBuffer, Nd4jLong numTads, Nd4jLong inputLength, Nd4jLong* tadOnlyInputShapeInfo, Nd4jLong *tadInputOffsets, Nd4jLong* tadOnlyOutputShapeInfo, Nd4jLong *tadOutputOffsets) { //auto tid = blockIdx.x * blockDim.x; // + threadIdx.x; // int totalThreads = gridDim.x * blockDim.x; int totalThreads = blockDim.x; //const auto resultLength = shape::length(outputShape); for (Nd4jLong i = blockIdx.x; i < numTads; i += gridDim.x) { auto yOffset = tadInputOffsets[i]; auto xOffset = tadOutputOffsets[i]; for (Nd4jLong j = threadIdx.x; j < inputLength; j += totalThreads) { auto outputOffset = shape::getIndexOrderOffset(j, tadOnlyOutputShapeInfo, inputLength, shape::order(tadOnlyInputShapeInfo)); auto inputOffset = shape::getIndexOrderOffset(j, tadOnlyInputShapeInfo, inputLength, shape::order(tadOnlyInputShapeInfo)); *(reinterpret_cast<T*>(outputBuffer) + xOffset + outputOffset) = *(reinterpret_cast<T const*>(inputBuffer) + yOffset + inputOffset); } } } BUILD_SINGLE_TEMPLATE(template __global__ void repeatKernel, (void const* inputBuffer, void* outputBuffer, Nd4jLong numTads, Nd4jLong inputLength, Nd4jLong* tadOnlyInputShapeInfo, Nd4jLong *tadInputOffsets, Nd4jLong* tadOnlyOutputShapeInfo, Nd4jLong *tadOutputOffsets), LIBND4J_TYPES); template <typename X, typename Y> static __global__ void repeatKernelDouble(void const* inputBuffer, void* outputBuffer, Nd4jLong numTads, Nd4jLong inputLength, Nd4jLong* tadOnlyInputShapeInfo, Nd4jLong *tadInputOffsets, Nd4jLong* tadOnlyOutputShapeInfo, Nd4jLong *tadOutputOffsets) { //auto tid = blockIdx.x * blockDim.x; // + threadIdx.x; int totalThreads = gridDim.x * blockDim.x; //const auto resultLength = shape::length(outputShape); for (Nd4jLong i = blockIdx.x; i < numTads; i += gridDim.x) { auto yOffset = tadInputOffsets[i]; auto xOffset = tadOutputOffsets[i]; for (Nd4jLong j = threadIdx.x; j < inputLength; j += totalThreads) { auto outputOffset = shape::getIndexOrderOffset(j, tadOnlyOutputShapeInfo, inputLength, shape::order(tadOnlyInputShapeInfo)); auto inputOffset = shape::getIndexOrderOffset(j, tadOnlyInputShapeInfo, inputLength, shape::order(tadOnlyInputShapeInfo)); *(reinterpret_cast<X*>(outputBuffer) + xOffset + outputOffset) = static_cast<X>(*(reinterpret_cast<Y const*>(inputBuffer) + yOffset + inputOffset)); } } } BUILD_DOUBLE_TEMPLATE(template __global__ void repeatKernelDouble, (void const* inputBuffer, void* outputBuffer, Nd4jLong numTads, Nd4jLong inputLength, Nd4jLong* tadOnlyInputShapeInfo, Nd4jLong *tadInputOffsets, Nd4jLong* tadOnlyOutputShapeInfo, Nd4jLong *tadOutputOffsets), LIBND4J_TYPES, LIBND4J_TYPES); template <typename T> void repeatKernelH(void const* inputBuffer, void* outputBuffer, Nd4jLong numTads, Nd4jLong inputLength, Nd4jLong outputLength, Nd4jLong *tadOnlyInputShapeInfo, Nd4jLong *tadInputOffsets, Nd4jLong *tadOnlyOutputShapeInfo,Nd4jLong *tadOutputOffsets, cudaStream_t stream) { dim3 launchDims(256, 512, 8192); repeatKernel<T><<<launchDims.x, launchDims.y, launchDims.z, stream>>>(inputBuffer, outputBuffer, numTads, inputLength, tadOnlyInputShapeInfo, tadInputOffsets, tadOnlyOutputShapeInfo, tadOutputOffsets); } BUILD_SINGLE_TEMPLATE(template void repeatKernelH, (void const* inputBuffer, void* outputBuffer, Nd4jLong numTads, Nd4jLong inputLength, Nd4jLong outputLength, Nd4jLong* tadOnlyInputShapeInfo, Nd4jLong *tadInputOffsets, Nd4jLong* tadOnlyOutputShapeInfo, Nd4jLong *tadOutputOffsets, cudaStream_t stream), LIBND4J_TYPES); template <typename X, typename Y> void repeatKernelHH(void const* inputBuffer, void* outputBuffer, Nd4jLong numTads, Nd4jLong inputLength, Nd4jLong *tadOnlyInputShapeInfo, Nd4jLong *tadInputOffsets, Nd4jLong *tadOnlyOutputShapeInfo,Nd4jLong *tadOutputOffsets, cudaStream_t stream) { dim3 launchDims(256, 512, 8192); repeatKernelDouble<X,Y><<<launchDims.x, launchDims.y, launchDims.z, stream>>>(inputBuffer, outputBuffer, numTads, inputLength, tadOnlyInputShapeInfo, tadInputOffsets, tadOnlyOutputShapeInfo, tadOutputOffsets); } BUILD_DOUBLE_TEMPLATE(template void repeatKernelHH, (void const* inputBuffer, void* outputBuffer, Nd4jLong numTads, Nd4jLong inputLength, Nd4jLong* tadOnlyInputShapeInfo, Nd4jLong *tadInputOffsets, Nd4jLong* tadOnlyOutputShapeInfo, Nd4jLong *tadOutputOffsets, cudaStream_t stream), LIBND4J_TYPES, LIBND4J_TYPES); }