cavis/libnd4j/include/loops/cuda/specials/repeatKernel.cu

97 lines
6.0 KiB
Plaintext
Raw Normal View History

2019-06-06 14:21:15 +02:00
/*******************************************************************************
* 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);
}