244 lines
12 KiB
Plaintext
244 lines
12 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 raver119@gmail.com
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//
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#include <op_boilerplate.h>
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#include <loops/broadcasting.h>
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#include <loops/legacy_ops.h>
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#include <types/types.h>
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#include <Environment.h>
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#include <cuda.h>
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#include <cuda_runtime.h>
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#include <string>
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#include <stdexcept>
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#include <StringUtils.h>
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#include <specials_cuda.h>
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using namespace simdOps;
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template<typename X, typename Y, typename Z, typename OpClass>
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static __global__ void broadcastSimple(
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void *x,
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Nd4jLong *xShapeInfo,
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void *y,
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Nd4jLong *yShapeInfo,
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void *z,
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Nd4jLong *zShapeInfo,
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int *dimension,
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int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) {
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functions::broadcast::Broadcast<X,Y,Z>::template transformCuda<OpClass>(x,xShapeInfo,y,yShapeInfo,z,zShapeInfo,dimension,dimensionLength,tadOnlyShapeInfo,tadOffsets,tadOnlyShapeInfoZ,tadOffsetsZ);
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}
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template<typename X, typename Y, typename Z, typename OpClass>
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static __global__ void broadcastInverseSimple(
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void *x,
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Nd4jLong *xShapeInfo,
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void *y,
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Nd4jLong *yShapeInfo,
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void *z,
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Nd4jLong *zShapeInfo,
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int *dimension,
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int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) {
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functions::broadcast::Broadcast<X,Y,Z>::template transformInverseCuda<OpClass>(x,xShapeInfo,y,yShapeInfo,z,zShapeInfo,dimension,dimensionLength,tadOnlyShapeInfo,tadOffsets,tadOnlyShapeInfoZ,tadOffsetsZ);
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}
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namespace functions {
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namespace broadcast {
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static Nd4jLong __device__ __noinline__ _getIndexOffset(Nd4jLong index, Nd4jLong *shapeInfo, Nd4jLong length) {
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return shape::getIndexOffset(index, shapeInfo, length);
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}
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static Nd4jLong __device__ __noinline__ _length(Nd4jLong *shapeInfo) {
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return shape::length(shapeInfo);
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}
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template<typename X, typename Y, typename Z>
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template <typename OpClass>
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__host__ void Broadcast<X,Y,Z>::intermediateBroadcast(dim3 launchDims, cudaStream_t *stream, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *z, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) {
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broadcastSimple<X, Y, Z, OpClass><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ);
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}
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template<typename X, typename Y, typename Z>
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__host__ void Broadcast<X,Y,Z>::execBroadcast(dim3 launchDims, cudaStream_t *stream, int opNum, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *z, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) {
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DISPATCH_BY_OPNUM_TTT(intermediateBroadcast, PARAMS(launchDims, stream, x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ), OPS_A(BROADCAST_OPS))
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DEBUG_KERNEL(stream, opNum);
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}
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template<typename X, typename Y, typename Z>
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template <typename OpClass>
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__host__ void Broadcast<X,Y,Z>::intermediateInverseBroadcast(dim3 launchDims, cudaStream_t *stream, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *z, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) {
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broadcastInverseSimple<X, Y, Z, OpClass><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ);
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}
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template<typename X, typename Y, typename Z>
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__host__ void Broadcast<X,Y,Z>::execInverseBroadcast(dim3 launchDims, cudaStream_t *stream, int opNum, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *z, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) {
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DISPATCH_BY_OPNUM_TTT(intermediateInverseBroadcast, PARAMS(launchDims, stream, x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ), OPS_A(BROADCAST_OPS))
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DEBUG_KERNEL(stream, opNum);
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}
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template<typename X, typename Y, typename Z>
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template <typename OpType>
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__device__ void Broadcast<X,Y,Z>::transformInverseCuda(
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void *vx, Nd4jLong *xShapeInfo,
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void *vy, Nd4jLong *yShapeInfo,
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void *vz, Nd4jLong *zShapeInfo,
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int *dimension, int dimensionLength,
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Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) {
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if (tadOnlyShapeInfoZ == nullptr) {
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tadOnlyShapeInfoZ = tadOnlyShapeInfo;
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tadOffsetsZ = tadOffsets;
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}
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auto x = reinterpret_cast<X*>(vx);
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auto y = reinterpret_cast<Y*>(vy);
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auto z = reinterpret_cast<Z*>(vz);
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//decompose in to several sub tads after
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//moving all dimensions (in sorted order)
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//to the back.
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//permuted version of the x shape info for setting up the tad problem
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__shared__ Nd4jLong tadLength;
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__shared__ Nd4jLong tadEWS;
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__shared__ int numTads;
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__shared__ Nd4jLong xEWS;
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__shared__ Nd4jLong zEWS;
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if (threadIdx.x == 0) {
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tadLength = _length(tadOnlyShapeInfo);
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tadEWS = shape::elementWiseStride(tadOnlyShapeInfo);
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numTads = _length(yShapeInfo) / tadLength;
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xEWS = shape::elementWiseStride(xShapeInfo);
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zEWS = shape::elementWiseStride(tadOnlyShapeInfoZ);
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}
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__syncthreads();
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auto xOrder = shape::order(xShapeInfo);
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auto yOrder = shape::order(tadOnlyShapeInfo);
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auto zOrder = shape::order(tadOnlyShapeInfoZ);
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for (int r = blockIdx.x; r < numTads; r += gridDim.x) {
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auto rY = y + tadOffsets[r];
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auto rZ = z + tadOffsetsZ[r];
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if(tadEWS > 0 && zEWS > 0 && xEWS > 0 && dimensionLength == 1 && xOrder == yOrder && xOrder == zOrder) {
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for (int i = threadIdx.x; i < tadLength; i+= blockDim.x)
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rZ[i * zEWS] = OpType::op(x[i * xEWS], rY[i * tadEWS]);
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}
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else {
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// it is expected that x and z tads and y array all have the same length
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for (Nd4jLong i = threadIdx.x; i < tadLength; i+= blockDim.x) {
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auto xOffset = _getIndexOffset(i, xShapeInfo, tadLength);
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auto yOffset = _getIndexOffset(i, tadOnlyShapeInfo, tadLength);
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auto zOffset = _getIndexOffset(i, tadOnlyShapeInfoZ, tadLength);
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rZ[zOffset] = OpType::op(x[xOffset], rY[yOffset]);
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}
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}
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}
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}
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template<typename X, typename Y, typename Z>
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template <typename OpType>
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__device__ void Broadcast<X,Y,Z>::transformCuda(
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void *vx, Nd4jLong *xShapeInfo,
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void *vy, Nd4jLong *yShapeInfo,
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void *vz, Nd4jLong *zShapeInfo,
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int *dimension, int dimensionLength,
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Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) {
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if (tadOnlyShapeInfoZ == nullptr) {
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tadOnlyShapeInfoZ = tadOnlyShapeInfo;
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tadOffsetsZ = tadOffsets;
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}
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auto x = reinterpret_cast<X*>(vx);
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auto y = reinterpret_cast<Y*>(vy);
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auto z = reinterpret_cast<Z*>(vz);
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//decompose in to several sub tads after
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//moving all dimensions (in sorted order)
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//to the back.
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//permuted version of the x shape info for setting up the tad problem
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__shared__ Nd4jLong tadLength;
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__shared__ Nd4jLong tadEWS;
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__shared__ int numTads;
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__shared__ Nd4jLong yEWS;
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__shared__ Nd4jLong zEWS;
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if (threadIdx.x == 0) {
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tadLength = _length(tadOnlyShapeInfo);
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tadEWS = shape::elementWiseStride(tadOnlyShapeInfo);
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numTads = _length(xShapeInfo) / tadLength;
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yEWS = shape::elementWiseStride(yShapeInfo);
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zEWS = shape::elementWiseStride(tadOnlyShapeInfoZ);
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}
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__syncthreads();
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auto xOrder = shape::order(tadOnlyShapeInfo);
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auto yOrder = shape::order(yShapeInfo);
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auto zOrder = shape::order(tadOnlyShapeInfoZ);
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for (int r = blockIdx.x; r < numTads; r += gridDim.x) {
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auto rX = x + tadOffsets[r];
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auto rZ = z + tadOffsetsZ[r];
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if(tadEWS > 0 && zEWS > 0 && yEWS > 0 && xOrder == yOrder && xOrder == zOrder) {
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for (int i = threadIdx.x; i < tadLength; i+= blockDim.x)
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rZ[i * zEWS] = OpType::op(rX[i * tadEWS], y[i * yEWS]);
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}
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else {
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// it is expected that x and z tads and y array all have the same length
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for (Nd4jLong i = threadIdx.x; i < tadLength; i+= blockDim.x) {
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auto xOffset = _getIndexOffset(i, tadOnlyShapeInfo, tadLength);
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auto yOffset = _getIndexOffset(i, yShapeInfo, tadLength);
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auto zOffset = _getIndexOffset(i, tadOnlyShapeInfoZ, tadLength);
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rZ[zOffset] = OpType::op(rX[xOffset], y[yOffset]);
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}
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}
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}
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}
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/*
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BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_0);
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BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_1);
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BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_2);
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BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_3);
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BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_4);
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BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_5);
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BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_6);
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BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_7);
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BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_8);
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BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_9);
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*/
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}
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} |