cavis/libnd4j/include/loops/cuda/scalar_int.cu
raver119 320924278d
Legacy API changes (#441)
* initial commit

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* another initial commit

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* another initial commit

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* one more initial commit

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* next step

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* next step

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* next step

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* next step

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* Refactored buffer() and shapeInfo() methods usage with NDArray class.

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* Adopt Graph class methods to use const shapes.

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* Adopt choose op to use constant shapes.

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* Adopt where op shape method to use constant shapes.

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* Adopt lstsq op to use constant empty shapes.

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* Adopt matrix_diag_part op shape routine to use constant shapes.

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* Adopt determinant ops to use constant shapes.

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* Adopt mean_pairwssqerr_loss ops to use constant shapes.

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* Adopt ops shape methods.

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* Adopt shape methods for loss ops.

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* Adopt log_loss op shape method.

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* Adopt shape methods for ops.

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* Adopt dilation2d ops shape methods.

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* Adopted deconv2d ops shape methods.

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* Adopted dynamicRNN op shape method.

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* Adopted shape methods for ops.

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* Adopted shape methods for lstm layer ops.

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* few updates

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* first cuda tweak

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* Adopt constant shapes for sconv2d ops.

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* Adopt constant shapes for gru ops.

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* Adopt constant shapes with shape methods for segment ops and so on.

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* Adopted constant shapes with unsorted_segment_* ops.

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* Adopted constant shapes with gamma op shape method.

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* Adopted shape methods of reduce_stddev ops.

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* Adopted shape methods for reduce_* ops.

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* Adopt shape method for squeeze op.

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* Adopt strided_slice shape method.

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* Refactored concat op shape method to adopt constant shapes.

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* Adopted shape method for mirror_pad op.

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* Adopted split op shape method.

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* Adopted tile ops shape methods.

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* Added const cast for mkldnn routines handles.

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* Refactored logSoftMaxForVector_ routine to conform with proper data and shape pointer casts.

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* Cosmetic changes to proper usage of constant pointers.

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* Refactored a couple shape comparators for strides and addBias helpers to proper use data pointers with inplace option.

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* Refactored depthToSpace helpers.

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* Refactored histogram helpers.

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* Refactored im2col helpers.

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* Refactored gather and gatherND helpers.

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* Fixed buffer usage on percentile helper.

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* Fixed gather shape with helpers and range buffer usage.

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* Fixed buffer usage with space to depth helpers.

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* Fixed buffer usage and constant shapes.

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* Fixed buffer usage with LUP decomposition>

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* Refactored onehot_ helper.

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* Refactored pad and prefix to use constant shapes.

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* Refactoed softmax helpers.

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* Fixed space to batch helpers to use buffers properly.

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* Fixed stack and split helpers.

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* Fixed buffer usage with sparse to dense helpers.

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* Fixed buffer usage with mindistance_ helpers.

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* Fixed buffer usage with tile helper.

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* Fixed constant shape usage.

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* Fixed constant shape usage with legacy pairwise bool ops.

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* Refactored a couple of methods to adopt constant shape usage.

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* Fixed broadcasting with constant shape."

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* Fixed const usage with inplace reverse and constant shapes with legacy reduction.

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* Refactored legacy ops with const shapes.

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* Refactored sort to adopt constant shapes.

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* Corrected sort for constant shape usage.

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* Fixed constant shape usage with special methods.

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* Refactored Context to conform with constant shape usage.

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* CUDA broadcasting headers

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* pairwise/indexreduce/random headers

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* Refactored native ops to adopt constant shapes.

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* legacy reduce3/scalar headers

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* Corrected pullRow signature and tests.

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* Corrected routines to proper use of constant shapes.

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* Refactored tests to use constant shapes properly.

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* Refactored legacy ops tests to use constant shapes properly.

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* Refactored buffer usage with NDArray tests.

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* Fixed native ops tests.

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* Fixed special concat routine.

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* Fixed buffer usage with test.

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* Fixed buffer usage with a test.

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* Refactored TAD.h and tests.

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* Refactored calcStrides* routines to use constant shapes.

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* Fixed miscelaneous errors with constant shapes.

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* NativeOps const changes

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* Corrected definitions for declared functions.

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* NativeOps const changes

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* few more const changes

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* Fixed const shapes with shape routines.

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* few more const changes

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* Fixed shape method for broadcastable case.

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* few more const changes

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* xw_plus_b BP shape fn restored

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* Fixed signatures with broadcasting.

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* Repaired backprops shape methods for a set of operations.

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* Refactored broadcast bool for cuda.

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* Refactored methods for 3 args with const qualifier.

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* Fixed a couple of kernel signatures for broadcasting.

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* Fixed kernels signatures for const buffers and shapes.

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* Refactored pairwise methods to persistent buffers and shapes usage.

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* Adopt const to buffers and shapes with kernels.

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* Adopt const to buffers and shapes with scalar kernels.

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* Refactored indexreduce kernels signatures to use const buffers and shapes.

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* Refactored pairwise kernels to adopt cons shapes and buffers.

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* Refactored pairwise bool kernels to adopt cons shapes and buffers.

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* Refactored random special ops to conform with const shapes and buffers.

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* Refactored native ops to conform with const shapes and buffers under cuda platform.

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* Cosmetical changes only.

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* Fixed const shapes and buffers error.

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* Corrected start pos routine.

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* Refactored methods to conform with const shapes and buffers.

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* Refactored helpers to use proper methods instead.

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* bunch of changes

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* next bunch of changes

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* next bunch of changes

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* Fixed execScalar declaration.

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* Fixed execScalar declaration.

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* Corrected const shape cases with sort and so on.

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* Fixed const shapes for sort.

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* Refactored kernel declarations to adopt const shapes.

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* Fixed kernels declarations to adopt const shapes.

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* Corrected kernel declarations to adopt const shapes and buffers.

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* Fixed kernels declarations to adopt const shapes.

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* Fixed segment helpers kernels declarations and so on to adopt const shapes.

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* Fixed const shape usage with segment and solve helpers.

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* Fixed kernel declaration with adjustWeight helper.

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* Fixed cuda implementations for constant shape helpers.

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* Adopted const shape usage with kernels.

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* Adopted top_k kernels to use const shapes and buffers.

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* Corrected kernels declarations to adopt const shapes with helpers.

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* Refactored NDArray definitions to adopt const shapes and buffers.

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* Fixed const shapes with image suppression helpers.

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* Slight improvement with buffers.

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* Refactored buffer usage.

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* Refactored buffer usage with tests.

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* Fixed const shape usage with definitions.

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* minor updates on cpu side

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* Refactored const shape usage with ConstantDescritor and native ops with cuda platform.

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* Refactored tear and tile kernels to adopt with const shapes.

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* softmax_loop fix

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* update missing signature

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* softmax again

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* few more missing consts

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* new methods updated

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Co-authored-by: shugeo <sgazeos@gmail.com>
2020-05-09 08:06:14 +03:00

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author Yurii Shyrma (iuriish@yahoo.com), created on 08.11.2018
// @author raver119@gmail.com
//
#include "../scalar_int.h"
#include <system/op_boilerplate.h>
#include <types/types.h>
#include "../legacy_ops.h"
using namespace simdOps;
////////////////////////////////////////////////////////////////////////
template <typename X, typename OpType>
__global__ void scalarAlongDimension(void const* x, Nd4jLong const* xShapeInfo,
void *extraParams,
void *z, Nd4jLong const* zShapeInfo,
void const* scalars,
int *dimension, int dimensionLength,
Nd4jLong const* tadShapeInfo, Nd4jLong const* tadOffsets,
Nd4jLong const* tadShapeInfoZ, Nd4jLong const* tadOffsetsZ) {
functions::scalar::ScalarIntTransform<X>::template transformCuda<OpType>(x, xShapeInfo, extraParams, z, zShapeInfo, scalars, dimension, dimensionLength, tadShapeInfo, tadOffsets, tadShapeInfoZ, tadOffsetsZ);
}
////////////////////////////////////////////////////////////////////////
template <typename X, typename OpType>
__global__ void scalarSimpleShaped(void const* x, void const* y, Nd4jLong const* xShapeInfo, void *params, void *z, Nd4jLong const* zShapeInfo, int *allocationBuffer) {
functions::scalar::ScalarIntTransform<X>::template transformCuda<OpType>(y, x, xShapeInfo, params, z, zShapeInfo, allocationBuffer);
}
// *********************************************************************//
// *********************************************************************//
namespace functions {
namespace scalar {
////////////////////////////////////////////////////////////////////////
template<typename X>
template<typename OpType>
__device__ void ScalarIntTransform<X>::transformCuda(void const* vscalar,
void const* vy, Nd4jLong const* yShapeInfo,
void *vparams,
void *vz, Nd4jLong const* zShapeInfo,
int *allocationBuffer) {
auto scalar = reinterpret_cast<X const*>(vscalar)[0];
auto y = reinterpret_cast<X const*>(vy);
auto params = reinterpret_cast<X*>(vparams);
auto z = reinterpret_cast<X*>(vz);
auto yRank = shape::rank(yShapeInfo);
auto yEWS = shape::elementWiseStride(yShapeInfo);
auto yShape = shape::shapeOf(yShapeInfo);
auto yStride = shape::stride(yShapeInfo);
auto zRank = shape::rank(zShapeInfo);
auto zEWS = shape::elementWiseStride(zShapeInfo);
auto zShape = shape::shapeOf(zShapeInfo);
auto zStride = shape::stride(zShapeInfo);
int totalThreads = gridDim.x * blockDim.x;
int tid = blockIdx.x * blockDim.x + threadIdx.x;
__shared__ int len;
if(threadIdx.x == 0)
len = shape::length(yShapeInfo);
__syncthreads();
if(yEWS >= 1 && zEWS >= 1 && shape::order(yShapeInfo) == shape::order(zShapeInfo)) {
transformCuda<OpType>(len, vscalar, vy, yEWS, vparams, vz, zEWS, allocationBuffer);
}
else {
for (Nd4jLong i = tid; i < len; i+= totalThreads)
z[shape::getIndexOffset(i, zShapeInfo)] = OpType::op(y[shape::getIndexOffset(i, yShapeInfo)], scalar, params);
}
}
////////////////////////////////////////////////////////////////////////
template<typename X>
template<typename OpType>
__device__ void ScalarIntTransform<X>::transformCuda(Nd4jLong len,
void const* vx,
void const* vy, Nd4jLong yEWS,
void *vparams,
void *vz, Nd4jLong zEWS,
int *allocationBuffer) {
auto x = reinterpret_cast<X const*>(vx)[0];
auto y = reinterpret_cast<X const*>(vy);
auto z = reinterpret_cast<X*>(vz);
auto params = reinterpret_cast<X*>(vparams);
int totalThreads = gridDim.x * blockDim.x;
int tid = blockIdx.x * blockDim.x + threadIdx.x;
Nd4jLong i = tid;
if(yEWS == 1 && zEWS == 1) {
for (; i < len; i += totalThreads)
z[i] = OpType::op(y[i], x, params);
}
else {
for (; i < len; i += totalThreads)
z[i * zEWS] = OpType::op(y[i * yEWS], x, params);
}
}
////////////////////////////////////////////////////////////////////////
template<typename X>
template<typename OpType>
__device__ void ScalarIntTransform<X>::transformCuda(void const* vx, Nd4jLong const* xShapeInfo,
void *vextraParams,
void *vz, Nd4jLong const* zShapeInfo,
void const* vscalars,
int *dimension, int dimensionLength,
Nd4jLong const* tadShapeInfo, Nd4jLong const* tadOffsets,
Nd4jLong const* tadShapeInfoZ, Nd4jLong const* tadOffsetsZ) {
auto x = reinterpret_cast<X const*>(vx);
auto scalars = reinterpret_cast<X const*>(vscalars);
auto z = reinterpret_cast<X*>(vz);
auto extraParams = reinterpret_cast<X*>(vextraParams);
if (tadShapeInfoZ == nullptr) {
tadShapeInfoZ = tadShapeInfo;
tadOffsetsZ = tadOffsets;
}
// tad preparation
auto tadEws = shape::elementWiseStride(tadShapeInfo);
auto zEws = shape::elementWiseStride(tadShapeInfoZ);
auto tadLength = shape::length(tadShapeInfo);//shape::tadLength(xShapeInfo, dimension, dimensionLength);
auto numTads =shape::length(xShapeInfo) / tadLength;
if (tadEws > 0 && zEws > 0 && shape::order(tadShapeInfo) == shape::order(zShapeInfo)) {
// main loop, rolling over tads
for (int r = blockIdx.x; r < numTads; r += gridDim.x) {
X *oZ = z + tadOffsetsZ[r];
auto oX = x + tadOffsets[r];
auto s = scalars[r];
for (int f = threadIdx.x; f < tadLength; f += blockDim.x)
oZ[f * zEws] = OpType::op(oX[f * tadEws], s, extraParams);
}
} else {
// main loop, rolling over tads
for (int r = blockIdx.x; r < numTads; r += gridDim.x) {
X *oZ = z + tadOffsetsZ[r];
auto oX = x + tadOffsets[r];
auto s = scalars[r];
for (int f = threadIdx.x; f < tadLength; f += blockDim.x)
oZ[shape::getIndexOffset(f, tadShapeInfoZ)] = OpType::op(oX[shape::getIndexOffset(f, tadShapeInfo)], s, extraParams);
}
}
}
////////////////////////////////////////////////////////////////////////
template<typename X>
template <typename OpType>
_CUDA_H void ScalarIntTransform<X>::intermediateAlongDimension(dim3& launchDims, cudaStream_t *stream,
void const* x, Nd4jLong const* xShapeInfo,
void *z, Nd4jLong const* zShapeInfo,
void const* scalars,
void *extraParams,
int *dimension, int dimensionLength,
Nd4jLong const* tadShapeInfo, Nd4jLong const* tadOffsets,
Nd4jLong const* tadShapeInfoZ, Nd4jLong const* tadOffsetsZ) {
scalarAlongDimension<X, OpType><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(x, xShapeInfo, extraParams, z, zShapeInfo, scalars, dimension, dimensionLength, tadShapeInfo, tadOffsets, tadShapeInfoZ, tadOffsetsZ);
}
////////////////////////////////////////////////////////////////////////
template<typename X>
template<typename OpType>
void _CUDA_H ScalarIntTransform<X>::intermediateShaped(dim3& launchDims, cudaStream_t *stream,
void const* vx, Nd4jLong const* xShapeInfo,
void *vz, Nd4jLong const* zShapeInfo,
void const* vscalar,
void *vextraParams, int *allocPointer){
scalarSimpleShaped<X, OpType><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(vx, vscalar, xShapeInfo, vextraParams, vz, zShapeInfo, allocPointer);
}
////////////////////////////////////////////////////////////////////////
template<typename X>
void ScalarIntTransform<X>::executeCudaShaped(dim3& launchDims, cudaStream_t *stream,
int opNum,
void const* vx, Nd4jLong const* xShapeInfo,
void *vz, Nd4jLong const* zShapeInfo,
void const* vscalar,
void* vextraParams) {
if (sd::Environment::getInstance()->isDebugAndVerbose())
printf("H14 opNum:[%i]\n", opNum);
DISPATCH_BY_OPNUM_T(intermediateShaped, PARAMS(launchDims, stream, vx, xShapeInfo, vz, zShapeInfo, vscalar, vextraParams, nullptr), SCALAR_INT_OPS);
}
////////////////////////////////////////////////////////////////////////
template<typename X>
void ScalarIntTransform<X>::executeCudaAlongDimension(dim3& launchDims, cudaStream_t *stream, int opNum, void const* vx, Nd4jLong const* xShapeInfo, void *vz, Nd4jLong const* zShapeInfo, void const* vscalars, void *vextraParams, int *dimension, int dimensionLength, Nd4jLong const* tadShapeInfo, Nd4jLong const* tadOffsets, Nd4jLong const* tadShapeInfoZ, Nd4jLong const* tadOffsetsZ) {
DISPATCH_BY_OPNUM_T(intermediateAlongDimension, PARAMS(launchDims, stream, vx, xShapeInfo, vz, zShapeInfo, vscalars, vextraParams, dimension, dimensionLength, tadShapeInfo, tadOffsets, tadShapeInfoZ, tadOffsetsZ), SCALAR_INT_OPS);
}
BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT ScalarIntTransform, , INTEGER_TYPES);
}
}