325 lines
15 KiB
C++
325 lines
15 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 <system/pointercast.h>
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#include <system/op_boilerplate.h>
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#include <helpers/shape.h>
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#include <cuda.h>
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#include <cuda_runtime.h>
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static Nd4jLong __device__ __noinline__ getIndexOffset(Nd4jLong index, const Nd4jLong *shapeInfo) {
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return shape::getIndexOffset(index, shapeInfo);
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}
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static Nd4jLong __device__ __noinline__ length(const Nd4jLong *shapeInfo) {
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return shape::length(shapeInfo);
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}
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template <typename T, typename Lambda> static _CUDA_G void lambdaKernel(const void* vx, const Nd4jLong *xShapeInfo, void *vz, const Nd4jLong *zShapeInfo, Lambda lambda);
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template <typename T, typename Lambda> static _CUDA_G void lambdaIndexedKernel(const void* vx, const Nd4jLong *xShapeInfo, void *vz, const Nd4jLong *zShapeInfo, Lambda lambda);
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template <typename T, typename Lambda> static _CUDA_G void lambdaIndexedPairwiseKernel(const void* vx, const Nd4jLong *xShapeInfo, const void* vy, const Nd4jLong *yShapeInfo, void *vz, const Nd4jLong *zShapeInfo, Lambda lambda);
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template <typename T, typename Lambda> static _CUDA_G void lambdaPairwiseKernel(const void* vx, const Nd4jLong *xShapeInfo, const void* vy, const Nd4jLong *yShapeInfo, void *vz, const Nd4jLong *zShapeInfo, Lambda lambda);
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template <typename T, typename Lambda> static _CUDA_G void lambdaTriplewiseKernel(const void* vw, const Nd4jLong *wShapeInfo, const void* vx, const Nd4jLong *xShapeInfo, const void* vy, const Nd4jLong *yShapeInfo, void *vz, const Nd4jLong *zShapeInfo, 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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FORCEINLINE static void lambdaLauncher(cudaStream_t *stream, const void* vx, const Nd4jLong *xShapeInfo, void *vz, const Nd4jLong *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)
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throw std::runtime_error("NDArray::applyLambda execution failed");
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}
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template <typename Lambda>
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FORCEINLINE static void lambdaIndexedLauncher(cudaStream_t *stream, const void* vx, const Nd4jLong *xShapeInfo, void *vz, const Nd4jLong *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)
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throw std::runtime_error("NDArray::applyIndexedLambda execution failed");
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}
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template <typename Lambda>
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FORCEINLINE static void lambdaPairwiseLauncher(cudaStream_t *stream, const void* vx, const Nd4jLong *xShapeInfo, const void* vy, const Nd4jLong *yShapeInfo, void *vz, const Nd4jLong *zShapeInfo, Lambda lambda) {
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lambdaPairwiseKernel<T, Lambda><<<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)
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throw std::runtime_error("NDArray::applyPairwiseLambda execution failed");
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}
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template <typename Lambda>
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FORCEINLINE static void lambdaIndexedPairwiseLauncher(cudaStream_t *stream, const void* vx, const Nd4jLong *xShapeInfo, const void* vy, const Nd4jLong *yShapeInfo, void *vz, const Nd4jLong *zShapeInfo, Lambda lambda) {
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lambdaIndexedPairwiseKernel<T, Lambda><<<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)
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throw std::runtime_error("NDArray::applyIndexedPairwiseLambda execution failed");
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}
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template <typename Lambda>
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FORCEINLINE static void lambdaTriplewiseLauncher(cudaStream_t *stream,const void* vw, const Nd4jLong *wShapeInfo, const void* vx, const Nd4jLong *xShapeInfo, const void* vy, const Nd4jLong *yShapeInfo, void *vz, const Nd4jLong *zShapeInfo, Lambda lambda) {
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lambdaTriplewiseKernel<T, Lambda><<<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)
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throw std::runtime_error("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 _CUDA_G void lambdaKernel(const void* vx, const Nd4jLong *xShapeInfo, void *vz, const Nd4jLong *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 xEws = shape::elementWiseStride(xShapeInfo);
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auto zEws = shape::elementWiseStride(zShapeInfo);
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auto xOrder = shape::order(xShapeInfo);
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auto zOrder = shape::order(zShapeInfo);
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auto zLength = length(zShapeInfo);
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auto tid = threadIdx.x + blockIdx.x * blockDim.x;
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if (xEws >= 1 && zEws >= 1 && xOrder == zOrder) {
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for (uint e = tid; e < zLength; e += blockDim.x * gridDim.x)
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z[e * zEws] = lambda(x[e * xEws]);
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} else {
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for (uint e = tid; e < zLength; e += blockDim.x * gridDim.x) {
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auto xOffset = getIndexOffset(e, xShapeInfo);
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auto zOffset = getIndexOffset(e, zShapeInfo);
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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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////////////////////////////////////////////////////////////////////////
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template <typename T, typename Lambda>
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static _CUDA_G void lambdaIndexedKernel(const void* vx, const Nd4jLong *xShapeInfo, void *vz, const Nd4jLong *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 xEws = shape::elementWiseStride(xShapeInfo);
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auto zEws = shape::elementWiseStride(zShapeInfo);
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auto xOrder = shape::order(xShapeInfo);
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auto zOrder = shape::order(zShapeInfo);
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auto zLength = length(zShapeInfo);
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auto tid = threadIdx.x + blockIdx.x * blockDim.x;
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if (xEws >= 1 && zEws >= 1 && xOrder == zOrder) {
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for (uint e = tid; e < zLength; e += blockDim.x * gridDim.x)
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z[e * zEws] = lambda(e, x[e * xEws]);
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} else {
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for (uint e = tid; e < zLength; e += blockDim.x * gridDim.x) {
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auto xOffset = getIndexOffset(e, xShapeInfo);
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auto zOffset = getIndexOffset(e, zShapeInfo);
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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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////////////////////////////////////////////////////////////////////////
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template <typename T, typename Lambda>
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static _CUDA_G void lambdaIndexedPairwiseKernel(const void* vx, const Nd4jLong *xShapeInfo, const void* vy, const Nd4jLong *yShapeInfo, void *vz, const Nd4jLong *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 xEws = shape::elementWiseStride(xShapeInfo);
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auto yEws = shape::elementWiseStride(yShapeInfo);
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auto zEws = shape::elementWiseStride(zShapeInfo);
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auto xOrder = shape::order(xShapeInfo);
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auto yOrder = shape::order(yShapeInfo);
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auto zOrder = shape::order(zShapeInfo);
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auto zLength = length(zShapeInfo);
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auto tid = threadIdx.x + blockIdx.x * blockDim.x;
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if (xEws >= 1 && yEws >= 1 && zEws >= 1 && xOrder == zOrder && yOrder == xOrder) {
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for (uint e = tid; e < zLength; e += blockDim.x * gridDim.x)
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z[e * zEws] = lambda(e, x[e * xEws], y[e * yEws]);
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} else {
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for (uint e = tid; e < zLength; e += blockDim.x * gridDim.x) {
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auto xOffset = getIndexOffset(e, xShapeInfo);
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auto yOffset = getIndexOffset(e, yShapeInfo);
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auto zOffset = getIndexOffset(e, zShapeInfo);
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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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////////////////////////////////////////////////////////////////////////
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template <typename T, typename Lambda>
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static _CUDA_G void lambdaPairwiseKernel(const void* vx, const Nd4jLong *xShapeInfo, const void* vy, const Nd4jLong *yShapeInfo, void *vz, const Nd4jLong *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 xEws = shape::elementWiseStride(xShapeInfo);
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auto yEws = shape::elementWiseStride(yShapeInfo);
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auto zEws = shape::elementWiseStride(zShapeInfo);
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auto xOrder = shape::order(xShapeInfo);
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auto yOrder = shape::order(yShapeInfo);
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auto zOrder = shape::order(zShapeInfo);
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auto zLength = length(zShapeInfo);
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auto tid = threadIdx.x + blockIdx.x * blockDim.x;
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if (xEws >= 1 && yEws >= 1 && zEws >= 1 && xOrder == zOrder && yOrder == xOrder) {
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for (uint e = tid; e < zLength; e += blockDim.x * gridDim.x)
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z[e * zEws] = lambda(x[e * xEws], y[e * yEws]);
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} else {
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for (uint e = tid; e < zLength; e += blockDim.x * gridDim.x) {
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auto xOffset = getIndexOffset(e, xShapeInfo);
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auto yOffset = getIndexOffset(e, yShapeInfo);
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auto zOffset = getIndexOffset(e, zShapeInfo);
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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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////////////////////////////////////////////////////////////////////////
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template <typename T, typename Lambda>
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static _CUDA_G void lambdaTriplewiseKernel(const void* vw, const Nd4jLong *wShapeInfo, const void* vx, const Nd4jLong *xShapeInfo, const void* vy, const Nd4jLong *yShapeInfo, void *vz, const Nd4jLong *zShapeInfo, 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 wEws = shape::elementWiseStride(wShapeInfo);
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auto xEws = shape::elementWiseStride(xShapeInfo);
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auto yEws = shape::elementWiseStride(yShapeInfo);
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auto zEws = shape::elementWiseStride(zShapeInfo);
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auto wOrder = shape::order(wShapeInfo);
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auto xOrder = shape::order(xShapeInfo);
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auto yOrder = shape::order(yShapeInfo);
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auto zOrder = shape::order(zShapeInfo);
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auto zLength = length(zShapeInfo);
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auto tid = threadIdx.x + blockIdx.x * blockDim.x;
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if (wEws > 1 && xEws >= 1 && yEws >= 1 && zEws >= 1 && xOrder == zOrder && yOrder == xOrder && wOrder == xOrder) {
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for (uint e = tid; e < zLength; e += blockDim.x * gridDim.x)
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z[e * zEws] = lambda(w[e * wEws], x[e * xEws], y[e * yEws]);
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} else {
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for (uint e = tid; e < zLength; e += blockDim.x * gridDim.x) {
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auto wOffset = getIndexOffset(e, wShapeInfo);
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auto xOffset = getIndexOffset(e, xShapeInfo);
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auto yOffset = getIndexOffset(e, yShapeInfo);
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auto zOffset = getIndexOffset(e, zShapeInfo);
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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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}
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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())
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throw std::runtime_error("NDArray::applyLambda X/Z data types must be the same");
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//throw datatype_exception::build("NDArray::applyLambda X/Z data types must be the same", dtype, target.dataType());
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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), LIBND4J_TYPES);
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registerSpecialUse({&target}, {this});
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename Lambda>
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void NDArray::applyPairwiseLambda(const NDArray& other, Lambda func, NDArray& target) {
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auto dtype = this->dataType();
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if (dtype != target.dataType() || dtype != other.dataType())
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throw std::runtime_error("NDArray::applyPairwiseLambda X/Y/Z data types must be the same");
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//throw datatype_exception::build("NDArray::applyLambda X/Z data types must be the same", dtype, target.dataType());
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prepareSpecialUse({&target}, {this, &other});
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BUILD_SINGLE_SELECTOR(dtype, LambdaHelper ,::lambdaPairwiseLauncher(this->_context->getCudaStream(), this->specialBuffer(), this->specialShapeInfo(), other.specialBuffer(), other.specialShapeInfo(), target.specialBuffer(), target.specialShapeInfo(), func), LIBND4J_TYPES);
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registerSpecialUse({&target}, {this, &other});
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename Lambda>
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void NDArray::applyIndexedLambda(Lambda func, NDArray& target) {
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auto dtype = this->dataType();
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if (dtype != target.dataType())
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throw std::runtime_error("NDArray::applyIndexedLambda X/Z data types must be the same");
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prepareSpecialUse({&target}, {this});
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BUILD_SINGLE_SELECTOR(dtype, LambdaHelper ,::lambdaIndexedLauncher(this->_context->getCudaStream(), this->specialBuffer(), this->specialShapeInfo(), target.specialBuffer(), target.specialShapeInfo(), func), LIBND4J_TYPES);
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registerSpecialUse({&target}, {this});
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename Lambda>
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void NDArray::applyIndexedPairwiseLambda(NDArray& other, Lambda func, NDArray& target) {
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auto dtype = this->dataType();
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if (dtype != target.dataType() || dtype != other.dataType())
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throw std::runtime_error("NDArray::applyIndexedPairwiseLambda X/Y/Z data types must be the same");
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prepareSpecialUse({&target}, {this, &other});
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BUILD_SINGLE_SELECTOR(dtype, LambdaHelper ,::lambdaIndexedPairwiseLauncher(this->_context->getCudaStream(), this->specialBuffer(), this->specialShapeInfo(), other.specialBuffer(), other.specialShapeInfo(), target.specialBuffer(), target.specialShapeInfo(), func), LIBND4J_TYPES);
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registerSpecialUse({&target}, {this, &other});
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename Lambda>
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void NDArray::applyTriplewiseLambda(NDArray& second, NDArray& third, Lambda func, NDArray& target) {
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auto dtype = this->dataType();
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if (dtype != target.dataType() || dtype != second.dataType() || dtype != third.dataType())
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throw std::runtime_error("NDArray::applyTriplewiseLambda X/Y/Z data types must be the same");
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prepareSpecialUse({&target}, {this, &second, &third});
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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), LIBND4J_TYPES);
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registerSpecialUse({&target}, {this, &second, &third});
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}
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