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solve.h
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////////////////////////////////////////////////////////////////////////////////
// BSD 3-Clause License
//
// Copyright (c) 2025, NVIDIA Corporation
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// 1. Redistributions of source code must retain the above copyright notice, this
// list of conditions and the following disclaimer.
//
// 2. Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// 3. Neither the name of the copyright holder nor the names of its
// contributors may be used to endorse or promote products derived from
// this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
// DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
// FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
// DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
// SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
// CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
// OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
/////////////////////////////////////////////////////////////////////////////////
#pragma once
#include "matx/core/type_utils.h"
#include "matx/operators/base_operator.h"
#ifdef MATX_EN_CUDSS
#include "matx/transforms/solve/solve_cudss.h"
#endif
namespace matx {
namespace detail {
template <typename OpA, typename OpB>
class SolveOp : public BaseOp<SolveOp<OpA, OpB>> {
private:
typename detail::base_type_t<OpA> a_;
typename detail::base_type_t<OpB> b_;
static constexpr int out_rank = OpB::Rank();
cuda::std::array<index_t, out_rank> out_dims_;
mutable detail::tensor_impl_t<typename OpA::value_type, out_rank> tmp_out_;
mutable typename OpA::value_type *ptr = nullptr;
public:
using matxop = bool;
using matx_transform_op = bool;
using solve_xform_op = bool;
using value_type = typename OpA::value_type;
__MATX_INLINE__ SolveOp(const OpA &a, const OpB &b) : a_(a), b_(b) {
for (int r = 0, rank = Rank(); r < rank; r++) {
out_dims_[r] = b_.Size(r);
}
}
__MATX_INLINE__ std::string str() const {
return "solve(" + get_type_str(a_) + "," + get_type_str(b_) + ")";
}
__MATX_HOST__ __MATX_INLINE__ auto Data() const noexcept { return ptr; }
template <typename... Is>
__MATX_INLINE__ __MATX_DEVICE__ __MATX_HOST__ decltype(auto)
operator()(Is... indices) const {
return tmp_out_(indices...);
}
static __MATX_INLINE__ constexpr __MATX_HOST__ __MATX_DEVICE__ int32_t
Rank() {
return remove_cvref_t<OpB>::Rank();
}
constexpr __MATX_INLINE__ __MATX_HOST__ __MATX_DEVICE__ index_t
Size(int dim) const {
return out_dims_[dim];
}
template <typename Out, typename Executor>
void Exec([[maybe_unused]] Out &&out, [[maybe_unused]] Executor &&ex) const {
static_assert(!is_sparse_tensor_v<OpB>, "sparse rhs not implemented");
if constexpr (is_sparse_tensor_v<OpA>) {
#ifdef MATX_EN_CUDSS
sparse_solve_impl(cuda::std::get<0>(out), a_, b_, ex);
#else
MATX_THROW(matxNotSupported, "Sparse direct solver requires cuDSS");
#endif
} else {
MATX_THROW(matxNotSupported,
"Direct solver currently only supports sparse system");
}
}
template <typename ShapeType, typename Executor>
__MATX_INLINE__ void
InnerPreRun([[maybe_unused]] ShapeType &&shape,
[[maybe_unused]] Executor &&ex) const noexcept {
static_assert(is_sparse_tensor_v<OpA>,
"Direct solver currently only supports sparse system");
if constexpr (is_matx_op<OpB>()) {
b_.PreRun(std::forward<ShapeType>(shape), std::forward<Executor>(ex));
}
}
template <typename ShapeType, typename Executor>
__MATX_INLINE__ void PreRun([[maybe_unused]] ShapeType &&shape,
[[maybe_unused]] Executor &&ex) const noexcept {
InnerPreRun(std::forward<ShapeType>(shape), std::forward<Executor>(ex));
detail::AllocateTempTensor(tmp_out_, std::forward<Executor>(ex), out_dims_,
&ptr);
Exec(cuda::std::make_tuple(tmp_out_), std::forward<Executor>(ex));
}
template <typename ShapeType, typename Executor>
__MATX_INLINE__ void PostRun([[maybe_unused]] ShapeType &&shape,
[[maybe_unused]] Executor &&ex) const noexcept {
static_assert(is_sparse_tensor_v<OpA>,
"Direct solver currently only supports sparse system");
if constexpr (is_matx_op<OpB>()) {
b_.PostRun(std::forward<ShapeType>(shape), std::forward<Executor>(ex));
}
matxFree(ptr);
}
};
} // end namespace detail
/**
* Run a direct SOLVE (viz. X = solve(A, B) solves system AX=B for unknown X).
*
* Note that currently, this operation is only implemented for solving
* a linear system with a very **sparse** matrix A.
*
* @tparam OpA
* Data type of A tensor (sparse)
* @tparam OpB
* Data type of B tensor
*
* @param A
* A Sparse tensor with system coefficients
* @param B
* B Dense tensor of known values
*
* @return
* Operator that produces the output tensor X with the solution
*/
template <typename OpA, typename OpB>
__MATX_INLINE__ auto solve(const OpA &A, const OpB &B) {
return detail::SolveOp(A, B);
}
} // end namespace matx