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cusolver.cc
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/* Copyright 2019 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://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.
==============================================================================*/
#include <algorithm>
#include <cstdint>
#include <stdexcept>
#include <utility>
#include <vector>
#include "absl/base/casts.h"
#include "absl/base/thread_annotations.h"
#include "absl/container/flat_hash_map.h"
#include "absl/memory/memory.h"
#include "absl/strings/str_format.h"
#include "absl/synchronization/mutex.h"
#include "include/pybind11/numpy.h"
#include "include/pybind11/pybind11.h"
#include "include/pybind11/stl.h"
#include "jaxlib/cuda_gpu_kernel_helpers.h"
#include "jaxlib/handle_pool.h"
#include "jaxlib/kernel_pybind11_helpers.h"
#include "third_party/gpus/cuda/include/cuda.h"
#include "third_party/gpus/cuda/include/cuda_runtime_api.h"
#include "third_party/gpus/cuda/include/cusolverDn.h"
namespace jax {
namespace {
namespace py = pybind11;
void ThrowIfErrorStatus(cusolverStatus_t status) {
switch (status) {
case CUSOLVER_STATUS_SUCCESS:
return;
case CUSOLVER_STATUS_NOT_INITIALIZED:
throw std::runtime_error("cuSolver has not been initialized");
case CUSOLVER_STATUS_ALLOC_FAILED:
throw std::runtime_error("cuSolver allocation failed");
case CUSOLVER_STATUS_INVALID_VALUE:
throw std::runtime_error("cuSolver invalid value error");
case CUSOLVER_STATUS_ARCH_MISMATCH:
throw std::runtime_error("cuSolver architecture mismatch error");
case CUSOLVER_STATUS_MAPPING_ERROR:
throw std::runtime_error("cuSolver mapping error");
case CUSOLVER_STATUS_EXECUTION_FAILED:
throw std::runtime_error("cuSolver execution failed");
case CUSOLVER_STATUS_INTERNAL_ERROR:
throw std::runtime_error("cuSolver internal error");
case CUSOLVER_STATUS_MATRIX_TYPE_NOT_SUPPORTED:
throw std::invalid_argument("cuSolver matrix type not supported error");
case CUSOLVER_STATUS_NOT_SUPPORTED:
throw std::runtime_error("cuSolver not supported error");
case CUSOLVER_STATUS_ZERO_PIVOT:
throw std::runtime_error("cuSolver zero pivot error");
case CUSOLVER_STATUS_INVALID_LICENSE:
throw std::runtime_error("cuSolver invalid license error");
default:
throw std::runtime_error("Unknown cuSolver error");
}
}
using SolverHandlePool = HandlePool<cusolverDnHandle_t, cudaStream_t>;
template <>
/*static*/ SolverHandlePool::Handle SolverHandlePool::Borrow(
cudaStream_t stream) {
SolverHandlePool* pool = Instance();
absl::MutexLock lock(&pool->mu_);
cusolverDnHandle_t handle;
if (pool->handles_.empty()) {
ThrowIfErrorStatus(cusolverDnCreate(&handle));
} else {
handle = pool->handles_.back();
pool->handles_.pop_back();
}
if (stream) {
ThrowIfErrorStatus(cusolverDnSetStream(handle, stream));
}
return Handle(pool, handle);
}
// Set of types known to Cusolver.
enum class Type {
F32,
F64,
C64,
C128,
};
// Converts a NumPy dtype to a Type.
Type DtypeToType(const py::dtype& np_type) {
static auto* types = new absl::flat_hash_map<std::pair<char, int>, Type>({
{{'f', 4}, Type::F32},
{{'f', 8}, Type::F64},
{{'c', 8}, Type::C64},
{{'c', 16}, Type::C128},
});
auto it = types->find({np_type.kind(), np_type.itemsize()});
if (it == types->end()) {
throw std::invalid_argument(
absl::StrFormat("Unsupported dtype %s", py::repr(np_type)));
}
return it->second;
}
int SizeOfType(Type type) {
switch (type) {
case Type::F32:
return sizeof(float);
case Type::F64:
return sizeof(double);
case Type::C64:
return sizeof(cuComplex);
case Type::C128:
return sizeof(cuDoubleComplex);
}
}
// potrf: Cholesky decomposition
struct PotrfDescriptor {
Type type;
cublasFillMode_t uplo;
std::int64_t batch, n;
int lwork;
};
// Returns the workspace size and a descriptor for a potrf operation.
std::pair<int, py::bytes> BuildPotrfDescriptor(const py::dtype& dtype,
bool lower, int b, int n) {
Type type = DtypeToType(dtype);
auto handle = SolverHandlePool::Borrow();
int lwork;
std::int64_t workspace_size;
cublasFillMode_t uplo =
lower ? CUBLAS_FILL_MODE_LOWER : CUBLAS_FILL_MODE_UPPER;
if (b == 1) {
switch (type) {
case Type::F32:
ThrowIfErrorStatus(cusolverDnSpotrf_bufferSize(handle.get(), uplo, n,
/*A=*/nullptr,
/*lda=*/n, &lwork));
workspace_size = lwork * sizeof(float);
break;
case Type::F64:
ThrowIfErrorStatus(cusolverDnDpotrf_bufferSize(handle.get(), uplo, n,
/*A=*/nullptr,
/*lda=*/n, &lwork));
workspace_size = lwork * sizeof(double);
break;
case Type::C64:
ThrowIfErrorStatus(cusolverDnCpotrf_bufferSize(handle.get(), uplo, n,
/*A=*/nullptr,
/*lda=*/n, &lwork));
workspace_size = lwork * sizeof(cuComplex);
break;
case Type::C128:
ThrowIfErrorStatus(cusolverDnZpotrf_bufferSize(handle.get(), uplo, n,
/*A=*/nullptr,
/*lda=*/n, &lwork));
workspace_size = lwork * sizeof(cuDoubleComplex);
break;
}
} else {
// We use the workspace buffer for our own scratch space.
workspace_size = sizeof(void*) * b;
}
return {workspace_size,
PackDescriptor(PotrfDescriptor{type, uplo, b, n, lwork})};
}
void Potrf(cudaStream_t stream, void** buffers, const char* opaque,
size_t opaque_len) {
const PotrfDescriptor& d =
*UnpackDescriptor<PotrfDescriptor>(opaque, opaque_len);
auto handle = SolverHandlePool::Borrow(stream);
if (buffers[1] != buffers[0]) {
ThrowIfError(cudaMemcpyAsync(buffers[1], buffers[0],
SizeOfType(d.type) * d.batch * d.n * d.n,
cudaMemcpyDeviceToDevice, stream));
}
int* info = static_cast<int*>(buffers[2]);
void* workspace = buffers[3];
if (d.batch == 1) {
switch (d.type) {
case Type::F32: {
float* a = static_cast<float*>(buffers[1]);
ThrowIfErrorStatus(cusolverDnSpotrf(handle.get(), d.uplo, d.n, a, d.n,
static_cast<float*>(workspace),
d.lwork, info));
break;
}
case Type::F64: {
double* a = static_cast<double*>(buffers[1]);
ThrowIfErrorStatus(cusolverDnDpotrf(handle.get(), d.uplo, d.n, a, d.n,
static_cast<double*>(workspace),
d.lwork, info));
break;
}
case Type::C64: {
cuComplex* a = static_cast<cuComplex*>(buffers[1]);
ThrowIfErrorStatus(cusolverDnCpotrf(handle.get(), d.uplo, d.n, a, d.n,
static_cast<cuComplex*>(workspace),
d.lwork, info));
break;
}
case Type::C128: {
cuDoubleComplex* a = static_cast<cuDoubleComplex*>(buffers[1]);
ThrowIfErrorStatus(cusolverDnZpotrf(
handle.get(), d.uplo, d.n, a, d.n,
static_cast<cuDoubleComplex*>(workspace), d.lwork, info));
break;
}
}
} else {
auto buffer_ptrs_host = MakeBatchPointers(
stream, buffers[1], workspace, d.batch, SizeOfType(d.type) * d.n * d.n);
// Make sure that accesses to buffer_ptrs_host complete before we delete it.
// TODO(phawkins): avoid synchronization here.
ThrowIfError(cudaStreamSynchronize(stream));
switch (d.type) {
case Type::F32: {
ThrowIfErrorStatus(cusolverDnSpotrfBatched(
handle.get(), d.uplo, d.n, static_cast<float**>(workspace), d.n,
info, d.batch));
break;
}
case Type::F64: {
ThrowIfErrorStatus(cusolverDnDpotrfBatched(
handle.get(), d.uplo, d.n, static_cast<double**>(workspace), d.n,
info, d.batch));
break;
}
case Type::C64: {
ThrowIfErrorStatus(cusolverDnCpotrfBatched(
handle.get(), d.uplo, d.n, static_cast<cuComplex**>(workspace), d.n,
info, d.batch));
break;
}
case Type::C128: {
ThrowIfErrorStatus(cusolverDnZpotrfBatched(
handle.get(), d.uplo, d.n,
static_cast<cuDoubleComplex**>(workspace), d.n, info, d.batch));
break;
}
}
}
}
// getrf: LU decomposition
struct GetrfDescriptor {
Type type;
int batch, m, n;
};
// Returns the workspace size and a descriptor for a getrf operation.
std::pair<int, py::bytes> BuildGetrfDescriptor(const py::dtype& dtype, int b,
int m, int n) {
Type type = DtypeToType(dtype);
auto handle = SolverHandlePool::Borrow();
int lwork;
switch (type) {
case Type::F32:
ThrowIfErrorStatus(cusolverDnSgetrf_bufferSize(handle.get(), m, n,
/*A=*/nullptr,
/*lda=*/m, &lwork));
break;
case Type::F64:
ThrowIfErrorStatus(cusolverDnDgetrf_bufferSize(handle.get(), m, n,
/*A=*/nullptr,
/*lda=*/m, &lwork));
break;
case Type::C64:
ThrowIfErrorStatus(cusolverDnCgetrf_bufferSize(handle.get(), m, n,
/*A=*/nullptr,
/*lda=*/m, &lwork));
break;
case Type::C128:
ThrowIfErrorStatus(cusolverDnZgetrf_bufferSize(handle.get(), m, n,
/*A=*/nullptr,
/*lda=*/m, &lwork));
break;
}
return {lwork, PackDescriptor(GetrfDescriptor{type, b, m, n})};
}
void Getrf(cudaStream_t stream, void** buffers, const char* opaque,
size_t opaque_len) {
const GetrfDescriptor& d =
*UnpackDescriptor<GetrfDescriptor>(opaque, opaque_len);
auto handle = SolverHandlePool::Borrow(stream);
if (buffers[1] != buffers[0]) {
ThrowIfError(cudaMemcpyAsync(
buffers[1], buffers[0],
SizeOfType(d.type) * static_cast<std::int64_t>(d.batch) *
static_cast<std::int64_t>(d.m) * static_cast<std::int64_t>(d.n),
cudaMemcpyDeviceToDevice, stream));
}
int* ipiv = static_cast<int*>(buffers[2]);
int* info = static_cast<int*>(buffers[3]);
void* workspace = buffers[4];
switch (d.type) {
case Type::F32: {
float* a = static_cast<float*>(buffers[1]);
for (int i = 0; i < d.batch; ++i) {
ThrowIfErrorStatus(cusolverDnSgetrf(handle.get(), d.m, d.n, a, d.m,
static_cast<float*>(workspace),
ipiv, info));
a += d.m * d.n;
ipiv += std::min(d.m, d.n);
++info;
}
break;
}
case Type::F64: {
double* a = static_cast<double*>(buffers[1]);
for (int i = 0; i < d.batch; ++i) {
ThrowIfErrorStatus(cusolverDnDgetrf(handle.get(), d.m, d.n, a, d.m,
static_cast<double*>(workspace),
ipiv, info));
a += d.m * d.n;
ipiv += std::min(d.m, d.n);
++info;
}
break;
}
case Type::C64: {
cuComplex* a = static_cast<cuComplex*>(buffers[1]);
for (int i = 0; i < d.batch; ++i) {
ThrowIfErrorStatus(cusolverDnCgetrf(handle.get(), d.m, d.n, a, d.m,
static_cast<cuComplex*>(workspace),
ipiv, info));
a += d.m * d.n;
ipiv += std::min(d.m, d.n);
++info;
}
break;
}
case Type::C128: {
cuDoubleComplex* a = static_cast<cuDoubleComplex*>(buffers[1]);
for (int i = 0; i < d.batch; ++i) {
ThrowIfErrorStatus(cusolverDnZgetrf(
handle.get(), d.m, d.n, a, d.m,
static_cast<cuDoubleComplex*>(workspace), ipiv, info));
a += d.m * d.n;
ipiv += std::min(d.m, d.n);
++info;
}
break;
}
}
}
// geqrf: QR decomposition
struct GeqrfDescriptor {
Type type;
int batch, m, n, lwork;
};
// Returns the workspace size and a descriptor for a geqrf operation.
std::pair<int, py::bytes> BuildGeqrfDescriptor(const py::dtype& dtype, int b,
int m, int n) {
Type type = DtypeToType(dtype);
auto handle = SolverHandlePool::Borrow();
int lwork;
switch (type) {
case Type::F32:
ThrowIfErrorStatus(cusolverDnSgeqrf_bufferSize(handle.get(), m, n,
/*A=*/nullptr,
/*lda=*/m, &lwork));
break;
case Type::F64:
ThrowIfErrorStatus(cusolverDnDgeqrf_bufferSize(handle.get(), m, n,
/*A=*/nullptr,
/*lda=*/m, &lwork));
break;
case Type::C64:
ThrowIfErrorStatus(cusolverDnCgeqrf_bufferSize(handle.get(), m, n,
/*A=*/nullptr,
/*lda=*/m, &lwork));
break;
case Type::C128:
ThrowIfErrorStatus(cusolverDnZgeqrf_bufferSize(handle.get(), m, n,
/*A=*/nullptr,
/*lda=*/m, &lwork));
break;
}
return {lwork, PackDescriptor(GeqrfDescriptor{type, b, m, n, lwork})};
}
void Geqrf(cudaStream_t stream, void** buffers, const char* opaque,
size_t opaque_len) {
const GeqrfDescriptor& d =
*UnpackDescriptor<GeqrfDescriptor>(opaque, opaque_len);
auto handle = SolverHandlePool::Borrow(stream);
if (buffers[1] != buffers[0]) {
ThrowIfError(cudaMemcpyAsync(
buffers[1], buffers[0],
SizeOfType(d.type) * static_cast<std::int64_t>(d.batch) *
static_cast<std::int64_t>(d.m) * static_cast<std::int64_t>(d.n),
cudaMemcpyDeviceToDevice, stream));
}
int* info = static_cast<int*>(buffers[3]);
void* workspace = buffers[4];
switch (d.type) {
case Type::F32: {
float* a = static_cast<float*>(buffers[1]);
float* tau = static_cast<float*>(buffers[2]);
for (int i = 0; i < d.batch; ++i) {
ThrowIfErrorStatus(cusolverDnSgeqrf(handle.get(), d.m, d.n, a, d.m, tau,
static_cast<float*>(workspace),
d.lwork, info));
a += d.m * d.n;
tau += std::min(d.m, d.n);
++info;
}
break;
}
case Type::F64: {
double* a = static_cast<double*>(buffers[1]);
double* tau = static_cast<double*>(buffers[2]);
for (int i = 0; i < d.batch; ++i) {
ThrowIfErrorStatus(cusolverDnDgeqrf(handle.get(), d.m, d.n, a, d.m, tau,
static_cast<double*>(workspace),
d.lwork, info));
a += d.m * d.n;
tau += std::min(d.m, d.n);
++info;
}
break;
}
case Type::C64: {
cuComplex* a = static_cast<cuComplex*>(buffers[1]);
cuComplex* tau = static_cast<cuComplex*>(buffers[2]);
for (int i = 0; i < d.batch; ++i) {
ThrowIfErrorStatus(cusolverDnCgeqrf(handle.get(), d.m, d.n, a, d.m, tau,
static_cast<cuComplex*>(workspace),
d.lwork, info));
a += d.m * d.n;
tau += std::min(d.m, d.n);
++info;
}
break;
}
case Type::C128: {
cuDoubleComplex* a = static_cast<cuDoubleComplex*>(buffers[1]);
cuDoubleComplex* tau = static_cast<cuDoubleComplex*>(buffers[2]);
for (int i = 0; i < d.batch; ++i) {
ThrowIfErrorStatus(cusolverDnZgeqrf(
handle.get(), d.m, d.n, a, d.m, tau,
static_cast<cuDoubleComplex*>(workspace), d.lwork, info));
a += d.m * d.n;
tau += std::min(d.m, d.n);
++info;
}
break;
}
}
}
// orgqr/ungqr: apply elementary Householder transformations
struct OrgqrDescriptor {
Type type;
int batch, m, n, k, lwork;
};
// Returns the workspace size and a descriptor for a geqrf operation.
std::pair<int, py::bytes> BuildOrgqrDescriptor(const py::dtype& dtype, int b,
int m, int n, int k) {
Type type = DtypeToType(dtype);
auto handle = SolverHandlePool::Borrow();
int lwork;
switch (type) {
case Type::F32:
ThrowIfErrorStatus(cusolverDnSorgqr_bufferSize(handle.get(), m, n, k,
/*A=*/nullptr,
/*lda=*/m, /*tau=*/nullptr,
&lwork));
break;
case Type::F64:
ThrowIfErrorStatus(cusolverDnDorgqr_bufferSize(handle.get(), m, n, k,
/*A=*/nullptr,
/*lda=*/m, /*tau=*/nullptr,
&lwork));
break;
case Type::C64:
ThrowIfErrorStatus(cusolverDnCungqr_bufferSize(handle.get(), m, n, k,
/*A=*/nullptr,
/*lda=*/m, /*tau=*/nullptr,
&lwork));
break;
case Type::C128:
ThrowIfErrorStatus(cusolverDnZungqr_bufferSize(handle.get(), m, n, k,
/*A=*/nullptr,
/*lda=*/m, /*tau=*/nullptr,
&lwork));
break;
}
return {lwork, PackDescriptor(OrgqrDescriptor{type, b, m, n, k, lwork})};
}
void Orgqr(cudaStream_t stream, void** buffers, const char* opaque,
size_t opaque_len) {
const OrgqrDescriptor& d =
*UnpackDescriptor<OrgqrDescriptor>(opaque, opaque_len);
auto handle = SolverHandlePool::Borrow(stream);
if (buffers[2] != buffers[0]) {
ThrowIfError(cudaMemcpyAsync(
buffers[2], buffers[0],
SizeOfType(d.type) * static_cast<std::int64_t>(d.batch) *
static_cast<std::int64_t>(d.m) * static_cast<std::int64_t>(d.n),
cudaMemcpyDeviceToDevice, stream));
}
int* info = static_cast<int*>(buffers[3]);
void* workspace = buffers[4];
switch (d.type) {
case Type::F32: {
float* a = static_cast<float*>(buffers[2]);
float* tau = static_cast<float*>(buffers[1]);
for (int i = 0; i < d.batch; ++i) {
ThrowIfErrorStatus(cusolverDnSorgqr(handle.get(), d.m, d.n, d.k, a, d.m,
tau, static_cast<float*>(workspace),
d.lwork, info));
a += d.m * d.n;
tau += d.k;
++info;
}
break;
}
case Type::F64: {
double* a = static_cast<double*>(buffers[2]);
double* tau = static_cast<double*>(buffers[1]);
for (int i = 0; i < d.batch; ++i) {
ThrowIfErrorStatus(
cusolverDnDorgqr(handle.get(), d.m, d.n, d.k, a, d.m, tau,
static_cast<double*>(workspace), d.lwork, info));
a += d.m * d.n;
tau += d.k;
++info;
}
break;
}
case Type::C64: {
cuComplex* a = static_cast<cuComplex*>(buffers[2]);
cuComplex* tau = static_cast<cuComplex*>(buffers[1]);
for (int i = 0; i < d.batch; ++i) {
ThrowIfErrorStatus(cusolverDnCungqr(
handle.get(), d.m, d.n, d.k, a, d.m, tau,
static_cast<cuComplex*>(workspace), d.lwork, info));
a += d.m * d.n;
tau += d.k;
++info;
}
break;
}
case Type::C128: {
cuDoubleComplex* a = static_cast<cuDoubleComplex*>(buffers[2]);
cuDoubleComplex* tau = static_cast<cuDoubleComplex*>(buffers[1]);
for (int i = 0; i < d.batch; ++i) {
ThrowIfErrorStatus(cusolverDnZungqr(
handle.get(), d.m, d.n, d.k, a, d.m, tau,
static_cast<cuDoubleComplex*>(workspace), d.lwork, info));
a += d.m * d.n;
tau += d.k;
++info;
}
break;
}
}
}
// Symmetric (Hermitian) eigendecomposition, QR algorithm: syevd/heevd
struct SyevdDescriptor {
Type type;
cublasFillMode_t uplo;
int batch, n;
int lwork;
};
// Returns the workspace size and a descriptor for a syevd operation.
std::pair<int, py::bytes> BuildSyevdDescriptor(const py::dtype& dtype,
bool lower, int b, int n) {
Type type = DtypeToType(dtype);
auto handle = SolverHandlePool::Borrow();
int lwork;
cusolverEigMode_t jobz = CUSOLVER_EIG_MODE_VECTOR;
cublasFillMode_t uplo =
lower ? CUBLAS_FILL_MODE_LOWER : CUBLAS_FILL_MODE_UPPER;
switch (type) {
case Type::F32:
ThrowIfErrorStatus(cusolverDnSsyevd_bufferSize(
handle.get(), jobz, uplo, n, /*A=*/nullptr, /*lda=*/n, /*W=*/nullptr,
&lwork));
break;
case Type::F64:
ThrowIfErrorStatus(cusolverDnDsyevd_bufferSize(
handle.get(), jobz, uplo, n, /*A=*/nullptr, /*lda=*/n, /*W=*/nullptr,
&lwork));
break;
case Type::C64:
ThrowIfErrorStatus(cusolverDnCheevd_bufferSize(
handle.get(), jobz, uplo, n, /*A=*/nullptr, /*lda=*/n, /*W=*/nullptr,
&lwork));
break;
case Type::C128:
ThrowIfErrorStatus(cusolverDnZheevd_bufferSize(
handle.get(), jobz, uplo, n, /*A=*/nullptr, /*lda=*/n, /*W=*/nullptr,
&lwork));
break;
}
return {lwork, PackDescriptor(SyevdDescriptor{type, uplo, b, n, lwork})};
}
void Syevd(cudaStream_t stream, void** buffers, const char* opaque,
size_t opaque_len) {
const SyevdDescriptor& d =
*UnpackDescriptor<SyevdDescriptor>(opaque, opaque_len);
auto handle = SolverHandlePool::Borrow(stream);
ThrowIfError(cudaMemcpyAsync(
buffers[1], buffers[0],
SizeOfType(d.type) * static_cast<std::int64_t>(d.batch) *
static_cast<std::int64_t>(d.n) * static_cast<std::int64_t>(d.n),
cudaMemcpyDeviceToDevice, stream));
cusolverEigMode_t jobz = CUSOLVER_EIG_MODE_VECTOR;
int* info = static_cast<int*>(buffers[3]);
void* work = buffers[4];
switch (d.type) {
case Type::F32: {
float* a = static_cast<float*>(buffers[1]);
float* w = static_cast<float*>(buffers[2]);
for (int i = 0; i < d.batch; ++i) {
ThrowIfErrorStatus(cusolverDnSsyevd(handle.get(), jobz, d.uplo, d.n, a,
d.n, w, static_cast<float*>(work),
d.lwork, info));
a += d.n * d.n;
w += d.n;
++info;
}
break;
}
case Type::F64: {
double* a = static_cast<double*>(buffers[1]);
double* w = static_cast<double*>(buffers[2]);
for (int i = 0; i < d.batch; ++i) {
ThrowIfErrorStatus(cusolverDnDsyevd(handle.get(), jobz, d.uplo, d.n, a,
d.n, w, static_cast<double*>(work),
d.lwork, info));
a += d.n * d.n;
w += d.n;
++info;
}
break;
}
case Type::C64: {
cuComplex* a = static_cast<cuComplex*>(buffers[1]);
float* w = static_cast<float*>(buffers[2]);
for (int i = 0; i < d.batch; ++i) {
ThrowIfErrorStatus(
cusolverDnCheevd(handle.get(), jobz, d.uplo, d.n, a, d.n, w,
static_cast<cuComplex*>(work), d.lwork, info));
a += d.n * d.n;
w += d.n;
++info;
}
break;
}
case Type::C128: {
cuDoubleComplex* a = static_cast<cuDoubleComplex*>(buffers[1]);
double* w = static_cast<double*>(buffers[2]);
for (int i = 0; i < d.batch; ++i) {
ThrowIfErrorStatus(cusolverDnZheevd(
handle.get(), jobz, d.uplo, d.n, a, d.n, w,
static_cast<cuDoubleComplex*>(work), d.lwork, info));
a += d.n * d.n;
w += d.n;
++info;
}
break;
}
}
}
// Symmetric (Hermitian) eigendecomposition, Jacobi algorithm: syevj/heevj
// Supports batches of matrices up to size 32.
struct SyevjDescriptor {
Type type;
cublasFillMode_t uplo;
int batch, n;
int lwork;
};
// Returns the workspace size and a descriptor for a syevj_batched operation.
std::pair<int, py::bytes> BuildSyevjDescriptor(const py::dtype& dtype,
bool lower, int batch, int n) {
Type type = DtypeToType(dtype);
auto handle = SolverHandlePool::Borrow();
int lwork;
syevjInfo_t params;
ThrowIfErrorStatus(cusolverDnCreateSyevjInfo(¶ms));
std::unique_ptr<syevjInfo, void (*)(syevjInfo*)> params_cleanup(
params, [](syevjInfo* p) { cusolverDnDestroySyevjInfo(p); });
cusolverEigMode_t jobz = CUSOLVER_EIG_MODE_VECTOR;
cublasFillMode_t uplo =
lower ? CUBLAS_FILL_MODE_LOWER : CUBLAS_FILL_MODE_UPPER;
if (batch == 1) {
switch (type) {
case Type::F32:
ThrowIfErrorStatus(cusolverDnSsyevj_bufferSize(
handle.get(), jobz, uplo, n, /*A=*/nullptr, /*lda=*/n,
/*W=*/nullptr, &lwork, params));
break;
case Type::F64:
ThrowIfErrorStatus(cusolverDnDsyevj_bufferSize(
handle.get(), jobz, uplo, n, /*A=*/nullptr, /*lda=*/n,
/*W=*/nullptr, &lwork, params));
break;
case Type::C64:
ThrowIfErrorStatus(cusolverDnCheevj_bufferSize(
handle.get(), jobz, uplo, n, /*A=*/nullptr, /*lda=*/n,
/*W=*/nullptr, &lwork, params));
break;
case Type::C128:
ThrowIfErrorStatus(cusolverDnZheevj_bufferSize(
handle.get(), jobz, uplo, n, /*A=*/nullptr, /*lda=*/n,
/*W=*/nullptr, &lwork, params));
break;
}
} else {
switch (type) {
case Type::F32:
ThrowIfErrorStatus(cusolverDnSsyevjBatched_bufferSize(
handle.get(), jobz, uplo, n, /*A=*/nullptr, /*lda=*/n,
/*W=*/nullptr, &lwork, params, batch));
break;
case Type::F64:
ThrowIfErrorStatus(cusolverDnDsyevjBatched_bufferSize(
handle.get(), jobz, uplo, n, /*A=*/nullptr, /*lda=*/n,
/*W=*/nullptr, &lwork, params, batch));
break;
case Type::C64:
ThrowIfErrorStatus(cusolverDnCheevjBatched_bufferSize(
handle.get(), jobz, uplo, n, /*A=*/nullptr, /*lda=*/n,
/*W=*/nullptr, &lwork, params, batch));
break;
case Type::C128:
ThrowIfErrorStatus(cusolverDnZheevjBatched_bufferSize(
handle.get(), jobz, uplo, n, /*A=*/nullptr, /*lda=*/n,
/*W=*/nullptr, &lwork, params, batch));
break;
}
}
return {lwork, PackDescriptor(SyevjDescriptor{type, uplo, batch, n, lwork})};
}
void Syevj(cudaStream_t stream, void** buffers, const char* opaque,
size_t opaque_len) {
const SyevjDescriptor& d =
*UnpackDescriptor<SyevjDescriptor>(opaque, opaque_len);
auto handle = SolverHandlePool::Borrow(stream);
if (buffers[1] != buffers[0]) {
ThrowIfError(cudaMemcpyAsync(
buffers[1], buffers[0],
SizeOfType(d.type) * static_cast<std::int64_t>(d.batch) *
static_cast<std::int64_t>(d.n) * static_cast<std::int64_t>(d.n),
cudaMemcpyDeviceToDevice, stream));
}
syevjInfo_t params;
ThrowIfErrorStatus(cusolverDnCreateSyevjInfo(¶ms));
std::unique_ptr<syevjInfo, void (*)(syevjInfo*)> params_cleanup(
params, [](syevjInfo* p) { cusolverDnDestroySyevjInfo(p); });
cusolverEigMode_t jobz = CUSOLVER_EIG_MODE_VECTOR;
int* info = static_cast<int*>(buffers[3]);
void* work = buffers[4];
if (d.batch == 1) {
switch (d.type) {
case Type::F32: {
float* a = static_cast<float*>(buffers[1]);
float* w = static_cast<float*>(buffers[2]);
ThrowIfErrorStatus(cusolverDnSsyevj(handle.get(), jobz, d.uplo, d.n, a,
d.n, w, static_cast<float*>(work),
d.lwork, info, params));
break;
}
case Type::F64: {
double* a = static_cast<double*>(buffers[1]);
double* w = static_cast<double*>(buffers[2]);
ThrowIfErrorStatus(cusolverDnDsyevj(handle.get(), jobz, d.uplo, d.n, a,
d.n, w, static_cast<double*>(work),
d.lwork, info, params));
break;
}
case Type::C64: {
cuComplex* a = static_cast<cuComplex*>(buffers[1]);
float* w = static_cast<float*>(buffers[2]);
ThrowIfErrorStatus(cusolverDnCheevj(
handle.get(), jobz, d.uplo, d.n, a, d.n, w,
static_cast<cuComplex*>(work), d.lwork, info, params));
break;
}
case Type::C128: {
cuDoubleComplex* a = static_cast<cuDoubleComplex*>(buffers[1]);
double* w = static_cast<double*>(buffers[2]);
ThrowIfErrorStatus(cusolverDnZheevj(
handle.get(), jobz, d.uplo, d.n, a, d.n, w,
static_cast<cuDoubleComplex*>(work), d.lwork, info, params));
break;
}
}
} else {
switch (d.type) {
case Type::F32: {
float* a = static_cast<float*>(buffers[1]);
float* w = static_cast<float*>(buffers[2]);
ThrowIfErrorStatus(cusolverDnSsyevjBatched(
handle.get(), jobz, d.uplo, d.n, a, d.n, w,
static_cast<float*>(work), d.lwork, info, params, d.batch));
break;
}
case Type::F64: {
double* a = static_cast<double*>(buffers[1]);
double* w = static_cast<double*>(buffers[2]);
ThrowIfErrorStatus(cusolverDnDsyevjBatched(
handle.get(), jobz, d.uplo, d.n, a, d.n, w,
static_cast<double*>(work), d.lwork, info, params, d.batch));
break;
}
case Type::C64: {
cuComplex* a = static_cast<cuComplex*>(buffers[1]);
float* w = static_cast<float*>(buffers[2]);
ThrowIfErrorStatus(cusolverDnCheevjBatched(
handle.get(), jobz, d.uplo, d.n, a, d.n, w,
static_cast<cuComplex*>(work), d.lwork, info, params, d.batch));
break;
}
case Type::C128: {
cuDoubleComplex* a = static_cast<cuDoubleComplex*>(buffers[1]);
double* w = static_cast<double*>(buffers[2]);
ThrowIfErrorStatus(
cusolverDnZheevjBatched(handle.get(), jobz, d.uplo, d.n, a, d.n, w,
static_cast<cuDoubleComplex*>(work),
d.lwork, info, params, d.batch));
break;
}
}
}
}
// Singular value decomposition using QR algorithm: gesvd
struct GesvdDescriptor {
Type type;
int batch, m, n;
int lwork;
signed char jobu, jobvt;
};
// Returns the workspace size and a descriptor for a gesvd operation.
std::pair<int, py::bytes> BuildGesvdDescriptor(const py::dtype& dtype, int b,
int m, int n, bool compute_uv,
bool full_matrices) {
Type type = DtypeToType(dtype);
auto handle = SolverHandlePool::Borrow();
int lwork;
switch (type) {
case Type::F32:
ThrowIfErrorStatus(
cusolverDnSgesvd_bufferSize(handle.get(), m, n, &lwork));
break;
case Type::F64:
ThrowIfErrorStatus(
cusolverDnDgesvd_bufferSize(handle.get(), m, n, &lwork));
break;
case Type::C64:
ThrowIfErrorStatus(
cusolverDnCgesvd_bufferSize(handle.get(), m, n, &lwork));
break;
case Type::C128:
ThrowIfErrorStatus(
cusolverDnZgesvd_bufferSize(handle.get(), m, n, &lwork));
break;
}
signed char jobu, jobvt;
if (compute_uv) {
if (full_matrices) {
jobu = jobvt = 'A';
} else {
jobu = jobvt = 'S';
}
} else {
jobu = jobvt = 'N';
}
return {lwork,
PackDescriptor(GesvdDescriptor{type, b, m, n, lwork, jobu, jobvt})};
}
void Gesvd(cudaStream_t stream, void** buffers, const char* opaque,
size_t opaque_len) {
const GesvdDescriptor& d =
*UnpackDescriptor<GesvdDescriptor>(opaque, opaque_len);
auto handle = SolverHandlePool::Borrow(stream);
ThrowIfError(cudaMemcpyAsync(
buffers[1], buffers[0],
SizeOfType(d.type) * static_cast<std::int64_t>(d.batch) *
static_cast<std::int64_t>(d.m) * static_cast<std::int64_t>(d.n),
cudaMemcpyDeviceToDevice, stream));
int* info = static_cast<int*>(buffers[5]);
void* work = buffers[6];
switch (d.type) {
case Type::F32: {
float* a = static_cast<float*>(buffers[1]);
float* s = static_cast<float*>(buffers[2]);
float* u = static_cast<float*>(buffers[3]);
float* vt = static_cast<float*>(buffers[4]);
for (int i = 0; i < d.batch; ++i) {
ThrowIfErrorStatus(cusolverDnSgesvd(handle.get(), d.jobu, d.jobvt, d.m,
d.n, a, d.m, s, u, d.m, vt, d.n,
static_cast<float*>(work), d.lwork,
/*rwork=*/nullptr, info));
a += d.m * d.n;
s += std::min(d.m, d.n);
u += d.m * d.m;
vt += d.n * d.n;
++info;
}
break;
}
case Type::F64: {
double* a = static_cast<double*>(buffers[1]);
double* s = static_cast<double*>(buffers[2]);
double* u = static_cast<double*>(buffers[3]);
double* vt = static_cast<double*>(buffers[4]);
for (int i = 0; i < d.batch; ++i) {
ThrowIfErrorStatus(cusolverDnDgesvd(handle.get(), d.jobu, d.jobvt, d.m,
d.n, a, d.m, s, u, d.m, vt, d.n,
static_cast<double*>(work), d.lwork,
/*rwork=*/nullptr, info));
a += d.m * d.n;
s += std::min(d.m, d.n);
u += d.m * d.m;
vt += d.n * d.n;
++info;
}
break;
}
case Type::C64: {
cuComplex* a = static_cast<cuComplex*>(buffers[1]);
float* s = static_cast<float*>(buffers[2]);
cuComplex* u = static_cast<cuComplex*>(buffers[3]);
cuComplex* vt = static_cast<cuComplex*>(buffers[4]);
for (int i = 0; i < d.batch; ++i) {
ThrowIfErrorStatus(cusolverDnCgesvd(
handle.get(), d.jobu, d.jobvt, d.m, d.n, a, d.m, s, u, d.m, vt, d.n,
static_cast<cuComplex*>(work), d.lwork, /*rwork=*/nullptr, info));
a += d.m * d.n;
s += std::min(d.m, d.n);
u += d.m * d.m;
vt += d.n * d.n;
++info;
}
break;
}
case Type::C128: {
cuDoubleComplex* a = static_cast<cuDoubleComplex*>(buffers[1]);
double* s = static_cast<double*>(buffers[2]);
cuDoubleComplex* u = static_cast<cuDoubleComplex*>(buffers[3]);
cuDoubleComplex* vt = static_cast<cuDoubleComplex*>(buffers[4]);
for (int i = 0; i < d.batch; ++i) {
ThrowIfErrorStatus(cusolverDnZgesvd(
handle.get(), d.jobu, d.jobvt, d.m, d.n, a, d.m, s, u, d.m, vt, d.n,
static_cast<cuDoubleComplex*>(work), d.lwork,
/*rwork=*/nullptr, info));
a += d.m * d.n;
s += std::min(d.m, d.n);
u += d.m * d.m;
vt += d.n * d.n;
++info;
}