/*******************************************************************************
* 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 20.04.2018
//
#include<ops/declarable/helpers/transforms.h>
#include <array/ResultSet.h>
#include <helpers/ShapeUtils.h>
#include <numeric>
#include <NDArrayFactory.h>
#include <helpers/TAD.h>
#include <exceptions/cuda_exception.h>
#include <PointersManager.h>
#include <ConstantTadHelper.h>
namespace nd4j {
namespace ops {
namespace helpers {
///////////////////////////////////////////////////////////////////
template<typename T>
__global__ static void concatCuda(const int numOfArrs, void* pVx, void* pxShapeInfo, void* pVz, void* pzShapeInfo) {
__shared__ int arrIdx, blocksPerArr;
__shared__ T *x, *z;
__shared__ Nd4jLong *zShapeInfo, *xShapeInfo, arrLen, arrLenPerBlock, start, end;
if (threadIdx.x == 0) {
blocksPerArr = (gridDim.x + numOfArrs - 1) / numOfArrs; // ceil
arrIdx = blockIdx.x / blocksPerArr;
x = reinterpret_cast<T*>(reinterpret_cast<void**>(pVx)[arrIdx]);
z = reinterpret_cast<T*>(reinterpret_cast<void**>(pVz)[arrIdx]);
xShapeInfo = reinterpret_cast<Nd4jLong**>(pxShapeInfo)[arrIdx];
zShapeInfo = reinterpret_cast<Nd4jLong**>(pzShapeInfo)[arrIdx];
arrLen = shape::length(xShapeInfo);
arrLenPerBlock = (arrLen + blocksPerArr - 1) / blocksPerArr; // ceil
start = (blockIdx.x % blocksPerArr) * arrLenPerBlock;
end = (start + arrLenPerBlock) > arrLen ? arrLen : (start + arrLenPerBlock);
}
__syncthreads();
for (Nd4jLong i = start + threadIdx.x; i < end; i += blockDim.x)
z[shape::getIndexOffset(i, zShapeInfo, arrLen)] = x[shape::getIndexOffset(i, xShapeInfo, arrLen)];
}
///////////////////////////////////////////////////////////////////
template<typename T>
__host__ static void concatCudaLauncher(const int numOfArrs, const cudaStream_t *stream, void* pVx, void* pxShapeInfo, void* pVz, void* pzShapeInfo) {
concatCuda<T><<<512, 256, 1024, *stream>>>(numOfArrs, pVx, pxShapeInfo, pVz, pzShapeInfo);
}
///////////////////////////////////////////////////////////////////
// x - input, y - paddings, z - output
template<typename X, typename Y>
__global__ static void padCuda(const int mode,
const void *vx, const Nd4jLong *xShapeInfo,
const void *vy, const Nd4jLong *yShapeInfo,
void *vz, const Nd4jLong *zShapeInfo,
const void *vPadVal) {
const X padVal = *reinterpret_cast<const X*>(vPadVal);
const auto x = reinterpret_cast<const X*>(vx);
const auto y = reinterpret_cast<const Y*>(vy);
auto z = reinterpret_cast<X*>(vz);
__shared__ int rank, rankMinusOne;
__shared__ Nd4jLong zLen, yLen, totalThreads, *coords, *xShape, *zShape, *xStride, *zStride, shift1, shift2, yStride0;
if (threadIdx.x == 0) {
extern __shared__ unsigned char shmem[];
coords = reinterpret_cast<Nd4jLong*>(shmem);
zLen = shape::length(zShapeInfo);
xShape = shape::shapeOf(const_cast<Nd4jLong*>(xShapeInfo));
zShape = shape::shapeOf(const_cast<Nd4jLong*>(zShapeInfo));
xStride = shape::stride(const_cast<Nd4jLong*>(xShapeInfo));
zStride = shape::stride(const_cast<Nd4jLong*>(zShapeInfo));
yStride0 = shape::stride(const_cast<Nd4jLong*>(yShapeInfo))[0];
rank = shape::rank(xShapeInfo);
zLen = shape::length(zShapeInfo);
yLen = 2 * rank;
rankMinusOne = rank - 1;
totalThreads = gridDim.x * blockDim.x;
shift1 = mode == 1 ? 0 : 1; // REFLECT : SYMMETRIC
shift2 = mode == 1 ? 2 : 1; // REFLECT : SYMMETRIC
}
__syncthreads();
auto xzCoord = coords + threadIdx.x * rank; // we use xzCoord storage both for x and z arrays
const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
if(mode == 0) { // CONSTANT case
for (Nd4jLong i = tid; i < zLen; i += totalThreads) {
shape::index2coords(rank, zShape, i, zLen, xzCoord);
const auto zOffset = shape::getOffset(0, zShape, zStride, xzCoord, rank);
bool within = true;
for(int j = rankMinusOne; j >= 0; --j) {
if(xShape[j] == zShape[j]) continue;
const auto left = y[shape::getIndexOffset(yStride0 * j, yShapeInfo, yLen)];
if(xzCoord[j] < left || xzCoord[j] >= left + xShape[j]) {within = false; break;}
else {xzCoord[j] = xzCoord[j] - left;}
}
if(within)
z[zOffset] = x[shape::getOffset(0, xShape, xStride, xzCoord, rank)];
else
z[zOffset] = padVal;
}
}
else { // REFLECT and SYMMETRIC cases
for (Nd4jLong i = tid; i < zLen; i += totalThreads) {
shape::index2coords(rank, zShape, i, zLen, xzCoord);
const auto zOffset = shape::getOffset(0, zShape, zStride, xzCoord, rank);
for(int j = rankMinusOne; j >= 0; --j) {
if(xShape[j] == zShape[j]) continue;
xzCoord[j] = xzCoord[j] - y[shape::getIndexOffset(yStride0 * j, yShapeInfo, yLen)]; // are ready to fill middle (within input dimension range)
if(xzCoord[j] < 0) xzCoord[j] = -xzCoord[j] - shift1; // means fill from left
else if(xzCoord[j] >= xShape[j]) xzCoord[j] = 2 * xShape[j] - xzCoord[j] - shift2; // means fill from right
}
const auto xOffset = shape::getOffset(0, xShape, xStride, xzCoord, rank);
z[zOffset] = x[xOffset];
}
}
}
///////////////////////////////////////////////////////////////////
template<typename X, typename Y>
static void padCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream,
const int mode,
const void *vx, const Nd4jLong *xShapeInfo,
const void *vy, const Nd4jLong *yShapeInfo,
void *vz, const Nd4jLong *zShapeInfo,
const void* padVal) {
padCuda<X,Y><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(mode, vx, xShapeInfo, vy, yShapeInfo, vz, zShapeInfo, padVal);
}
///////////////////////////////////////////////////////////////////
void pad(nd4j::LaunchContext * context, const int mode, const NDArray& input, const NDArray& paddings, NDArray& output, const NDArray& padValue) {
PointersManager manager(context, "pad");
NDArray::prepareSpecialUse({&output}, {&input, &paddings, &padValue});
const int threadsPerBlock = MAX_NUM_THREADS / 4;
const int blocksPerGrid = (output.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
const int sharedMem = 8 * threadsPerBlock * output.rankOf() + 128;
const auto xType = input.dataType();
const auto yType = paddings.dataType();
BUILD_DOUBLE_SELECTOR(xType, yType, padCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), mode, input.getSpecialBuffer(), input.getSpecialShapeInfo(), paddings.getSpecialBuffer(), paddings.getSpecialShapeInfo(), output.getSpecialBuffer(), output.getSpecialShapeInfo(), padValue.getSpecialBuffer()), LIBND4J_TYPES, INTEGER_TYPES);
NDArray::registerSpecialUse({&output}, {&input, &paddings, &padValue});
manager.synchronize();
}
///////////////////////////////////////////////////////////////////
template<typename T>
__global__ static void invertPermutationCuda(const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo) {
const T* x = reinterpret_cast<const T*>(vx);
T* z = reinterpret_cast<T*>(vz);
__shared__ Nd4jLong len, totalThreads;
if (threadIdx.x == 0) {
len = shape::length(xShapeInfo);
totalThreads = gridDim.x * blockDim.x;
}
__syncthreads();
const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
for (Nd4jLong i = tid; i < len; i += totalThreads) {
const auto xOffset = shape::getIndexOffset(i, xShapeInfo, len);
const Nd4jLong index = x[xOffset];
const auto zOffset = shape::getIndexOffset(index, zShapeInfo, len);
z[zOffset] = i;
}
}
///////////////////////////////////////////////////////////////////
template<typename T>
__host__ static void invertPermutationCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream,
const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo) {
invertPermutationCuda<T><<<blocksPerGrid, threadsPerBlock, 1024, *stream>>>(vx, xShapeInfo, vz, zShapeInfo);
}
BUILD_SINGLE_TEMPLATE(template void invertPermutationCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream, const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo), LIBND4J_TYPES);
////////////////////////////////////////////////////////////////////////
void invertPermutation(nd4j::LaunchContext* context, const NDArray& input, NDArray& output) {
const int threadsPerBlock = MAX_NUM_THREADS;
const int blocksPerGrid = (input.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
PointersManager manager(context, "invertPermutation");
NDArray::prepareSpecialUse({&output}, {&input});
BUILD_SINGLE_SELECTOR(input.dataType(), invertPermutationCudaLauncher, (blocksPerGrid, threadsPerBlock, context->getCudaStream(), input.getSpecialBuffer(), input.getSpecialShapeInfo(), output.getSpecialBuffer(), output.getSpecialShapeInfo()), LIBND4J_TYPES);
NDArray::registerSpecialUse({&output}, {&input});
manager.synchronize();
}
//////////////////////////////////////////////////////////////////////////
template<typename T>
__global__ static void traceCuda(const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const uint diagLen) {
const auto x = reinterpret_cast<const T*>(vx);
auto z = reinterpret_cast<T*>(vz);
__shared__ T* sharedMem;
__shared__ int xRank, zRank; // xRank = zRank + 2
__shared__ Nd4jLong xLen, zLen, *coordsMem;
if (threadIdx.x == 0) {
extern __shared__ unsigned char shmem[];
sharedMem = reinterpret_cast<T*>(shmem);
coordsMem = reinterpret_cast<Nd4jLong*>(shmem + blockDim.x * sizeof(T));
xRank = shape::rank(xShapeInfo);
zRank = shape::rank(zShapeInfo);
xLen = shape::length(xShapeInfo);
zLen = shape::length(zShapeInfo); // corresponds to number of matrices
}
__syncthreads();
Nd4jLong* coords = coordsMem + threadIdx.x * xRank;
for (uint m = blockIdx.x; m < zLen; m += gridDim.x) { // one block per each element of z, that is per each matrix
shape::index2coords(zRank, shape::shapeOf(const_cast<Nd4jLong*>(zShapeInfo)), m, zLen, coords);
const auto zOffset = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(zShapeInfo)), shape::stride(const_cast<Nd4jLong*>(zShapeInfo)), coords, zRank);
sharedMem[threadIdx.x] = 0;
for (uint i = threadIdx.x; i < diagLen; i += blockDim.x) {
coords[zRank] = coords[zRank + 1] = i;
const auto xOffset = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(xShapeInfo)), shape::stride(const_cast<Nd4jLong*>(xShapeInfo)), coords, xRank);
sharedMem[threadIdx.x] += x[xOffset];
}
__syncthreads();
// aggregate sum
for (Nd4jLong activeThreads = blockDim.x / 2; activeThreads > 0; activeThreads /= 2) {
if (threadIdx.x < activeThreads)
sharedMem[threadIdx.x] += sharedMem[threadIdx.x + activeThreads];
__syncthreads();
}
if (threadIdx.x == 0)
z[zOffset] = *sharedMem;
}
}
///////////////////////////////////////////////////////////////////
template<typename T>
static void traceCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream,
const void *vx, const Nd4jLong *xShapeInfo,
void *vz, const Nd4jLong *zShapeInfo,
const uint diagLen) {
traceCuda<T><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vz, zShapeInfo, diagLen);
}
BUILD_SINGLE_TEMPLATE(template void traceCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const uint diagLen), LIBND4J_TYPES);
///////////////////////////////////////////////////////////////////
void trace(nd4j::LaunchContext* context, const NDArray& input, NDArray& output) {
PointersManager manager(context, "trace");
const uint diagLen = input.sizeAt(-1) < input.sizeAt(-2) ? input.sizeAt(-1) : input.sizeAt(-2);
const int threadsPerBlock = MAX_NUM_THREADS / 4;
const int blocksPerGrid = (output.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
const int sharedMem = threadsPerBlock * (sizeof(Nd4jLong) * input.rankOf() + input.sizeOfT()) + 128;
NDArray::prepareSpecialUse({&output}, {&input});
BUILD_SINGLE_SELECTOR(input.dataType(), traceCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), input.getSpecialBuffer(), input.getSpecialShapeInfo(), output.specialBuffer(), output.specialShapeInfo(), diagLen), LIBND4J_TYPES);
NDArray::registerSpecialUse({&output}, {&input});
manager.synchronize();
}
///////////////////////////////////////////////////////////////////
template<typename T>
__global__ static void triuBPCuda(const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const int diag) {
// x and z have same shapes
const auto x = reinterpret_cast<const T*>(vx); // gradO
auto z = reinterpret_cast<T*>(vz); // gradI
__shared__ int rank, areSameOffsets; // xRank = zRank
__shared__ Nd4jLong len, totalThreads, *sharedMem; // xLen = zLen
if (threadIdx.x == 0) {
extern __shared__ unsigned char shmem[];
sharedMem = reinterpret_cast<Nd4jLong*>(shmem);
areSameOffsets = shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo);
rank = shape::rank(xShapeInfo);
len = shape::length(zShapeInfo);
totalThreads = gridDim.x * blockDim.x;
}
__syncthreads();
auto coords = sharedMem + threadIdx.x * rank;
const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
for (Nd4jLong i = tid; i < len; i += totalThreads) {
shape::index2coords(rank, zShapeInfo + 1, i, len, coords);
const auto zOffset = shape::getOffset(0, zShapeInfo + 1, zShapeInfo + rank + 1, coords, rank);
if((coords[rank - 2] + diag > coords[rank - 1])) // row + diag > col
z[zOffset] = 0;
else
z[zOffset] = x[areSameOffsets ? zOffset : shape::getOffset(0, xShapeInfo + 1, xShapeInfo + rank + 1, coords, rank)];
}
}
///////////////////////////////////////////////////////////////////
template<typename T>
static void triuBPCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const int diag) {
triuBPCuda<T><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vz, zShapeInfo, diag);
}
BUILD_SINGLE_TEMPLATE(template void triuBPCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const int diag), LIBND4J_TYPES);
///////////////////////////////////////////////////////////////////
void triuBP(nd4j::LaunchContext* context, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int diagonal) {
const int threadsPerBlock = MAX_NUM_THREADS / 4;
const int blocksPerGrid = (gradO.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
const int sharedMem = threadsPerBlock * sizeof(Nd4jLong) * gradO.rankOf() + 128;
PointersManager manager(context, "triuBP");
NDArray::prepareSpecialUse({&gradI}, {&gradO});
BUILD_SINGLE_SELECTOR(gradI.dataType(), triuBPCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), gradO.getSpecialBuffer(), gradO.getSpecialShapeInfo(), gradI.specialBuffer(), gradI.specialShapeInfo(), diagonal), LIBND4J_TYPES);
NDArray::registerSpecialUse({&gradI}, {&gradO});
manager.synchronize();
}
///////////////////////////////////////////////////////////////////
template<typename T>
__global__ static void tileBPCuda(const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, Nd4jLong* globMem) {
// x and z have same shapes
const auto x = reinterpret_cast<const T*>(vx); // gradO
auto z = reinterpret_cast<T*>(vz); // gradI
__shared__ int xRank, zRank; // xRank >= zRank
__shared__ Nd4jLong numOfXOffsets, zLen, totalThreads, *sharedMem; // xLen >= zLen
if (threadIdx.x == 0) {
extern __shared__ unsigned char shmem[];
sharedMem = reinterpret_cast<Nd4jLong*>(shmem);
xRank = shape::rank(zShapeInfo);
zLen = shape::length(zShapeInfo);
numOfXOffsets = shape::length(xShapeInfo) / zLen;
totalThreads = gridDim.x * blockDim.x;
}
__syncthreads();
const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
auto memBuff = sharedMem + threadIdx.x * 2 * xRank;
auto xOffsets = globMem + tid * numOfXOffsets;
for (Nd4jLong i = tid; i < zLen; i += totalThreads) {
const auto zOffset = shape::getIndexOffset(i, zShapeInfo, zLen);
shape::outerArrayOffsets(xOffsets, i, xShapeInfo, zShapeInfo, memBuff);
z[zOffset] = x[xOffsets[0]]; // first offset
for (Nd4jLong j = 1; j < numOfXOffsets; ++j) // rest offsets
z[zOffset] += x[xOffsets[j]];
}
}
///////////////////////////////////////////////////////////////////
template<typename T>
static void tileBPCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, Nd4jLong* globMem) {
tileBPCuda<T><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vz, zShapeInfo, globMem);
}
BUILD_SINGLE_TEMPLATE(template void tileBPCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, Nd4jLong* globMem), FLOAT_TYPES);
//////////////////////////////////////////////////////////////////////////
void tileBP(nd4j::LaunchContext * context, const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector<Nd4jLong> reps) {
NDArray memBuff('c', gradO.getShapeAsVector(), nd4j::DataType::INT64, context); // empty auxiliary array for storing device memory which will be used in kernel calculations
const int threadsPerBlock = MAX_NUM_THREADS / 4;
const int blocksPerGrid = (gradI.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
const int sharedMem = threadsPerBlock * sizeof(Nd4jLong) * 2 * gradO.rankOf() + 128;
PointersManager manager(context, "tileBP");
NDArray::prepareSpecialUse({&gradI}, {&gradO, &memBuff});
BUILD_SINGLE_SELECTOR(gradI.dataType(), tileBPCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), gradO.getSpecialBuffer(), gradO.getSpecialShapeInfo(), gradI.specialBuffer(), gradI.specialShapeInfo(), reinterpret_cast<Nd4jLong*>(memBuff.specialBuffer())), FLOAT_TYPES);
NDArray::registerSpecialUse({&gradI}, {&gradO, &memBuff});
manager.synchronize();
}
///////////////////////////////////////////////////////////////////
template<typename T>
__global__ static void scatterUpdateCuda(const int opCode, const int numOfInd,
void* vx, const Nd4jLong *xShapeInfo, const Nd4jLong *xOffsets,
void* vy, const Nd4jLong *yShapeInfo, const Nd4jLong *yOffsets,
const int* indexes) {
__shared__ T *x, *y;
__shared__ Nd4jLong arrLenX, arrLenY;
for (int e = 0; e < numOfInd; e++ ) {
const auto xIndex = indexes[e];
const bool isOwner = xIndex < gridDim.x ? blockIdx.x == xIndex : blockIdx.x == xIndex % gridDim.x;
if (!isOwner)
continue;
if (threadIdx.x == 0) {
x = reinterpret_cast<T*>(vx) + xOffsets[xIndex];
y = reinterpret_cast<T*>(vy) + yOffsets[e];
arrLenX = shape::length(xShapeInfo);
arrLenY = shape::length(yShapeInfo);
}
__syncthreads();
if (arrLenX != arrLenY)
return;
for (Nd4jLong i = threadIdx.x; i < arrLenX; i += blockDim.x) {
const auto xOffset = shape::getIndexOffset(i, xShapeInfo, arrLenX);
const auto yOffset = shape::getIndexOffset(i, yShapeInfo, arrLenY);
switch (opCode) {
case 0:
x[xOffset] += y[yOffset];
break;
case 1:
x[xOffset] -= y[yOffset];
break;
case 2:
x[xOffset] *= y[yOffset];
break;
case 3:
x[xOffset] /= y[yOffset];
break;
case 4:
x[xOffset] = y[yOffset] - x[xOffset];
break;
case 5:
x[xOffset] = y[yOffset] / x[xOffset];
break;
case 6:
x[xOffset] = y[yOffset];
break;
default:
continue;
}
}
__syncthreads();
}
}
template<typename T>
__host__ static void scatterUpdateCudaLauncher(const cudaStream_t* stream, const int opCode, const int numOfInd, void* vx, const Nd4jLong *xShapeInfo, const Nd4jLong *xOffsets, void* vy, const Nd4jLong *yShapeInfo, const Nd4jLong *yOffsets, const int* indexes) {
scatterUpdateCuda<T><<<512, 256, MAX_NUM_THREADS, *stream>>>(opCode, numOfInd, vx, xShapeInfo, xOffsets, vy, yShapeInfo, yOffsets, indexes);
}
//////////////////////////////////////////////////////////////////////////
void scatterUpdate(nd4j::LaunchContext* context, NDArray& input, NDArray& updates, const std::vector<int>* intArgs) {
const int opCode = (*intArgs)[0];
const int numOfDims = (*intArgs)[1];
const int numOfInd = (*intArgs)[2 + numOfDims];
std::vector<int> tadDimensions(numOfDims);
for (int e = 2; e < 2 + numOfDims; e++)
tadDimensions[e-2] = (*intArgs)[e];
auto packX = ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), tadDimensions);
auto packY = ConstantTadHelper::getInstance()->tadForDimensions(updates.getShapeInfo(), tadDimensions);
NDArray indices(const_cast<int*>(intArgs->data()) + numOfDims + 3, 'c', {numOfInd}, nd4j::DataType::INT32, context);
PointersManager manager(context, "scatterUpdate");
NDArray::prepareSpecialUse({&input}, {&input, &updates, &indices});
BUILD_SINGLE_SELECTOR(input.dataType(), scatterUpdateCudaLauncher, (context->getCudaStream(), opCode, numOfInd, input.specialBuffer(), packX.platformShapeInfo(), packX.platformOffsets(), updates.specialBuffer(), packY.platformShapeInfo(), packY.platformOffsets(), reinterpret_cast<int*>(indices.getSpecialBuffer())), LIBND4J_TYPES);
NDArray::registerSpecialUse({&input}, {&input, &updates, &indices});
manager.synchronize();
}
template <typename T>
static __global__ void swapShuffleKernel(T* input, Nd4jLong* shape, Nd4jLong firstDim, Nd4jLong len, nd4j::graph::RandomGenerator* rng) {
auto tid = blockIdx.x * blockDim.x;
auto step = blockDim.x * gridDim.x;
for (int i = firstDim - 1 - tid - threadIdx.x; i > 0; i -= step) {
int r = rng->relativeInt(i) % i;
if (i != r) {
T e0 = input[shape::getIndexOffset(i, shape, len)];
T e1 = input[shape::getIndexOffset(r, shape, len)];
//math::nd4j_swap<T>(input(i), input(r));
input[shape::getIndexOffset(i, shape, len)] = e1;
input[shape::getIndexOffset(r, shape, len)] = e0;
}
}
}
template <typename T>
static __global__ void fillShuffleKernel(T* input, Nd4jLong* inputShape, T* output, Nd4jLong* outputShape, Nd4jLong firstDim, Nd4jLong len, int* indices, nd4j::graph::RandomGenerator* rng) {
// PRAGMA_OMP_PARALLEL_FOR_IF((firstDim-1) > Environment::getInstance()->tadThreshold())
auto tid = blockIdx.x * blockDim.x;
auto step = blockDim.x * gridDim.x;
for(int i = firstDim - 1 - tid - threadIdx.x; i > 0; i -= step) {
int r = rng->relativeInt(i) % i;
output[shape::getIndexOffset(i, outputShape, len)] = input[shape::getIndexOffset(indices[r], inputShape, len)];
if(i != r) {
output[shape::getIndexOffset(r, outputShape, len)] = input[shape::getIndexOffset(indices[i], inputShape, len)];
// output.p(r, input.e<T>(indices[i]));
// math::nd4j_swap<int>(indices[i], indices[r]);
atomicExch(&indices[i], indices[r]);
}
}
}
//////////////////////////////////////////////////////////////////////////
template <typename T>
void randomShuffle_(nd4j::LaunchContext * context, NDArray& input, NDArray& output, nd4j::graph::RandomGenerator& rng, const bool isInplace) {
// check edge cases first
int temp;
const int firstDim = input.sizeAt(0);
auto stream = context->getCudaStream();
NDArray::prepareSpecialUse({&output}, {&input});
if(input.lengthOf() == 1 || firstDim == 1) {
if(!isInplace)
output.assign(input);
}
else if (input.isVector() || shape::isLikeVector(input.getShapeInfo(), temp)) {
// apply Fisher-Yates shuffle
nd4j::graph::RandomGenerator* dRandom = nullptr;
cudaMalloc(&dRandom, sizeof(nd4j::graph::RandomGenerator));
cudaMemcpy(dRandom, &rng, sizeof(nd4j::graph::RandomGenerator), cudaMemcpyHostToDevice);
T* inputBuf = reinterpret_cast<T*>(input.specialBuffer());
if(isInplace) {
swapShuffleKernel<T><<<128, 256, 1024, *stream>>>(inputBuf, input.specialShapeInfo(), firstDim, input.lengthOf(), dRandom);
}
else {
std::vector<int> indices(firstDim);
std::iota(indices.begin(), indices.end(), 0);
cudaMemcpy(output.specialBuffer(), input.specialBuffer(), sizeof(T), cudaMemcpyDeviceToDevice);
//output.p<T>(Nd4jLong(0), input.e<T>(0));
PointersManager pointersManager(context, "helper::randomShuffle_");
int* indicesDev = reinterpret_cast<int*>(pointersManager.replicatePointer(indices.data(), indices.size() * sizeof(int)));
T* outputBuf = reinterpret_cast<T*>(output.specialBuffer());
fillShuffleKernel<T><<<128, 256, 1024, *stream>>>(inputBuf, input.specialShapeInfo(), outputBuf, output.specialShapeInfo(), firstDim, input.lengthOf(), indicesDev, dRandom);
pointersManager.synchronize();
}
// rng.rewindH(firstDim - 1);
cudaFree(dRandom);
}
else {
// evaluate sub-arrays list of input array through all dimensions excluding first one
std::vector<int> dimensions = ShapeUtils::evalDimsToExclude(input.rankOf(), {0});
auto subArrsListIn = input.allTensorsAlongDimension(dimensions);
// apply Fisher-Yates shuffle
if(isInplace) {
PRAGMA_OMP_PARALLEL_FOR_IF((firstDim-1) > Environment::getInstance()->elementwiseThreshold())
for(int i = firstDim - 1; i > 0; --i) {
int r = rng.relativeInt(i) % i;
if(i != r)
subArrsListIn->at(i)->swapUnsafe(*subArrsListIn->at(r));
}
}
else {
// evaluate sub-arrays list of output array through all dimensions excluding first one
auto subArrsListOut = output.allTensorsAlongDimension(dimensions);
std::vector<int> indices(firstDim);
std::iota(indices.begin(), indices.end(), 0);
bool isZeroShuffled = false;
PRAGMA_OMP_PARALLEL_FOR_IF((firstDim-1) > Environment::getInstance()->tadThreshold())
for(int i = firstDim - 1; i > 0; --i) {
int r = rng.relativeInt(i) % i;
subArrsListOut->at(i)->assign(subArrsListIn->at(indices[r]));
if(r == 0)
isZeroShuffled = true;
if(i != r) {
subArrsListOut->at(r)->assign(subArrsListIn->at(indices[i]));
math::nd4j_swap<int>(indices[i], indices[r]);
}
}
if(!isZeroShuffled)
subArrsListOut->at(0)->assign(subArrsListIn->at(0));
delete subArrsListOut;
}
rng.rewindH(firstDim-1);
delete subArrsListIn;
}
NDArray::registerSpecialUse({&output}, {&input});
}
void randomShuffle(nd4j::LaunchContext * context, NDArray& input, NDArray& output, nd4j::graph::RandomGenerator& rng, const bool isInplace) {
BUILD_SINGLE_SELECTOR(input.dataType(), randomShuffle_, (context, input, output, rng, isInplace), LIBND4J_TYPES);
}
BUILD_SINGLE_TEMPLATE(template void randomShuffle_, (nd4j::LaunchContext * context, NDArray& input, NDArray& output, nd4j::graph::RandomGenerator& rng, const bool isInplace), LIBND4J_TYPES);
////////////////////////////////////////////////////////////////////////
template<typename T>
static void gatherND_(nd4j::LaunchContext * context, NDArray& input, NDArray& indices, NDArray& output) {
}
void gatherND(nd4j::LaunchContext * context, NDArray& input, NDArray& indices, NDArray& output) {
BUILD_SINGLE_SELECTOR(input.dataType(), gatherND_, (context, input, indices, output), LIBND4J_TYPES);
}
BUILD_SINGLE_TEMPLATE(template void gatherND_, (nd4j::LaunchContext * context, NDArray& input, NDArray& indices, NDArray& output), LIBND4J_TYPES);
//////////////////////////////////////////////////////////////////////////
void eye(nd4j::LaunchContext * context, NDArray& output) {
output.setIdentity();
}
//////////////////////////////////////////////////////////////////////////
template <typename T, typename Z>
static __global__ void global_mergeMaxIndex_(void **inArrs, void **inShapes, const int numArrays, void *voutput, Nd4jLong *outputShape, Nd4jLong length) {
auto output = reinterpret_cast<Z*>(voutput);
const auto tid = blockIdx.x * gridDim.x + threadIdx.x;
const auto step = gridDim.x * blockDim.x;
for (Nd4jLong e = tid; e < length; e += step) {
T mVal = -DataTypeUtils::max<T>();
Z mIdx(0);
for (int i = 0; i < numArrays; i++) {
auto x = reinterpret_cast<T*>(inArrs[i]);
auto xShape = reinterpret_cast<Nd4jLong *>(inShapes[i]);
auto val = x[shape::getIndexOffset(e, xShape, length)];;
if (mVal < val)
mIdx = static_cast<Z>(e);
}
__syncthreads();
output[shape::getIndexOffset(e, outputShape, length)] = mIdx;
}
}
template <typename T, typename Z>
static void mergeMaxIndex_(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
std::vector<void *> inBuffers(inArrs.size());
std::vector<void *> inShapes(inArrs.size());
for (int e = 0; e < inArrs.size(); e++) {
inBuffers[e] = inArrs[e]->getSpecialBuffer();
inShapes[e] = inArrs[e]->getSpecialShapeInfo();
}
PointersManager manager(context, "mergeMaxIndex");
auto pInBuffers = reinterpret_cast<void **>(manager.replicatePointer(inBuffers.data(), inBuffers.size() * sizeof(void *)));
auto pInShapes = reinterpret_cast<void **>(manager.replicatePointer(inShapes.data(), inShapes.size() * sizeof(void *)));
auto length = output.lengthOf();
global_mergeMaxIndex_<T,Z><<<512, 512, 512, *context->getCudaStream()>>>(pInBuffers, pInShapes, (int) inArrs.size(), output.getSpecialBuffer(), output.getSpecialShapeInfo(), length);
manager.synchronize();
}
void mergeMaxIndex(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
BUILD_DOUBLE_SELECTOR(inArrs[0]->dataType(), output.dataType(), mergeMaxIndex_, (context, inArrs, output), LIBND4J_TYPES, INTEGER_TYPES);
}
BUILD_DOUBLE_TEMPLATE(template void mergeMaxIndex_, (nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output), LIBND4J_TYPES, INTEGER_TYPES);
//////////////////////////////////////////////////////////////////////////
template <typename T>
static __global__ void global_mergeMax_(void **inArrs, void **inShapes, const int numArrays, void *voutput, Nd4jLong *outputShape, Nd4jLong length) {
auto output = reinterpret_cast<T*>(voutput);
const auto tid = blockIdx.x * gridDim.x + threadIdx.x;
const auto step = gridDim.x * blockDim.x;
for (Nd4jLong e = tid; e < length; e += step) {
T mVal = -DataTypeUtils::max<T>();
for (int i = 0; i < numArrays; i++) {
auto x = reinterpret_cast<T*>(inArrs[i]);
auto xShape = reinterpret_cast<Nd4jLong *>(inShapes[i]);
auto val = x[shape::getIndexOffset(e, xShape, length)];;
if (mVal < val)
mVal = val;
}
__syncthreads();
output[shape::getIndexOffset(e, outputShape, length)] = mVal;
}
}
template<typename T>
static void mergeMax_(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
std::vector<void *> inBuffers(inArrs.size());
std::vector<void *> inShapes(inArrs.size());
for (int e = 0; e < inArrs.size(); e++) {
inBuffers[e] = inArrs[e]->getSpecialBuffer();
inShapes[e] = inArrs[e]->getSpecialShapeInfo();
}
PointersManager manager(context, "mergeMax");
auto pInBuffers = reinterpret_cast<void **>(manager.replicatePointer(inBuffers.data(), inBuffers.size() * sizeof(void *)));
auto pInShapes = reinterpret_cast<void **>(manager.replicatePointer(inShapes.data(), inShapes.size() * sizeof(void *)));
auto length = output.lengthOf();
global_mergeMax_<T><<<512, 512, 512, *context->getCudaStream()>>>(pInBuffers, pInShapes, (int) inArrs.size(), output.getSpecialBuffer(), output.getSpecialShapeInfo(), length);
manager.synchronize();
}
BUILD_SINGLE_TEMPLATE(template void mergeMax_, (nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output), LIBND4J_TYPES);
void mergeMax(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
BUILD_SINGLE_SELECTOR(output.dataType(), mergeMax_, (context, inArrs, output), LIBND4J_TYPES);
}
//////////////////////////////////////////////////////////////////////////
template <typename T>
static __global__ void global_mergeAvg_(void **inArrs, void **inShapes, const int numArrays, void *voutput, Nd4jLong *outputShape, Nd4jLong length) {
auto output = reinterpret_cast<T*>(voutput);
const auto tid = blockIdx.x * gridDim.x + threadIdx.x;
const auto step = gridDim.x * blockDim.x;
for (Nd4jLong e = tid; e < length; e += step) {
T sum(0.0f);
for (int i = 0; i < numArrays; i++) {
auto x = reinterpret_cast<T*>(inArrs[i]);
auto xShape = reinterpret_cast<Nd4jLong *>(inShapes[i]);
sum += x[shape::getIndexOffset(e, xShape, length)];
}
output[shape::getIndexOffset(e, outputShape, length)] = sum / numArrays;
}
}
template<typename T>
static void mergeAvg_(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
std::vector<void *> inBuffers(inArrs.size());
std::vector<void *> inShapes(inArrs.size());
for (int e = 0; e < inArrs.size(); e++) {
inBuffers[e] = inArrs[e]->getSpecialBuffer();
inShapes[e] = inArrs[e]->getSpecialShapeInfo();
}
PointersManager manager(context, "mergeAvg");
auto pInBuffers = reinterpret_cast<void **>(manager.replicatePointer(inBuffers.data(), inBuffers.size() * sizeof(void *)));
auto pInShapes = reinterpret_cast<void **>(manager.replicatePointer(inShapes.data(), inShapes.size() * sizeof(void *)));
auto length = output.lengthOf();
global_mergeAvg_<T><<<512, 512, 512, *context->getCudaStream()>>>(pInBuffers, pInShapes, (int) inArrs.size(), output.getSpecialBuffer(), output.getSpecialShapeInfo(), length);
manager.synchronize();
}
BUILD_SINGLE_TEMPLATE(template void mergeAvg_, (nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output), LIBND4J_TYPES);
void mergeAvg(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
BUILD_SINGLE_SELECTOR(output.dataType(), mergeAvg_, (context, inArrs, output), LIBND4J_TYPES);
}
//////////////////////////////////////////////////////////////////////////
template <typename T>
static __global__ void global_mergeAdd_(void **inArrs, void **inShapes, const int numArrays, void *voutput, Nd4jLong *outputShape, Nd4jLong length) {
auto output = reinterpret_cast<T*>(voutput);
const auto tid = blockIdx.x * gridDim.x + threadIdx.x;
const auto step = gridDim.x * blockDim.x;
for (Nd4jLong e = tid; e < length; e += step) {
T sum(0.0f);
for (int i = 0; i < numArrays; i++) {
auto x = reinterpret_cast<T*>(inArrs[i]);
auto xShape = reinterpret_cast<Nd4jLong *>(inShapes[i]);
sum += x[shape::getIndexOffset(e, xShape, length)];
}
output[shape::getIndexOffset(e, outputShape, length)] = sum;
}
}
template<typename T>
static void mergeAdd_(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
std::vector<void *> inBuffers(inArrs.size());
std::vector<void *> inShapes(inArrs.size());
for (int e = 0; e < inArrs.size(); e++) {
inBuffers[e] = inArrs[e]->getSpecialBuffer();
inShapes[e] = inArrs[e]->getSpecialShapeInfo();
}
PointersManager manager(context, "mergeAdd");
auto pInBuffers = reinterpret_cast<void **>(manager.replicatePointer(inBuffers.data(), inBuffers.size() * sizeof(void *)));
auto pInShapes = reinterpret_cast<void **>(manager.replicatePointer(inShapes.data(), inShapes.size() * sizeof(void *)));
auto length = output.lengthOf();
global_mergeAdd_<T><<<512, 512, 512, *context->getCudaStream()>>>(pInBuffers, pInShapes, (int) inArrs.size(), output.getSpecialBuffer(), output.getSpecialShapeInfo(), length);
manager.synchronize();
}
BUILD_SINGLE_TEMPLATE(template void mergeAdd_, (nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output), LIBND4J_TYPES);
void mergeAdd(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
BUILD_SINGLE_SELECTOR(output.dataType(), mergeAdd_, (context, inArrs, output), LIBND4J_TYPES);
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename T>
static __global__ void clipByNormInplaceKernel(Nd4jLong numOfSubArrs, T* inputBuffer, Nd4jLong* shape, Nd4jLong* inputOffsets, T* norm2Buf, Nd4jLong* norm2shape, T clipNorm) {
for (int arr = blockIdx.x; arr < numOfSubArrs; arr += gridDim.x) {
__shared__ T* z;
__shared__ Nd4jLong len;
if (threadIdx.x == 0) {
len = shape::length(shape);
z = inputBuffer + inputOffsets[arr];
}
__syncthreads();
for (int j = threadIdx.x; j < len; j+= blockDim.x) {
auto xIndex = shape::getIndexOffset(j, shape, len);
if(norm2Buf[arr] > clipNorm)
z[xIndex] *= clipNorm / norm2Buf[arr]; // case with ews = 1 and ordering is 'c'
}
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename T>
static __global__ void clipByNormKernel(Nd4jLong numOfSubArrs, T* inputBuffer, Nd4jLong* shape, Nd4jLong* inputOffsets, T* outputBuffer, Nd4jLong* outputShape, Nd4jLong* outputOffsets, T* norm2Buf, Nd4jLong* norm2shape, T clipNorm) {
for (Nd4jLong arr = blockIdx.x; arr < numOfSubArrs; arr += gridDim.x) {
__shared__ T* x, *z;
__shared__ Nd4jLong lenX, lenZ;
__shared__ T norm2;
if (threadIdx.x == 0) {
lenX = shape::length(shape);
x = inputBuffer + inputOffsets[arr];
z = outputBuffer + outputOffsets[arr];
lenZ = shape::length(outputShape);
norm2 = norm2Buf[shape::getIndexOffset(arr, norm2shape, numOfSubArrs)];
//printf("%d: %lf (vs %lf) %lld %lld\n", arr, norm2, clipNorm, lenX, lenZ);
}
__syncthreads();
for (Nd4jLong j = threadIdx.x; j < lenZ; j+= blockDim.x) {
auto xIndex = shape::getIndexOffset(j, shape, lenX);
auto zIndex = shape::getIndexOffset(j, outputShape, lenZ);
if(norm2 > clipNorm) {
z[zIndex] = x[xIndex] * clipNorm / norm2; // case with ews = 1 and ordering is 'c'
} else {
z[zIndex] = x[xIndex];
}
//printf("%lld: %lf %lf\n", j, z[zIndex], x[xIndex]);
}
__syncthreads();
}
}
//////////////////////////////////////////////////////////////////////////
template<typename T>
static void clipByNorm_(nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector<int>& dimensions, NDArray const& clipNormA, const bool isInplace) {
const int rank = input.rankOf();
auto norm2 = input.reduceAlongDims(reduce::Norm2, dimensions);
clipNormA.syncToHost();
//norm2.printBuffer("Norm2");
T const clipNorm = clipNormA.e<T>(0);
//clipNormA.printBuffer("ClipNorm");
auto stream = context->getCudaStream();
if (isInplace) {
if(norm2.lengthOf() == 1) {
norm2.syncToHost();
T norm2Val = norm2.e<T>(0);
if(norm2Val > clipNorm)
input *= clipNorm / norm2Val;
}
else {
std::vector<int> dimsToExclude = ShapeUtils::evalDimsToExclude(rank, dimensions);
const Nd4jLong numOfSubArrs = ShapeUtils::getNumOfSubArrs(input.getShapeInfo(), dimsToExclude);
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimensions);
//auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output.getShapeInfo(), dimsToExclude);
T* inputBuffer = reinterpret_cast<T*>(input.specialBuffer());
T* norm2buf = reinterpret_cast<T*>(norm2.specialBuffer());
clipByNormInplaceKernel<T><<<256, 512, 1024, *stream>>>(numOfSubArrs, inputBuffer, packX.specialShapeInfo(), packX.specialOffsets(), norm2buf, norm2.specialShapeInfo(), clipNorm);
}
}
else {
if(norm2.lengthOf() == 1) {
norm2.syncToHost();
T norm2Val = norm2.e<T>(0);
if(norm2Val > clipNorm)
output.assign( input * (clipNorm / norm2Val));
else
output.assign( input );
}
else {
std::vector<int> dimsToExclude = ShapeUtils::evalDimsToExclude(rank, dimensions);
const Nd4jLong numOfSubArrs = ShapeUtils::getNumOfSubArrs(input.getShapeInfo(), dimsToExclude);
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimensions);
auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output.getShapeInfo(), dimensions);
T* inputBuffer = reinterpret_cast<T*>(input.specialBuffer());
T* norm2buf = reinterpret_cast<T*>(norm2.specialBuffer());
T* outputBuffer = reinterpret_cast<T*>(output.specialBuffer());
clipByNormKernel<T><<<256, 512, 1024, *stream>>>(numOfSubArrs, inputBuffer, packX.specialShapeInfo(), packX.specialOffsets(), outputBuffer, packZ.specialShapeInfo(), packZ.specialOffsets(), norm2buf, norm2.specialShapeInfo(), clipNorm);
}
}
}
void clipByNorm(nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace) {
BUILD_SINGLE_SELECTOR(output.dataType(), clipByNorm_, (context, input, output, dimensions, clipNorm, isInplace), FLOAT_TYPES);
}
BUILD_SINGLE_TEMPLATE(template void clipByNorm_, (nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace), FLOAT_TYPES);
template <typename T>
static void clipByGlobalNorm_(nd4j::LaunchContext * context, std::vector<NDArray*> const& inputs, double clipNorm, nd4j::memory::Workspace* workspace, std::vector<NDArray*>& outputs, bool isInplace) {
}
void clipByGlobalNorm(nd4j::LaunchContext * context, std::vector<NDArray*> const& inputs, double clipNorm, nd4j::memory::Workspace* workspace, std::vector<NDArray*>& outputs, bool isInplace) {
BUILD_SINGLE_SELECTOR(outputs[0]->dataType(), clipByGlobalNorm_, (context, inputs, clipNorm, workspace, outputs, isInplace), FLOAT_TYPES);
}
BUILD_SINGLE_TEMPLATE(template void clipByGlobalNorm_, (nd4j::LaunchContext * context, std::vector<NDArray*> const& inputs, double clipNorm, nd4j::memory::Workspace* workspace, std::vector<NDArray*>& outputs, bool isInplace), FLOAT_TYPES);
//////////////////////////////////////////////////////////////////////////
template<typename T>
static void clipByNormBP_(nd4j::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI /*output*/, const std::vector<int>& dimensions, const NDArray& clipNorm) {
}
void clipByNormBP(nd4j::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI /*output*/, const std::vector<int>& dimensions, const NDArray& clipNorm) {
BUILD_SINGLE_SELECTOR(gradI.dataType(), clipByNormBP_, (context, input, gradO, gradI, dimensions, clipNorm), FLOAT_TYPES);
}
BUILD_SINGLE_TEMPLATE(template void clipByNormBP_, (nd4j::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI /*output*/, const std::vector<int>& dimensions, const NDArray& clipNorm), FLOAT_TYPES);
//////////////////////////////////////////////////////////////////////////
template<typename T>
static void clipByAveraged_(nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace) {
auto cn = clipNorm.e<T>(0);
if (dimensions.size() == 0) {
// all-reduce
T n2 = input.reduceNumber(reduce::Norm2).e<T>(0) / input.lengthOf();
if (n2 <= cn) {
if (!isInplace)
output.assign(input);
}
else {
const T factor = cn / n2;
//auto lambda = LAMBDA_T(_x, factor) { return _x * factor; };
//input.applyLambda<T>(lambda, &output);
output.assign(input * factor);
}
}
else {
// along dimension
auto norm2 = input.reduceAlongDims(reduce::Norm2, dimensions, false);
if (!isInplace)
output.assign(input);
auto tads = output.allTensorsAlongDimension(dimensions);
auto outTads = output.allTensorsAlongDimension(dimensions);
// TODO: make this CUDA-compliant somehow
for (int e = 0; e < tads->size(); e++) {
T n2 = norm2.e<T>(e) / tads->at(e)->lengthOf();
const T factor = cn / n2;
if (n2 > cn) {
//auto lambda = LAMBDA_T(_x, factor) {return _x * factor;};
tads->at(e)->applyScalar(scalar::Multiply, factor, outTads->at(e));//applyLambda<T>(lambda, &output);
}
}
delete tads;
delete outTads;
}
}
void clipByAveraged(nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace) {
BUILD_SINGLE_SELECTOR(input.dataType(), clipByAveraged_, (context, input, output, dimensions, clipNorm, isInplace), FLOAT_TYPES);
}
BUILD_SINGLE_TEMPLATE(template void clipByAveraged_, (nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace), FLOAT_TYPES);
/*
if (d1 > params[1])
return params[1];
else if (d1 < params[0])
return params[0];
else return d1;
*/
template <typename T>
static void __global__ clipByValueKernel(void* input, Nd4jLong* inputShape, void* output, Nd4jLong* outputShape, double leftBound, double rightBound) {
__shared__ T* outputBuf;
__shared__ T* inputBuf;
__shared__ Nd4jLong length;
__shared__ bool linearBuffers;
if (threadIdx.x == 0) {
outputBuf = reinterpret_cast<T *>(output);
inputBuf = reinterpret_cast<T *>(input);
length = shape::length(inputShape);
linearBuffers = shape::elementWiseStride(inputShape) == shape::elementWiseStride(outputShape) && shape::elementWiseStride(inputShape) == 1;
}
__syncthreads();
const auto tid = blockIdx.x * gridDim.x + threadIdx.x;
const auto step = gridDim.x * blockDim.x;
for (Nd4jLong e = tid; e < length; e += step) {
if (linearBuffers) {
if (inputBuf[e] > rightBound) outputBuf[e] = (T) rightBound;
else if (inputBuf[e] < leftBound) outputBuf[e] = (T) leftBound;
else outputBuf[e] = inputBuf[e];
}
else {
auto inputOffset = shape::getIndexOffset(e, inputShape, length);
auto outputOffset = shape::getIndexOffset(e, outputShape, length);
if (inputBuf[inputOffset] > rightBound) outputBuf[outputOffset] = (T) rightBound;
else if (inputBuf[inputOffset] < leftBound) outputBuf[outputOffset] = (T) leftBound;
else outputBuf[outputOffset] = inputBuf[outputOffset];
}
}
}
template <typename T>
static void clipByValue_(nd4j::LaunchContext * context, NDArray& input, double leftBound, double rightBound, NDArray& output) {
auto stream = context->getCudaStream();
if (!input.isActualOnDeviceSide())
input.syncToDevice();
NDArray::prepareSpecialUse({&output}, {&input});
clipByValueKernel<T><<<256, 512, 8192, *stream>>>(input.specialBuffer(), input.specialShapeInfo(), output.specialBuffer(), output.specialShapeInfo(), leftBound, rightBound);
NDArray::registerSpecialUse({&output}, {&input});
}
void clipByValue(nd4j::LaunchContext * context, NDArray& input, double leftBound, double rightBound, NDArray& output) {
BUILD_SINGLE_SELECTOR(input.dataType(), clipByValue_, (context, input, leftBound, rightBound, output), FLOAT_TYPES);
}
BUILD_SINGLE_TEMPLATE(template void clipByValue_, (nd4j::LaunchContext * context, NDArray& input, double leftBound, double rightBound, NDArray& output);, FLOAT_TYPES);
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename T>
static __global__ void mirrorPadLinearKernel(void const* vx, Nd4jLong* xShape, void* vz, Nd4jLong* zShape, Nd4jLong leftSide, Nd4jLong leftSideCorrected, Nd4jLong xLen, Nd4jLong len, Nd4jLong zLen) {
__shared__ T const* x;
__shared__ T* z;
if (threadIdx.x == 0) {
x = reinterpret_cast<T const*>(vx);
z = reinterpret_cast<T*>(vz);
}
__syncthreads();
auto start = blockIdx.x * blockDim.x + threadIdx.x;
auto step = blockDim.x * gridDim.x;
for(int i = start; i < zLen; i+= step) {
auto zIndex = shape::getIndexOffset(i, zShape, zLen);
auto xIndex = shape::getIndexOffset(len - i, xShape, xLen);
if (i < leftSide) // left side
xIndex = shape::getIndexOffset(leftSideCorrected - i, xShape, xLen);
else if(i >= leftSide && i < leftSide + xLen) // middle
xIndex = shape::getIndexOffset(i - leftSide, xShape, xLen);
// else // right side
// z[i] = x[len - i];
z[zIndex] = x[xIndex];
}
}
template <typename F, typename I>
static __global__ void mirrorPadKernel(void const* vx, Nd4jLong* xShape, void* vz, Nd4jLong* zShape, Nd4jLong outLen, void const* paddings, Nd4jLong* paddingShape, int reflBorder) {
__shared__ F const* x;
__shared__ I const* pads;
__shared__ F* z;
__shared__ Nd4jLong zRank, rank;
__shared__ Nd4jLong* xShapeOf, *xStrideOf, *padsShapeOf, *padsStrideOf;
__shared__ Nd4jLong* zShapeOf, *zStrideOf;
__shared__ Nd4jLong* xIdx;
if (threadIdx.x == 0) {
extern __shared__ unsigned char shmem[];
xIdx = reinterpret_cast<Nd4jLong*>(shmem);
rank = shape::rank(xShape);
x = reinterpret_cast<F const*>(vx);//
pads = reinterpret_cast<I const*>(paddings);
z = reinterpret_cast<F*>(vz);
xShapeOf = shape::shapeOf(xShape);
xStrideOf = shape::stride(xShape);
zShapeOf = shape::shapeOf(zShape);
zRank = shape::rank(zShape);
zStrideOf = shape::stride(zShape);
padsShapeOf = shape::shapeOf(paddingShape);
padsStrideOf = shape::stride(paddingShape);
}
__syncthreads();
auto start = threadIdx.x + blockIdx.x * blockDim.x;
auto step = blockDim.x * gridDim.x;
for(Nd4jLong i = start; i < outLen; i+= step) {
auto xzCoord = xIdx + threadIdx.x * rank;
//auto zxCoord = xIdx + (threadIdx.x + threadIdx.x % 2 + 1) * rank;
shape::index2coords(rank, zShapeOf, i, xzCoord);
auto outOffset = shape::getOffset(0, zShapeOf, zStrideOf, xzCoord, rank);
// auto intStep = blockDim.y * gridDim.y;
for(int j = 0; j < rank; j++) {
const Nd4jLong inLen = shape::sizeAt(xShape, j);
Nd4jLong coords[2] = {j, 0};
auto padOffset = shape::getOffset(0, padsShapeOf, padsStrideOf, coords, 2); // padding already has rank 2
const auto leftSide = pads[padOffset];
const auto leftSideCorrected = leftSide - reflBorder;
const Nd4jLong len = 2 * (inLen - 1) + leftSide + reflBorder;
if(xzCoord[j] < leftSide) // left side
xzCoord[j] = leftSideCorrected - xzCoord[j];
else if(xzCoord[j] >= leftSide && xzCoord[j] < leftSide + inLen) // middle
xzCoord[j] = xzCoord[j] - leftSide;
else if (len > xzCoord[j]) // right side
xzCoord[j] = len - xzCoord[j];
else
xzCoord[j] = xzCoord[j] - len;
}
auto inOffset = shape::getOffset(0, xShapeOf, xStrideOf, xzCoord, rank);
z[outOffset] = x[inOffset];
}
}
template<typename F, typename I>
static void mirrorPad_(nd4j::LaunchContext * context, const NDArray& input, const NDArray& paddings, NDArray& output, const int mode) {
// mode: 0 - REFLECT, else - SYMMETRIC
const int reflBorder = (bool)mode ? 1 : 0;
const int rank = input.rankOf();
const Nd4jLong outLen = output.lengthOf();
auto stream = context->getCudaStream();
NDArray::prepareSpecialUse({&output}, {&input, &paddings});
if(rank <= 1) {
const Nd4jLong inLen = input.lengthOf();
const auto leftSide = paddings.e<Nd4jLong>(0);
const auto leftSideCorrected = leftSide - reflBorder;
const Nd4jLong len = 2*(inLen-1) + leftSide + reflBorder;
mirrorPadLinearKernel<F><<<256, 512, 256, *stream>>>(input.getSpecialBuffer(), input.getSpecialShapeInfo(), output.specialBuffer(), output.specialShapeInfo(), leftSide, leftSideCorrected, inLen, len, outLen);
nd4j::DebugHelper::checkErrorCode(stream, "helpers::mirrorPadLinearKernel(...) failed");
}
else {
mirrorPadKernel<F, I><<<256, 256, 8192, *stream>>>(input.getSpecialBuffer(), input.getSpecialShapeInfo(), output.specialBuffer(), output.specialShapeInfo(), outLen, paddings.getSpecialBuffer(), paddings.getSpecialShapeInfo(), reflBorder);
nd4j::DebugHelper::checkErrorCode(stream, "helpers::mirrorPadKernel(...) failed");
}
NDArray::registerSpecialUse({&output}, {&input, &paddings});
}
void mirrorPad(nd4j::LaunchContext * context, const NDArray& input, const NDArray& paddings, NDArray& output, const int mode) {
BUILD_DOUBLE_SELECTOR(input.dataType(), paddings.dataType(), mirrorPad_, (context, input, paddings, output, mode), LIBND4J_TYPES, INTEGER_TYPES);
}
BUILD_DOUBLE_TEMPLATE(template void mirrorPad_, (nd4j::LaunchContext * context, const NDArray& input, const NDArray& paddings, NDArray& output, const int mode), LIBND4J_TYPES, INTEGER_TYPES);
//////////////////////////////////////////////////////////////////////////
void concat(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output, const int axis) {
const int numOfArrs = inArrs.size();
for(int i = 0; i < numOfArrs; ++i)
if(!inArrs[i]->isActualOnDeviceSide()) inArrs[i]->syncToDevice();
const int rank = inArrs[0]->rankOf();
const int rank2 = 2*rank;
std::vector<std::vector<Nd4jLong>> indices(numOfArrs, std::vector<Nd4jLong>(rank2,0));
// take into account indices for first array
indices[0][2 * axis + 1] = inArrs[0]->sizeAt(axis);
// loop through the rest of input arrays
for(int i = 1; i < numOfArrs; ++i) {
indices[i][2 * axis] = indices[i-1][2 * axis + 1]; // index start from
indices[i][2 * axis + 1] = indices[i-1][2 * axis + 1] + inArrs[i]->sizeAt(axis); // index end with (excluding)
}
std::vector<NDArray*> outSubArrs(numOfArrs);
for(int i = 0; i < numOfArrs; ++i)
outSubArrs[i] = new NDArray(output(indices[i], true));
// prepare arrays of pointers on buffers and shapes
std::vector<void*> hOutBuffers(numOfArrs), hInBuffers(numOfArrs);
std::vector<Nd4jLong*> hOutShapeInfo(numOfArrs), hInShapeInfo(numOfArrs);
for(int i = 0; i < numOfArrs; ++i) {
hOutBuffers[i] = outSubArrs[i]->getSpecialBuffer();
hInBuffers[i] = inArrs[i]->getSpecialBuffer();
hOutShapeInfo[i] = outSubArrs[i]->getSpecialShapeInfo();
hInShapeInfo[i] = inArrs[i]->getSpecialShapeInfo();
}
// allocate and copy all buffers and shapes arrays to global memory
PointersManager manager(context, "helpers::concat");
void* dOutBuffers = manager.replicatePointer(hOutBuffers.data(), hOutBuffers.size() * sizeof(void*));
void* dInBuffers = manager.replicatePointer(hInBuffers.data(), hInBuffers.size() * sizeof(void*));
void* dInShapeInfo = manager.replicatePointer(hInShapeInfo.data(), hInShapeInfo.size() * sizeof(Nd4jLong*));
void* dOutShapeInfo = manager.replicatePointer(hOutShapeInfo.data(), hOutShapeInfo.size() * sizeof(Nd4jLong*));
BUILD_SINGLE_SELECTOR(inArrs[0]->dataType(), concatCudaLauncher, (numOfArrs, context->getCudaStream(), dInBuffers, dInShapeInfo, dOutBuffers, dOutShapeInfo), LIBND4J_TYPES);
manager.synchronize();
for(int i = 0; i < numOfArrs; ++i)
delete outSubArrs[i];
for(int i = 0; i < numOfArrs; ++i)
inArrs[i]->tickReadHost();
output.tickWriteDevice();
}
template <typename X, typename Y>
static _CUDA_G void scatterSimpleKernel(void *vx, Nd4jLong *xTadShape, Nd4jLong *xTadOffsets, Nd4jLong xLength, Nd4jLong numTads, void *vi, Nd4jLong *iShapeInfo, Nd4jLong iLength, void *vu, Nd4jLong *uShapeInfo, Nd4jLong uLength) {
auto u = reinterpret_cast<X*>(vu);
auto indices = reinterpret_cast<Y*>(vi);
auto tid = threadIdx.x + blockIdx.x * blockDim.x;
for (int i = tid; i < iLength; i += blockDim.x * gridDim.x) {
auto x = reinterpret_cast<X*>(vx) + xTadOffsets[i];
auto idx = indices[shape::getIndexOffset(i, iShapeInfo, iLength)];
x[shape::getIndexOffset(idx, xTadShape, xLength)] = u[shape::getIndexOffset(i, uShapeInfo, uLength)];
}
}
template <typename X, typename Y>
void scatterSimple_(nd4j::LaunchContext * context, const int opId, NDArray& input, const NDArray& updates, const NDArray& indices, const std::vector<int>& dimensions) {
auto dims = ShapeUtils::evalDimsToExclude(input.rankOf(), dimensions);
auto packX = ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dims);
auto xLength = shape::length(packX.primaryShapeInfo());
auto iLength = indices.lengthOf();
auto uLength = updates.lengthOf();
scatterSimpleKernel<X,Y><<<256, 256, 1024, *context->getCudaStream()>>>(input.getSpecialBuffer(), packX.platformShapeInfo(), packX.platformOffsets(), xLength, packX.numberOfTads(), indices.getSpecialBuffer(), indices.getSpecialShapeInfo(), iLength, updates.getSpecialBuffer(), updates.getSpecialShapeInfo(), uLength);
}
void scatterSimple(nd4j::LaunchContext * context, const int opId, NDArray& input, const NDArray& updates, const NDArray& indices, const std::vector<int>& dimensions) {
auto xType = input.dataType();
auto yType = indices.dataType();
if (opId != 6)
throw std::runtime_error("scatterSimple: only copy op is supported");
NDArray::prepareSpecialUse({&input}, {&updates, &indices});
BUILD_DOUBLE_SELECTOR(xType, yType, scatterSimple_, (context, opId, input, updates, indices, dimensions), LIBND4J_TYPES, INTEGER_TYPES);
NDArray::registerSpecialUse({&input}, {&updates, &indices});
}
BUILD_SINGLE_TEMPLATE(template void concatCudaLauncher, (const int numOfArrs, const cudaStream_t *stream, void* pVx, void* pxShapeInfo, void* pVz, void* pzShapeInfo), LIBND4J_TYPES);
BUILD_DOUBLE_TEMPLATE(template void padCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const int mode, const void *vx, const Nd4jLong *xShapeInfo, const void *vy, const Nd4jLong *yShapeInfo, void *vz, const Nd4jLong *zShapeInfo, const void* vPadVal), LIBND4J_TYPES, INTEGER_TYPES);
}
}
}