Array<T> unaryOp(const Array<T> &in, dim4 outDim = dim4(-1, -1, -1, -1)) {
jit::Node_ptr in_node = in.getNode();
jit::UnaryNode<T, T, op> *node = new jit::UnaryNode<T, T, op>(in_node);
if (outDim == dim4(-1, -1, -1, -1)) { outDim = in.dims(); }
return createNodeArray<T>(outDim, jit::Node_ptr(node));
}
void sortBatched(Array<T>& val, bool isAscending) {
af::dim4 inDims = val.dims();
// Sort dimension
af::dim4 tileDims(1);
af::dim4 seqDims = inDims;
tileDims[dim] = inDims[dim];
seqDims[dim] = 1;
Array<uint> key = iota<uint>(seqDims, tileDims);
Array<uint> resKey = createEmptyArray<uint>(dim4());
Array<T> resVal = createEmptyArray<T>(dim4());
val.setDataDims(inDims.elements());
key.setDataDims(inDims.elements());
sort_by_key<T, uint>(resVal, resKey, val, key, 0, isAscending);
// Needs to be ascending (true) in order to maintain the indices properly
sort_by_key<uint, T>(key, val, resKey, resVal, 0, true);
val.eval();
val.setDataDims(inDims); // This is correct only for dim0
}
void assign(Array<T>& out, const af_index_t idxrs[], const Array<T>& rhs)
{
out.eval();
rhs.eval();
vector<bool> isSeq(4);
vector<af_seq> seqs(4, af_span);
// create seq vector to retrieve output dimensions, offsets & offsets
for (dim_t x=0; x<4; ++x) {
if (idxrs[x].isSeq) {
seqs[x] = idxrs[x].idx.seq;
}
isSeq[x] = idxrs[x].isSeq;
}
vector< Array<uint> > idxArrs(4, createEmptyArray<uint>(dim4()));
// look through indexs to read af_array indexs
for (dim_t x=0; x<4; ++x) {
if (!isSeq[x]) {
idxArrs[x] = castArray<uint>(idxrs[x].idx.arr);
idxArrs[x].eval();
}
}
getQueue().enqueue(kernel::assign<T>, out, rhs, std::move(isSeq),
std::move(seqs), std::move(idxArrs));
}
Array<T>* setUnique(const Array<T> &in,
const bool is_sorted)
{
if ((std::is_same<T, double>::value || std::is_same<T, cdouble>::value) &&
!isDoubleSupported(getActiveDeviceId())) {
OPENCL_NOT_SUPPORTED();
}
Array<T> *out = copyArray<T>(in);
compute::command_queue queue(getQueue()());
compute::buffer out_data((*out->get())());
compute::buffer_iterator<T> begin(out_data, 0);
compute::buffer_iterator<T> end(out_data, out->dims()[0]);
if (!is_sorted) {
compute::sort(begin, end, queue);
}
end = compute::unique(begin, end, queue);
out->resetDims(dim4(std::distance(begin, end), 1, 1, 1));
return out;
}
Array<T> diagCreate(const Array<T> &in, const int num)
{
int size = in.dims()[0] + std::abs(num);
int batch = in.dims()[1];
Array<T> out = createEmptyArray<T>(dim4(size, size, batch));
const T *iptr = in.get();
T *optr = out.get();
for (int k = 0; k < batch; k++) {
for (int j = 0; j < size; j++) {
for (int i = 0; i < size; i++) {
T val = scalar<T>(0);
if (i == j - num) {
val = (num > 0) ? iptr[i] : iptr[j];
}
optr[i + j * out.strides()[1]] = val;
}
}
optr += out.strides()[2];
iptr += in.strides()[1];
}
return out;
}
Array<T> diagExtract(const Array<T> &in, const int num)
{
const dim_t *idims = in.dims().get();
dim_t size = std::max(idims[0], idims[1]) - std::abs(num);
Array<T> out = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
const dim_t *odims = out.dims().get();
const int i_off = (num > 0) ? (num * in.strides()[1]) : (-num);
for (int l = 0; l < (int)odims[3]; l++) {
for (int k = 0; k < (int)odims[2]; k++) {
const T *iptr = in.get() + l * in.strides()[3] + k * in.strides()[2] + i_off;
T *optr = out.get() + l * out.strides()[3] + k * out.strides()[2];
for (int i = 0; i < (int)odims[0]; i++) {
T val = scalar<T>(0);
if (i < idims[0] && i < idims[1]) val = iptr[i * in.strides()[1] + i];
optr[i] = val;
}
}
}
return out;
}
Array<T>::Array(dim4 dims, const T * const in_data):
ArrayInfo(getActiveDeviceId(), dims, dim4(0,0,0,0), calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
data(memAlloc<T>(dims.elements()), memFree<T>), data_dims(dims),
node(), ready(true), offset(0), owner(true)
{
std::copy(in_data, in_data + dims.elements(), data.get());
}
Array<T> diagCreate(const Array<T> &in, const int num)
{
int size = in.dims()[0] + std::abs(num);
int batch = in.dims()[1];
Array<T> out = createEmptyArray<T>(dim4(size, size, batch));
kernel::diagCreate<T>(out, in, num);
return out;
}
Array<T> diagExtract(const Array<T> &in, const int num)
{
const dim_t *idims = in.dims().get();
dim_t size = std::min(idims[0], idims[1]) - std::abs(num);
Array<T> out = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
kernel::diagExtract<T>(out, in, num);
return out;
}
Array<T> index(const Array<T>& in, const af_index_t idxrs[])
{
kernel::IndexKernelParam_t p;
std::vector<af_seq> seqs(4, af_span);
// create seq vector to retrieve output
// dimensions, offsets & offsets
for (dim_t x=0; x<4; ++x) {
if (idxrs[x].isSeq) {
seqs[x] = idxrs[x].idx.seq;
}
}
// retrieve dimensions, strides and offsets
dim4 iDims = in.dims();
dim4 dDims = in.getDataDims();
dim4 oDims = toDims (seqs, iDims);
dim4 iOffs = toOffset(seqs, dDims);
dim4 iStrds= toStride(seqs, dDims);
for (dim_t i=0; i<4; ++i) {
p.isSeq[i] = idxrs[i].isSeq;
p.offs[i] = iOffs[i];
p.strds[i] = iStrds[i];
}
Buffer* bPtrs[4];
std::vector< Array<uint> > idxArrs(4, createEmptyArray<uint>(dim4()));
// look through indexs to read af_array indexs
for (dim_t x=0; x<4; ++x) {
// set index pointers were applicable
if (!p.isSeq[x]) {
idxArrs[x] = castArray<uint>(idxrs[x].idx.arr);
bPtrs[x] = idxArrs[x].get();
// set output array ith dimension value
oDims[x] = idxArrs[x].elements();
}
else {
// alloc an 1-element buffer to avoid OpenCL from failing
bPtrs[x] = bufferAlloc(sizeof(uint));
}
}
Array<T> out = createEmptyArray<T>(oDims);
if(oDims.elements() == 0) { return out; }
kernel::index<T>(out, in, p, bPtrs);
for (dim_t x=0; x<4; ++x) {
if (p.isSeq[x]) bufferFree(bPtrs[x]);
}
return out;
}
Array<T> diagExtract(const Array<T> &in, const int num)
{
in.eval();
const dim4 idims = in.dims();
dim_t size = std::min(idims[0], idims[1]) - std::abs(num);
Array<T> out = createEmptyArray<T>(dim4(size, 1, idims[2], idims[3]));
getQueue().enqueue(kernel::diagExtract<T>, out, in, num);
return out;
}
SparseArray<T> sparseConvertDenseToCOO(const Array<T> &in)
{
in.eval();
Array<uint> nonZeroIdx_ = where<T>(in);
Array<int> nonZeroIdx = cast<int, uint>(nonZeroIdx_);
dim_t nNZ = nonZeroIdx.elements();
Array<int> constNNZ = createValueArray<int>(dim4(nNZ), nNZ);
constNNZ.eval();
Array<int> rowIdx = arithOp<int, af_mod_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
Array<int> colIdx = arithOp<int, af_div_t>(nonZeroIdx, constNNZ, nonZeroIdx.dims());
Array<T> values = copyArray<T>(in);
values.modDims(dim4(values.elements()));
values = lookup<T, int>(values, nonZeroIdx, 0);
return createArrayDataSparseArray<T>(in.dims(), values, rowIdx, colIdx, AF_STORAGE_COO);
}
Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
{
a.eval();
b.eval();
if (options & AF_MAT_UPPER || options & AF_MAT_LOWER) {
return triangleSolve<T>(a, b, options);
}
int M = a.dims()[0];
int N = a.dims()[1];
int K = b.dims()[1];
Array<T> A = copyArray<T>(a);
Array<T> B = padArray<T, T>(b, dim4(max(M, N), K));
if(M == N) {
Array<int> pivot = createEmptyArray<int>(dim4(N, 1, 1));
auto func = [=] (Array<T> A, Array<T> B, Array<int> pivot, int N, int K) {
gesv_func<T>()(AF_LAPACK_COL_MAJOR, N, K, A.get(), A.strides()[1],
pivot.get(), B.get(), B.strides()[1]);
};
getQueue().enqueue(func, A, B, pivot, N, K);
} else {
auto func = [=] (Array<T> A, Array<T> B, int M, int N, int K) {
int sM = A.strides()[1];
int sN = A.strides()[2] / sM;
gels_func<T>()(AF_LAPACK_COL_MAJOR, 'N',
M, N, K,
A.get(), A.strides()[1],
B.get(), max(sM, sN));
};
B.resetDims(dim4(N, K));
getQueue().enqueue(func, A, B, M, N, K);
}
return B;
}
Array<T>* setIntersect(const Array<T> &first,
const Array<T> &second,
const bool is_unique)
{
if ((std::is_same<T, double>::value || std::is_same<T, cdouble>::value) &&
!isDoubleSupported(getActiveDeviceId())) {
OPENCL_NOT_SUPPORTED();
}
Array<T> unique_first = first;
Array<T> unique_second = second;
if (!is_unique) {
unique_first = *setUnique(first, false);
unique_second = *setUnique(second, false);
}
size_t out_size = std::max(unique_first.dims()[0], unique_second.dims()[0]);
Array<T> *out = createEmptyArray<T>(dim4(out_size, 1, 1, 1));
compute::command_queue queue(getQueue()());
compute::buffer first_data((*unique_first.get())());
compute::buffer second_data((*unique_second.get())());
compute::buffer out_data((*out->get())());
compute::buffer_iterator<T> first_begin(first_data, 0);
compute::buffer_iterator<T> first_end(first_data, unique_first.dims()[0]);
compute::buffer_iterator<T> second_begin(second_data, 0);
compute::buffer_iterator<T> second_end(second_data, unique_second.dims()[0]);
compute::buffer_iterator<T> out_begin(out_data, 0);
compute::buffer_iterator<T> out_end = compute::set_intersection(
first_begin, first_end, second_begin, second_end, out_begin, queue
);
out->resetDims(dim4(std::distance(out_begin, out_end), 1, 1, 1));
return out;
}
Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
{
if (options & AF_MAT_UPPER ||
options & AF_MAT_LOWER) {
return triangleSolve<T>(a, b, options);
}
int M = a.dims()[0];
int N = a.dims()[1];
int K = b.dims()[1];
Array<T> A = copyArray<T>(a);
Array<T> B = padArray<T, T>(b, dim4(max(M, N), K), scalar<T>(0));
std::shared_ptr<T> aPtr = A.getMappedPtr();
std::shared_ptr<T> bPtr = B.getMappedPtr();
if(M == N) {
std::vector<int> pivot(N);
gesv_func<T>()(AF_LAPACK_COL_MAJOR, N, K,
aPtr.get(), A.strides()[1],
&pivot.front(),
bPtr.get(), B.strides()[1]);
} else {
int sM = a.strides()[1];
int sN = a.strides()[2] / sM;
gels_func<T>()(AF_LAPACK_COL_MAJOR, 'N',
M, N, K,
aPtr.get(), A.strides()[1],
bPtr.get(), max(sM, sN));
B.resetDims(dim4(N, K));
}
return B;
}
Array<int> lu_inplace(Array<T> &in, const bool convert_pivot) {
in.eval();
dim4 iDims = in.dims();
Array<int> pivot =
createEmptyArray<int>(af::dim4(min(iDims[0], iDims[1]), 1, 1, 1));
auto func = [=](Param<T> in, Param<int> pivot) {
dim4 iDims = in.dims();
getrf_func<T>()(AF_LAPACK_COL_MAJOR, iDims[0], iDims[1], in.get(),
in.strides(1), pivot.get());
};
getQueue().enqueue(func, in, pivot);
if (convert_pivot) {
Array<int> p = range<int>(dim4(iDims[0]), 0);
getQueue().enqueue(kernel::convertPivot, p, pivot);
return p;
} else {
return pivot;
}
}
static dim4 getDims(const af_array arr)
{
dim_t d0, d1, d2, d3;
AF_THROW(af_get_dims(&d0, &d1, &d2, &d3, arr));
return dim4(d0, d1, d2, d3);
}
// Helper functions
dim4 array::dims() const
{
dim_type d0, d1, d2, d3;
AF_THROW(af_get_dims(&d0, &d1, &d2, &d3, arr));
return dim4(d0, d1, d2, d3);
}
namespace cuda
{
template<typename T>
Array<T> iota(const dim4 &dim, const dim4 &tile_dims = dim4(1));
}
array randn(const dim_type d0, af_dtype ty)
{
return randn(dim4(d0), ty);
}
Array<T> leastSquares(const Array<T> &a, const Array<T> &b)
{
int M = a.dims()[0];
int N = a.dims()[1];
int K = b.dims()[1];
int MN = std::min(M, N);
Array<T> B = createEmptyArray<T>(dim4());
gpu_blas_trsm_func<T> gpu_blas_trsm;
cl_event event;
cl_command_queue queue = getQueue()();
if (M < N) {
#define UNMQR 0 // FIXME: UNMQR == 1 should be faster but does not work
// Least squres for this case is solved using the following
// solve(A, B) == matmul(Q, Xpad);
// Where:
// Xpad == pad(Xt, N - M, 1);
// Xt == tri_solve(R1, B);
// R1 == R(seq(M), seq(M));
// transpose(A) == matmul(Q, R);
// QR is performed on the transpose of A
Array<T> A = transpose<T>(a, true);
#if UNMQR
B = padArray<T, T>(b, dim4(N, K), scalar<T>(0));
B.resetDims(dim4(M, K));
#else
B = copyArray<T>(b);
#endif
int NB = magma_get_geqrf_nb<T>(A.dims()[1]);
int NUM = (2*MN + ((M+31)/32)*32)*NB;
Array<T> tmp = createEmptyArray<T>(dim4(NUM));
std::vector<T> h_tau(MN);
int info = 0;
cl::Buffer *dA = A.get();
cl::Buffer *dT = tmp.get();
cl::Buffer *dB = B.get();
magma_geqrf3_gpu<T>(A.dims()[0], A.dims()[1],
(*dA)(), A.getOffset(), A.strides()[1],
&h_tau[0], (*dT)(), tmp.getOffset(), getQueue()(), &info);
A.resetDims(dim4(M, M));
magmablas_swapdblk<T>(MN-1, NB,
(*dA)(), A.getOffset(), A.strides()[1], 1,
(*dT)(), tmp.getOffset() + MN * NB, NB, 0, queue);
CLBLAS_CHECK(gpu_blas_trsm(
clblasLeft, clblasUpper,
clblasConjTrans, clblasNonUnit,
B.dims()[0], B.dims()[1],
scalar<T>(1),
(*dA)(), A.getOffset(), A.strides()[1],
(*dB)(), B.getOffset(), B.strides()[1],
1, &queue, 0, nullptr, &event));
magmablas_swapdblk<T>(MN - 1, NB,
(*dT)(), tmp.getOffset() + MN * NB, NB, 0,
(*dA)(), A.getOffset(), A.strides()[1], 1, queue);
#if UNMQR
int lwork = (B.dims()[0]-A.dims()[0]+NB)*(B.dims()[1]+2*NB);
std::vector<T> h_work(lwork);
B.resetDims(dim4(N, K));
magma_unmqr_gpu<T>(MagmaLeft, MagmaNoTrans,
B.dims()[0], B.dims()[1], A.dims()[0],
(*dA)(), A.getOffset(), A.strides()[1],
&h_tau[0],
(*dB)(), B.getOffset(), B.strides()[1],
&h_work[0], lwork,
(*dT)(), tmp.getOffset(), NB, queue, &info);
#else
A.resetDims(dim4(N, M));
magma_ungqr_gpu<T>(A.dims()[0], A.dims()[1], std::min(M, N),
(*dA)(), A.getOffset(), A.strides()[1],
&h_tau[0],
(*dT)(), tmp.getOffset(), NB, queue, &info);
B = matmul(A, B, AF_MAT_NONE, AF_MAT_NONE);
#endif
} else if (M > N) {
// Least squres for this case is solved using the following
// solve(A, B) == tri_solve(R1, Bt);
// Where:
// R1 == R(seq(N), seq(N));
// Bt == matmul(transpose(Q1), B);
// Q1 == Q(span, seq(N));
// A == matmul(Q, R);
Array<T> A = copyArray<T>(a);
B = copyArray(b);
int MN = std::min(M, N);
int NB = magma_get_geqrf_nb<T>(M);
int NUM = (2*MN + ((N+31)/32)*32)*NB;
Array<T> tmp = createEmptyArray<T>(dim4(NUM));
std::vector<T> h_tau(NUM);
int info = 0;
cl::Buffer *A_buf = A.get();
cl::Buffer *B_buf = B.get();
cl::Buffer *dT = tmp.get();
magma_geqrf3_gpu<T>(M, N,
(*A_buf)(), A.getOffset(), A.strides()[1],
&h_tau[0], (*dT)(), tmp.getOffset(), getQueue()(), &info);
int NRHS = B.dims()[1];
int lhwork = (M - N + NB) * (NRHS + NB) + NRHS * NB;
std::vector<T> h_work(lhwork);
h_work[0] = scalar<T>(lhwork);
magma_unmqr_gpu<T>(MagmaLeft, MagmaConjTrans,
M, NRHS, N,
(*A_buf)(), A.getOffset(), A.strides()[1],
&h_tau[0],
(*B_buf)(), B.getOffset(), B.strides()[1],
&h_work[0], lhwork,
(*dT)(), tmp.getOffset(), NB,
queue, &info);
magmablas_swapdblk<T>(MN - 1, NB,
(*A_buf)(), A.getOffset(), A.strides()[1], 1,
(*dT)(), tmp.getOffset() + NB * MN,
NB, 0, queue);
if(getActivePlatform() == AFCL_PLATFORM_NVIDIA)
{
Array<T> AT = transpose<T>(A, true);
cl::Buffer* AT_buf = AT.get();
CLBLAS_CHECK(gpu_blas_trsm(
clblasLeft, clblasLower, clblasConjTrans, clblasNonUnit,
N, NRHS, scalar<T>(1),
(*AT_buf)(), AT.getOffset(), AT.strides()[1],
(*B_buf)(), B.getOffset(), B.strides()[1],
1, &queue, 0, nullptr, &event));
} else {
CLBLAS_CHECK(gpu_blas_trsm(
clblasLeft, clblasUpper, clblasNoTrans, clblasNonUnit,
N, NRHS, scalar<T>(1),
(*A_buf)(), A.getOffset(), A.strides()[1],
(*B_buf)(), B.getOffset(), B.strides()[1],
1, &queue, 0, nullptr, &event));
}
B.resetDims(dim4(N, K));
}
return B;
}
array randn(const dim_t d0,
const dim_t d1, const dim_t d2,
const dim_t d3, const af::dtype ty)
{
return randn(dim4(d0, d1, d2, d3), ty);
}
Array<T> *initArray() {
return new Array<T>(dim4());
}
array constant(T val, const dim_t d0, const dim_t d1, const dim_t d2, const dim_t d3, const af::dtype ty)
{
return constant(val, dim4(d0, d1, d2, d3), ty);
}
array constant(T val, const dim_t d0, const af::dtype ty)
{
return constant(val, dim4(d0), ty);
}
array randu(const dim_t d0,
const dim_t d1, const dim_t d2, const af::dtype ty)
{
return randu(dim4(d0, d1, d2), ty);
}
array identity(const dim_t d0, const af::dtype ty)
{
return identity(dim4(d0), ty);
}
array range(const dim_t d0, const dim_t d1, const dim_t d2,
const dim_t d3, const int seq_dim, const af::dtype ty)
{
return range(dim4(d0, d1, d2, d3), seq_dim, ty);
}
array identity(const dim_t d0,
const dim_t d1, const dim_t d2,
const dim_t d3, const af::dtype ty)
{
return identity(dim4(d0, d1, d2, d3), ty);
}
array randn(const dim_t d0, const af::dtype ty)
{
return randn(dim4(d0), ty);
}
