wehub-resource-sync
508 lines
21 KiB
C++
508 lines
21 KiB
C++
/* ******************************************************************************
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*
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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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* See the NOTICE file distributed with this work for additional
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* information regarding copyright ownership.
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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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#ifndef CUDA_LAMBDA_HELPER
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#define CUDA_LAMBDA_HELPER
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#include <cuda.h>
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#include <cuda_runtime.h>
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#include <helpers/shape.h>
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#include <system/op_boilerplate.h>
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static sd::LongType SD_DEVICE length(const sd::LongType *shapeInfo) { return shape::length(shapeInfo); }
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template <typename T, typename Lambda>
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static SD_KERNEL void lambdaKernel(const void *vx, const sd::LongType *xShapeInfo, void *vz,
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const sd::LongType *zShapeInfo, Lambda lambda);
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template <typename T, typename Lambda>
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static SD_KERNEL void lambdaIndexedKernel(const void *vx, const sd::LongType *xShapeInfo, void *vz,
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const sd::LongType *zShapeInfo, Lambda lambda);
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template <typename T, typename Lambda>
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static SD_KERNEL void lambdaIndexedPairwiseKernel(const void *vx, const sd::LongType *xShapeInfo, const void *vy,
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const sd::LongType *yShapeInfo, void *vz,
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const sd::LongType *zShapeInfo, Lambda lambda);
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template <typename T, typename Lambda>
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static SD_KERNEL void lambdaPairwiseKernel(const void *vx, const sd::LongType *xShapeInfo, const void *vy,
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const sd::LongType *yShapeInfo, void *vz, const sd::LongType *zShapeInfo,
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Lambda lambda);
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template <typename T, typename Lambda>
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static SD_KERNEL void lambdaPairwiseKernel(const void *scalarPtr, const void *vx, const sd::LongType *xShapeInfo, void *vz, const sd::LongType *zShapeInfo,
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Lambda lambda);
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template <typename T, typename Lambda>
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static SD_KERNEL void lambdaTriplewiseKernel(const void *vw, const sd::LongType *wShapeInfo, const void *vx,
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const sd::LongType *xShapeInfo, const void *vy,
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const sd::LongType *yShapeInfo, void *vz, const sd::LongType *zShapeInfo,
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Lambda lambda);
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template <typename T>
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class LambdaHelper {
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public:
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template <typename Lambda>
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SD_INLINE static void lambdaLauncher(cudaStream_t *stream, const void *vx, const sd::LongType *xShapeInfo, void *vz,
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const sd::LongType *zShapeInfo, Lambda lambda) {
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lambdaKernel<T, Lambda><<<256, 512, 1024, *stream>>>(vx, xShapeInfo, vz, zShapeInfo, lambda);
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auto err = cudaStreamSynchronize(*stream);
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if (err != 0) THROW_EXCEPTION("NDArray::applyLambda execution failed");
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}
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template <typename Lambda>
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SD_INLINE static void lambdaIndexedLauncher(cudaStream_t *stream, const void *vx, const sd::LongType *xShapeInfo,
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void *vz, const sd::LongType *zShapeInfo, Lambda lambda) {
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lambdaIndexedKernel<T, Lambda><<<256, 512, 1024, *stream>>>(vx, xShapeInfo, vz, zShapeInfo, lambda);
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auto err = cudaStreamSynchronize(*stream);
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if (err != 0) THROW_EXCEPTION("NDArray::applyIndexedLambda execution failed");
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}
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template <typename Lambda>
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SD_INLINE static void lambdaPairwiseLauncher(cudaStream_t *stream, const void *vx, const sd::LongType *xShapeInfo, bool otherIsScalar,
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const void *vy, const sd::LongType *yShapeInfo, void *vz,
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const sd::LongType *zShapeInfo, Lambda lambda) {
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if (otherIsScalar) {
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lambdaPairwiseKernel<T, Lambda><<<256, 512, 1024, *stream>>>(vy, vx, xShapeInfo, vz, zShapeInfo, lambda);
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} else {
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lambdaPairwiseKernel<T, Lambda>
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<<<256, 512, 1024, *stream>>>(vx, xShapeInfo, vy, yShapeInfo, vz, zShapeInfo, lambda);
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}
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auto err = cudaStreamSynchronize(*stream);
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if (err != 0) THROW_EXCEPTION("NDArray::applyPairwiseLambda execution failed");
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}
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template <typename Lambda>
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SD_INLINE static void lambdaIndexedPairwiseLauncher(cudaStream_t *stream, const void *vx,
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const sd::LongType *xShapeInfo, const void *vy,
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const sd::LongType *yShapeInfo, void *vz,
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const sd::LongType *zShapeInfo, Lambda lambda) {
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lambdaIndexedPairwiseKernel<T, Lambda>
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<<<256, 512, 1024, *stream>>>(vx, xShapeInfo, vy, yShapeInfo, vz, zShapeInfo, lambda);
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auto err = cudaStreamSynchronize(*stream);
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if (err != 0) THROW_EXCEPTION("NDArray::applyIndexedPairwiseLambda execution failed");
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}
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template <typename Lambda>
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SD_INLINE static void lambdaTriplewiseLauncher(cudaStream_t *stream, const void *vw, const sd::LongType *wShapeInfo,
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const void *vx, const sd::LongType *xShapeInfo, const void *vy,
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const sd::LongType *yShapeInfo, void *vz,
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const sd::LongType *zShapeInfo, Lambda lambda) {
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lambdaTriplewiseKernel<T, Lambda>
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<<<256, 512, 1024, *stream>>>(vw, wShapeInfo, vx, xShapeInfo, vy, yShapeInfo, vz, zShapeInfo, lambda);
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auto err = cudaStreamSynchronize(*stream);
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if (err != 0) THROW_EXCEPTION("NDArray::applyTriplewiseLambda execution failed");
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}
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};
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////////////////////////////////////////////////////////////////////////
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template <typename T, typename Lambda>
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static SD_KERNEL void lambdaKernel(const void *vx, const sd::LongType *xShapeInfo, void *vz,
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const sd::LongType *zShapeInfo, Lambda lambda) {
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auto x = reinterpret_cast<const T *>(vx);
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auto z = reinterpret_cast<T *>(vz);
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auto zLength = length(zShapeInfo);
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auto tid = threadIdx.x + blockIdx.x * blockDim.x;
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__shared__ sd::LongType xRank;
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__shared__ sd::LongType *xShape;
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__shared__ sd::LongType *xStride;
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__shared__ sd::LongType zRank;
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__shared__ sd::LongType *zShape;
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__shared__ sd::LongType *zStride;
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if(threadIdx.x == 0) {
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xRank = shape::rank(xShapeInfo);
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xShape = shape::shapeOf(xShapeInfo);
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xStride = shape::stride(xShapeInfo);
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zRank = shape::rank(zShapeInfo);
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zShape = shape::shapeOf(zShapeInfo);
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zStride = shape::stride(zShapeInfo);
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}
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__syncthreads();
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for (sd::LongType e = tid; e < zLength; e += blockDim.x * gridDim.x) {
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sd::LongType xCoords[SD_MAX_RANK];
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sd::LongType zCoords[SD_MAX_RANK];
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sd::LongType xOffset;
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sd::LongType zOffset;
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INDEX2COORDS(e, xRank, xShape, xCoords);
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COORDS2INDEX(xRank,xStride, xCoords, xOffset);
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INDEX2COORDS(e, zRank, zShape, zCoords);
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COORDS2INDEX(zRank, zStride, zCoords, zOffset);
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z[zOffset] = lambda(x[xOffset]);
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}
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}
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////////////////////////////////////////////////////////////////////////
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template <typename T, typename Lambda>
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static SD_KERNEL void lambdaIndexedKernel(const void *vx, const sd::LongType *xShapeInfo, void *vz,
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const sd::LongType *zShapeInfo, Lambda lambda) {
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auto x = reinterpret_cast<const T *>(vx);
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auto z = reinterpret_cast<T *>(vz);
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auto zLength = length(zShapeInfo);
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auto tid = threadIdx.x + blockIdx.x * blockDim.x;
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__shared__ sd::LongType xRank;
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__shared__ sd::LongType *xShape;
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__shared__ sd::LongType *xStride;
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__shared__ sd::LongType zRank;
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__shared__ sd::LongType *zShape;
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__shared__ sd::LongType *zStride;
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if(threadIdx.x == 0) {
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xRank = shape::rank(xShapeInfo);
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xShape = shape::shapeOf(xShapeInfo);
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xStride = shape::stride(xShapeInfo);
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zRank = shape::rank(zShapeInfo);
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zShape = shape::shapeOf(zShapeInfo);
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zStride = shape::stride(zShapeInfo);
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}
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for (sd::LongType e = tid; e < zLength; e += blockDim.x * gridDim.x) {
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sd::LongType xCoords[SD_MAX_RANK];
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sd::LongType zCoords[SD_MAX_RANK];
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sd::LongType xOffset;
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sd::LongType zOffset;
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INDEX2COORDS(e, xRank, xShape, xCoords);
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COORDS2INDEX(xRank,xStride, xCoords, xOffset);
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INDEX2COORDS(e, zRank, zShape, zCoords);
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COORDS2INDEX(zRank, zStride, zCoords, zOffset);
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z[zOffset] = lambda(e, x[xOffset]);
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}
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}
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////////////////////////////////////////////////////////////////////////
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template <typename T, typename Lambda>
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static SD_KERNEL void lambdaIndexedPairwiseKernel(const void *vx, const sd::LongType *xShapeInfo, const void *vy,
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const sd::LongType *yShapeInfo, void *vz,
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const sd::LongType *zShapeInfo, Lambda lambda) {
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auto x = reinterpret_cast<const T *>(vx);
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auto y = reinterpret_cast<const T *>(vy);
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auto z = reinterpret_cast<T *>(vz);
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auto zLength = length(zShapeInfo);
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auto tid = threadIdx.x + blockIdx.x * blockDim.x;
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__shared__ sd::LongType xRank;
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__shared__ sd::LongType *xShape;
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__shared__ sd::LongType *xStride;
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__shared__ sd::LongType yRank;
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__shared__ sd::LongType *yShape;
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__shared__ sd::LongType *yStride;
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__shared__ sd::LongType zRank;
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__shared__ sd::LongType *zShape;
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__shared__ sd::LongType *zStride;
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if(threadIdx.x == 0) {
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xRank = shape::rank(xShapeInfo);
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xShape = shape::shapeOf(xShapeInfo);
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xStride = shape::stride(xShapeInfo);
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yRank = shape::rank(yShapeInfo);
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yShape = shape::shapeOf(yShapeInfo);
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yStride = shape::stride(yShapeInfo);
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zRank = shape::rank(zShapeInfo);
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zShape = shape::shapeOf(zShapeInfo);
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zStride = shape::stride(zShapeInfo);
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}
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__syncthreads();
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for (sd::LongType e = tid; e < zLength; e += blockDim.x * gridDim.x) {
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sd::LongType xCoords[SD_MAX_RANK];
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sd::LongType yCoords[SD_MAX_RANK];
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sd::LongType zCoords[SD_MAX_RANK];
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sd::LongType xOffset;
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sd::LongType yOffset;
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sd::LongType zOffset;
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INDEX2COORDS(e, xRank,xShape, xCoords);
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COORDS2INDEX(xRank, xStride, xCoords, xOffset);
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INDEX2COORDS(e, yRank, yShape, yCoords);
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COORDS2INDEX(yRank, yStride, yCoords, yOffset);
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INDEX2COORDS(e, zRank, zShape, zCoords);
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COORDS2INDEX(zRank,zStride, zCoords, zOffset);
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z[zOffset] = lambda(e, x[xOffset], y[yOffset]);
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}
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}
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////////////////////////////////////////////////////////////////////////
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template <typename T, typename Lambda>
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static SD_KERNEL void lambdaPairwiseKernel(const void *vx, const sd::LongType *xShapeInfo, const void *vy,
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const sd::LongType *yShapeInfo, void *vz, const sd::LongType *zShapeInfo,
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Lambda lambda) {
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auto x = reinterpret_cast<const T *>(vx);
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auto y = reinterpret_cast<const T *>(vy);
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auto z = reinterpret_cast<T *>(vz);
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auto zLength = length(zShapeInfo);
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auto tid = threadIdx.x + blockIdx.x * blockDim.x;
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__shared__ sd::LongType xRank;
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__shared__ sd::LongType *xShape;
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__shared__ sd::LongType *xStride;
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__shared__ sd::LongType yRank;
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__shared__ sd::LongType *yShape;
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__shared__ sd::LongType *yStride;
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__shared__ sd::LongType zRank;
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__shared__ sd::LongType *zShape;
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__shared__ sd::LongType *zStride;
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if(threadIdx.x == 0) {
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xRank = shape::rank(xShapeInfo);
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xShape = shape::shapeOf(xShapeInfo);
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xStride = shape::stride(xShapeInfo);
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yRank = shape::rank(yShapeInfo);
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yShape = shape::shapeOf(yShapeInfo);
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yStride = shape::stride(yShapeInfo);
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zRank = shape::rank(zShapeInfo);
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zShape = shape::shapeOf(zShapeInfo);
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zStride = shape::stride(zShapeInfo);
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}
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__syncthreads();
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for (sd::LongType e = tid; e < zLength; e += blockDim.x * gridDim.x) {
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sd::LongType xCoords[SD_MAX_RANK];
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sd::LongType yCoords[SD_MAX_RANK];
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sd::LongType zCoords[SD_MAX_RANK];
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sd::LongType xOffset;
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sd::LongType yOffset;
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sd::LongType zOffset;
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INDEX2COORDS(e, xRank, xShape, xCoords);
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COORDS2INDEX(xRank, xStride, xCoords, xOffset);
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INDEX2COORDS(e, yRank, yShape, yCoords);
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COORDS2INDEX(yRank, yStride, yCoords, yOffset);
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INDEX2COORDS(e, zRank, zShape, zCoords);
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COORDS2INDEX(zRank,zStride, zCoords, zOffset);
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z[zOffset] = lambda(x[xOffset], y[yOffset]);
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}
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}
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///////////////////////////////////////////////////////////////////////
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template <typename T, typename Lambda>
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static SD_KERNEL void lambdaPairwiseKernel(const void *scalarPtr, const void *vx, const sd::LongType *xShapeInfo,
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void *vz, const sd::LongType *zShapeInfo, Lambda lambda) {
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auto x = reinterpret_cast<const T *>(vx);
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auto y = reinterpret_cast<const T *>(scalarPtr);
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auto z = reinterpret_cast<T *>(vz);
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auto yVal = *y;
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auto zLength = length(zShapeInfo);
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auto tid = threadIdx.x + blockIdx.x * blockDim.x;
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__shared__ sd::LongType xRank;
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__shared__ sd::LongType *xShape;
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__shared__ sd::LongType *xStride;
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__shared__ sd::LongType yRank;
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__shared__ sd::LongType *yShape;
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__shared__ sd::LongType *yStride;
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__shared__ sd::LongType zRank;
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__shared__ sd::LongType *zShape;
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__shared__ sd::LongType *zStride;
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if(threadIdx.x == 0) {
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xRank = shape::rank(xShapeInfo);
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xShape = shape::shapeOf(xShapeInfo);
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xStride = shape::stride(xShapeInfo);
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yRank = shape::rank(yShapeInfo);
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yShape = shape::shapeOf(yShapeInfo);
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yStride = shape::stride(yShapeInfo);
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zRank = shape::rank(zShapeInfo);
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zShape = shape::shapeOf(zShapeInfo);
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zStride = shape::stride(zShapeInfo);
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}
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__syncthreads();
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for (sd::LongType e = tid; e < zLength; e += blockDim.x * gridDim.x) {
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sd::LongType xCoords[SD_MAX_RANK];
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sd::LongType zCoords[SD_MAX_RANK];
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sd::LongType xOffset;
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sd::LongType zOffset;
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INDEX2COORDS(e,xRank,xShape, xCoords);
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COORDS2INDEX(xRank, xStride, xCoords, xOffset);
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INDEX2COORDS(e, zRank, zShape, zCoords);
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COORDS2INDEX(zRank, zStride, zCoords, zOffset);
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z[zOffset] = lambda(x[xOffset], yVal);
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}
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}
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////////////////////////////////////////////////////////////////////////
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template <typename T, typename Lambda>
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static SD_KERNEL void lambdaTriplewiseKernel(const void *vw, const sd::LongType *wShapeInfo, const void *vx,
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const sd::LongType *xShapeInfo, const void *vy,
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const sd::LongType *yShapeInfo, void *vz, const sd::LongType *zShapeInfo,
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Lambda lambda) {
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auto w = reinterpret_cast<const T *>(vw);
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auto x = reinterpret_cast<const T *>(vx);
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auto y = reinterpret_cast<const T *>(vy);
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auto z = reinterpret_cast<T *>(vz);
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auto zLength = length(zShapeInfo);
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auto tid = threadIdx.x + blockIdx.x * blockDim.x;
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__shared__ sd::LongType xRank;
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__shared__ sd::LongType *xShape;
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__shared__ sd::LongType *xStride;
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__shared__ sd::LongType yRank;
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__shared__ sd::LongType *yShape;
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__shared__ sd::LongType *yStride;
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__shared__ sd::LongType zRank;
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__shared__ sd::LongType *zShape;
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__shared__ sd::LongType *zStride;
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__shared__ sd::LongType wRank;
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__shared__ sd::LongType *wShape;
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__shared__ sd::LongType *wStride;
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if(threadIdx.x == 0) {
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xRank = shape::rank(xShapeInfo);
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xShape = shape::shapeOf(xShapeInfo);
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xStride = shape::stride(xShapeInfo);
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yRank = shape::rank(yShapeInfo);
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yShape = shape::shapeOf(yShapeInfo);
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yStride = shape::stride(yShapeInfo);
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zRank = shape::rank(zShapeInfo);
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zShape = shape::shapeOf(zShapeInfo);
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zStride = shape::stride(zShapeInfo);
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wRank = shape::rank(wShapeInfo);
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wShape = shape::shapeOf(wShapeInfo);
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wStride = shape::stride(wShapeInfo);
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}
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__syncthreads();
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for (sd::LongType e = tid; e < zLength; e += blockDim.x * gridDim.x) {
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sd::LongType wCoords[SD_MAX_RANK];
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sd::LongType xCoords[SD_MAX_RANK];
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sd::LongType yCoords[SD_MAX_RANK];
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sd::LongType zCoords[SD_MAX_RANK];
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sd::LongType wOffset;
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sd::LongType xOffset;
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sd::LongType yOffset;
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sd::LongType zOffset;
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INDEX2COORDS(e, wRank, wShape, wCoords);
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COORDS2INDEX(wRank, wStride, wCoords, wOffset);
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INDEX2COORDS(e, xRank,xShape, xCoords);
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COORDS2INDEX(xRank, xStride, xCoords, xOffset);
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INDEX2COORDS(e, yRank, yStride, yCoords);
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COORDS2INDEX(yRank,yStride, yCoords, yOffset);
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INDEX2COORDS(e, zRank, zShape, zCoords);
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COORDS2INDEX(zRank, zStride, zCoords, zOffset);
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z[zOffset] = lambda(w[wOffset], x[xOffset], y[yOffset]);
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}
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}
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#endif
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//////////////////////////////////////////////////////////////////////////
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template <typename Lambda>
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void NDArray::applyLambda(Lambda func, NDArray *target) {
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auto dtype = this->dataType();
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if (dtype != target->dataType()) THROW_EXCEPTION("NDArray::applyLambda X/Z data types must be the same");
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prepareSpecialUse({&target}, {this});
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BUILD_SINGLE_SELECTOR(
|
|
dtype, LambdaHelper,
|
|
::lambdaLauncher(this->_context->getCudaStream(), this->specialBuffer(), this->specialShapeInfo(),
|
|
target->specialBuffer(), target->specialShapeInfo(), func),
|
|
SD_COMMON_TYPES);
|
|
registerSpecialUse({&target}, {this});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename Lambda>
|
|
void NDArray::applyPairwiseLambda(NDArray *other, Lambda func, NDArray *target) {
|
|
auto dtype = this->dataType();
|
|
|
|
if (dtype != target->dataType() || dtype != other->dataType())
|
|
THROW_EXCEPTION("NDArray::applyPairwiseLambda X/Y/Z data types must be the same");
|
|
bool otherIsScalar = other->isScalar();
|
|
prepareSpecialUse({&target}, {this, &other});
|
|
BUILD_SINGLE_SELECTOR(
|
|
dtype, LambdaHelper,
|
|
::lambdaPairwiseLauncher(this->_context->getCudaStream(), this->specialBuffer(), this->specialShapeInfo(), otherIsScalar,
|
|
other->specialBuffer(), other->specialShapeInfo(), target->specialBuffer(),
|
|
target->specialShapeInfo(), func),
|
|
SD_COMMON_TYPES);
|
|
registerSpecialUse({&target}, {this, &other});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename Lambda>
|
|
void NDArray::applyIndexedLambda(Lambda func, NDArray *target) {
|
|
auto dtype = this->dataType();
|
|
if (dtype != target->dataType())
|
|
THROW_EXCEPTION("NDArray::applyIndexedLambda X/Z data types must be the same");
|
|
|
|
prepareSpecialUse({&target}, {this});
|
|
BUILD_SINGLE_SELECTOR(
|
|
dtype, LambdaHelper,
|
|
::lambdaIndexedLauncher(this->_context->getCudaStream(), this->specialBuffer(), this->specialShapeInfo(),
|
|
target->specialBuffer(), target->specialShapeInfo(), func),
|
|
SD_COMMON_TYPES);
|
|
registerSpecialUse({&target}, {this});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename Lambda>
|
|
void NDArray::applyIndexedPairwiseLambda(NDArray *other, Lambda func, NDArray *target) {
|
|
auto dtype = this->dataType();
|
|
if (dtype != target->dataType() || dtype != other->dataType())
|
|
THROW_EXCEPTION("NDArray::applyIndexedPairwiseLambda X/Y/Z data types must be the same");
|
|
|
|
prepareSpecialUse({&target}, {this, &other});
|
|
BUILD_SINGLE_SELECTOR(
|
|
dtype, LambdaHelper,
|
|
::lambdaIndexedPairwiseLauncher(this->_context->getCudaStream(), this->specialBuffer(), this->specialShapeInfo(),
|
|
other->specialBuffer(), other->specialShapeInfo(), target->specialBuffer(),
|
|
target->specialShapeInfo(), func),
|
|
SD_COMMON_TYPES);
|
|
registerSpecialUse({&target}, {this, &other});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename Lambda>
|
|
void NDArray::applyTriplewiseLambda(NDArray *second, NDArray *third, Lambda func, NDArray *target) {
|
|
auto dtype = this->dataType();
|
|
|
|
if (dtype != target->dataType() || dtype != second.dataType() || dtype != third.dataType())
|
|
THROW_EXCEPTION("NDArray::applyTriplewiseLambda X/Y/Z data types must be the same");
|
|
|
|
prepareSpecialUse({&target}, {this, &second, &third});
|
|
BUILD_SINGLE_SELECTOR(
|
|
dtype, LambdaHelper,
|
|
::lambdaTriplewiseLauncher(this->_context->getCudaStream(), this->specialBuffer(), this->specialShapeInfo(),
|
|
second.specialBuffer(), second.specialShapeInfo(), third.specialBuffer(),
|
|
third.specialShapeInfo(), target->specialBuffer(), target->specialShapeInfo(), func),
|
|
SD_COMMON_TYPES);
|
|
registerSpecialUse({&target}, {this, &second, &third});
|
|
}
|