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https://github.com/ceres-solver/ceres-solver.git
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fc826c5780
* All Cuda* objects now take in a ContextImpl* during construction, and save the context instead of individual handles. * Since we no longer use the legacy default stream, we need to explicitly synchronize the stream before performing GPU->CPU transfers, and CudaBuffer is responsible for such synchronization when asked to perform GPU to CPU transfers. * Remove all manual syncs and relegate syncing to CudaBuffer before performing GPU to CPU transfers. Change-Id: Ic73cb24174a1e09842827323280e90241716cc20
163 lines
6.1 KiB
C++
163 lines
6.1 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2022 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: joydeepb@cs.utexas.edu (Joydeep Biswas)
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//
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// A CUDA sparse matrix linear operator.
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// This include must come before any #ifndef check on Ceres compile options.
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// clang-format off
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#include "ceres/internal/config.h"
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// clang-format on
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#include "ceres/cuda_sparse_matrix.h"
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#include <math.h>
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#include <memory>
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#include "ceres/block_sparse_matrix.h"
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#include "ceres/compressed_row_sparse_matrix.h"
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#include "ceres/context_impl.h"
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#include "ceres/crs_matrix.h"
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#include "ceres/internal/export.h"
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#include "ceres/types.h"
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#include "ceres/wall_time.h"
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#ifndef CERES_NO_CUDA
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#include "ceres/cuda_buffer.h"
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#include "ceres/cuda_kernels.h"
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#include "ceres/cuda_vector.h"
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#include "cuda_runtime_api.h"
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#include "cusparse.h"
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namespace ceres::internal {
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CudaSparseMatrix::CudaSparseMatrix(ContextImpl* context,
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const CompressedRowSparseMatrix& crs_matrix)
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: context_(context),
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rows_{context},
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cols_{context},
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values_{context},
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spmv_buffer_{context} {
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DCHECK_NE(context, nullptr);
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CHECK(context->IsCudaInitialized());
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num_rows_ = crs_matrix.num_rows();
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num_cols_ = crs_matrix.num_cols();
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num_nonzeros_ = crs_matrix.num_nonzeros();
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rows_.CopyFromCpu(crs_matrix.rows(), num_rows_ + 1);
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cols_.CopyFromCpu(crs_matrix.cols(), num_nonzeros_);
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values_.CopyFromCpu(crs_matrix.values(), num_nonzeros_);
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cusparseCreateCsr(&descr_,
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num_rows_,
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num_cols_,
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num_nonzeros_,
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rows_.data(),
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cols_.data(),
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values_.data(),
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CUSPARSE_INDEX_32I,
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CUSPARSE_INDEX_32I,
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CUSPARSE_INDEX_BASE_ZERO,
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CUDA_R_64F);
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}
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CudaSparseMatrix::~CudaSparseMatrix() {
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CHECK_EQ(cusparseDestroySpMat(descr_), CUSPARSE_STATUS_SUCCESS);
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descr_ = nullptr;
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}
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void CudaSparseMatrix::CopyValuesFromCpu(
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const CompressedRowSparseMatrix& crs_matrix) {
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// There is no quick and easy way to verify that the structure is unchanged,
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// but at least we can check that the size of the matrix and the number of
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// nonzeros is unchanged.
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CHECK_EQ(num_rows_, crs_matrix.num_rows());
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CHECK_EQ(num_cols_, crs_matrix.num_cols());
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CHECK_EQ(num_nonzeros_, crs_matrix.num_nonzeros());
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values_.CopyFromCpu(crs_matrix.values(), num_nonzeros_);
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}
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void CudaSparseMatrix::SpMv(cusparseOperation_t op,
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const CudaVector& x,
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CudaVector* y) {
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size_t buffer_size = 0;
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const double alpha = 1.0;
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const double beta = 1.0;
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// Starting in CUDA 11.2.1, CUSPARSE_MV_ALG_DEFAULT was deprecated in favor of
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// CUSPARSE_SPMV_ALG_DEFAULT.
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#if CUDART_VERSION >= 11021
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const auto algorithm = CUSPARSE_SPMV_ALG_DEFAULT;
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#else // CUDART_VERSION >= 11021
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const auto algorithm = CUSPARSE_MV_ALG_DEFAULT;
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#endif // CUDART_VERSION >= 11021
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CHECK_EQ(cusparseSpMV_bufferSize(context_->cusparse_handle_,
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op,
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&alpha,
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descr_,
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x.descr(),
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&beta,
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y->descr(),
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CUDA_R_64F,
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algorithm,
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&buffer_size),
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CUSPARSE_STATUS_SUCCESS);
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spmv_buffer_.Reserve(buffer_size);
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CHECK_EQ(cusparseSpMV(context_->cusparse_handle_,
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op,
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&alpha,
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descr_,
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x.descr(),
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&beta,
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y->descr(),
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CUDA_R_64F,
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algorithm,
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spmv_buffer_.data()),
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CUSPARSE_STATUS_SUCCESS);
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}
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void CudaSparseMatrix::RightMultiplyAndAccumulate(const CudaVector& x,
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CudaVector* y) {
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SpMv(CUSPARSE_OPERATION_NON_TRANSPOSE, x, y);
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}
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void CudaSparseMatrix::LeftMultiplyAndAccumulate(const CudaVector& x,
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CudaVector* y) {
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// TODO(Joydeep Biswas): We should consider storing a transposed copy of the
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// matrix by converting CSR to CSC. From the cuSPARSE documentation:
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// "In general, opA == CUSPARSE_OPERATION_NON_TRANSPOSE is 3x faster than opA
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// != CUSPARSE_OPERATION_NON_TRANSPOSE"
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SpMv(CUSPARSE_OPERATION_TRANSPOSE, x, y);
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}
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} // namespace ceres::internal
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#endif // CERES_NO_CUDA
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