297 lines
14 KiB
Plaintext
297 lines
14 KiB
Plaintext
/* ******************************************************************************
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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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//
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// @author raver119@gmail.com
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//
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#include <system/op_boilerplate.h>
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#include <loops/broadcasting_int.h>
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#include <loops/legacy_ops.h>
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#include <types/types.h>
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#include <system/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 <helpers/StringUtils.h>
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using namespace simdOps;
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//////////////////////////////////////////////////////////////////////////
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template<typename X, typename OpClass>
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static __global__ void broadcastIntSimple(
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void const* x,
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Nd4jLong const* xShapeInfo,
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void const* y,
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Nd4jLong const* yShapeInfo,
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void *z,
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Nd4jLong const* zShapeInfo,
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int *dimension,
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int dimensionLength, Nd4jLong const* tadOnlyShapeInfo, Nd4jLong const* tadOffsets, Nd4jLong const* tadOnlyShapeInfoZ, Nd4jLong const* tadOffsetsZ) {
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functions::broadcast::BroadcastInt<X>::template transformCuda<OpClass>(x,xShapeInfo,y,yShapeInfo,z,zShapeInfo,dimension,dimensionLength,tadOnlyShapeInfo,tadOffsets,tadOnlyShapeInfoZ,tadOffsetsZ);
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename X, typename OpClass>
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static __global__ void broadcastIntSimple(const void *x, const Nd4jLong const* xShapeInfo,
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const void *y, const Nd4jLong const* yShapeInfo,
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void *z, const Nd4jLong const* zShapeInfo) {
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functions::broadcast::BroadcastInt<X>::template transformCuda<OpClass>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo);
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename X, typename OpClass>
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static __global__ void broadcastBoolInverseSimple(
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void const* x,
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Nd4jLong const* xShapeInfo,
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void const* y,
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Nd4jLong const* yShapeInfo,
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void *z,
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Nd4jLong const* zShapeInfo,
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int *dimension,
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int dimensionLength, Nd4jLong const* tadOnlyShapeInfo, Nd4jLong const* tadOffsets, Nd4jLong const* tadOnlyShapeInfoZ, Nd4jLong const* tadOffsetsZ) {
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functions::broadcast::BroadcastInt<X>::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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//////////////////////////////////////////////////////////////////////////
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template<typename X>
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template <typename OpClass>
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__host__ void BroadcastInt<X>::intermediateBroadcast(dim3 launchDims, cudaStream_t *stream, void const* x, Nd4jLong const* xShapeInfo, void const* y, Nd4jLong const* yShapeInfo, void *z, Nd4jLong const* zShapeInfo, int *dimension, int dimensionLength, Nd4jLong const* tadOnlyShapeInfo, Nd4jLong const* tadOffsets, Nd4jLong const* tadOnlyShapeInfoZ, Nd4jLong const* tadOffsetsZ) {
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broadcastIntSimple<X, 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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//////////////////////////////////////////////////////////////////////////
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template<typename X>
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template <typename OpClass>
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__host__ void BroadcastInt<X>::intermediateBroadcast(dim3 launchDims, cudaStream_t *stream,
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const void *x, const Nd4jLong *xShapeInfo,
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const void *y, const Nd4jLong *yShapeInfo,
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void *z, const Nd4jLong *zShapeInfo) {
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broadcastIntSimple<X, OpClass><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo);
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename X>
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__host__ void BroadcastInt<X>::execBroadcast(dim3 launchDims, cudaStream_t *stream, int opNum, void const* x, Nd4jLong const* xShapeInfo, void const* y, Nd4jLong const* yShapeInfo, void *z, Nd4jLong const* zShapeInfo, int *dimension, int dimensionLength, Nd4jLong const* tadOnlyShapeInfo, Nd4jLong const* tadOffsets, Nd4jLong const* tadOnlyShapeInfoZ, Nd4jLong const* tadOffsetsZ) {
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DISPATCH_BY_OPNUM_T(intermediateBroadcast, PARAMS(launchDims, stream, x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ), OPS_A(BROADCAST_INT_OPS))
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename X>
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__host__ void BroadcastInt<X>::execBroadcast(dim3 launchDims, cudaStream_t *stream, const int opNum,
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const void *x, const Nd4jLong const* xShapeInfo,
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const void *y, const Nd4jLong const* yShapeInfo,
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void *z, const Nd4jLong const* zShapeInfo) {
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DISPATCH_BY_OPNUM_T(intermediateBroadcast, PARAMS(launchDims, stream, x, xShapeInfo, y, yShapeInfo, z, zShapeInfo), OPS_A(BROADCAST_INT_OPS))
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename X>
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template <typename OpClass>
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__host__ void BroadcastInt<X>::intermediateInverseBroadcast(dim3 launchDims, cudaStream_t *stream, void const* x, Nd4jLong const* xShapeInfo, void const* y, Nd4jLong const* yShapeInfo, void *z, Nd4jLong const* zShapeInfo, int *dimension, int dimensionLength, Nd4jLong const* tadOnlyShapeInfo, Nd4jLong const* tadOffsets, Nd4jLong const* tadOnlyShapeInfoZ, Nd4jLong const* tadOffsetsZ) {
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broadcastBoolInverseSimple<X, 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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//////////////////////////////////////////////////////////////////////////
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template<typename X>
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__host__ void BroadcastInt<X>::execInverseBroadcast(dim3 launchDims, cudaStream_t *stream, int opNum, void const* x, Nd4jLong const* xShapeInfo, void const* y, Nd4jLong const* yShapeInfo, void *z, Nd4jLong const* zShapeInfo, int *dimension, int dimensionLength, Nd4jLong const* tadOnlyShapeInfo, Nd4jLong const* tadOffsets, Nd4jLong const* tadOnlyShapeInfoZ, Nd4jLong const* tadOffsetsZ) {
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DISPATCH_BY_OPNUM_T(intermediateInverseBroadcast, PARAMS(launchDims, stream, x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ), OPS_A(BROADCAST_INT_OPS))
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename X>
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template <typename OpType>
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__device__ void BroadcastInt<X>::transformInverseCuda(
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void const* vx, Nd4jLong const* xShapeInfo,
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void const* vy, Nd4jLong const* yShapeInfo,
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void *vz, Nd4jLong const* zShapeInfo,
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int *dimension, int dimensionLength,
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Nd4jLong const* tadOnlyShapeInfo, Nd4jLong const* tadOffsets, Nd4jLong const* tadOnlyShapeInfoZ, Nd4jLong const* 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 const*>(vx);
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auto y = reinterpret_cast<X const*>(vy);
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auto z = reinterpret_cast<X*>(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 = shape::length(tadOnlyShapeInfo);//shape::tadLength(xShapeInfo, dimension, dimensionLength);
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tadEWS = shape::elementWiseStride(tadOnlyShapeInfo);
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numTads = shape::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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for (int r = blockIdx.x; r < numTads; r += gridDim.x) {
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auto rZ = z + tadOffsetsZ[r];
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auto rY = y + tadOffsets[r];
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if(tadEWS > 0 && zEWS > 0 && xEWS > 0 && dimensionLength == 1) {
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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 = shape::getIndexOffset(i, xShapeInfo);
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auto yOffset = shape::getIndexOffset(i, tadOnlyShapeInfo);
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auto zOffset = shape::getIndexOffset(i, tadOnlyShapeInfoZ);
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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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//////////////////////////////////////////////////////////////////////////
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template<typename X>
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template <typename OpType>
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__device__ void BroadcastInt<X>::transformCuda(
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void const* vx, Nd4jLong const* xShapeInfo,
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void const* vy, Nd4jLong const* yShapeInfo,
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void *vz, Nd4jLong const* zShapeInfo,
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int *dimension, int dimensionLength,
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Nd4jLong const* tadOnlyShapeInfo, Nd4jLong const* tadOffsets, Nd4jLong const* tadOnlyShapeInfoZ, Nd4jLong const* 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 const*>(vx);
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auto y = reinterpret_cast<X const*>(vy);
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auto z = reinterpret_cast<X*>(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 = shape::length(tadOnlyShapeInfo);//shape::tadLength(xShapeInfo, dimension, dimensionLength);
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tadEWS = shape::elementWiseStride(tadOnlyShapeInfo);
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numTads = shape::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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__shared__ X *rZ;
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__shared__ X const* rX;
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for (int r = blockIdx.x; r < numTads; r += gridDim.x) {
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if (threadIdx.x == 0) {
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rZ = z + tadOffsetsZ[r];
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rX = x + tadOffsets[r];
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}
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__syncthreads();
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if(tadEWS > 0 && zEWS > 0 && yEWS > 0 && dimensionLength == 1) {
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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 = shape::getIndexOffset(i, tadOnlyShapeInfo);
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auto yOffset = shape::getIndexOffset(i, yShapeInfo);
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auto zOffset = shape::getIndexOffset(i, tadOnlyShapeInfoZ);
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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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template<typename X>
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template <typename OpType>
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__device__ void BroadcastInt<X>::transformCuda(const void *vx, const Nd4jLong const* xShapeInfo,
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const void *vy, const Nd4jLong const* yShapeInfo,
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void *vz, const Nd4jLong const* zShapeInfo) {
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const X* x = reinterpret_cast<const X*>(vx);
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const X* y = reinterpret_cast<const X*>(vy);
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X* z = reinterpret_cast<X*>(vz);
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__shared__ Nd4jLong zLen;
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__shared__ int rank;
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__shared__ bool xzSameOffsets, yzSameOffsets;
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if (threadIdx.x == 0) {
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zLen = shape::length(zShapeInfo);
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rank = shape::rank(zShapeInfo);
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xzSameOffsets = shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo);
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yzSameOffsets = shape::haveSameShapeAndStrides(yShapeInfo, zShapeInfo);
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}
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__syncthreads();
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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int coords[MAX_RANK];
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for (int i = tid; i < zLen; i += blockDim.x * gridDim.x) {
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shape::index2coords(i, zShapeInfo, coords);
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const auto zOffset = shape::getOffset(zShapeInfo, coords);
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const auto xOffset = xzSameOffsets ? zOffset : shape::getOffset(xShapeInfo, coords);
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const auto yOffset = yzSameOffsets ? zOffset : shape::getOffset(yShapeInfo, coords);
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z[zOffset] = OpType::op(x[xOffset], y[yOffset]);
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}
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}
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BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT BroadcastInt, , INTEGER_TYPES);
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}
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} |