448 lines
22 KiB
Plaintext
448 lines
22 KiB
Plaintext
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/*******************************************************************************
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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/random.h>
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#include <dll.h>
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#include <cuda.h>
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#include <cuda_runtime.h>
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#include <helpers/DebugHelper.h>
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#include <specials_cuda.h>
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using namespace randomOps;
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template <typename T, typename OpClass>
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static inline __device__ void randomSingleGeneric(
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Nd4jPointer state,
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void *z,
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Nd4jLong *zShapeBuffer,
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void *extraArguments) {
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functions::random::RandomFunction<T>::template execTransformCuda<OpClass>(
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state,
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z,
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zShapeBuffer,
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extraArguments);
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}
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template <typename T, typename OpClass>
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static inline __device__ void randomDoubleGeneric(
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Nd4jPointer state,
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void *x,
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Nd4jLong *xShapeBuffer,
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void *z,
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Nd4jLong *zShapeBuffer,
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void *extraArguments) {
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functions::random::RandomFunction<T>::template execTransformCuda<OpClass>(
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state,
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x,
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xShapeBuffer,
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z,
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zShapeBuffer,
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extraArguments);
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}
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template <typename T, typename OpClass>
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static inline __device__ void randomTripleGeneric(
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Nd4jPointer state,
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void *x,
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Nd4jLong *xShapeBuffer,
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void *y,
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Nd4jLong *yShapeBuffer,
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void *z,
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Nd4jLong *zShapeBuffer,
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void *extraArguments) {
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functions::random::RandomFunction<T>::template execTransformCuda<OpClass>(
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state,
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x,
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xShapeBuffer,
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y,
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yShapeBuffer,
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z,
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zShapeBuffer,
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extraArguments);
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}
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#ifndef __CLION_IDE__
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// here we generate kernels for target operations
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DISPATCH_KERNEL_SIMPLE(randomSingle_, randomSingleGeneric, float, INPUT(Nd4jPointer state, void *z, Nd4jLong *zShapeBuffer, void *extraArguments), PARAMS(state, z, zShapeBuffer, extraArguments), OPS_A(RANDOM_OPS))
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DISPATCH_KERNEL_SIMPLE(randomSingle_, randomSingleGeneric, double, INPUT(Nd4jPointer state, void *z, Nd4jLong *zShapeBuffer, void *extraArguments), PARAMS(state, z, zShapeBuffer, extraArguments), OPS_A(RANDOM_OPS))
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DISPATCH_KERNEL_SIMPLE(randomSingle_, randomSingleGeneric, float16, INPUT(Nd4jPointer state, void *z, Nd4jLong *zShapeBuffer, void *extraArguments), PARAMS(state, z, zShapeBuffer, extraArguments), OPS_A(RANDOM_OPS))
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DISPATCH_KERNEL_SIMPLE(randomSingle_, randomSingleGeneric, bfloat16, INPUT(Nd4jPointer state, void *z, Nd4jLong *zShapeBuffer, void *extraArguments), PARAMS(state, z, zShapeBuffer, extraArguments), OPS_A(RANDOM_OPS))
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DISPATCH_KERNEL_SIMPLE(randomDouble_, randomDoubleGeneric, float, INPUT(Nd4jPointer state, void *x, Nd4jLong *xShapeBuffer, void *z, Nd4jLong *zShapeBuffer, void *extraArguments), PARAMS(state, x, xShapeBuffer, z, zShapeBuffer, extraArguments), OPS_A(RANDOM_OPS))
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DISPATCH_KERNEL_SIMPLE(randomDouble_, randomDoubleGeneric, double, INPUT(Nd4jPointer state, void *x, Nd4jLong *xShapeBuffer, void *z, Nd4jLong *zShapeBuffer, void *extraArguments), PARAMS(state, x, xShapeBuffer, z, zShapeBuffer, extraArguments), OPS_A(RANDOM_OPS))
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DISPATCH_KERNEL_SIMPLE(randomDouble_, randomDoubleGeneric, float16, INPUT(Nd4jPointer state, void *x, Nd4jLong *xShapeBuffer, void *z, Nd4jLong *zShapeBuffer, void *extraArguments), PARAMS(state, x, xShapeBuffer, z, zShapeBuffer, extraArguments), OPS_A(RANDOM_OPS))
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DISPATCH_KERNEL_SIMPLE(randomDouble_, randomDoubleGeneric, bfloat16, INPUT(Nd4jPointer state, void *x, Nd4jLong *xShapeBuffer, void *z, Nd4jLong *zShapeBuffer, void *extraArguments), PARAMS(state, x, xShapeBuffer, z, zShapeBuffer, extraArguments), OPS_A(RANDOM_OPS))
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DISPATCH_KERNEL_SIMPLE(randomTriple_, randomTripleGeneric, float, INPUT(Nd4jPointer state, void *x, Nd4jLong *xShapeBuffer, void *y, Nd4jLong *yShapeBuffer, void *z, Nd4jLong *zShapeBuffer, void *extraArguments), PARAMS(state, x, xShapeBuffer, y, yShapeBuffer, z, zShapeBuffer, extraArguments), OPS_A(RANDOM_OPS))
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DISPATCH_KERNEL_SIMPLE(randomTriple_, randomTripleGeneric, double, INPUT(Nd4jPointer state, void *x, Nd4jLong *xShapeBuffer, void *y, Nd4jLong *yShapeBuffer, void *z, Nd4jLong *zShapeBuffer, void *extraArguments), PARAMS(state, x, xShapeBuffer, y, yShapeBuffer, z, zShapeBuffer, extraArguments), OPS_A(RANDOM_OPS))
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DISPATCH_KERNEL_SIMPLE(randomTriple_, randomTripleGeneric, float16, INPUT(Nd4jPointer state, void *x, Nd4jLong *xShapeBuffer, void *y, Nd4jLong *yShapeBuffer, void *z, Nd4jLong *zShapeBuffer, void *extraArguments), PARAMS(state, x, xShapeBuffer, y, yShapeBuffer, z, zShapeBuffer, extraArguments), OPS_A(RANDOM_OPS))
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DISPATCH_KERNEL_SIMPLE(randomTriple_, randomTripleGeneric, bfloat16, INPUT(Nd4jPointer state, void *x, Nd4jLong *xShapeBuffer, void *y, Nd4jLong *yShapeBuffer, void *z, Nd4jLong *zShapeBuffer, void *extraArguments), PARAMS(state, x, xShapeBuffer, y, yShapeBuffer, z, zShapeBuffer, extraArguments), OPS_A(RANDOM_OPS))
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#endif
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namespace functions {
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namespace random {
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template<typename T>
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template<typename OpClass>
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void _CUDA_D RandomFunction<T>::execTransformCuda(Nd4jPointer state, void *vx, Nd4jLong *xShapeBuffer, void *vy, Nd4jLong *yShapeBuffer, void *vz, Nd4jLong *zShapeBuffer, void *vextraArguments) {
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auto x = reinterpret_cast<T*>(vx);
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auto y = reinterpret_cast<T*>(vy);
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auto z = reinterpret_cast<T*>(vz);
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auto extraArguments = reinterpret_cast<T*>(vextraArguments);
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if (OpClass::requiresSpecial) {
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OpClass::specialOpCuda(state, x, xShapeBuffer, y, yShapeBuffer, z, zShapeBuffer, extraArguments);
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return;
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} else {
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__shared__ Nd4jLong length;
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__shared__ int xEWS;
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__shared__ int yEWS;
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__shared__ int zEWS;
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__shared__ char xOrder;
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__shared__ char yOrder;
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__shared__ char zOrder;
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__shared__ nd4j::graph::RandomGenerator *buffer;
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__shared__ unsigned char *cB;
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__shared__ unsigned char *dB;
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nd4j::graph::RandomGenerator *devBuffer;
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if (threadIdx.x == 0) {
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length = shape::length(zShapeBuffer);
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xEWS = shape::elementWiseStride(xShapeBuffer);
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yEWS = shape::elementWiseStride(yShapeBuffer);
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zEWS = shape::elementWiseStride(zShapeBuffer);
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xOrder = shape::order(xShapeBuffer);
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yOrder = shape::order(yShapeBuffer);
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zOrder = shape::order(zShapeBuffer);
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extern __shared__ unsigned char shmem[];
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buffer = (nd4j::graph::RandomGenerator *) shmem;
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cB = shmem;
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devBuffer = reinterpret_cast<nd4j::graph::RandomGenerator *> (state);
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dB = reinterpret_cast<unsigned char *> (state);
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}
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__syncthreads();
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// using this loop instead of memcpy
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for (int e = threadIdx.x; e < sizeof(nd4j::graph::RandomGenerator); e+= blockDim.x) {
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cB[e] = dB[e];
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}
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__syncthreads();
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int tid = blockIdx.x * blockDim.x + threadIdx.x;
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if (xEWS >= 1 && yEWS >= 1 && zEWS >= 1 && xOrder == yOrder && xOrder == zOrder) {
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for (Nd4jLong e = tid; e < length; e += blockDim.x * gridDim.x) {
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z[e * zEWS] = OpClass::op(x[e * xEWS], y[e * yEWS], e, length, buffer, extraArguments);
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}
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} else {
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for (Nd4jLong i = tid; i < length; i += blockDim.x * gridDim.x) {
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auto xOffset2 = shape::getIndexOffset(i, xShapeBuffer, length);
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auto yOffset2 = shape::getIndexOffset(i, yShapeBuffer, length);
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auto zOffset2 = shape::getIndexOffset(i, zShapeBuffer, length);
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z[zOffset2] = OpClass::op(x[xOffset2], y[yOffset2], i, length, buffer, extraArguments);
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}
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}
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}
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};
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template<typename T>
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template<typename OpClass>
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void _CUDA_D RandomFunction<T>::execTransformCuda(Nd4jPointer state, void *vx, Nd4jLong *xShapeBuffer, void *vz, Nd4jLong *zShapeBuffer, void *vextraArguments) {
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auto x = reinterpret_cast<T*>(vx);
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auto z = reinterpret_cast<T*>(vz);
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auto extraArguments = reinterpret_cast<T*>(vextraArguments);
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__shared__ Nd4jLong length;
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__shared__ int xEWS;
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__shared__ int zEWS;
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__shared__ char xOrder;
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__shared__ char zOrder;
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__shared__ nd4j::graph::RandomGenerator *buffer;
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__shared__ unsigned char *cB;
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__shared__ unsigned char *dB;
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__shared__ nd4j::graph::RandomGenerator *devBuffer;
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if (threadIdx.x == 0) {
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extern __shared__ unsigned char shmem[];
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buffer = (nd4j::graph::RandomGenerator *) shmem;
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cB = shmem;
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devBuffer = reinterpret_cast<nd4j::graph::RandomGenerator *> (state);
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dB = reinterpret_cast<unsigned char *> (state);
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length = shape::length(zShapeBuffer);
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xEWS = shape::elementWiseStride(xShapeBuffer);
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zEWS = shape::elementWiseStride(zShapeBuffer);
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xOrder = shape::order(xShapeBuffer);
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zOrder = shape::order(zShapeBuffer);
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}
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__syncthreads();
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// using this loop instead of memcpy
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for (int e = threadIdx.x; e < sizeof(nd4j::graph::RandomGenerator); e+= blockDim.x) {
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cB[e] = dB[e];
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}
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__syncthreads();
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if (xEWS >= 1 && zEWS >= 1 && xOrder == zOrder) {
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for (Nd4jLong e = blockIdx.x * blockDim.x + threadIdx.x; e < length; e += blockDim.x * gridDim.x) {
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z[e * zEWS] = OpClass::op(x[e * xEWS], e, length, buffer, extraArguments);
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}
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} else {
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for (Nd4jLong i = blockIdx.x * blockDim.x + threadIdx.x; i < length; i += blockDim.x * gridDim.x) {
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auto xOffset2 = shape::getIndexOffset(i, xShapeBuffer, length);
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auto zOffset2 = shape::getIndexOffset(i, zShapeBuffer, length);
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z[zOffset2] = OpClass::op(x[xOffset2], i, length, buffer, extraArguments);
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}
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}
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}
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template<typename T>
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template<typename OpClass>
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void _CUDA_D RandomFunction<T>::execTransformCuda(Nd4jPointer state, void *vz, Nd4jLong *zShapeBuffer, void *vextraArguments) {
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auto z = reinterpret_cast<T*>(vz);
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auto extraArguments = reinterpret_cast<T*>(vextraArguments);
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__shared__ Nd4jLong length;
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__shared__ Nd4jLong ews;
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__shared__ nd4j::graph::RandomGenerator *buffer;
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__shared__ unsigned char *cB;
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__shared__ unsigned char *dB;
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__shared__ nd4j::graph::RandomGenerator *devBuffer;
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if (threadIdx.x == 0) {
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extern __shared__ unsigned char shmem[];
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buffer = (nd4j::graph::RandomGenerator *) shmem;
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cB = shmem;
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devBuffer = reinterpret_cast<nd4j::graph::RandomGenerator *> (state);
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dB = reinterpret_cast<unsigned char *> (state);
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length = shape::length(zShapeBuffer);
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ews = shape::elementWiseStride(zShapeBuffer);
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}
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__syncthreads();
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// using this loop instead of memcpy
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for (int e = threadIdx.x; e < sizeof(nd4j::graph::RandomGenerator); e+= blockDim.x) {
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cB[e] = dB[e];
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}
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__syncthreads();
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int tid = blockIdx.x * blockDim.x + threadIdx.x;
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if (ews > 0) {
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for (Nd4jLong i = tid; i < length; i += blockDim.x * gridDim.x) {
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z[i * ews] = OpClass::op(i, length, buffer, extraArguments);
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}
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} else {
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for (Nd4jLong i = tid; i < length; i += blockDim.x * gridDim.x) {
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auto zOffset2 = shape::getIndexOffset(i, zShapeBuffer, length);
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z[zOffset2] = OpClass::op(i, length, buffer, extraArguments);
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}
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}
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}
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template <>
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_CUDA_H void RandomFunction<float>::executeCudaSingle(dim3& launchDims, cudaStream_t* stream, int opNum, Nd4jPointer stateHost, void *vz, Nd4jLong *zShapeBuffer, void *vextraArguments) {
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auto z = reinterpret_cast<float*>(vz);
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auto extraArguments = reinterpret_cast<float*>(vextraArguments);
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// this macro builds bunch of IF/ELSE selectors for kernel launch
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DISPATCH_SIMPLE(randomSingle, float, PARAMS(stateHost, z, zShapeBuffer, extraArguments), OPS_A(RANDOM_OPS))
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DEBUG_KERNEL(stream, opNum);
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}
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template <>
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_CUDA_H void RandomFunction<float16>::executeCudaSingle(dim3& launchDims, cudaStream_t* stream, int opNum, Nd4jPointer stateHost, void *vz, Nd4jLong *zShapeBuffer, void *vextraArguments) {
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auto z = reinterpret_cast<float16*>(vz);
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auto extraArguments = reinterpret_cast<float16*>(vextraArguments);
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// this macro builds bunch of IF/ELSE selectors for kernel launch
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DISPATCH_SIMPLE(randomSingle, float16, PARAMS(stateHost, z, zShapeBuffer, extraArguments), OPS_A(RANDOM_OPS))
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DEBUG_KERNEL(stream, opNum);
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}
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template <>
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_CUDA_H void RandomFunction<bfloat16>::executeCudaSingle(dim3& launchDims, cudaStream_t* stream, int opNum, Nd4jPointer stateHost, void *vz, Nd4jLong *zShapeBuffer, void *vextraArguments) {
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auto z = reinterpret_cast<bfloat16*>(vz);
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auto extraArguments = reinterpret_cast<bfloat16*>(vextraArguments);
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// this macro builds bunch of IF/ELSE selectors for kernel launch
|
||
|
|
DISPATCH_SIMPLE(randomSingle, bfloat16, PARAMS(stateHost, z, zShapeBuffer, extraArguments), OPS_A(RANDOM_OPS))
|
||
|
|
|
||
|
|
DEBUG_KERNEL(stream, opNum);
|
||
|
|
}
|
||
|
|
|
||
|
|
template <>
|
||
|
|
_CUDA_H void RandomFunction<double>::executeCudaSingle(dim3& launchDims, cudaStream_t *stream, int opNum, Nd4jPointer stateHost, void *vz, Nd4jLong *zShapeBuffer, void *vextraArguments) {
|
||
|
|
|
||
|
|
auto z = reinterpret_cast<double*>(vz);
|
||
|
|
auto extraArguments = reinterpret_cast<double*>(vextraArguments);
|
||
|
|
|
||
|
|
// this macro builds bunch of IF/ELSE selectors for kernel launch
|
||
|
|
DISPATCH_SIMPLE(randomSingle, double, PARAMS(stateHost, z, zShapeBuffer, extraArguments), OPS_A(RANDOM_OPS))
|
||
|
|
|
||
|
|
DEBUG_KERNEL(stream, opNum);
|
||
|
|
}
|
||
|
|
|
||
|
|
template <>
|
||
|
|
_CUDA_H void RandomFunction<float>::executeCudaDouble(dim3& launchDims, cudaStream_t* stream, int opNum, Nd4jPointer stateHost, void *vx, Nd4jLong *xShapeBuffer, void *vz, Nd4jLong *zShapeBuffer, void *vextraArguments) {
|
||
|
|
|
||
|
|
auto x = reinterpret_cast<float*>(vx);
|
||
|
|
auto z = reinterpret_cast<float*>(vz);
|
||
|
|
auto extraArguments = reinterpret_cast<float*>(vextraArguments);
|
||
|
|
|
||
|
|
// this macro builds bunch of IF/ELSE selectors for kernel launch
|
||
|
|
DISPATCH_SIMPLE(randomDouble, float, PARAMS(stateHost, x, xShapeBuffer, z, zShapeBuffer, extraArguments), OPS_A(RANDOM_OPS))
|
||
|
|
|
||
|
|
DEBUG_KERNEL(stream, opNum);
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
template <>
|
||
|
|
_CUDA_H void RandomFunction<float16>::executeCudaDouble(dim3& launchDims, cudaStream_t* stream, int opNum, Nd4jPointer stateHost, void *vx, Nd4jLong *xShapeBuffer, void *vz, Nd4jLong *zShapeBuffer, void *vextraArguments) {
|
||
|
|
|
||
|
|
auto x = reinterpret_cast<float16*>(vx);
|
||
|
|
auto z = reinterpret_cast<float16*>(vz);
|
||
|
|
auto extraArguments = reinterpret_cast<float16*>(vextraArguments);
|
||
|
|
|
||
|
|
// this macro builds bunch of IF/ELSE selectors for kernel launch
|
||
|
|
DISPATCH_SIMPLE(randomDouble, float16, PARAMS(stateHost, x, xShapeBuffer, z, zShapeBuffer, extraArguments), OPS_A(RANDOM_OPS))
|
||
|
|
|
||
|
|
DEBUG_KERNEL(stream, opNum);
|
||
|
|
}
|
||
|
|
|
||
|
|
template <>
|
||
|
|
_CUDA_H void RandomFunction<bfloat16>::executeCudaDouble(dim3& launchDims, cudaStream_t* stream, int opNum, Nd4jPointer stateHost, void *vx, Nd4jLong *xShapeBuffer, void *vz, Nd4jLong *zShapeBuffer, void *vextraArguments) {
|
||
|
|
|
||
|
|
auto x = reinterpret_cast<bfloat16*>(vx);
|
||
|
|
auto z = reinterpret_cast<bfloat16*>(vz);
|
||
|
|
auto extraArguments = reinterpret_cast<bfloat16*>(vextraArguments);
|
||
|
|
|
||
|
|
// this macro builds bunch of IF/ELSE selectors for kernel launch
|
||
|
|
DISPATCH_SIMPLE(randomDouble, bfloat16, PARAMS(stateHost, x, xShapeBuffer, z, zShapeBuffer, extraArguments), OPS_A(RANDOM_OPS))
|
||
|
|
|
||
|
|
DEBUG_KERNEL(stream, opNum);
|
||
|
|
}
|
||
|
|
|
||
|
|
template <>
|
||
|
|
_CUDA_H void RandomFunction<double>::executeCudaDouble(dim3& launchDims, cudaStream_t* stream, int opNum, Nd4jPointer stateHost, void *vx, Nd4jLong *xShapeBuffer, void *vz, Nd4jLong *zShapeBuffer, void *vextraArguments) {
|
||
|
|
|
||
|
|
auto x = reinterpret_cast<double*>(vx);
|
||
|
|
auto z = reinterpret_cast<double*>(vz);
|
||
|
|
auto extraArguments = reinterpret_cast<double*>(vextraArguments);
|
||
|
|
|
||
|
|
// this macro builds bunch of IF/ELSE selectors for kernel launch
|
||
|
|
DISPATCH_SIMPLE(randomDouble, double, PARAMS(stateHost, x, xShapeBuffer, z, zShapeBuffer, extraArguments), OPS_A(RANDOM_OPS))
|
||
|
|
|
||
|
|
DEBUG_KERNEL(stream, opNum);
|
||
|
|
}
|
||
|
|
|
||
|
|
template <>
|
||
|
|
_CUDA_H void RandomFunction<float>::executeCudaTriple(dim3& launchDims, cudaStream_t* stream, int opNum, Nd4jPointer stateHost, void *vx, Nd4jLong *xShapeBuffer, void *vy, Nd4jLong *yShapeBuffer, void *vz, Nd4jLong *zShapeBuffer, void *vextraArguments) {
|
||
|
|
|
||
|
|
|
||
|
|
auto x = reinterpret_cast<float*>(vx);
|
||
|
|
auto y = reinterpret_cast<float*>(vy);
|
||
|
|
auto z = reinterpret_cast<float*>(vz);
|
||
|
|
auto extraArguments = reinterpret_cast<float*>(vextraArguments);
|
||
|
|
|
||
|
|
// this macro builds bunch of IF/ELSE selectors for kernel launch
|
||
|
|
DISPATCH_SIMPLE(randomTriple, float, PARAMS(stateHost, x, xShapeBuffer, y, yShapeBuffer, z, zShapeBuffer, extraArguments), OPS_A(RANDOM_OPS))
|
||
|
|
|
||
|
|
DEBUG_KERNEL(stream, opNum);
|
||
|
|
}
|
||
|
|
|
||
|
|
template <>
|
||
|
|
_CUDA_H void RandomFunction<float16>::executeCudaTriple(dim3& launchDims, cudaStream_t* stream, int opNum, Nd4jPointer stateHost, void *vx, Nd4jLong *xShapeBuffer, void *vy, Nd4jLong *yShapeBuffer, void *vz, Nd4jLong *zShapeBuffer, void *vextraArguments) {
|
||
|
|
|
||
|
|
auto x = reinterpret_cast<float16*>(vx);
|
||
|
|
auto y = reinterpret_cast<float16*>(vy);
|
||
|
|
auto z = reinterpret_cast<float16*>(vz);
|
||
|
|
auto extraArguments = reinterpret_cast<float16*>(vextraArguments);
|
||
|
|
|
||
|
|
// this macro builds bunch of IF/ELSE selectors for kernel launch
|
||
|
|
DISPATCH_SIMPLE(randomTriple, float16, PARAMS(stateHost, x, xShapeBuffer, y, yShapeBuffer, z, zShapeBuffer, extraArguments), OPS_A(RANDOM_OPS))
|
||
|
|
|
||
|
|
DEBUG_KERNEL(stream, opNum);
|
||
|
|
}
|
||
|
|
|
||
|
|
template <>
|
||
|
|
_CUDA_H void RandomFunction<bfloat16>::executeCudaTriple(dim3& launchDims, cudaStream_t* stream, int opNum, Nd4jPointer stateHost, void *vx, Nd4jLong *xShapeBuffer, void *vy, Nd4jLong *yShapeBuffer, void *vz, Nd4jLong *zShapeBuffer, void *vextraArguments) {
|
||
|
|
|
||
|
|
auto x = reinterpret_cast<bfloat16*>(vx);
|
||
|
|
auto y = reinterpret_cast<bfloat16*>(vy);
|
||
|
|
auto z = reinterpret_cast<bfloat16*>(vz);
|
||
|
|
auto extraArguments = reinterpret_cast<bfloat16*>(vextraArguments);
|
||
|
|
|
||
|
|
// this macro builds bunch of IF/ELSE selectors for kernel launch
|
||
|
|
DISPATCH_SIMPLE(randomTriple, bfloat16, PARAMS(stateHost, x, xShapeBuffer, y, yShapeBuffer, z, zShapeBuffer, extraArguments), OPS_A(RANDOM_OPS))
|
||
|
|
|
||
|
|
DEBUG_KERNEL(stream, opNum);
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
template <>
|
||
|
|
_CUDA_H void RandomFunction<double>::executeCudaTriple(dim3& launchDims, cudaStream_t* stream, int opNum, Nd4jPointer stateHost, void *vx, Nd4jLong *xShapeBuffer, void *vy, Nd4jLong *yShapeBuffer, void *vz, Nd4jLong *zShapeBuffer, void *vextraArguments) {
|
||
|
|
|
||
|
|
auto x = reinterpret_cast<double*>(vx);
|
||
|
|
auto y = reinterpret_cast<double*>(vy);
|
||
|
|
auto z = reinterpret_cast<double*>(vz);
|
||
|
|
auto extraArguments = reinterpret_cast<double*>(vextraArguments);
|
||
|
|
|
||
|
|
// this macro builds bunch of IF/ELSE selectors for kernel launch
|
||
|
|
DISPATCH_SIMPLE(randomTriple, double, PARAMS(stateHost, x, xShapeBuffer, y, yShapeBuffer, z, zShapeBuffer, extraArguments), OPS_A(RANDOM_OPS))
|
||
|
|
|
||
|
|
DEBUG_KERNEL(stream, opNum);
|
||
|
|
}
|
||
|
|
|
||
|
|
BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT RandomFunction, , FLOAT_TYPES);
|
||
|
|
}
|
||
|
|
}
|