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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2023 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * 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.
// * Neither the name of Google Inc. 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 OWNER 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.
//
// Author: joydeepb@cs.utexas.edu (Joydeep Biswas)
//
// A simple CUDA vector class.
#ifndef CERES_INTERNAL_CUDA_VECTOR_H_
#define CERES_INTERNAL_CUDA_VECTOR_H_
// This include must come before any #ifndef check on Ceres compile options.
// clang-format off
#include "ceres/internal/config.h"
// clang-format on
#include <math.h>
#include <memory>
#include <string>
#include "ceres/context_impl.h"
#include "ceres/internal/export.h"
#include "ceres/types.h"
#ifndef CERES_NO_CUDA
#include "ceres/cuda_buffer.h"
#include "ceres/cuda_kernels_vector_ops.h"
#include "ceres/internal/eigen.h"
#include "cublas_v2.h"
#include "cusparse.h"
namespace ceres::internal {
// An Nx1 vector, denoted y hosted on the GPU, with CUDA-accelerated operations.
class CERES_NO_EXPORT CudaVector {
public:
// Create a pre-allocated vector of size N and return a pointer to it. The
// caller must ensure that InitCuda() has already been successfully called on
// context before calling this method.
CudaVector(ContextImpl* context, int size);
CudaVector(CudaVector&& other);
~CudaVector();
void Resize(int size);
// Perform a deep copy of the vector.
CudaVector& operator=(const CudaVector&);
// Return the inner product x' * y.
double Dot(const CudaVector& x) const;
// Return the L2 norm of the vector (||y||_2).
double Norm() const;
// Set all elements to zero.
void SetZero();
// Copy from Eigen vector.
void CopyFromCpu(const Vector& x);
// Copy from CPU memory array.
void CopyFromCpu(const double* x);
// Copy to Eigen vector.
void CopyTo(Vector* x) const;
// Copy to CPU memory array. It is the caller's responsibility to ensure
// that the array is large enough.
void CopyTo(double* x) const;
// y = a * x + b * y.
void Axpby(double a, const CudaVector& x, double b);
// y = diag(d)' * diag(d) * x + y.
void DtDxpy(const CudaVector& D, const CudaVector& x);
// y = s * y.
void Scale(double s);
int num_rows() const { return num_rows_; }
int num_cols() const { return 1; }
const double* data() const { return data_.data(); }
double* mutable_data() { return data_.data(); }
const cusparseDnVecDescr_t& descr() const { return descr_; }
private:
CudaVector(const CudaVector&) = delete;
void DestroyDescriptor();
int num_rows_ = 0;
ContextImpl* context_ = nullptr;
CudaBuffer<double> data_;
// CuSparse object that describes this dense vector.
cusparseDnVecDescr_t descr_ = nullptr;
};
// Blas1 operations on Cuda vectors. These functions are needed as an
// abstraction layer so that we can use different versions of a vector style
// object in the conjugate gradients linear solver.
// Context and num_threads arguments are not used by CUDA implementation,
// context embedded into CudaVector is used instead.
inline double Norm(const CudaVector& x,
ContextImpl* context = nullptr,
int num_threads = 1) {
(void)context;
(void)num_threads;
return x.Norm();
}
inline void SetZero(CudaVector& x,
ContextImpl* context = nullptr,
int num_threads = 1) {
(void)context;
(void)num_threads;
x.SetZero();
}
inline void Axpby(double a,
const CudaVector& x,
double b,
const CudaVector& y,
CudaVector& z,
ContextImpl* context = nullptr,
int num_threads = 1) {
(void)context;
(void)num_threads;
if (&x == &y && &y == &z) {
// z = (a + b) * z;
z.Scale(a + b);
} else if (&x == &z) {
// x is aliased to z.
// z = x
// = b * y + a * x;
z.Axpby(b, y, a);
} else if (&y == &z) {
// y is aliased to z.
// z = y = a * x + b * y;
z.Axpby(a, x, b);
} else {
// General case: all inputs and outputs are distinct.
z = y;
z.Axpby(a, x, b);
}
}
inline double Dot(const CudaVector& x,
const CudaVector& y,
ContextImpl* context = nullptr,
int num_threads = 1) {
(void)context;
(void)num_threads;
return x.Dot(y);
}
inline void Copy(const CudaVector& from,
CudaVector& to,
ContextImpl* context = nullptr,
int num_threads = 1) {
(void)context;
(void)num_threads;
to = from;
}
} // namespace ceres::internal
#endif // CERES_NO_CUDA
#endif // CERES_INTERNAL_CUDA_SPARSE_LINEAR_OPERATOR_H_