From 634634d8948083e3e03ffa1ea1593ced3b99bae5 Mon Sep 17 00:00:00 2001 From: Pierre Marchand Date: Mon, 12 Jun 2023 17:10:33 +0200 Subject: [PATCH 01/52] update CI --- .github/workflows/CI.yml | 56 +++++++++++++++++++++------------------- 1 file changed, 30 insertions(+), 26 deletions(-) diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml index 7d84eeb..050a6f4 100644 --- a/.github/workflows/CI.yml +++ b/.github/workflows/CI.yml @@ -29,27 +29,27 @@ jobs: - name: "Ubuntu OpenMPI g++" CC_COMPILER: gcc CXX_COMPILER: g++ - container: "ubuntu_openmpi" + container: "ubuntu_gcc_openmpi" MPIEXEC_PREFLAGS: "--allow-run-as-root --oversubscribe" USE_SANITIZER: "" CMAKE_BUILD_TYPE: Release - DOCKER_OPTIONS: " " + DOCKER_OPTIONS: "--cap-add SYS_PTRACE" CODE_COVERAGE: "OFF" - name: "Ubuntu OpenMPI clang++" CC_COMPILER: clang CXX_COMPILER: clang++ - container: "ubuntu_openmpi" + container: "ubuntu_clang_openmpi" MPIEXEC_PREFLAGS: "--allow-run-as-root --oversubscribe" USE_SANITIZER: "" CMAKE_BUILD_TYPE: Release - DOCKER_OPTIONS: " " + DOCKER_OPTIONS: "--cap-add SYS_PTRACE" CODE_COVERAGE: "OFF" - name: "Ubuntu MPICH g++" CC_COMPILER: gcc CXX_COMPILER: g++ - container: "ubuntu_mpich" + container: "ubuntu_gcc_mpich" MPIEXEC_PREFLAGS: "" CMAKE_BUILD_TYPE: Debug DOCKER_OPTIONS: " " @@ -58,35 +58,37 @@ jobs: - name: "Ubuntu MPICH clang++" CC_COMPILER: clang CXX_COMPILER: clang++ - container: "ubuntu_mpich" + container: "ubuntu_clang_mpich" MPIEXEC_PREFLAGS: "" CMAKE_BUILD_TYPE: Release DOCKER_OPTIONS: " " CODE_COVERAGE: "OFF" # Hangs on github - # - name: "Debian OpenMPI g++" - # CC_COMPILER: gcc - # CXX_COMPILER: g++ - # container: "debian_openmpi" - # MPIEXEC_PREFLAGS: "--allow-run-as-root --oversubscribe --mca btl_vader_single_copy_mechanism none" - # USE_SANITIZER: "" - # CMAKE_BUILD_TYPE: Debug - # DOCKER_OPTIONS: "--cap-add SYS_PTRACE" - - # - name: "Debian OpenMPI clang++" - # CC_COMPILER: clang - # CXX_COMPILER: clang++ - # container: "debian_openmpi" - # MPIEXEC_PREFLAGS: "--allow-run-as-root --oversubscribe --mca btl_vader_single_copy_mechanism none" - # USE_SANITIZER: "" - # CMAKE_BUILD_TYPE: Debug - # DOCKER_OPTIONS: "--cap-add SYS_PTRACE" + - name: "Debian OpenMPI g++" + CC_COMPILER: gcc + CXX_COMPILER: g++ + container: "debian_gcc_openmpi" + MPIEXEC_PREFLAGS: "--allow-run-as-root --oversubscribe" + USE_SANITIZER: "" + CMAKE_BUILD_TYPE: Release + DOCKER_OPTIONS: "--cap-add SYS_PTRACE" + CODE_COVERAGE: "OFF" + + - name: "Debian OpenMPI clang++" + CC_COMPILER: clang + CXX_COMPILER: clang++ + container: "debian_clang_openmpi" + MPIEXEC_PREFLAGS: "--allow-run-as-root --oversubscribe" + USE_SANITIZER: "" + CMAKE_BUILD_TYPE: Release + DOCKER_OPTIONS: "--cap-add SYS_PTRACE" + CODE_COVERAGE: "OFF" - name: "Debian MPICH g++" CC_COMPILER: gcc CXX_COMPILER: g++ - container: "debian_mpich" + container: "debian_gcc_mpich" MPIEXEC_PREFLAGS: "" CMAKE_BUILD_TYPE: Release DOCKER_OPTIONS: " " @@ -95,7 +97,7 @@ jobs: - name: "Debian MPICH clang++" CC_COMPILER: clang CXX_COMPILER: clang++ - container: "debian_mpich" + container: "debian_clang_mpich" MPIEXEC_PREFLAGS: "" CMAKE_BUILD_TYPE: Release DOCKER_OPTIONS: " " @@ -115,7 +117,7 @@ jobs: if: "!contains(github.event.head_commit.message, '[ci skip]')" steps: - name: Checkout htool-python - uses: actions/checkout@v2 + uses: actions/checkout@v3 with: submodules: "true" @@ -141,7 +143,9 @@ jobs: - name: Check format if: matrix.CODE_COVERAGE == 'ON' + # https://github.com/actions/checkout/pull/762 run: | + git config --global --add safe.directory `pwd` cd build make format make cmake-format From 03ab7e73e6b3c330371f237b083c0e3bed5535a0 Mon Sep 17 00:00:00 2001 From: Pierre Marchand Date: Thu, 13 Jul 2023 17:42:47 +0200 Subject: [PATCH 02/52] update with new htool interface --- CMakeLists.txt | 10 +- example/cluster.py | 61 --- example/create_geometry.py | 93 ++++ example/ddm_solver.py | 149 +++--- .../define_custom_dense_blocks_generator.py | 16 + example/define_custom_generators.py | 96 ++++ example/define_custom_local_operator.py | 52 ++ example/define_custom_low_rank_generator.py | 23 + example/partition_example.py | 105 ---- example/smallest_example.py | 95 ---- example/solver_scipy.py | 81 --- example/use_block_jacobi_solver.py | 95 ++++ example/use_cluster.py | 58 ++ example/use_cluster_with_given_partition.py | 49 ++ example/use_custom_dense_block_generator.py | 95 ++++ example/use_custom_local_operator.py | 76 +++ example/use_custom_low_rank_approximation.py | 95 ++++ example/use_default_build.py | 99 ++++ example/use_local_build.py | 181 +++++++ example/use_no_default_build.py | 123 +++++ lib/hpddm | 2 +- lib/htool | 2 +- lib/pybind11 | 2 +- pyproject.toml | 41 ++ setup.py | 216 +++++--- src/Htool.cpython-311-darwin.so | Bin 0 -> 11551272 bytes src/Htool.cpython-39-x86_64-linux-gnu.so | Bin 0 -> 7816064 bytes src/htool.egg-info/PKG-INFO | 89 ++++ src/htool.egg-info/SOURCES.txt | 16 + src/htool.egg-info/dependency_links.txt | 1 + src/htool.egg-info/not-zip-safe | 1 + src/htool.egg-info/requires.txt | 11 + src/htool.egg-info/top_level.txt | 2 + src/htool/cluster.hpp | 146 ----- src/htool/clustering/cluster_builder.hpp | 36 ++ src/htool/clustering/cluster_node.hpp | 44 ++ .../implementation/direction_computation.hpp | 19 + .../clustering/implementation/splitting.hpp | 19 + .../interface/direction_computation.hpp | 11 + src/htool/clustering/interface/splitting.hpp | 11 + src/htool/clustering/utility.hpp | 13 + src/htool/dense_blocks_generator.hpp | 67 --- .../distributed_operator.hpp | 62 +++ .../implementation/partition_from_cluster.hpp | 16 + .../interface/partition.hpp | 32 ++ src/htool/distributed_operator/utility.hpp | 33 ++ src/htool/hmatrix.hpp | 309 ----------- src/htool/hmatrix/hmatrix.hpp | 188 +++++++ src/htool/hmatrix/hmatrix_builder.hpp | 27 + .../virtual_dense_blocks_generator.hpp | 63 +++ .../hmatrix/interfaces/virtual_generator.hpp | 96 ++++ .../interfaces/virtual_low_rank_generator.hpp | 91 ++++ .../local_operator/local_dense_operator.hpp | 15 + src/htool/local_operator/local_hmatrix.hpp | 47 ++ src/htool/local_operator/local_operator.hpp | 101 ++++ .../local_operator/virtual_local_operator.hpp | 18 + src/htool/lrmat_generator.hpp | 88 --- src/htool/main.cpp | 96 +++- src/htool/matplotlib/cluster.hpp | 64 +++ src/htool/matplotlib/hmatrix.hpp | 92 ++++ src/htool/matrix.hpp | 56 -- src/htool/misc.hpp | 7 - src/htool/misc/logger.hpp | 41 ++ src/htool/misc/utility.hpp | 22 + src/htool/{ => misc}/wrapper_mpi.hpp | 6 +- .../{ddm_solver.hpp => solver/solver.hpp} | 51 +- src/htool/solver/utility.hpp | 23 + tests/conftest.py | 417 +++++++++++++++ tests/test_cluster.py | 80 +-- tests/test_complex_hmatrix.py | 128 ----- tests/test_ddm_solver.py | 503 ++++++++---------- tests/test_distributed_operator.py | 59 ++ tests/test_hmatrix.py | 183 +------ tests/test_imatrix.py | 23 - 74 files changed, 3565 insertions(+), 1873 deletions(-) delete mode 100644 example/cluster.py create mode 100644 example/create_geometry.py create mode 100644 example/define_custom_dense_blocks_generator.py create mode 100644 example/define_custom_generators.py create mode 100644 example/define_custom_local_operator.py create mode 100644 example/define_custom_low_rank_generator.py delete mode 100644 example/partition_example.py delete mode 100644 example/smallest_example.py delete mode 100644 example/solver_scipy.py create mode 100644 example/use_block_jacobi_solver.py create mode 100644 example/use_cluster.py create mode 100644 example/use_cluster_with_given_partition.py create mode 100644 example/use_custom_dense_block_generator.py create mode 100644 example/use_custom_local_operator.py create mode 100644 example/use_custom_low_rank_approximation.py create mode 100644 example/use_default_build.py create mode 100644 example/use_local_build.py create mode 100644 example/use_no_default_build.py create mode 100644 pyproject.toml create mode 100755 src/Htool.cpython-311-darwin.so create mode 100755 src/Htool.cpython-39-x86_64-linux-gnu.so create mode 100644 src/htool.egg-info/PKG-INFO create mode 100644 src/htool.egg-info/SOURCES.txt create mode 100644 src/htool.egg-info/dependency_links.txt create mode 100644 src/htool.egg-info/not-zip-safe create mode 100644 src/htool.egg-info/requires.txt create mode 100644 src/htool.egg-info/top_level.txt delete mode 100644 src/htool/cluster.hpp create mode 100644 src/htool/clustering/cluster_builder.hpp create mode 100644 src/htool/clustering/cluster_node.hpp create mode 100644 src/htool/clustering/implementation/direction_computation.hpp create mode 100644 src/htool/clustering/implementation/splitting.hpp create mode 100644 src/htool/clustering/interface/direction_computation.hpp create mode 100644 src/htool/clustering/interface/splitting.hpp create mode 100644 src/htool/clustering/utility.hpp delete mode 100644 src/htool/dense_blocks_generator.hpp create mode 100644 src/htool/distributed_operator/distributed_operator.hpp create mode 100644 src/htool/distributed_operator/implementation/partition_from_cluster.hpp create mode 100644 src/htool/distributed_operator/interface/partition.hpp create mode 100644 src/htool/distributed_operator/utility.hpp delete mode 100644 src/htool/hmatrix.hpp create mode 100644 src/htool/hmatrix/hmatrix.hpp create mode 100644 src/htool/hmatrix/hmatrix_builder.hpp create mode 100644 src/htool/hmatrix/interfaces/virtual_dense_blocks_generator.hpp create mode 100644 src/htool/hmatrix/interfaces/virtual_generator.hpp create mode 100644 src/htool/hmatrix/interfaces/virtual_low_rank_generator.hpp create mode 100644 src/htool/local_operator/local_dense_operator.hpp create mode 100644 src/htool/local_operator/local_hmatrix.hpp create mode 100644 src/htool/local_operator/local_operator.hpp create mode 100644 src/htool/local_operator/virtual_local_operator.hpp delete mode 100644 src/htool/lrmat_generator.hpp create mode 100644 src/htool/matplotlib/cluster.hpp create mode 100644 src/htool/matplotlib/hmatrix.hpp delete mode 100644 src/htool/matrix.hpp delete mode 100644 src/htool/misc.hpp create mode 100644 src/htool/misc/logger.hpp create mode 100644 src/htool/misc/utility.hpp rename src/htool/{ => misc}/wrapper_mpi.hpp (94%) rename src/htool/{ddm_solver.hpp => solver/solver.hpp} (64%) create mode 100644 src/htool/solver/utility.hpp create mode 100644 tests/conftest.py delete mode 100644 tests/test_complex_hmatrix.py create mode 100644 tests/test_distributed_operator.py delete mode 100644 tests/test_imatrix.py diff --git a/CMakeLists.txt b/CMakeLists.txt index 41d53ce..540c3f8 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -13,13 +13,13 @@ else() LANGUAGES CXX) endif() -# To force c++11 +# To force c++14 if(${CMAKE_VERSION} VERSION_LESS 3.1) - add_compile_options(-std=c++11) + add_compile_options(-std=c++14) elseif(${CMAKE_VERSION} VERSION_LESS 3.6.3 AND ${CMAKE_CXX_COMPILER_ID} STREQUAL "Intel") - add_compile_options(-std=c++11) + add_compile_options(-std=c++14) else() - set(CMAKE_CXX_STANDARD 11) + set(CMAKE_CXX_STANDARD 14) set(CMAKE_CXX_STANDARD_REQUIRED ON) endif() @@ -85,4 +85,4 @@ if(CODE_COVERAGE AND (CMAKE_C_COMPILER_ID MATCHES "GNU" OR CMAKE_CXX_COMPILER_ID target_link_libraries(Htool PRIVATE gcov) endif() -# target_compile_features(Htool INTERFACE cxx_std_11) +# target_compile_features(Htool INTERFACE cxx_std_14) diff --git a/example/cluster.py b/example/cluster.py deleted file mode 100644 index 42a5b47..0000000 --- a/example/cluster.py +++ /dev/null @@ -1,61 +0,0 @@ -import Htool -import numpy as np -import mpi4py - -# Parameters -minclustersize = 5 -Number_points = 500 -np.random.seed(0) -points_3D = np.zeros((3, Number_points)) -points_2D = np.zeros((2, Number_points)) - -# Random 3D geometry -points_3D[0, :] = np.random.random(Number_points) -points_3D[1, :] = np.random.random(Number_points) -points_3D[2, :] = np.random.random(Number_points) - - -# Cluster 3D -cluster = Htool.BoundingBox1RegularClustering(3) -cluster.set_minclustersize(minclustersize) -cluster.build(Number_points, points_3D, 2) -cluster.display(points_3D, 1) -cluster.display(points_3D, 2) - -# Cluster 3D with given partition - -sizeworld = mpi4py.MPI.COMM_WORLD.Get_size() -local_size = int(Number_points/sizeworld) -MasterOffset = np.zeros((2, sizeworld)) -for i in range(0, sizeworld-1): - MasterOffset[0, i] = i*local_size - MasterOffset[1, i] = local_size - points_3D[0, i*local_size:(i+1)*local_size] = i -points_3D[0, (sizeworld-1)*local_size:] = sizeworld-1 -MasterOffset[0, sizeworld-1] = (sizeworld-1)*local_size -MasterOffset[1, sizeworld-1] = Number_points-(sizeworld-1)*local_size - - -cluster = Htool.BoundingBox1RegularClustering(3) -cluster.set_minclustersize(minclustersize) -cluster.build(Number_points, points_3D, MasterOffset, 2) -cluster.display(points_3D, 1) -cluster.display(points_3D, 2) - -# Random 2D geometry -points_2D = np.zeros((2, Number_points)) -# points_2D[0, :] = np.random.random(Number_points) -# points_2D[1, :] = np.random.random(Number_points) - -r = np.random.uniform(low=0, high=1, size=Number_points) # radius -theta = np.random.uniform(low=0, high=2*np.pi, size=Number_points) # angle - -points_2D[0, :] = np.sqrt(r) * np.cos(theta) -points_2D[1, :] = np.sqrt(r) * np.sin(theta) - -# Cluster 2D -cluster = Htool.PCAGeometricClustering(2) -cluster.set_minclustersize(minclustersize) -cluster.build(Number_points, points_2D, 2) -cluster.display(points_2D, 1) -cluster.display(points_2D, 2) diff --git a/example/create_geometry.py b/example/create_geometry.py new file mode 100644 index 0000000..639be2d --- /dev/null +++ b/example/create_geometry.py @@ -0,0 +1,93 @@ +import math +import mpi4py +import numpy as np + + +def create_random_points_in_disk(nb_points: int): + u = np.random.rand(nb_points) + theta = 2 * np.pi * np.random.rand(nb_points) + + x = np.sqrt(u) * np.cos(theta) + y = np.sqrt(u) * np.sin(theta) + return np.array([x, y]) + + +def create_random_points_in_sphere(nb_points: int): + u = np.random.rand(nb_points) + theta = 2 * np.pi * np.random.rand(nb_points) + phi = np.arccos(2 * np.random.rand(nb_points) - 1) + + r = np.cbrt(u) + x = r * np.sin(theta) * np.cos(phi) + y = r * np.sin(theta) * np.sin(phi) + z = r * np.cos(theta) + return np.array([x, y, z]) + + +def create_random_geometries(dimension: int, nb_rows: int, nb_cols: int): + np.random.seed(0) + + if dimension == 3: + target_points = create_random_points_in_sphere(nb_rows) + source_points = create_random_points_in_sphere(nb_cols) + + if dimension == 2: + target_points = create_random_points_in_disk(nb_rows) + source_points = create_random_points_in_disk(nb_cols) + + source_points[0, :] += 2 + return [target_points, source_points] + + +def create_partitionned_geometries( + dimension: int, nb_rows: int, nb_cols: int, sizeWorld: int +): + np.random.seed(0) + target_local_size = int(nb_rows / sizeWorld) + target_partition = np.zeros((2, sizeWorld), dtype=int) + + if dimension == 2: + target_points = np.array([[], []]) + for i in range(0, sizeWorld - 1): + target_partition[0, i] = i * target_local_size + target_partition[1, i] = target_local_size + new_target_points = create_random_points_in_disk(target_partition[1, i]) + new_target_points[0, :] += 3 * i + target_points = np.concatenate((target_points, new_target_points), axis=1) + + target_partition[0, sizeWorld - 1] = (sizeWorld - 1) * target_local_size + target_partition[1, sizeWorld - 1] = ( + nb_rows - (sizeWorld - 1) * target_local_size + ) + new_target_points = create_random_points_in_disk( + target_partition[1, sizeWorld - 1] + ) + new_target_points[0, :] += 3 * (sizeWorld - 1) + target_points = np.concatenate((target_points, new_target_points), axis=1) + source_points = create_random_points_in_disk(nb_cols) + source_points[0, :] += 3 * (sizeWorld - 1) / 2 + source_points[1, :] += 3 + + if dimension == 3: + target_points = np.array([[], [], []]) + for i in range(0, sizeWorld - 1): + target_partition[0, i] = i * target_local_size + target_partition[1, i] = target_local_size + new_target_points = create_random_points_in_sphere(target_partition[1, i]) + new_target_points[0, :] += 3 * i + target_points = np.concatenate((target_points, new_target_points), axis=1) + + target_partition[0, sizeWorld - 1] = (sizeWorld - 1) * target_local_size + target_partition[1, sizeWorld - 1] = ( + nb_rows - (sizeWorld - 1) * target_local_size + ) + new_target_points = create_random_points_in_sphere( + target_partition[1, sizeWorld - 1] + ) + new_target_points[0, :] += 3 * (sizeWorld - 1) + target_points = np.concatenate((target_points, new_target_points), axis=1) + source_points = create_random_points_in_sphere(nb_cols) + source_points[0, :] += 3 * (sizeWorld - 1) / 2 + source_points[1, :] += 3 + + return [target_points, source_points, target_partition] diff --git a/example/ddm_solver.py b/example/ddm_solver.py index 7052b6f..5838a62 100644 --- a/example/ddm_solver.py +++ b/example/ddm_solver.py @@ -1,88 +1,97 @@ import Htool import numpy as np import mpi4py +from define_custom_generators import CustomGeneratorWithPermutation +from create_geometry import create_random_geometries +import matplotlib.pyplot as plt -# MPI -comm = mpi4py.MPI.COMM_WORLD -rank = comm.Get_rank() - -# Custom generator - - -class Generator(Htool.VirtualGenerator): - - def __init__(self, points_target, points_source): - super().__init__(points_target.shape[1], points_source.shape[1]) - self.points_target = points_target - self.points_source = points_source - - def get_coef(self, i, j): - return 1.0 / (1e-5 + np.linalg.norm(self.points_target[i, :] - self.points_source[j, :])) - - def build_submatrix(self, J, K, mat): - for j in range(0, len(J)): - for k in range(0, len(K)): - mat[j, k] = 1.0 / (1.e-5 + np.linalg.norm( - self.points_target[:, J[j]] - self.points_source[:, K[k]])) - - def mat_vec(self, x): - y = np.zeros(self.nb_rows()) - for i in range(0, self.nb_rows()): - for j in range(0, self.nb_cols()): - y[i] += self.get_coef(i, j)*x[j] - return y - - def mat_mat(self, X): - Y = np.zeros((self.nb_rows(), X.shape[1])) - - for i in range(0, self.nb_rows()): - for j in range(0, X.shape[1]): - for k in range(0, self.nb_cols()): - Y[i, j] += self.get_coef(i, k)*X[k, j] - return Y +# Random geometry +size = 500 +dimension = 3 +[points, _] = create_random_geometries(dimension, size, size) -# Random geometry -Size = 500 -np.random.seed(0) -points_target = np.zeros((2, Size)) -points_target[0, :] = np.random.random(Size) -points_target[1, :] = np.random.random(Size) # Htool parameters -eta = 5 +eta = 10 epsilon = 1e-3 minclustersize = 10 +number_of_children = 2 + +# Build clusters +cluster_builder = Htool.ClusterBuilder() +cluster_builder.set_minclustersize(minclustersize) +cluster: Htool.Cluster = cluster_builder.create_cluster_tree( + points, number_of_children, mpi4py.MPI.COMM_WORLD.size +) + +# Build generator +generator = CustomGeneratorWithPermutation( + cluster.get_permutation(), points, cluster.get_permutation(), points +) + +# Build distributed operator +default_approximation = Htool.DefaultApproximationBuilder( + generator, + cluster, + cluster, + epsilon, + eta, + "S", + "L", + mpi4py.MPI.COMM_WORLD, +) + +# Solver with block Jacobi preconditionner +default_solver_builder = Htool.DefaultSolverBuilder( + default_approximation.distributed_operator, + default_approximation.block_diagonal_hmatrix, +) +solver = default_solver_builder.solver -# Build H matrix -generator = Generator(points_target, points_target) -cluster = Htool.PCARegularClustering(2) -cluster.build(Size, points_target, 2) -cluster.set_minclustersize(minclustersize) -hmat = Htool.HMatrix(cluster, cluster, epsilon, eta, 'S', 'L') -hmat.build(generator, points_target) # Solver with block Jacobi -x_ref = np.random.random(Size) -b = hmat*x_ref -x = np.zeros(Size) -ddm_solver = Htool.DDM(hmat) +x_ref = np.random.random(size) +b = default_approximation.distributed_operator * x_ref +x = np.zeros(size) hpddm_args = "-hpddm_compute_residual l2 " -if (rank == 0): +if mpi4py.MPI.COMM_WORLD.Get_rank() == 0: hpddm_args += "-hpddm_verbosity 10" -ddm_solver.set_hpddm_args(hpddm_args) -ddm_solver.facto_one_level() -ddm_solver.solve(x, b) +solver.set_hpddm_args(hpddm_args) +solver.facto_one_level() +solver.solve(x, b) -# Several ways to display information -print(hmat) -hmat.print_infos() -hmat.display() -hmat.display_cluster(points_target, 2, "target") -ddm_solver.print_infos() -if rank == 0: - nb_it = ddm_solver.get_infos("Nb_it") - print("Nb_it", nb_it) - print(np.linalg.norm(x-x_ref)/np.linalg.norm(x_ref)) +# Several ways to display information +if mpi4py.MPI.COMM_WORLD.Get_rank() == 0: + print(np.linalg.norm(x - x_ref) / np.linalg.norm(x_ref)) + hmatrix = default_approximation.hmatrix + local_block_hmatrix = default_approximation.block_diagonal_hmatrix + print(hmatrix.get_tree_parameters()) + print(hmatrix.get_information()) + + fig = plt.figure() + ax1 = None + ax2 = None + ax3 = None + ax4 = None + if dimension == 2: + ax1 = fig.add_subplot(2, 2, 1) + ax2 = fig.add_subplot(2, 2, 2) + ax3 = fig.add_subplot(2, 2, 3) + ax4 = fig.add_subplot(2, 2, 4) + elif dimension == 3: + ax1 = fig.add_subplot(2, 2, 1, projection="3d") + ax2 = fig.add_subplot(2, 2, 2, projection="3d") + ax3 = fig.add_subplot(2, 2, 3) + ax4 = fig.add_subplot(2, 2, 4) + + ax1.set_title("cluster at depth 1") + ax2.set_title("cluster at depth 2") + ax4.set_title("Hmatrix on rank 0") + Htool.plot(ax1, cluster, points, 1) + Htool.plot(ax2, cluster, points, 2) + Htool.plot(ax3, hmatrix) + Htool.plot(ax4, local_block_hmatrix) + plt.show() diff --git a/example/define_custom_dense_blocks_generator.py b/example/define_custom_dense_blocks_generator.py new file mode 100644 index 0000000..262ca37 --- /dev/null +++ b/example/define_custom_dense_blocks_generator.py @@ -0,0 +1,16 @@ +import Htool +import mpi4py + + +class CustomDenseBlocksGenerator(Htool.VirtualDenseBlocksGenerator): + def __init__( + self, + generator, + ): + super().__init__() + self.generator = generator + + def build_dense_blocks(self, rows_offsets, cols_offsets, blocks): + nb_blocks = len(blocks) + for i in range(nb_blocks): + self.generator.build_submatrix(rows_offsets[i], cols_offsets[i], blocks[i]) diff --git a/example/define_custom_generators.py b/example/define_custom_generators.py new file mode 100644 index 0000000..bf528bf --- /dev/null +++ b/example/define_custom_generators.py @@ -0,0 +1,96 @@ +import Htool +import numpy as np +import mpi4py + + +class CustomGenerator(Htool.VirtualGenerator): + def __init__(self, target_cluster, target_points, source_cluster, source_points): + super().__init__() + self.target_points = target_points + self.target_permutation = target_cluster.get_permutation() + self.source_points = source_points + self.source_permutation = source_cluster.get_permutation() + self.nb_rows = len(self.target_permutation) + self.nb_cols = len(self.source_permutation) + + def get_coef(self, i, j): + return 1.0 / ( + 1e-1 + + np.linalg.norm( + self.target_points[:, self.target_permutation[i]] + - self.source_points[:, self.source_permutation[j]] + ) + ) + + def build_submatrix(self, row_offset, col_offset, mat): + for j in range(0, mat.shape[0]): + for k in range(0, mat.shape[1]): + mat[j, k] = 1.0 / ( + 1.0e-1 + + np.linalg.norm( + self.target_points[:, self.target_permutation[j + row_offset]] + - self.source_points[:, self.source_permutation[k + col_offset]] + ) + ) + + def mat_vec(self, x): + y = np.zeros(self.nb_rows) + for i in range(0, self.nb_rows): + for j in range(0, self.nb_cols): + y[self.target_permutation[i]] += ( + self.get_coef(i, j) * x[self.source_permutation[j]] + ) + return y + + def mat_mat(self, X): + Y = np.zeros((self.nb_rows, X.shape[1])) + + for i in range(0, self.nb_rows): + for j in range(0, X.shape[1]): + for k in range(0, self.nb_cols): + Y[self.target_permutation[i], j] += ( + self.get_coef(i, k) * X[self.source_permutation[k], j] + ) + return Y + + +class CustomGeneratorWithPermutation(Htool.VirtualGeneratorWithPermutation): + def __init__( + self, target_permutation, target_points, source_permutation, source_points + ): + super().__init__(target_permutation, source_permutation) + self.target_points = target_points + self.source_points = source_points + self.nb_rows = len(target_permutation) + self.nb_cols = len(source_permutation) + + def get_coef(self, i, j): + return 1.0 / ( + 1e-1 + np.linalg.norm(self.target_points[:, i] - self.source_points[:, j]) + ) + + def build_submatrix(self, J, K, mat): + for j in range(0, len(J)): + for k in range(0, len(K)): + mat[j, k] = 1.0 / ( + 1e-1 + + np.linalg.norm( + self.target_points[:, J[j]] - self.source_points[:, K[k]] + ) + ) + + def mat_vec(self, x): + y = np.zeros(self.nb_rows) + for i in range(0, self.nb_rows): + for j in range(0, self.nb_cols): + y[i] += self.get_coef(i, j) * x[j] + return y + + def mat_mat(self, X): + Y = np.zeros((self.nb_rows, X.shape[1])) + + for i in range(0, self.nb_rows): + for j in range(0, X.shape[1]): + for k in range(0, self.nb_cols): + Y[i, j] += self.get_coef(i, k) * X[k, j] + return Y diff --git a/example/define_custom_local_operator.py b/example/define_custom_local_operator.py new file mode 100644 index 0000000..7026e6f --- /dev/null +++ b/example/define_custom_local_operator.py @@ -0,0 +1,52 @@ +import Htool +import numpy as np +import mpi4py + + +class CustomLocalOperator(Htool.LocalOperator): + def __init__( + self, + generator: Htool.VirtualGenerator, + target_cluster: Htool.Cluster, + source_cluster: Htool.Cluster, + symmetry: str = "N", + UPLO: str = "N", + target_use_permutation_to_mvprod: bool = False, + source_use_permutation_to_mvprod: bool = False, + ) -> None: + super().__init__( + target_cluster, + source_cluster, + symmetry, + UPLO, + target_use_permutation_to_mvprod, + source_use_permutation_to_mvprod, + ) + self.data = np.zeros((target_cluster.get_size(), source_cluster.get_size())) + generator.build_submatrix( + target_cluster.get_offset(), source_cluster.get_offset(), self.data + ) + + def add_vector_product( + self, trans, alpha, input: np.array, beta, output: np.array + ) -> None: + # Beware, you need to use inplace operation for output to keep the underlying data + output *= beta + if trans == "N": + output += alpha * self.data.dot(input) + elif trans == "T": + output += alpha * np.transpose(self.data).dot(input) + elif trans == "C": + output += alpha * np.vdot(np.transpose(self.data), input) + + def add_matrix_product_row_major( + self, trans, alpha, input: np.array, beta, output: np.array + ) -> None: + output *= beta + if trans == "N": + output += alpha * self.data @ input + elif trans == "T": + output += alpha * np.transpose(self.data) @ input + elif trans == "C": + output += alpha * np.matrix.H(self.data) @ input + output = np.asfortranarray(output) diff --git a/example/define_custom_low_rank_generator.py b/example/define_custom_low_rank_generator.py new file mode 100644 index 0000000..01aaa1a --- /dev/null +++ b/example/define_custom_low_rank_generator.py @@ -0,0 +1,23 @@ +import math +import Htool +import numpy as np + + +class CustomSVD(Htool.VirtualLowRankGenerator): + def build_low_rank_approximation( + self, generator, target_size, source_size, target_offset, source_offset, epsilon + ): + submat = np.zeros((target_size, source_size), order="F") + generator.build_submatrix(target_offset, source_offset, submat) + u, s, vh = np.linalg.svd(submat, full_matrices=False) + + norm = np.linalg.norm(submat) + svd_norm = 0 + count = len(s) - 1 + while count > 0 and math.sqrt(svd_norm) / norm < epsilon: + svd_norm += s[count] ** 2 + count -= 1 + count = min(count + 1, min(target_size, source_size)) + self.set_U(u[:, 0:count] * s[0:count]) + self.set_V(vh[0:count, :]) + self.set_rank(count) diff --git a/example/partition_example.py b/example/partition_example.py deleted file mode 100644 index e47777b..0000000 --- a/example/partition_example.py +++ /dev/null @@ -1,105 +0,0 @@ -import Htool -import numpy as np -import mpi4py - -# Custom generator - - -class Generator(Htool.VirtualGenerator): - - def __init__(self, points_target, points_source): - super().__init__(points_target.shape[1], points_source.shape[1]) - self.points_target = points_target - self.points_source = points_source - - def get_coef(self, i, j): - return 1.0 / (1e-5 + np.linalg.norm(self.points_target[:, i] - self.points_source[:, j])) - - def build_submatrix(self, J, K, mat): - for j in range(0, len(J)): - for k in range(0, len(K)): - mat[j, k] = 1.0 / (1.e-5 + np.linalg.norm( - self.points_target[:, J[j]] - self.points_source[:, K[k]])) - - def mat_vec(self, x): - y = np.zeros(self.nb_rows()) - for i in range(0, self.nb_rows()): - for j in range(0, self.nb_cols()): - y[i] += self.get_coef(i, j)*x[j] - return y - - def mat_mat(self, X): - Y = np.zeros((self.nb_rows(), X.shape[1])) - - for i in range(0, self.nb_rows()): - for j in range(0, X.shape[1]): - for k in range(0, self.nb_cols()): - Y[i, j] += self.get_coef(i, k)*X[k, j] - return Y - - -# Htool parameters -eta = 10 -epsilon = 1e-3 -minclustersize = 10 - -# Random geometry -NbRows = 500 -NbCols = 500 -np.random.seed(0) -points_target = np.zeros((2, NbRows)) -sizeworld = mpi4py.MPI.COMM_WORLD.Get_size() -local_size = int(NbRows/sizeworld) -MasterOffset = np.zeros((2, sizeworld)) -for i in range(0, sizeworld-1): - MasterOffset[0, i] = i*local_size - MasterOffset[1, i] = local_size - points_target[0, i*local_size:(i+1)*local_size] = i - -points_target[0, (sizeworld-1)*local_size:] = sizeworld-1 -MasterOffset[0, sizeworld-1] = (sizeworld-1)*local_size -MasterOffset[1, sizeworld-1] = NbRows-(sizeworld-1)*local_size - -points_target[1, :] = np.random.random(NbRows) - -# Cluster target -cluster_target = Htool.PCARegularClustering(2) -cluster_target.set_minclustersize(minclustersize) -cluster_target.build(NbRows, points_target, MasterOffset, 2) - -if NbRows == NbCols: - points_source = points_target - cluster_source = cluster_target -else: - points_source = np.zeros((2, NbCols)) - points_source[0, :] = np.random.random(NbCols) - points_source[1, :] = np.random.random(NbCols) - cluster_source = None - - -# Build H matrix -generator = Generator(points_target, points_source) -HMatrix_test = Htool.HMatrix(cluster_target, cluster_source, epsilon, eta) -HMatrix_test.build(generator, points_target, points_source) - -# Test matrix vector product -x = np.random.rand(NbCols) -y_1 = HMatrix_test*x -y_2 = generator.mat_vec(x) - -print(np.linalg.norm(y_1-y_2)/np.linalg.norm(y_2)) - -# Test matrix matrix product -X = np.random.rand(NbCols, 2) -Y_1 = HMatrix_test @ X -Y_2 = generator.mat_mat(X) - -print(np.linalg.norm(Y_1-Y_2)/np.linalg.norm(Y_2)) - -# Several ways to display information -HMatrix_test.print_infos() -print(HMatrix_test) -print("Number_of_MPI_tasks:", HMatrix_test.get_infos("Number_of_MPI_tasks")) -HMatrix_test.display() -HMatrix_test.display_cluster(points_target, 2, "target") -HMatrix_test.display_cluster(points_source, 2, "source") diff --git a/example/smallest_example.py b/example/smallest_example.py deleted file mode 100644 index 34ca05b..0000000 --- a/example/smallest_example.py +++ /dev/null @@ -1,95 +0,0 @@ -import Htool -import numpy as np -import mpi4py - -# Custom generator - - -class Generator(Htool.VirtualGenerator): - - def __init__(self, points_target, points_source): - super().__init__(points_target.shape[1], points_source.shape[1]) - self.points_target = points_target - self.points_source = points_source - - def get_coef(self, i, j): - return 1.0 / (1e-5 + np.linalg.norm(self.points_target[:, i] - self.points_source[:, j])) - - def build_submatrix(self, J, K, mat): - for j in range(0, len(J)): - for k in range(0, len(K)): - mat[j, k] = 1.0 / (1.e-5 + np.linalg.norm( - self.points_target[:, J[j]] - self.points_source[:, K[k]])) - - def mat_vec(self, x): - y = np.zeros(self.nb_rows()) - for i in range(0, self.nb_rows()): - for j in range(0, self.nb_cols()): - y[i] += self.get_coef(i, j)*x[j] - return y - - def mat_mat(self, X): - Y = np.zeros((self.nb_rows(), X.shape[1])) - - for i in range(0, self.nb_rows()): - for j in range(0, X.shape[1]): - for k in range(0, self.nb_cols()): - Y[i, j] += self.get_coef(i, k)*X[k, j] - return Y - - -# Random geometry -NbRows = 500 -NbCols = 500 -np.random.seed(0) -points_target = np.zeros((2, NbRows)) -points_target[0, :] = np.random.random(NbRows) -points_target[1, :] = np.random.random(NbRows) - -if NbRows == NbCols: - points_source = points_target -else: - points_source = np.zeros((2, NbCols)) - points_source[0, :] = np.random.random(NbCols) - points_source[1, :] = np.random.random(NbCols) - -# Htool parameters -eta = -1000 -epsilon = 1e-1 -minclustersize = 10 - -# Build clusters -dimension = 2 -cluster_target = Htool.PCARegularClustering(dimension) -cluster_source = Htool.PCARegularClustering(dimension) -cluster_target.build(NbRows, points_target, 2) -cluster_source.build(NbCols, points_source, 2) -cluster_target.set_minclustersize(minclustersize) -cluster_source.set_minclustersize(minclustersize) - -# Build H matrix -generator = Generator(points_target, points_source) -HMatrix_test = Htool.HMatrix(cluster_target, cluster_source, epsilon, eta) -HMatrix_test.build(generator, points_target, points_source) - -# Test matrix vector product -x = np.random.rand(NbCols) -y_1 = HMatrix_test*x -y_2 = generator.mat_vec(x) - -print(np.linalg.norm(y_1-y_2)/np.linalg.norm(y_2)) - -# Test matrix matrix product -X = np.random.rand(NbCols, 2) -Y_1 = HMatrix_test @ X -Y_2 = generator.mat_mat(X) - -print(np.linalg.norm(Y_1-Y_2)/np.linalg.norm(Y_2)) - -# Several ways to display information -HMatrix_test.print_infos() -print(HMatrix_test) -print("Number_of_MPI_tasks:", HMatrix_test.get_infos("Number_of_MPI_tasks")) -HMatrix_test.display() -HMatrix_test.display_cluster(points_target, 2, "target") -HMatrix_test.display_cluster(points_source, 2, "source") diff --git a/example/solver_scipy.py b/example/solver_scipy.py deleted file mode 100644 index ca3d0e0..0000000 --- a/example/solver_scipy.py +++ /dev/null @@ -1,81 +0,0 @@ -import Htool -from mpi4py import MPI -import numpy as np -from numpy.linalg import norm -from scipy.sparse.linalg import gmres - -# Custom generator - - -class Generator(Htool.VirtualGenerator): - - def __init__(self, points): - super().__init__(points.shape[1], points.shape[1]) - self.points = points - - def get_coef(self, i, j): - return 1.0 / (1e-5 + np.linalg.norm(self.points[:, i] - self.points[:, j])) - - def build_submatrix(self, J, K, mat): - for j in range(0, len(J)): - for k in range(0, len(K)): - mat[j, k] = 1.0 / \ - (1.e-5 + - np.linalg.norm(self.points[:, J[j]] - self.points[:, K[k]])) - - def mat_vec(self, x): - y = np.zeros(self.nb_rows()) - for i in range(0, self.nb_rows()): - for j in range(0, self.nb_cols()): - y[i] += self.get_coef(i, j)*x[j] - return y - - -# SETUP -# n² points on a regular grid in a square -n = int(np.sqrt(4761)) -points = np.zeros((2, n*n)) -for j in range(0, n): - for k in range(0, n): - points[:, j+k*n] = (j, k) - -# Htool parameters -eta = 10 -epsilon = 1e-3 -minclustersize = 25 - -# Build H matrix -generator = Generator(points) -symmetric = 'S' -UPLO = 'L' -cluster = Htool.PCARegularClustering(2) -cluster.build(n*n, points, 2) -cluster.set_minclustersize(minclustersize) -HMatrix = Htool.HMatrix(cluster, cluster, epsilon, eta, symmetric, UPLO) -HMatrix.build(generator, points) - -# Dense matrix -Full_H = 1.0 / (1e-5 + norm(points.T.reshape(1, n*n, 2) - - points.T.reshape(n*n, 1, 2), axis=2)) - -# GMRES -y = np.ones((n*n,)) -x, _ = gmres(HMatrix, y) -x_full, _ = gmres(Full_H, y) - -err_gmres_hmat = norm(HMatrix @ x - y) -err_gmres_dense = norm(Full_H @ x_full - y) -err_comp = norm(x - x_full)/norm(x) - -comm = MPI.COMM_WORLD -rank = comm.Get_rank() -if rank == 0: - print("Error from gmres (Hmatrix):", err_gmres_hmat) - print("Error from gmres (full matrix):", err_gmres_dense) - print("Error between the two solutions:", err_comp) - -# Several ways to display information -print(HMatrix) -HMatrix.print_infos() -HMatrix.display() -HMatrix.display_cluster(points, 2, "target") diff --git a/example/use_block_jacobi_solver.py b/example/use_block_jacobi_solver.py new file mode 100644 index 0000000..7630e9b --- /dev/null +++ b/example/use_block_jacobi_solver.py @@ -0,0 +1,95 @@ +import Htool +import numpy as np +import mpi4py +from define_custom_generators import CustomGenerator +from create_geometry import create_random_geometries +import matplotlib.pyplot as plt + +# Random geometry +size = 500 +dimension = 3 + +[points, _] = create_random_geometries(dimension, size, size) + + +# Htool parameters +eta = 10 +epsilon = 1e-3 +minclustersize = 10 +number_of_children = 2 + +# Build clusters +cluster_builder = Htool.ClusterBuilder() +cluster_builder.set_minclustersize(minclustersize) +cluster: Htool.Cluster = cluster_builder.create_cluster_tree( + points, number_of_children, mpi4py.MPI.COMM_WORLD.size +) + +# Build generator +generator = CustomGenerator(cluster, points, cluster, points) + +# Build distributed operator +default_approximation = Htool.DefaultApproximationBuilder( + generator, + cluster, + cluster, + epsilon, + eta, + "S", + "L", + mpi4py.MPI.COMM_WORLD, +) + +# Solver with block Jacobi preconditionner +default_solver_builder = Htool.DefaultSolverBuilder( + default_approximation.distributed_operator, + default_approximation.block_diagonal_hmatrix, +) +solver = default_solver_builder.solver + + +# Solver with block Jacobi +x_ref = np.random.random(size) +b = default_approximation.distributed_operator * x_ref +x = np.zeros(size) + +hpddm_args = "-hpddm_compute_residual l2 " +if mpi4py.MPI.COMM_WORLD.Get_rank() == 0: + hpddm_args += "-hpddm_verbosity 10" +solver.set_hpddm_args(hpddm_args) +solver.facto_one_level() +solver.solve(x, b) + + +# Several ways to display information +if mpi4py.MPI.COMM_WORLD.Get_rank() == 0: + print(np.linalg.norm(x - x_ref) / np.linalg.norm(x_ref)) + hmatrix = default_approximation.hmatrix + local_block_hmatrix = default_approximation.block_diagonal_hmatrix + print(hmatrix.get_tree_parameters()) + print(hmatrix.get_information()) + + fig = plt.figure() + ax1 = None + ax2 = None + ax3 = None + ax4 = None + if dimension == 2: + ax1 = fig.add_subplot(2, 2, 1) + ax2 = fig.add_subplot(2, 2, 2) + ax3 = fig.add_subplot(2, 2, 3) + ax4 = fig.add_subplot(2, 2, 4) + elif dimension == 3: + ax1 = fig.add_subplot(2, 2, 1, projection="3d") + ax2 = fig.add_subplot(2, 2, 2, projection="3d") + ax3 = fig.add_subplot(2, 2, 3) + ax4 = fig.add_subplot(2, 2, 4) + + ax1.set_title("cluster at depth 1") + ax2.set_title("cluster at depth 2") + ax4.set_title("Hmatrix on rank 0") + Htool.plot(ax1, cluster, points, 1) + Htool.plot(ax2, cluster, points, 2) + Htool.plot(ax3, hmatrix) + Htool.plot(ax4, local_block_hmatrix) + plt.show() diff --git a/example/use_cluster.py b/example/use_cluster.py new file mode 100644 index 0000000..7c4ff51 --- /dev/null +++ b/example/use_cluster.py @@ -0,0 +1,58 @@ +import Htool +import numpy as np +import mpi4py +from create_geometry import create_random_geometries +import matplotlib.pyplot as plt + +# Random geometry +nb_rows = 500 +nb_cols = 500 +dimension = 2 +[target_points, _] = create_random_geometries(dimension, nb_rows, nb_cols) + + +# Parameters +minclustersize = 10 +number_of_children = 2 + +# Cluster builder +cluster_builder = Htool.ClusterBuilder() +cluster_builder.set_minclustersize(minclustersize) + +# Strategies +direction_computation_strategy = Htool.ComputeBoundingBox() +# direction_computation_strategy = Htool.ComputeLargestExtent() # default +splitting_strategy = Htool.RegularSplitting() # default +# splitting_strategy = Htool.GeometricSplitting() +cluster_builder.set_direction_computation_strategy(direction_computation_strategy) +cluster_builder.set_splitting_strategy(splitting_strategy) + + +# Build cluster +target_cluster: Htool.Cluster = cluster_builder.create_cluster_tree( + target_points, number_of_children, mpi4py.MPI.COMM_WORLD.size +) + + +# Local cluster +local_target_cluster: Htool.Cluster = target_cluster.get_cluster_on_partition( + mpi4py.MPI.COMM_WORLD.rank +) + +if mpi4py.MPI.COMM_WORLD.Get_rank() == 0: + fig = plt.figure() + ax1 = None + ax2 = None + + if dimension == 2: + ax1 = fig.add_subplot(1, 2, 1) + ax2 = fig.add_subplot(1, 2, 2) + elif dimension == 3: + ax1 = fig.add_subplot(1, 2, 1, projection="3d") + ax2 = fig.add_subplot(1, 2, 2, projection="3d") + + ax1.set_title("target cluster\ndepth 2") + ax2.set_title("local cluster\ntarget partition number 0\ndepth 1") + Htool.plot(ax1, target_cluster, target_points, 1) + Htool.plot(ax2, local_target_cluster, target_points, 3) + plt.show() diff --git a/example/use_cluster_with_given_partition.py b/example/use_cluster_with_given_partition.py new file mode 100644 index 0000000..f1c8079 --- /dev/null +++ b/example/use_cluster_with_given_partition.py @@ -0,0 +1,49 @@ +import Htool +import numpy as np +import mpi4py +from create_geometry import create_partitionned_geometries +import matplotlib.pyplot as plt + +# Random geometry +nb_rows = 500 +nb_cols = 500 +dimension = 3 +[target_points, _, target_partition] = create_partitionned_geometries( + dimension, nb_rows, nb_cols, mpi4py.MPI.COMM_WORLD.size +) + + +# Parameters +minclustersize = 10 +number_of_children = 2 + +# Build clusters +cluster_builder = Htool.ClusterBuilder() +cluster_builder.set_minclustersize(minclustersize) + +target_cluster: Htool.Cluster = cluster_builder.create_cluster_tree( + target_points, number_of_children, mpi4py.MPI.COMM_WORLD.size, target_partition +) + +# Local cluster +local_target_cluster: Htool.Cluster = target_cluster.get_cluster_on_partition( + mpi4py.MPI.COMM_WORLD.rank +) + +if mpi4py.MPI.COMM_WORLD.Get_rank() == 0: + fig = plt.figure() + ax1 = None + ax2 = None + + if dimension == 2: + ax1 = fig.add_subplot(1, 2, 1) + ax2 = fig.add_subplot(1, 2, 2) + elif dimension == 3: + ax1 = fig.add_subplot(1, 2, 1, projection="3d") + ax2 = fig.add_subplot(1, 2, 2, projection="3d") + + ax1.set_title("target cluster\ndepth 2") + ax2.set_title("local cluster\ntarget partition number 0\ndepth 1") + Htool.plot(ax1, target_cluster, target_points, 1) + Htool.plot(ax2, local_target_cluster, target_points, 1) + plt.show() diff --git a/example/use_custom_dense_block_generator.py b/example/use_custom_dense_block_generator.py new file mode 100644 index 0000000..440e28d --- /dev/null +++ b/example/use_custom_dense_block_generator.py @@ -0,0 +1,95 @@ +import Htool +import numpy as np +import mpi4py +from define_custom_generators import CustomGenerator +from define_custom_dense_blocks_generator import CustomDenseBlocksGenerator +from create_geometry import create_partitionned_geometries +import matplotlib.pyplot as plt + +# Random geometry +nb_rows = 500 +nb_cols = 500 +dimension = 3 +[target_points, source_points, target_partition] = create_partitionned_geometries( + dimension, nb_rows, nb_cols, mpi4py.MPI.COMM_WORLD.size +) + + +# Htool parameters +eta = 10 +epsilon = 1e-3 +minclustersize = 10 +number_of_children = 2 + +# Build clusters +cluster_builder = Htool.ClusterBuilder() +cluster_builder.set_minclustersize(minclustersize) +target_cluster: Htool.Cluster = cluster_builder.create_cluster_tree( + target_points, number_of_children, mpi4py.MPI.COMM_WORLD.size, target_partition +) +source_cluster: Htool.Cluster = cluster_builder.create_cluster_tree( + source_points, number_of_children, mpi4py.MPI.COMM_WORLD.size +) + + +# Build generator +generator = CustomGenerator( + target_cluster, target_points, source_cluster, source_points +) + +# +custom_dense_blocks_generator = CustomDenseBlocksGenerator(generator) + +# Build HMatrix +hmatrix_builder = Htool.HMatrixBuilder( + target_cluster, + source_cluster, + epsilon, + eta, + "N", + "N", + -1, + mpi4py.MPI.COMM_WORLD.rank, +) + +hmatrix_builder.set_dense_blocks_generator(custom_dense_blocks_generator) +hmatrix: Htool.HMatrix = hmatrix_builder.build(generator) + +# Build local operator +local_operator = Htool.LocalHMatrix( + hmatrix, + target_cluster.get_cluster_on_partition(mpi4py.MPI.COMM_WORLD.rank), + source_cluster, + "N", + "N", + False, + False, +) + +# Build distributed operator +target_partition_from_cluster = Htool.PartitionFromCluster(target_cluster) +source_partition_from_cluster = Htool.PartitionFromCluster(source_cluster) +distributed_operator = Htool.DistributedOperator( + target_partition_from_cluster, + source_partition_from_cluster, + "N", + "N", + mpi4py.MPI.COMM_WORLD, +) + +distributed_operator.add_local_operator(local_operator) + + +# Test matrix vector product +np.random.seed(0) +x = np.random.rand(nb_cols) +y_1 = distributed_operator * x +y_2 = generator.mat_vec(x) +print(mpi4py.MPI.COMM_WORLD.rank, np.linalg.norm(y_1 - y_2) / np.linalg.norm(y_2)) + + +# Test matrix matrix product +X = np.asfortranarray(np.random.rand(nb_cols, 2)) +Y_1 = distributed_operator @ X +Y_2 = generator.mat_mat(X) +print(mpi4py.MPI.COMM_WORLD.rank, np.linalg.norm(Y_1 - Y_2) / np.linalg.norm(Y_2)) diff --git a/example/use_custom_local_operator.py b/example/use_custom_local_operator.py new file mode 100644 index 0000000..293eb22 --- /dev/null +++ b/example/use_custom_local_operator.py @@ -0,0 +1,76 @@ +import Htool +import numpy as np +import mpi4py +from define_custom_generators import CustomGenerator +from define_custom_local_operator import CustomLocalOperator +from create_geometry import create_partitionned_geometries + +# Random geometry +nb_rows = 500 +nb_cols = 500 +dimension = 3 +[target_points, source_points, target_partition] = create_partitionned_geometries( + dimension, nb_rows, nb_cols, mpi4py.MPI.COMM_WORLD.size +) + + +# Htool parameters +eta = 10 +epsilon = 1e-3 +minclustersize = 10 +number_of_children = 2 + +# Build clusters +cluster_builder = Htool.ClusterBuilder() +cluster_builder.set_minclustersize(minclustersize) +target_cluster: Htool.Cluster = cluster_builder.create_cluster_tree( + target_points, number_of_children, mpi4py.MPI.COMM_WORLD.size, target_partition +) +source_cluster: Htool.Cluster = cluster_builder.create_cluster_tree( + source_points, number_of_children, mpi4py.MPI.COMM_WORLD.size +) + + +# Build generator +generator = CustomGenerator( + target_cluster, target_points, source_cluster, source_points +) + +# Build local operator +local_operator = CustomLocalOperator( + generator, + target_cluster.get_cluster_on_partition(mpi4py.MPI.COMM_WORLD.rank), + source_cluster, + "N", + "N", + False, + False, +) + +# Build distributed operator +target_partition_from_cluster = Htool.PartitionFromCluster(target_cluster) +source_partition_from_cluster = Htool.PartitionFromCluster(source_cluster) +distributed_operator = Htool.DistributedOperator( + target_partition_from_cluster, + source_partition_from_cluster, + "N", + "N", + mpi4py.MPI.COMM_WORLD, +) + +distributed_operator.add_local_operator(local_operator) + + +# Test matrix vector product +np.random.seed(0) +x = np.random.rand(nb_cols) +y_1 = distributed_operator * x +y_2 = generator.mat_vec(x) +print(mpi4py.MPI.COMM_WORLD.rank, np.linalg.norm(y_1 - y_2) / np.linalg.norm(y_2)) + + +# Test matrix matrix product +X = np.asfortranarray(np.random.rand(nb_cols, 2)) +Y_1 = distributed_operator @ X +Y_2 = generator.mat_mat(X) +print(mpi4py.MPI.COMM_WORLD.rank, np.linalg.norm(Y_1 - Y_2) / np.linalg.norm(Y_2)) diff --git a/example/use_custom_low_rank_approximation.py b/example/use_custom_low_rank_approximation.py new file mode 100644 index 0000000..aede659 --- /dev/null +++ b/example/use_custom_low_rank_approximation.py @@ -0,0 +1,95 @@ +import Htool +import numpy as np +import mpi4py +from define_custom_generators import CustomGenerator +from define_custom_low_rank_generator import CustomSVD +from create_geometry import create_partitionned_geometries + +# Random geometry +nb_rows = 500 +nb_cols = 500 +dimension = 3 +[target_points, source_points, target_partition] = create_partitionned_geometries( + dimension, nb_rows, nb_cols, mpi4py.MPI.COMM_WORLD.size +) + + +# Htool parameters +eta = 100 +epsilon = 1e-3 +minclustersize = 10 +number_of_children = 2 + +# Build clusters +cluster_builder = Htool.ClusterBuilder() +cluster_builder.set_minclustersize(minclustersize) +target_cluster: Htool.Cluster = cluster_builder.create_cluster_tree( + target_points, number_of_children, mpi4py.MPI.COMM_WORLD.size, target_partition +) +source_cluster: Htool.Cluster = cluster_builder.create_cluster_tree( + source_points, number_of_children, mpi4py.MPI.COMM_WORLD.size +) + + +# Build generator +generator = CustomGenerator( + target_cluster, target_points, source_cluster, source_points +) + +# Low rank generator + +low_rank_generator = CustomSVD() + +# Build HMatrix +hmatrix_builder = Htool.HMatrixBuilder( + target_cluster, + source_cluster, + epsilon, + eta, + "N", + "N", + -1, + mpi4py.MPI.COMM_WORLD.rank, +) +# hmatrix_builder.set_low_rank_generator(low_rank_generator) +hmatrix: Htool.HMatrix = hmatrix_builder.build(generator) + + +# Build local operator +local_operator = Htool.LocalHMatrix( + hmatrix, + target_cluster.get_cluster_on_partition(mpi4py.MPI.COMM_WORLD.rank), + source_cluster, + "N", + "N", + False, + False, +) + +# Build distributed operator +target_partition_from_cluster = Htool.PartitionFromCluster(target_cluster) +source_partition_from_cluster = Htool.PartitionFromCluster(source_cluster) +distributed_operator = Htool.DistributedOperator( + target_partition_from_cluster, + source_partition_from_cluster, + "N", + "N", + mpi4py.MPI.COMM_WORLD, +) + +distributed_operator.add_local_operator(local_operator) + + +# Test matrix vector product +np.random.seed(0) +x = np.random.rand(nb_cols) +y_1 = distributed_operator * x +y_2 = generator.mat_vec(x) +print(mpi4py.MPI.COMM_WORLD.rank, np.linalg.norm(y_1 - y_2) / np.linalg.norm(y_2)) + + +# Test matrix matrix product +X = np.asfortranarray(np.random.rand(nb_cols, 2)) +Y_1 = distributed_operator @ X +Y_2 = generator.mat_mat(X) +print(mpi4py.MPI.COMM_WORLD.rank, np.linalg.norm(Y_1 - Y_2) / np.linalg.norm(Y_2)) diff --git a/example/use_default_build.py b/example/use_default_build.py new file mode 100644 index 0000000..cdc73fa --- /dev/null +++ b/example/use_default_build.py @@ -0,0 +1,99 @@ +import Htool +import numpy as np +import mpi4py +from define_custom_generators import CustomGenerator, CustomGeneratorWithPermutation +from create_geometry import create_partitionned_geometries + +import matplotlib.pyplot as plt +import logging + +# Random geometry +nb_rows = 500 +nb_cols = 500 +dimension = 3 +[target_points, source_points, target_partition] = create_partitionned_geometries( + dimension, nb_rows, nb_cols, mpi4py.MPI.COMM_WORLD.size +) + + +# Htool parameters +eta = 10 +epsilon = 1e-3 +minclustersize = 10 +number_of_children = 2 + +# Build clusters +cluster_builder = Htool.ClusterBuilder() +cluster_builder.set_minclustersize(minclustersize) +target_cluster: Htool.Cluster = cluster_builder.create_cluster_tree( + target_points, number_of_children, mpi4py.MPI.COMM_WORLD.size, target_partition +) +source_cluster: Htool.Cluster = cluster_builder.create_cluster_tree( + source_points, number_of_children, mpi4py.MPI.COMM_WORLD.size +) + + +# Build generator +generator = CustomGenerator( + target_cluster, target_points, source_cluster, source_points +) + +# Build distributed operator +default_approximation = Htool.DefaultApproximationBuilder( + generator, + target_cluster, + source_cluster, + epsilon, + eta, + "N", + "N", + mpi4py.MPI.COMM_WORLD, +) + +distributed_operator = default_approximation.distributed_operator + +# Test matrix vector product +np.random.seed(0) +x = np.random.rand(nb_cols) +y_1 = distributed_operator * x +y_2 = generator.mat_vec(x) +print(mpi4py.MPI.COMM_WORLD.rank, np.linalg.norm(y_1 - y_2) / np.linalg.norm(y_2)) + + +# Test matrix matrix product +X = np.asfortranarray(np.random.rand(nb_cols, 2)) +Y_1 = distributed_operator @ X +Y_2 = generator.mat_mat(X) +print(mpi4py.MPI.COMM_WORLD.rank, np.linalg.norm(Y_1 - Y_2) / np.linalg.norm(Y_2)) + +# Several ways to display information +if mpi4py.MPI.COMM_WORLD.Get_rank() == 0: + hmatrix = default_approximation.hmatrix + print(hmatrix.get_tree_parameters()) + print(hmatrix.get_information()) + + fig = plt.figure() + ax1 = None + ax2 = None + ax3 = None + ax4 = None + if dimension == 2: + ax1 = fig.add_subplot(2, 2, 1) + ax2 = fig.add_subplot(2, 2, 2) + ax3 = fig.add_subplot(2, 2, 3) + ax4 = fig.add_subplot(2, 2, 4) + elif dimension == 3: + ax1 = fig.add_subplot(2, 2, 1, projection="3d") + ax2 = fig.add_subplot(2, 2, 2, projection="3d") + ax3 = fig.add_subplot(2, 2, 3, projection="3d") + ax4 = fig.add_subplot(2, 2, 4) + + ax1.set_title("target cluster at depth 1") + ax2.set_title("target cluster at depth 2") + ax3.set_title("source cluster at depth 1") + ax4.set_title("Hmatrix on rank 0") + Htool.plot(ax1, target_cluster, target_points, 1) + Htool.plot(ax2, target_cluster, target_points, 2) + Htool.plot(ax3, source_cluster, source_points, 1) + Htool.plot(ax4, hmatrix) + plt.show() diff --git a/example/use_local_build.py b/example/use_local_build.py new file mode 100644 index 0000000..74bfc58 --- /dev/null +++ b/example/use_local_build.py @@ -0,0 +1,181 @@ +import Htool +import numpy as np +import mpi4py +from define_custom_generators import CustomGenerator +from define_custom_local_operator import CustomLocalOperator +from create_geometry import create_partitionned_geometries, create_random_geometries + +import matplotlib.pyplot as plt +import logging + +# Random geometry +target_size = 500 +source_size = 500 +dimension = 3 +# [points, _, partition] = create_partitionned_geometries( +# dimension, size, size, mpi4py.MPI.COMM_WORLD.size +# ) + +[target_points, source_points] = create_random_geometries( + dimension, target_size, source_size +) + +# Htool parameters +eta = 10 +epsilon = 1e-3 +minclustersize = 10 +number_of_children = 2 + +# Build clusters +cluster_builder = Htool.ClusterBuilder() +cluster_builder.set_minclustersize(minclustersize) +target_cluster: Htool.Cluster = cluster_builder.create_cluster_tree( + target_points, + number_of_children, + mpi4py.MPI.COMM_WORLD.size, +) + +source_cluster: Htool.Cluster = cluster_builder.create_cluster_tree( + source_points, + number_of_children, + mpi4py.MPI.COMM_WORLD.size, +) + +local_target_cluster: Htool.Cluster = target_cluster.get_cluster_on_partition( + mpi4py.MPI.COMM_WORLD.rank +) +local_source_cluster: Htool.Cluster = source_cluster.get_cluster_on_partition( + mpi4py.MPI.COMM_WORLD.rank +) + +# +source_permutation = source_cluster.get_permutation() +permuted_source_points = np.zeros((dimension, source_size)) +for i in range(0, source_size): + permuted_source_points[:, i] = source_points[:, source_permutation[i]] + +# Build generator +generator = CustomGenerator( + target_cluster, target_points, source_cluster, source_points +) + + +# Build distributed operator +default_local_approximation = Htool.DefaultLocalApproximationBuilder( + generator, + target_cluster, + source_cluster, + epsilon, + eta, + "N", + "N", + mpi4py.MPI.COMM_WORLD, +) +distributed_operator = default_local_approximation.distributed_operator + + +# Build off diagonal operators +off_diagonal_nc_1 = local_source_cluster.get_offset() +off_diagonal_nc_2 = ( + source_cluster.get_size() + - local_source_cluster.get_size() + - local_source_cluster.get_offset() +) +local_nc = local_source_cluster.get_size() + +off_diagonal_partition = np.zeros((2, 2), dtype=int) +off_diagonal_partition[0, 0] = 0 +off_diagonal_partition[1, 0] = off_diagonal_nc_1 +off_diagonal_partition[0, 1] = off_diagonal_nc_1 + local_nc +off_diagonal_partition[1, 1] = off_diagonal_nc_2 +off_diagonal_cluster: Htool.Cluster = cluster_builder.create_cluster_tree( + permuted_source_points, number_of_children, 2, off_diagonal_partition +) + +off_diagonal_generator = CustomGenerator( + target_cluster, + target_points, + off_diagonal_cluster, + permuted_source_points, +) + +local_operator_1 = None +if off_diagonal_nc_1 > 0: + local_operator_1 = CustomLocalOperator( + off_diagonal_generator, + local_target_cluster, + off_diagonal_cluster.get_cluster_on_partition(0), + "N", + "N", + False, + True, + ) + +local_operator_2 = None +if off_diagonal_nc_2 > 0: + local_operator_2 = CustomLocalOperator( + off_diagonal_generator, + local_target_cluster, + off_diagonal_cluster.get_cluster_on_partition(1), + "N", + "N", + False, + True, + ) + +if off_diagonal_nc_1 > 0: + distributed_operator.add_local_operator(local_operator_1) +if off_diagonal_nc_2 > 0: + distributed_operator.add_local_operator(local_operator_2) + +# Test matrix vector product +np.random.seed(0) +x = np.random.rand(source_size) +x = np.ones(source_size) +y_1 = distributed_operator * x +y_2 = generator.mat_vec(x) +print(mpi4py.MPI.COMM_WORLD.rank, np.linalg.norm(y_1 - y_2) / np.linalg.norm(y_2)) + + +# Test matrix matrix product +X = np.asfortranarray(np.random.rand(source_size, 2)) +Y_1 = distributed_operator @ X +Y_2 = generator.mat_mat(X) +print(mpi4py.MPI.COMM_WORLD.rank, np.linalg.norm(Y_1 - Y_2) / np.linalg.norm(Y_2)) + +# Several ways to display information +if mpi4py.MPI.COMM_WORLD.Get_rank() == 0: + hmatrix = default_local_approximation.hmatrix + print(hmatrix.get_tree_parameters()) + print(hmatrix.get_information()) + + fig = plt.figure() + ax1 = None + ax2 = None + ax3 = None + ax4 = None + if dimension == 2: + ax1 = fig.add_subplot(2, 2, 1) + ax2 = fig.add_subplot(2, 2, 2) + ax3 = fig.add_subplot(2, 2, 3) + ax4 = fig.add_subplot(2, 2, 4) + elif dimension == 3: + ax1 = fig.add_subplot(2, 2, 1, projection="3d") + ax2 = fig.add_subplot(2, 2, 2, projection="3d") + ax3 = fig.add_subplot(2, 2, 3, projection="3d") + ax4 = fig.add_subplot(2, 2, 4) + + ax1.set_title("source cluster at depth 1") + ax2.set_title("source cluster at depth 2") + ax3.set_title("off diagonal cluster on rank 0 at depth 2") + ax4.set_title("Hmatrix on rank 0") + Htool.plot(ax1, source_cluster, source_points, 1) + Htool.plot(ax2, source_cluster, source_points, 2) + Htool.plot( + ax3, + off_diagonal_cluster.get_cluster_on_partition(1), + permuted_source_points, + 2, + ) + Htool.plot(ax4, hmatrix) + plt.show() diff --git a/example/use_no_default_build.py b/example/use_no_default_build.py new file mode 100644 index 0000000..d093418 --- /dev/null +++ b/example/use_no_default_build.py @@ -0,0 +1,123 @@ +import Htool +import numpy as np +import mpi4py +from define_custom_generators import CustomGenerator +from create_geometry import create_partitionned_geometries +import matplotlib.pyplot as plt + +# Random geometry +nb_rows = 500 +nb_cols = 500 +dimension = 3 +[target_points, source_points, target_partition] = create_partitionned_geometries( + dimension, nb_rows, nb_cols, mpi4py.MPI.COMM_WORLD.size +) + + +# Htool parameters +eta = 10 +epsilon = 1e-3 +minclustersize = 10 +number_of_children = 2 + +# Build clusters +cluster_builder = Htool.ClusterBuilder() +cluster_builder.set_minclustersize(minclustersize) +target_cluster: Htool.Cluster = cluster_builder.create_cluster_tree( + target_points, number_of_children, mpi4py.MPI.COMM_WORLD.size, target_partition +) +source_cluster: Htool.Cluster = cluster_builder.create_cluster_tree( + source_points, number_of_children, mpi4py.MPI.COMM_WORLD.size +) + + +# Build generator +generator = CustomGenerator( + target_cluster, target_points, source_cluster, source_points +) + +# Build HMatrix +hmatrix_builder = Htool.HMatrixBuilder( + target_cluster, + source_cluster, + epsilon, + eta, + "N", + "N", + -1, + mpi4py.MPI.COMM_WORLD.rank, +) + +hmatrix: Htool.HMatrix = hmatrix_builder.build(generator) + + +# Build local operator +local_operator = Htool.LocalHMatrix( + hmatrix, + target_cluster.get_cluster_on_partition(mpi4py.MPI.COMM_WORLD.rank), + source_cluster, + "N", + "N", + False, + False, +) + +# Build distributed operator +target_partition_from_cluster = Htool.PartitionFromCluster(target_cluster) +source_partition_from_cluster = Htool.PartitionFromCluster(source_cluster) +distributed_operator = Htool.DistributedOperator( + target_partition_from_cluster, + source_partition_from_cluster, + "N", + "N", + mpi4py.MPI.COMM_WORLD, +) + +distributed_operator.add_local_operator(local_operator) + + +# Test matrix vector product +np.random.seed(0) +x = np.random.rand(nb_cols) +y_1 = distributed_operator * x +y_2 = generator.mat_vec(x) +print(mpi4py.MPI.COMM_WORLD.rank, np.linalg.norm(y_1 - y_2) / np.linalg.norm(y_2)) + + +# Test matrix matrix product +X = np.asfortranarray(np.random.rand(nb_cols, 2)) +Y_1 = distributed_operator @ X +Y_2 = generator.mat_mat(X) +print(mpi4py.MPI.COMM_WORLD.rank, np.linalg.norm(Y_1 - Y_2) / np.linalg.norm(Y_2)) + + +# Outputs +if mpi4py.MPI.COMM_WORLD.Get_rank() == 0: + print(hmatrix.get_tree_parameters()) + print(hmatrix.get_information()) + + fig = plt.figure() + ax1 = None + ax2 = None + ax3 = None + ax4 = None + if dimension == 2: + ax1 = fig.add_subplot(2, 2, 1) + ax2 = fig.add_subplot(2, 2, 2) + ax3 = fig.add_subplot(2, 2, 3) + ax4 = fig.add_subplot(2, 2, 4) + elif dimension == 3: + ax1 = fig.add_subplot(2, 2, 1, projection="3d") + ax2 = fig.add_subplot(2, 2, 2, projection="3d") + ax3 = fig.add_subplot(2, 2, 3, projection="3d") + ax4 = fig.add_subplot(2, 2, 4) + + ax1.set_title("target cluster at depth 1") + ax2.set_title("target cluster at depth 2") + ax3.set_title("source cluster at depth 1") + ax4.set_title("Hmatrix on rank 0") + Htool.plot(ax1, target_cluster, target_points, 1) + Htool.plot(ax2, target_cluster, target_points, 2) + Htool.plot(ax3, source_cluster, source_points, 1) + Htool.plot(ax4, hmatrix) + plt.show() diff --git a/lib/hpddm b/lib/hpddm index 9c5092c..1af2600 160000 --- a/lib/hpddm +++ b/lib/hpddm @@ -1 +1 @@ -Subproject commit 9c5092ca711678ebc257ffadc7d29057d4566a87 +Subproject commit 1af2600a191428d4fc2cc1f630bf14b5878675ff diff --git a/lib/htool b/lib/htool index b6e9169..e8baf29 160000 --- a/lib/htool +++ b/lib/htool @@ -1 +1 @@ -Subproject commit b6e91690f8d7c20d67dfb3b8db2e7ea674405a37 +Subproject commit e8baf29836dee66d99ee10429f55d63663cd9d41 diff --git a/lib/pybind11 b/lib/pybind11 index af8849f..0e43fcc 160000 --- a/lib/pybind11 +++ b/lib/pybind11 @@ -1 +1 @@ -Subproject commit af8849f47eea35fe16df0c5df64221b33b655968 +Subproject commit 0e43fcc75e6b7429e3511dfb44343ec05a0ab843 diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..7c7341c --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,41 @@ +[project] +name = "htool" +version = "0.1" +authors = [{ name = "Pierre Marchand", email = "test@test.com" }] +description = "CLI utility to automate asciinema" +readme = "README.md" +requires-python = ">=3.7" +license = { text = "MIT License" } +classifiers = [ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: MIT License", + "Operating System :: MacOS", + "Operating System :: Unix", +] +keywords = [ + "kernel approximation", + "hierarchical matrix", + "domain decomposition method", +] +dependencies = ["mpi4py", "numpy"] + + +[project.optional-dependencies] +dev = ["black", "bumpver", "isort", "pytest"] +test = ["pytest"] + + +[project.urls] +"Homepage" = "https://github.com/htool-ddm/htool_python" +"Bug Tracker" = "https://github.com/htool-ddm/htool_python/issues" + +[tool.black] +line-length = 88 + +[tool.isort] +profile = "black" + + +[build-system] +requires = ["setuptools>=42", "wheel", "ninja", "cmake>=3.12"] +build-backend = "setuptools.build_meta" diff --git a/setup.py b/setup.py index b24df7e..6a7121f 100644 --- a/setup.py +++ b/setup.py @@ -1,85 +1,175 @@ import os import re -import sys -import platform import subprocess +import sys +from pathlib import Path -from setuptools import setup, Extension +from setuptools import Extension, setup from setuptools.command.build_ext import build_ext -from distutils.version import LooseVersion + +# Convert distutils Windows platform specifiers to CMake -A arguments +PLAT_TO_CMAKE = { + "win32": "Win32", + "win-amd64": "x64", + "win-arm32": "ARM", + "win-arm64": "ARM64", +} class CMakeExtension(Extension): - def __init__(self, name, sourcedir=''): - Extension.__init__(self, name, sources=[]) - self.sourcedir = os.path.abspath(sourcedir) + def __init__(self, name: str, sourcedir: str = "") -> None: + super().__init__(name, sources=[]) + self.sourcedir = os.fspath(Path(sourcedir).resolve()) class CMakeBuild(build_ext): - def run(self): - try: - out = subprocess.check_output(['cmake', '--version']) - except OSError: - raise RuntimeError("CMake must be installed to build the following extensions: " + - ", ".join(e.name for e in self.extensions)) - - if platform.system() == "Windows": - cmake_version = LooseVersion( - re.search(r'version\s*([\d.]+)', out.decode()).group(1)) - if cmake_version < '3.1.0': - raise RuntimeError("CMake >= 3.1.0 is required on Windows") - - for ext in self.extensions: - self.build_extension(ext) - def build_extension(self, ext): - extdir = os.path.abspath(os.path.dirname( - self.get_ext_fullpath(ext.name))) - # required for auto-detection of auxiliary "native" libs - if not extdir.endswith(os.path.sep): - extdir += os.path.sep - - cmake_args = ['-DCMAKE_LIBRARY_OUTPUT_DIRECTORY=' + extdir, - '-DPYTHON_EXECUTABLE=' + sys.executable] - - cfg = 'Debug' if self.debug else 'Release' - build_args = ['--config', cfg] - - if platform.system() == "Windows": - cmake_args += [ - '-DCMAKE_LIBRARY_OUTPUT_DIRECTORY_{}={}'.format(cfg.upper(), extdir)] - if sys.maxsize > 2**32: - cmake_args += ['-A', 'x64'] - build_args += ['--', '/m'] + # Must be in this form due to bug in .resolve() only fixed in Python 3.10+ + ext_fullpath = Path.cwd() / self.get_ext_fullpath(ext.name) + extdir = ext_fullpath.parent.resolve() + + # Using this requires trailing slash for auto-detection & inclusion of + # auxiliary "native" libs + + debug = int(os.environ.get("DEBUG", 0)) if self.debug is None else self.debug + cfg = "Debug" if debug else "Release" + + # CMake lets you override the generator - we need to check this. + # Can be set with Conda-Build, for example. + cmake_generator = os.environ.get("CMAKE_GENERATOR", "") + + # Set Python_EXECUTABLE instead if you use PYBIND11_FINDPYTHON + # EXAMPLE_VERSION_INFO shows you how to pass a value into the C++ code + # from Python. + cmake_args = [ + f"-DCMAKE_LIBRARY_OUTPUT_DIRECTORY={extdir}{os.sep}", + f"-DPYTHON_EXECUTABLE={sys.executable}", + f"-DCMAKE_BUILD_TYPE={cfg}", # not used on MSVC, but no harm + ] + build_args = [] + # Adding CMake arguments set as environment variable + # (needed e.g. to build for ARM OSx on conda-forge) + if "CMAKE_ARGS" in os.environ: + cmake_args += [item for item in os.environ["CMAKE_ARGS"].split(" ") if item] + + # # In this example, we pass in the version to C++. You might not need to. + # cmake_args += [f"-DEXAMPLE_VERSION_INFO={self.distribution.get_version()}"] + + if self.compiler.compiler_type != "msvc": + # Using Ninja-build since it a) is available as a wheel and b) + # multithreads automatically. MSVC would require all variables be + # exported for Ninja to pick it up, which is a little tricky to do. + # Users can override the generator with CMAKE_GENERATOR in CMake + # 3.15+. + if not cmake_generator or cmake_generator == "Ninja": + try: + import ninja + + ninja_executable_path = Path(ninja.BIN_DIR) / "ninja" + cmake_args += [ + "-GNinja", + f"-DCMAKE_MAKE_PROGRAM:FILEPATH={ninja_executable_path}", + ] + except ImportError: + pass + else: - cmake_args += ['-DCMAKE_BUILD_TYPE=' + cfg] - build_args += ['--', '-j2'] + # Single config generators are handled "normally" + single_config = any(x in cmake_generator for x in {"NMake", "Ninja"}) + + # CMake allows an arch-in-generator style for backward compatibility + contains_arch = any(x in cmake_generator for x in {"ARM", "Win64"}) + + # Specify the arch if using MSVC generator, but only if it doesn't + # contain a backward-compatibility arch spec already in the + # generator name. + if not single_config and not contains_arch: + cmake_args += ["-A", PLAT_TO_CMAKE[self.plat_name]] + + # Multi-config generators have a different way to specify configs + if not single_config: + cmake_args += [ + f"-DCMAKE_LIBRARY_OUTPUT_DIRECTORY_{cfg.upper()}={extdir}" + ] + build_args += ["--config", cfg] + + if sys.platform.startswith("darwin"): + # Cross-compile support for macOS - respect ARCHFLAGS if set + archs = re.findall(r"-arch (\S+)", os.environ.get("ARCHFLAGS", "")) + if archs: + cmake_args += ["-DCMAKE_OSX_ARCHITECTURES={}".format(";".join(archs))] + + # Set CMAKE_BUILD_PARALLEL_LEVEL to control the parallel build level + # across all generators. + if "CMAKE_BUILD_PARALLEL_LEVEL" not in os.environ: + # self.parallel is a Python 3 only way to set parallel jobs by hand + # using -j in the build_ext call, not supported by pip or PyPA-build. + if hasattr(self, "parallel") and self.parallel: + # CMake 3.12+ only. + build_args += [f"-j{self.parallel}"] + + build_temp = Path(self.build_temp) / ext.name + if not build_temp.exists(): + build_temp.mkdir(parents=True) + + subprocess.run( + ["cmake", ext.sourcedir, *cmake_args], cwd=build_temp, check=True + ) + subprocess.run( + ["cmake", "--build", ".", *build_args], cwd=build_temp, check=True + ) + + # debug = int(os.environ.get("DEBUG", 0)) if self.debug is None else self.debug + # cfg = "Debug" if debug else "Release" + + # cmake_args = [ + # "-DCMAKE_LIBRARY_OUTPUT_DIRECTORY=" + extdir, + # "-DPYTHON_EXECUTABLE=" + sys.executable, + # ] + # cfg = "Debug" if self.debug else "Release" + # build_args = ["--config", cfg] - env = os.environ.copy() - env['CXXFLAGS'] = '{} -DVERSION_INFO=\\"{}\\"'.format( - env.get('CXXFLAGS', ''), self.distribution.get_version()) - if not os.path.exists(self.build_temp): - os.makedirs(self.build_temp) - subprocess.check_call(['cmake', ext.sourcedir] + - cmake_args, cwd=self.build_temp, env=env) - subprocess.check_call(['cmake', '--build', '.'] + - build_args, cwd=self.build_temp) + # if platform.system() == "Windows": + # cmake_args += [ + # "-DCMAKE_LIBRARY_OUTPUT_DIRECTORY_{}={}".format(cfg.upper(), extdir) + # ] + # if sys.maxsize > 2**32: + # cmake_args += ["-A", "x64"] + # build_args += ["--", "/m"] + # else: + # cmake_args += ["-DCMAKE_BUILD_TYPE=" + cfg] + # if self.debug: + # cmake_args += ["-DUSE_SANITIZER=Address"] + # build_args += ["--", "-j2"] + # print("BOUH", cmake_args) + # env = os.environ.copy() + # env["CXXFLAGS"] = '{} -DVERSION_INFO=\\"{}\\"'.format( + # env.get("CXXFLAGS", ""), self.distribution.get_version() + # ) + # if not os.path.exists(self.build_temp): + # os.makedirs(self.build_temp) + # subprocess.check_call( + # ["cmake", ext.sourcedir] + cmake_args, cwd=self.build_temp, env=env + # ) + # subprocess.check_call( + # ["cmake", "--build", "."] + build_args, cwd=self.build_temp + # ) setup( - name='Htool', - version='1.0.0', - author='Pierre Marchand', - author_email='', - description='A pybind11 interface to Htool, a header only c++ library that provides Hierarchical matrices.', - long_description='', - ext_modules=[CMakeExtension('Htool')], + name="Htool", + version="1.0.0", + author="Pierre Marchand", + author_email="", + description="A pybind11 interface to Htool, a header only c++ library that provides Hierarchical matrices.", + long_description="", + ext_modules=[CMakeExtension("Htool")], cmdclass=dict(build_ext=CMakeBuild), zip_safe=False, install_requires=[ - 'numpy>=1.0.0', - 'scipy>=1.0.0', - 'mpi4py>=3.0.0', - 'matplotlib>=3.0.0' + "numpy>=1.0.0", + "scipy>=1.0.0", + "mpi4py>=3.0.0", + "matplotlib>=3.0.0", ], ) diff --git a/src/Htool.cpython-311-darwin.so b/src/Htool.cpython-311-darwin.so new file mode 100755 index 0000000000000000000000000000000000000000..df5b42744d4cbb0869864f115b9b1a1a57dc009e GIT binary patch literal 11551272 zcmeF43!GI|+5h*MGXrNpLJx`9QXwzorBcZpJ7pMF zY~#&aADzEv7Qm$?{qbmf8IbSe=n>V3%`xCH~*v+H0M&_ntE_koLaszv1&4 z&faUU^{i(-_w}r`*Z#rZfB%nNJTKw#FU8;P{4E*kc^_%HMMd6T{9Rw=c}-1copA11 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