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