| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2022 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.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(); |
| |
| 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 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 CudaBuffer<double>& data() const { return 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. |
| inline double Norm(const CudaVector& x) { return x.Norm(); } |
| inline void SetZero(CudaVector& x) { x.SetZero(); } |
| inline void Axpby(double a, |
| const CudaVector& x, |
| double b, |
| const CudaVector& y, |
| CudaVector& z) { |
| 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) { return x.Dot(y); } |
| inline void Copy(const CudaVector& from, CudaVector& to) { to = from; } |
| |
| } // namespace ceres::internal |
| |
| #endif // CERES_NO_CUDA |
| #endif // CERES_INTERNAL_CUDA_SPARSE_LINEAR_OPERATOR_H_ |