diff --git a/.editorconfig b/.editorconfig new file mode 100644 index 0000000000000000000000000000000000000000..dcbc21d86f9e4b17ea7e8803d538c4c0f0b6276a --- /dev/null +++ b/.editorconfig @@ -0,0 +1,32 @@ +# https://editorconfig.org/ +# https://github.com/editorconfig/editorconfig-vim +# https://github.com/editorconfig/editorconfig-emacs + +root = true + +[*] +indent_style = space +end_of_line = lf +charset = utf-8 +trim_trailing_whitespace = true +insert_final_newline = true + +[*.py] +indent_size = 4 + +[*.rst] +indent_size = 4 + +[*.cpp] +indent_size = 2 + +[*.hpp] +indent_size = 2 + +# There may be one in doc/ +[Makefile] +indent_style = tab + +# https://github.com/microsoft/vscode/issues/1679 +[*.md] +trim_trailing_whitespace = false diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index beae4df5fa21c61ae1d356b6921c9231e57bdd15..82b4c637c4f8d9e9a72fa5c155b189d639e399cc 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -18,7 +18,7 @@ jobs: - uses: actions/setup-python@v1 with: - python-version: '3.x' + python-version: '3.x' - name: "Main Script" run: | curl -L -O -k https://gitlab.tiker.net/inducer/ci-support/raw/master/prepare-and-run-flake8.sh @@ -29,15 +29,9 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - - - uses: actions/setup-python@v1 - with: - python-version: '3.x' - name: "Main Script" run: | - set -e - CONDA_ENVIRONMENT=.test-conda-env-py3-pylint.yml - cp .test-conda-env-py3.yml $CONDA_ENVIRONMENT + CONDA_ENVIRONMENT=.test-conda-env-py3.yml echo "- matplotlib" >> $CONDA_ENVIRONMENT echo "-------------------------------------------" cat $CONDA_ENVIRONMENT @@ -50,31 +44,39 @@ jobs: . ./prepare-and-run-pylint.sh pytential test/test_*.py - pytest2: - name: Pytest on Py2 + pytest3: + name: Pytest Linux runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - name: "Main Script" run: | - set -e - sed 's/python=3/python=2.7/' .test-conda-env-py3.yml > .test-conda-env-py2-pre.yml - grep -v symengine .test-conda-env-py2-pre.yml > .test-conda-env-py2.yml - cat .test-conda-env-py2.yml - CONDA_ENVIRONMENT=.test-conda-env-py2.yml + grep -v symengine .test-conda-env-py3.yml > .test-conda-env.yml + CONDA_ENVIRONMENT=.test-conda-env.yml + + # https://gitlab.tiker.net/inducer/pytential/-/issues/146 + echo "- _openmp_mutex=*=*gnu*" >> $CONDA_ENVIRONMENT + + CISUPPORT_PARALLEL_PYTEST=no + curl -L -O -k https://gitlab.tiker.net/inducer/ci-support/raw/master/build-and-test-py-project-within-miniconda.sh . ./build-and-test-py-project-within-miniconda.sh - pytest3: - name: Pytest on Py3 - runs-on: ubuntu-latest + pytest3mac: + name: Pytest Mac + runs-on: macos-latest steps: - uses: actions/checkout@v2 - name: "Main Script" run: | - set -e + export LC_ALL=en_US.UTF-8 + export LANG=en_US.UTF-8 grep -v symengine .test-conda-env-py3.yml > .test-conda-env.yml + echo "- compilers" >> .test-conda-env.yml + echo "- llvm-openmp" >> .test-conda-env.yml CONDA_ENVIRONMENT=.test-conda-env.yml + export PYTEST_ADDOPTS=${PYTEST_ADDOPTS:--k-slowtest} + curl -L -O -k https://gitlab.tiker.net/inducer/ci-support/raw/master/build-and-test-py-project-within-miniconda.sh . ./build-and-test-py-project-within-miniconda.sh @@ -85,10 +87,14 @@ jobs: - uses: actions/checkout@v2 - name: "Main Script" run: | - set -e CONDA_ENVIRONMENT=.test-conda-env-py3.yml + + # https://gitlab.tiker.net/inducer/pytential/-/issues/146 + echo "- _openmp_mutex=*=*gnu*" >> $CONDA_ENVIRONMENT + + CISUPPORT_PARALLEL_PYTEST=no + curl -L -O -k https://gitlab.tiker.net/inducer/ci-support/raw/master/build-and-test-py-project-within-miniconda.sh . ./build-and-test-py-project-within-miniconda.sh # vim: sw=4 - diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index effbd8ce64d935a2821477a5598d923b3e47a8a9..cf9a7713fa8ea2f82a33eba781a66e8647c3c5ac 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -4,24 +4,6 @@ # which skips the slow running tests. # * SKIP_EXAMPLES, if non-empty, can be used to skip the examples job. -Python 2.7 POCL: - script: - - export PY_EXE=python2.7 - - export PYOPENCL_TEST=portable:pthread - - export PYTEST_ADDOPTS=${PYTEST_ADDOPTS:--k-slowtest} - - export EXTRA_INSTALL="Cython pybind11 numpy scipy mako" - - curl -L -O -k https://gitlab.tiker.net/inducer/ci-support/raw/master/build-and-test-py-project.sh - - ". ./build-and-test-py-project.sh" - tags: - - python2.7 - - pocl - - large-node - except: - - tags - artifacts: - reports: - junit: test/pytest.xml - Python 3 POCL: script: - export PY_EXE=python3 @@ -79,36 +61,17 @@ Python 3 Conda: - export SUMPY_FORCE_SYMBOLIC_BACKEND=symengine - export CONDA_ENVIRONMENT=.test-conda-env-py3.yml - export PYTEST_ADDOPTS=${PYTEST_ADDOPTS:--k-slowtest} - - curl -L -O -k https://gitlab.tiker.net/inducer/ci-support/raw/master/build-and-test-py-project-within-miniconda.sh - - ". ./build-and-test-py-project-within-miniconda.sh" - tags: - - linux - - large-node - except: - - tags - artifacts: - reports: - junit: test/pytest.xml + # https://gitlab.tiker.net/inducer/pytential/-/issues/146 + - echo "- _openmp_mutex=*=*gnu*" >> $CONDA_ENVIRONMENT -Python 3 Conda Apple: - script: - - export LC_ALL=en_US.UTF-8 - - export LANG=en_US.UTF-8 - - export CONDA_ENVIRONMENT=.test-conda-env-py3-macos.yml - - export PYTEST_ADDOPTS=${PYTEST_ADDOPTS:--k-slowtest} - - export CC=clang - # https://stackoverflow.com/q/60934005; https://reviews.llvm.org/D71579 - - export LDFLAGS="-mlinker-version=519" - - set -o xtrace - curl -L -O -k https://gitlab.tiker.net/inducer/ci-support/raw/master/build-and-test-py-project-within-miniconda.sh - ". ./build-and-test-py-project-within-miniconda.sh" - tags: - - apple + - linux + - large-node except: - tags - retry: 2 artifacts: reports: diff --git a/.test-conda-env-py3-macos.yml b/.test-conda-env-py3-macos.yml deleted file mode 100644 index 901576dcf85d5fae32db5acb200702e2ad73c352..0000000000000000000000000000000000000000 --- a/.test-conda-env-py3-macos.yml +++ /dev/null @@ -1,30 +0,0 @@ -name: test-conda-env-py3-macos -channels: -- conda-forge -- nodefaults -dependencies: -- git -- conda-forge::numpy -- conda-forge::sympy -- scipy -- pocl -- islpy -- pyopencl -- python>=3.6 -- python-symengine=0.6.0 -- pyfmmlib -# for OpenMP support in pyfmmlib -- libgfortran>=3.0.1 -- clangdev -- openmp -- cython -- gmsh - -- pip -- pip: - - git+https://github.com/inducer/pytools - - git+https://gitlab.tiker.net/inducer/boxtree - - git+https://github.com/inducer/pymbolic - - git+https://github.com/inducer/loopy - - git+https://gitlab.tiker.net/inducer/sumpy - - git+https://github.com/inducer/meshmode diff --git a/.test-conda-env-py3.yml b/.test-conda-env-py3.yml index 748855b09faf2accacd8b0970597d4732740a2a4..6c840cd81ca44dc9688ec7c437a729792bb78858 100644 --- a/.test-conda-env-py3.yml +++ b/.test-conda-env-py3.yml @@ -15,12 +15,3 @@ dependencies: - pyfmmlib - cython - gmsh - -- pip -- pip: - - git+https://github.com/inducer/pytools - - git+https://gitlab.tiker.net/inducer/boxtree - - git+https://github.com/inducer/pymbolic - - git+https://github.com/inducer/loopy - - git+https://gitlab.tiker.net/inducer/sumpy - - git+https://github.com/inducer/meshmode diff --git a/examples/cost.py b/examples/cost.py index 4d57dcd5c6a2b41eba253715c307aa36c6f4fe11..da2f34f4e59f78962791e8de40b01bbe1bc8f8b5 100644 --- a/examples/cost.py +++ b/examples/cost.py @@ -29,6 +29,8 @@ THE SOFTWARE. import pyopencl as cl import numpy as np +from meshmode.array_context import PyOpenCLArrayContext +from meshmode.dof_array import thaw from pytential import sym, bind from pytential.qbx.cost import QBXCostModel @@ -54,7 +56,7 @@ TRAINING_ARMS = (10, 15, 25) TESTING_ARMS = (20,) -def starfish_lpot_source(queue, n_arms): +def starfish_lpot_source(actx, n_arms): from meshmode.discretization import Discretization from meshmode.discretization.poly_element import ( InterpolatoryQuadratureSimplexGroupFactory) @@ -67,7 +69,7 @@ def starfish_lpot_source(queue, n_arms): TARGET_ORDER) pre_density_discr = Discretization( - queue.context, mesh, + actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(TARGET_ORDER)) lpot_kwargs = DEFAULT_LPOT_KWARGS.copy() @@ -88,14 +90,14 @@ def starfish_lpot_source(queue, n_arms): # }}} -def training_geometries(queue): +def training_geometries(actx): for n_arms in TRAINING_ARMS: - yield starfish_lpot_source(queue, n_arms) + yield starfish_lpot_source(actx, n_arms) -def test_geometries(queue): +def test_geometries(actx): for n_arms in TESTING_ARMS: - yield starfish_lpot_source(queue, n_arms) + yield starfish_lpot_source(actx, n_arms) def get_bound_op(places): @@ -107,21 +109,21 @@ def get_bound_op(places): return bind(places, op) -def get_test_density(queue, density_discr): - nodes = density_discr.nodes().with_queue(queue) - sigma = cl.clmath.sin(10 * nodes[0]) - +def get_test_density(actx, density_discr): + nodes = thaw(actx, density_discr.nodes()) + sigma = actx.np.sin(10 * nodes[0]) return sigma def calibrate_cost_model(ctx): queue = cl.CommandQueue(ctx) - cost_model = QBXCostModel(queue) + actx = PyOpenCLArrayContext(queue) + cost_model = QBXCostModel(actx) model_results = [] timing_results = [] - for lpot_source in training_geometries(queue): + for lpot_source in training_geometries(actx): lpot_source = lpot_source.copy(cost_model=cost_model) from pytential import GeometryCollection @@ -129,16 +131,17 @@ def calibrate_cost_model(ctx): density_discr = places.get_discretization(places.auto_source.geometry) bound_op = get_bound_op(places) - sigma = get_test_density(queue, density_discr) + sigma = get_test_density(actx, density_discr) - modeled_cost, _ = bound_op.cost_per_stage(queue, "constant_one", sigma=sigma) + modeled_cost, _ = bound_op.cost_per_stage("constant_one", sigma=sigma) # Warm-up run. - bound_op.eval(queue, {"sigma": sigma}) + bound_op.eval({"sigma": sigma}, array_context=actx) for _ in range(RUNS): timing_data = {} - bound_op.eval(queue, {"sigma": sigma}, timing_data=timing_data) + bound_op.eval({"sigma": sigma}, array_context=actx, + timing_data=timing_data) model_results.append(modeled_cost) timing_results.append(timing_data) @@ -152,9 +155,10 @@ def calibrate_cost_model(ctx): def test_cost_model(ctx, calibration_params): queue = cl.CommandQueue(ctx) - cost_model = QBXCostModel(queue) + actx = PyOpenCLArrayContext(queue) + cost_model = QBXCostModel(actx) - for lpot_source in test_geometries(queue): + for lpot_source in test_geometries(actx): lpot_source = lpot_source.copy(cost_model=cost_model) from pytential import GeometryCollection @@ -162,18 +166,19 @@ def test_cost_model(ctx, calibration_params): density_discr = places.get_discretization(places.auto_source.geometry) bound_op = get_bound_op(places) - sigma = get_test_density(queue, density_discr) + sigma = get_test_density(actx, density_discr) - cost_S, _ = bound_op.cost_per_stage(queue, calibration_params, sigma=sigma) + cost_S, _ = bound_op.cost_per_stage(calibration_params, sigma=sigma) model_result = one(cost_S.values()) # Warm-up run. - bound_op.eval(queue, {"sigma": sigma}) + bound_op.eval({"sigma": sigma}, array_context=actx) temp_timing_results = [] for _ in range(RUNS): timing_data = {} - bound_op.eval(queue, {"sigma": sigma}, timing_data=timing_data) + bound_op.eval({"sigma": sigma}, + array_context=actx, timing_data=timing_data) temp_timing_results.append(one(timing_data.values())) timing_result = {} @@ -197,6 +202,9 @@ def test_cost_model(ctx, calibration_params): def predict_cost(ctx): + import logging + logging.basicConfig(level=logging.WARNING) # INFO for more progress info + params = calibrate_cost_model(ctx) test_cost_model(ctx, params) diff --git a/examples/fmm-error.py b/examples/fmm-error.py index a6d19bb150499c121c38994fcaffdd3e1bca9f50..4f766ff0be5b408982fe5f345bdf7c210d7579f1 100644 --- a/examples/fmm-error.py +++ b/examples/fmm-error.py @@ -1,6 +1,8 @@ from __future__ import division import numpy as np import pyopencl as cl +from meshmode.array_context import PyOpenCLArrayContext +from meshmode.dof_array import thaw from meshmode.mesh.generation import ( # noqa make_curve_mesh, starfish, ellipse, drop) from sumpy.visualization import FieldPlotter @@ -13,6 +15,7 @@ def main(): cl_ctx = cl.create_some_context() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) target_order = 16 qbx_order = 3 @@ -37,13 +40,13 @@ def main(): InterpolatoryQuadratureSimplexGroupFactory pre_density_discr = Discretization( - cl_ctx, mesh, + actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order)) unaccel_qbx = QBXLayerPotentialSource( pre_density_discr, fine_order=2*target_order, qbx_order=qbx_order, fmm_order=False, - target_association_tolerance=.05 + target_association_tolerance=.05, ) from pytential.target import PointsTarget @@ -57,24 +60,35 @@ def main(): }) density_discr = places.get_discretization("unaccel_qbx") - nodes = density_discr.nodes().with_queue(queue) - angle = cl.clmath.atan2(nodes[1], nodes[0]) + nodes = thaw(actx, density_discr.nodes()) + angle = actx.np.arctan2(nodes[1], nodes[0]) from pytential import bind, sym - #op = sym.d_dx(sym.S(kernel, sym.var("sigma")), qbx_forced_limit=None) - #op = sym.D(kernel, sym.var("sigma"), qbx_forced_limit=None) - op = sym.S(kernel, sym.var("sigma"), qbx_forced_limit=None) + if k: + kernel_kwargs = {"k": sym.var("k")} + else: + kernel_kwargs = {} + + def get_op(): + kwargs = dict(qbx_forced_limit=None) + kwargs.update(kernel_kwargs) + # return sym.d_dx(2, sym.S(kernel, sym.var("sigma"), **kwargs)) + # return sym.D(kernel, sym.var("sigma"), **kwargs) + return sym.S(kernel, sym.var("sigma"), **kwargs) + + op = get_op() - sigma = cl.clmath.cos(mode_nr*angle) + sigma = actx.np.cos(mode_nr*angle) if isinstance(kernel, HelmholtzKernel): - sigma = sigma.astype(np.complex128) + for i, elem in np.ndenumerate(sigma): + sigma[i] = elem.astype(np.complex128) fld_in_vol = bind(places, op, auto_where=("unaccel_qbx", "targets"))( - queue, sigma=sigma, k=k).get() + actx, sigma=sigma, k=k).get() fmm_fld_in_vol = bind(places, op, auto_where=("qbx", "targets"))( - queue, sigma=sigma, k=k).get() + actx, sigma=sigma, k=k).get() err = fmm_fld_in_vol-fld_in_vol diff --git a/examples/helmholtz-dirichlet.py b/examples/helmholtz-dirichlet.py index 75115da4a896638c1a18fcc50ae3345704b801ce..aee8c39086fd203e11703f8f762aaeb6c3455d5d 100644 --- a/examples/helmholtz-dirichlet.py +++ b/examples/helmholtz-dirichlet.py @@ -3,6 +3,7 @@ import numpy.linalg as la import pyopencl as cl import pyopencl.clmath # noqa +from meshmode.array_context import PyOpenCLArrayContext from meshmode.discretization import Discretization from meshmode.discretization.poly_element import \ InterpolatoryQuadratureSimplexGroupFactory @@ -29,6 +30,7 @@ def main(mesh_name="ellipse", visualize=False): cl_ctx = cl.create_some_context() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) from meshmode.mesh.generation import ellipse, make_curve_mesh from functools import partial @@ -67,7 +69,7 @@ def main(mesh_name="ellipse", visualize=False): raise ValueError("unknown mesh name: {}".format(mesh_name)) pre_density_discr = Discretization( - cl_ctx, mesh, + actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(bdry_quad_order)) from pytential.qbx import ( @@ -79,7 +81,7 @@ def main(mesh_name="ellipse", visualize=False): from sumpy.visualization import FieldPlotter fplot = FieldPlotter(np.zeros(2), extent=5, npoints=500) - targets = cl.array.to_device(queue, fplot.points) + targets = actx.from_numpy(fplot.points) from pytential import GeometryCollection places = GeometryCollection({ @@ -120,21 +122,22 @@ def main(mesh_name="ellipse", visualize=False): # {{{ fix rhs and solve - nodes = density_discr.nodes().with_queue(queue) + from meshmode.dof_array import thaw + nodes = thaw(actx, density_discr.nodes()) k_vec = np.array([2, 1]) k_vec = k * k_vec / la.norm(k_vec, 2) def u_incoming_func(x): - return cl.clmath.exp( + return actx.np.exp( 1j * (x[0] * k_vec[0] + x[1] * k_vec[1])) bc = -u_incoming_func(nodes) - bvp_rhs = bind(places, sqrt_w*sym.var("bc"))(queue, bc=bc) + bvp_rhs = bind(places, sqrt_w*sym.var("bc"))(actx, bc=bc) from pytential.solve import gmres gmres_result = gmres( - bound_op.scipy_op(queue, sigma_sym.name, dtype=np.complex128, k=k), + bound_op.scipy_op(actx, sigma_sym.name, dtype=np.complex128, k=k), bvp_rhs, tol=1e-8, progress=True, stall_iterations=0, hard_failure=True) @@ -152,15 +155,18 @@ def main(mesh_name="ellipse", visualize=False): - sym.D(kernel, inv_sqrt_w_sigma, k=k_sym, **repr_kwargs)) u_incoming = u_incoming_func(targets) - ones_density = density_discr.zeros(queue) - ones_density.fill(1) + ones_density = density_discr.zeros(actx) + for elem in ones_density: + elem.fill(1) - indicator = bind(places, sym.D(LaplaceKernel(2), sigma_sym, **repr_kwargs))( - queue, sigma=ones_density).get() + indicator = actx.to_numpy( + bind(places, sym.D(LaplaceKernel(2), sigma_sym, **repr_kwargs))( + actx, sigma=ones_density)) try: - fld_in_vol = bind(places, representation_sym)( - queue, sigma=gmres_result.solution, k=k).get() + fld_in_vol = actx.to_numpy( + bind(places, representation_sym)( + actx, sigma=gmres_result.solution, k=k)) except QBXTargetAssociationFailedException as e: fplot.write_vtk_file("helmholtz-dirichlet-failed-targets.vts", [ ("failed", e.failed_target_flags.get(queue)) @@ -171,7 +177,7 @@ def main(mesh_name="ellipse", visualize=False): fplot.write_vtk_file("helmholtz-dirichlet-potential.vts", [ ("potential", fld_in_vol), ("indicator", indicator), - ("u_incoming", u_incoming.get()), + ("u_incoming", actx.to_numpy(u_incoming)), ]) # }}} diff --git a/examples/laplace-dirichlet-3d.py b/examples/laplace-dirichlet-3d.py index 984f1de10694a493e1b708f35118897c21a25cc5..7f6bba45890308b5ac27b80c836c1916ecd08be3 100644 --- a/examples/laplace-dirichlet-3d.py +++ b/examples/laplace-dirichlet-3d.py @@ -3,6 +3,7 @@ import numpy.linalg as la import pyopencl as cl import pyopencl.clmath # noqa +from meshmode.array_context import PyOpenCLArrayContext from meshmode.discretization import Discretization from meshmode.discretization.poly_element import \ InterpolatoryQuadratureSimplexGroupFactory @@ -28,6 +29,7 @@ def main(mesh_name="torus", visualize=False): cl_ctx = cl.create_some_context() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) if mesh_name == "torus": rout = 10 @@ -61,7 +63,7 @@ def main(mesh_name="torus", visualize=False): raise ValueError("unknown mesh name: {}".format(mesh_name)) pre_density_discr = Discretization( - cl_ctx, mesh, + actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(bdry_quad_order)) from pytential.qbx import ( @@ -73,7 +75,7 @@ def main(mesh_name="torus", visualize=False): from sumpy.visualization import FieldPlotter fplot = FieldPlotter(np.zeros(3), extent=20, npoints=50) - targets = cl.array.to_device(queue, fplot.points) + targets = actx.from_numpy(fplot.points) from pytential import GeometryCollection places = GeometryCollection({ @@ -109,33 +111,39 @@ def main(mesh_name="torus", visualize=False): # {{{ fix rhs and solve - nodes = density_discr.nodes().with_queue(queue) + from meshmode.dof_array import thaw, flatten, unflatten + nodes = thaw(actx, density_discr.nodes()) source = np.array([rout, 0, 0]) def u_incoming_func(x): + from pytools.obj_array import obj_array_vectorize + x = obj_array_vectorize(actx.to_numpy, flatten(x)) + x = np.array(list(x)) # return 1/cl.clmath.sqrt( (x[0] - source[0])**2 # +(x[1] - source[1])**2 # +(x[2] - source[2])**2 ) - return 1.0/la.norm(x.get()-source[:, None], axis=0) + return 1.0/la.norm(x - source[:, None], axis=0) - bc = cl.array.to_device(queue, u_incoming_func(nodes)) + bc = unflatten(actx, + density_discr, + actx.from_numpy(u_incoming_func(nodes))) - bvp_rhs = bind(places, sqrt_w*sym.var("bc"))(queue, bc=bc) + bvp_rhs = bind(places, sqrt_w*sym.var("bc"))(actx, bc=bc) from pytential.solve import gmres gmres_result = gmres( - bound_op.scipy_op(queue, "sigma", dtype=np.float64), + bound_op.scipy_op(actx, "sigma", dtype=np.float64), bvp_rhs, tol=1e-14, progress=True, stall_iterations=0, hard_failure=True) sigma = bind(places, sym.var("sigma")/sqrt_w)( - queue, sigma=gmres_result.solution) + actx, sigma=gmres_result.solution) # }}} from meshmode.discretization.visualization import make_visualizer - bdry_vis = make_visualizer(queue, density_discr, 20) + bdry_vis = make_visualizer(actx, density_discr, 20) bdry_vis.write_vtk_file("laplace.vtu", [ ("sigma", sigma), ]) @@ -151,8 +159,8 @@ def main(mesh_name="torus", visualize=False): + sym.D(kernel, inv_sqrt_w_sigma, **repr_kwargs)) try: - fld_in_vol = bind(places, representation_sym)( - queue, sigma=sigma).get() + fld_in_vol = actx.to_numpy( + bind(places, representation_sym)(actx, sigma=sigma)) except QBXTargetAssociationFailedException as e: fplot.write_vtk_file("laplace-dirichlet-3d-failed-targets.vts", [ ("failed", e.failed_target_flags.get(queue)), diff --git a/examples/layerpot-3d.py b/examples/layerpot-3d.py index ecace75de9c67dfdb4899f688737aeda14b05fc8..75fb3412649f2ec0a32b8b84a6dc6b3d8fec6a41 100644 --- a/examples/layerpot-3d.py +++ b/examples/layerpot-3d.py @@ -1,5 +1,5 @@ -from __future__ import division - +from meshmode.array_context import PyOpenCLArrayContext +from meshmode.dof_array import thaw import numpy as np import pyopencl as cl @@ -22,6 +22,7 @@ def main(mesh_name="ellipsoid"): cl_ctx = cl.create_some_context() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) if mesh_name == "ellipsoid": cad_file_name = "geometries/ellipsoid.step" @@ -55,7 +56,7 @@ def main(mesh_name="ellipsoid"): InterpolatoryQuadratureSimplexGroupFactory density_discr = Discretization( - cl_ctx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order)) + actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order)) qbx = QBXLayerPotentialSource(density_discr, 4*target_order, qbx_order, fmm_order=qbx_order + 3, @@ -71,8 +72,8 @@ def main(mesh_name="ellipsoid"): }, auto_where="qbx") density_discr = places.get_discretization("qbx") - nodes = density_discr.nodes().with_queue(queue) - angle = cl.clmath.atan2(nodes[1], nodes[0]) + nodes = thaw(actx, density_discr.nodes()) + angle = actx.np.arctan2(nodes[1], nodes[0]) if k: kernel = HelmholtzKernel(3) @@ -83,18 +84,22 @@ def main(mesh_name="ellipsoid"): op = sym.D(kernel, sym.var("sigma"), qbx_forced_limit=None) #op = sym.S(kernel, sym.var("sigma"), qbx_forced_limit=None) - sigma = cl.clmath.cos(mode_nr*angle) + sigma = actx.np.cos(mode_nr*angle) if 0: - sigma = 0*angle + from meshmode.dof_array import flatten, unflatten + sigma = flatten(0 * angle) from random import randrange for i in range(5): sigma[randrange(len(sigma))] = 1 + sigma = unflatten(actx, density_discr, sigma) if isinstance(kernel, HelmholtzKernel): - sigma = sigma.astype(np.complex128) + for i, elem in np.ndenumerate(sigma): + sigma[i] = elem.astype(np.complex128) - fld_in_vol = bind(places, op, auto_where=("qbx", "targets"))( - queue, sigma=sigma, k=k).get() + fld_in_vol = actx.to_numpy( + bind(places, op, auto_where=("qbx", "targets"))( + actx, sigma=sigma, k=k)) #fplot.show_scalar_in_mayavi(fld_in_vol.real, max_val=5) fplot.write_vtk_file("layerpot-3d-potential.vts", [ @@ -102,11 +107,10 @@ def main(mesh_name="ellipsoid"): ]) bdry_normals = bind(places, - sym.normal(density_discr.ambient_dim))(queue).as_vector(dtype=object) + sym.normal(density_discr.ambient_dim))(actx).as_vector(dtype=object) from meshmode.discretization.visualization import make_visualizer - bdry_vis = make_visualizer(queue, density_discr, target_order) - + bdry_vis = make_visualizer(actx, density_discr, target_order) bdry_vis.write_vtk_file("layerpot-3d-density.vtu", [ ("sigma", sigma), ("bdry_normals", bdry_normals), diff --git a/examples/layerpot.py b/examples/layerpot.py index e01a24eb8ffbc15e232839e7015a2f5feeae35a5..605faf177cdeacda07d0d708a1bf672c4fcd5fab 100644 --- a/examples/layerpot.py +++ b/examples/layerpot.py @@ -36,12 +36,15 @@ def main(curve_fn=starfish, visualize=True): target_order) from pytential.qbx import QBXLayerPotentialSource + from meshmode.array_context import PyOpenCLArrayContext from meshmode.discretization import Discretization from meshmode.discretization.poly_element import \ InterpolatoryQuadratureSimplexGroupFactory + actx = PyOpenCLArrayContext(queue) + pre_density_discr = Discretization( - cl_ctx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order)) + actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order)) qbx = QBXLayerPotentialSource(pre_density_discr, 4*target_order, qbx_order, fmm_order=qbx_order+3, @@ -56,10 +59,12 @@ def main(curve_fn=starfish, visualize=True): "qbx": qbx, "targets": PointsTarget(targets_dev), }, auto_where="qbx") + density_discr = places.get_discretization("qbx") - nodes = density_discr.nodes().with_queue(queue) - angle = cl.clmath.atan2(nodes[1], nodes[0]) + from meshmode.dof_array import thaw + nodes = thaw(actx, density_discr.nodes()) + angle = actx.np.arctan2(nodes[1], nodes[0]) if k: kernel = HelmholtzKernel(2) @@ -75,22 +80,26 @@ def main(curve_fn=starfish, visualize=True): return sym.D(kernel, sym.var("sigma"), **kwargs) #op = sym.S(kernel, sym.var("sigma"), qbx_forced_limit=None, **kwargs) - sigma = cl.clmath.cos(mode_nr*angle) + sigma = actx.np.cos(mode_nr*angle) if 0: - sigma = 0*angle + from meshmode.dof_array import flatten, unflatten + sigma = flatten(0 * angle) from random import randrange for i in range(5): sigma[randrange(len(sigma))] = 1 + sigma = unflatten(actx, density_discr, sigma) if isinstance(kernel, HelmholtzKernel): - sigma = sigma.astype(np.complex128) + for i, elem in np.ndenumerate(sigma): + sigma[i] = elem.astype(np.complex128) bound_bdry_op = bind(places, op()) if visualize: - fld_in_vol = bind(places, op( - source="qbx", - target="targets", - qbx_forced_limit=None))(queue, sigma=sigma, k=k).get() + fld_in_vol = actx.to_numpy( + bind(places, op( + source="qbx", + target="targets", + qbx_forced_limit=None))(actx, sigma=sigma, k=k)) if enable_mayavi: fplot.show_scalar_in_mayavi(fld_in_vol.real, max_val=5) @@ -100,13 +109,9 @@ def main(curve_fn=starfish, visualize=True): ]) if 0: - def apply_op(density): - return bound_bdry_op( - queue, sigma=cl.array.to_device(queue, density), k=k).get() - + apply_op = bound_bdry_op.scipy_op(actx, "sigma", np.float64, k=k) from sumpy.tools import build_matrix - n = len(sigma) - mat = build_matrix(apply_op, dtype=np.float64, shape=(n, n)) + mat = build_matrix(apply_op) import matplotlib.pyplot as pt pt.imshow(mat) @@ -116,18 +121,20 @@ def main(curve_fn=starfish, visualize=True): if enable_mayavi: # {{{ plot boundary field - fld_on_bdry = bound_bdry_op(queue, sigma=sigma, k=k).get() + from pytential.utils import flatten_to_numpy + + fld_on_bdry = flatten_to_numpy( + actx, bound_bdry_op(actx, sigma=sigma, k=k)) + nodes_host = flatten_to_numpy(actx, density_discr.nodes()) - nodes_host = density_discr.nodes().get(queue=queue) mlab.points3d(nodes_host[0], nodes_host[1], fld_on_bdry.real, scale_factor=0.03) - # }}} - - if enable_mayavi: mlab.colorbar() mlab.show() + # }}} + if __name__ == "__main__": main() diff --git a/examples/scaling-study.py b/examples/scaling-study.py index 21a85019ff03214f314265999598ed000350e3d5..8f3b4639eeeac5a12ad05036940e847710a73803 100644 --- a/examples/scaling-study.py +++ b/examples/scaling-study.py @@ -2,6 +2,7 @@ import numpy as np import pyopencl as cl import pyopencl.clmath # noqa +from meshmode.array_context import PyOpenCLArrayContext from meshmode.discretization import Discretization from meshmode.discretization.poly_element import \ InterpolatoryQuadratureSimplexGroupFactory @@ -58,11 +59,12 @@ def timing_run(nx, ny, visualize=False): cl_ctx = cl.create_some_context() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) mesh = make_mesh(nx=nx, ny=ny, visualize=visualize) density_discr = Discretization( - cl_ctx, mesh, + actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(bdry_quad_order)) from pytential.qbx import ( @@ -76,7 +78,7 @@ def timing_run(nx, ny, visualize=False): if visualize: from sumpy.visualization import FieldPlotter fplot = FieldPlotter(np.zeros(2), extent=5, npoints=1500) - targets = PointsTarget(cl.array.to_device(queue, fplot.points)) + targets = PointsTarget(actx.from_numpy(fplot.points)) places.update({ "plot-targets": targets, @@ -119,10 +121,12 @@ def timing_run(nx, ny, visualize=False): # {{{ fix rhs and solve mode_nr = 3 - nodes = density_discr.nodes().with_queue(queue) - angle = cl.clmath.atan2(nodes[1], nodes[0]) - sigma = cl.clmath.cos(mode_nr*angle) + from meshmode.dof_array import thaw + nodes = thaw(actx, density_discr.nodes()) + angle = actx.np.arctan2(nodes[1], nodes[0]) + + sigma = actx.np.cos(mode_nr*angle) # }}} @@ -134,17 +138,17 @@ def timing_run(nx, ny, visualize=False): bound_op = bind(places, sym_op) print("FMM WARM-UP RUN 1: %5d elements" % mesh.nelements) - bound_op(queue, sigma=sigma, k=k) + bound_op(actx, sigma=sigma, k=k) queue.finish() print("FMM WARM-UP RUN 2: %5d elements" % mesh.nelements) - bound_op(queue, sigma=sigma, k=k) + bound_op(actx, sigma=sigma, k=k) queue.finish() from time import time t_start = time() - bound_op(queue, sigma=sigma, k=k) - queue.finish() + bound_op(actx, sigma=sigma, k=k) + actx.queue.finish() elapsed = time() - t_start print("FMM TIMING RUN: %5d elements -> %g s" diff --git a/pytential/__init__.py b/pytential/__init__.py index 728ce196fd33a55566ea9f21e920e2b59332453b..1f548debac95ea850c6c7798b632c076cf2a3d56 100644 --- a/pytential/__init__.py +++ b/pytential/__init__.py @@ -66,8 +66,8 @@ def _integral_op(discr): discr.ambient_dim, discr.dim, sym.var("integrand"))) -def integral(discr, queue, x): - return _integral_op(discr)(queue, integrand=x) +def integral(discr, x): + return _integral_op(discr)(integrand=x) @memoize_on_first_arg @@ -97,23 +97,26 @@ def _norm_inf_op(discr, num_components): return bind(discr, sym.NodeMax(max_arg)) -def norm(discr, queue, x, p=2): +def norm(discr, x, p=2): from pymbolic.geometric_algebra import MultiVector if isinstance(x, MultiVector): x = x.as_vector(np.object) + from meshmode.dof_array import DOFArray num_components = None - if isinstance(x, np.ndarray): + if (isinstance(x, np.ndarray) + and x.dtype.char == "O" + and not isinstance(x, DOFArray)): num_components, = x.shape if p == 2: norm_op = _norm_2_op(discr, num_components) from math import sqrt - return sqrt(norm_op(queue, integrand=x)) + return sqrt(norm_op(integrand=x)) elif p == np.inf or p == "inf": norm_op = _norm_inf_op(discr, num_components) - norm_res = norm_op(queue, arg=x) + norm_res = norm_op(arg=x) if isinstance(norm_res, np.ndarray): return max(norm_res) else: diff --git a/pytential/linalg/proxy.py b/pytential/linalg/proxy.py index ba2e2ea96990c6b57457e9a01d3e6cdba5920e33..0f79148e8961433de65fc960487c5e4cd6202e94 100644 --- a/pytential/linalg/proxy.py +++ b/pytential/linalg/proxy.py @@ -26,10 +26,6 @@ THE SOFTWARE. import numpy as np import numpy.linalg as la -import pyopencl as cl -import pyopencl.array # noqa -from pyopencl.array import to_device - from pytools.obj_array import make_obj_array from pytools import memoize_method, memoize_in from sumpy.tools import BlockIndexRanges @@ -54,16 +50,7 @@ Proxy Point Generation # {{{ point index partitioning -def _element_node_range(group, ielement): - istart = group.node_nr_base + group.nunit_nodes * ielement - iend = group.node_nr_base + group.nunit_nodes * (ielement + 1) - - return np.arange(istart, iend) - - -def partition_by_nodes(discr, - use_tree=True, - max_nodes_in_box=None): +def partition_by_nodes(actx, discr, use_tree=True, max_nodes_in_box=None): """Generate equally sized ranges of nodes. The partition is created at the lowest level of granularity, i.e. nodes. This results in balanced ranges of points, but will split elements across different ranges. @@ -82,112 +69,42 @@ def partition_by_nodes(discr, # FIXME: this is just an arbitrary value max_nodes_in_box = 32 - with cl.CommandQueue(discr.cl_context) as queue: - if use_tree: - from boxtree import box_flags_enum - from boxtree import TreeBuilder + if use_tree: + from boxtree import box_flags_enum + from boxtree import TreeBuilder - builder = TreeBuilder(discr.cl_context) + builder = TreeBuilder(actx.context) - tree, _ = builder(queue, discr.nodes(), + from meshmode.dof_array import flatten, thaw + tree, _ = builder(actx.queue, + flatten(thaw(actx, discr.nodes())), max_particles_in_box=max_nodes_in_box) - tree = tree.get(queue) - leaf_boxes, = (tree.box_flags - & box_flags_enum.HAS_CHILDREN == 0).nonzero() + tree = tree.get(actx.queue) + leaf_boxes, = (tree.box_flags + & box_flags_enum.HAS_CHILDREN == 0).nonzero() - indices = np.empty(len(leaf_boxes), dtype=np.object) - for i, ibox in enumerate(leaf_boxes): - box_start = tree.box_source_starts[ibox] - box_end = box_start + tree.box_source_counts_cumul[ibox] - indices[i] = tree.user_source_ids[box_start:box_end] + indices = np.empty(len(leaf_boxes), dtype=np.object) + for i, ibox in enumerate(leaf_boxes): + box_start = tree.box_source_starts[ibox] + box_end = box_start + tree.box_source_counts_cumul[ibox] + indices[i] = tree.user_source_ids[box_start:box_end] - ranges = to_device(queue, - np.cumsum([0] + [box.shape[0] for box in indices])) - indices = to_device(queue, np.hstack(indices)) - else: - indices = cl.array.arange(queue, 0, discr.nnodes, - dtype=np.int) - ranges = cl.array.arange(queue, 0, discr.nnodes + 1, - discr.nnodes // max_nodes_in_box, - dtype=np.int) - assert ranges[-1] == discr.nnodes - - return BlockIndexRanges(discr.cl_context, - indices.with_queue(None), - ranges.with_queue(None)) - - -def partition_from_coarse(resampler, from_indices): - """Generate a partition of nodes from an existing partition on a - coarser discretization. The new partition is generated based on element - refinement relationships in *resampler*, so the existing partition - needs to be created using :func:`partition_by_elements`, - since we assume that each range contains all the nodes in an element. - - The new partition will have the same number of ranges as the old partition. - The nodes inside each range in the new partition are all the nodes in - *resampler.to_discr* that were refined from elements in the same - range from *resampler.from_discr*. - - :arg resampler: a - :class:`meshmode.discretization.connection.DirectDiscretizationConnection`. - :arg from_indices: a :class:`sumpy.tools.BlockIndexRanges`. + ranges = actx.from_numpy( + np.cumsum([0] + [box.shape[0] for box in indices]) + ) + indices = actx.from_numpy(np.hstack(indices)) + else: + indices = actx.from_numpy(np.arange(0, discr.ndofs, dtype=np.int)) + ranges = actx.from_numpy(np.arange( + 0, + discr.ndofs + 1, + discr.ndofs // max_nodes_in_box, dtype=np.int)) - :return: a :class:`sumpy.tools.BlockIndexRanges`. - """ + assert ranges[-1] == discr.ndofs - if not hasattr(resampler, "groups"): - raise ValueError("resampler must be a DirectDiscretizationConnection.") - - with cl.CommandQueue(resampler.cl_context) as queue: - from_indices = from_indices.get(queue) - - # construct ranges - from_discr = resampler.from_discr - from_grp_ranges = np.cumsum( - [0] + [grp.nelements for grp in from_discr.mesh.groups]) - from_el_ranges = np.hstack([ - np.arange(grp.node_nr_base, grp.nnodes + 1, grp.nunit_nodes) - for grp in from_discr.groups]) - - # construct coarse element arrays in each from_range - el_indices = np.empty(from_indices.nblocks, dtype=np.object) - el_ranges = np.full(from_grp_ranges[-1], -1, dtype=np.int) - for i in range(from_indices.nblocks): - ifrom = from_indices.block_indices(i) - el_indices[i] = np.unique(np.digitize(ifrom, from_el_ranges)) - 1 - el_ranges[el_indices[i]] = i - el_indices = np.hstack(el_indices) - - # construct lookup table - to_el_table = [np.full(g.nelements, -1, dtype=np.int) - for g in resampler.to_discr.groups] - - for igrp, grp in enumerate(resampler.groups): - for batch in grp.batches: - to_el_table[igrp][batch.to_element_indices.get(queue)] = \ - from_grp_ranges[igrp] + batch.from_element_indices.get(queue) - - # construct fine node index list - indices = [np.empty(0, dtype=np.int) - for _ in range(from_indices.nblocks)] - for igrp in range(len(resampler.groups)): - to_element_indices = \ - np.where(np.isin(to_el_table[igrp], el_indices))[0] - - for i, j in zip(el_ranges[to_el_table[igrp][to_element_indices]], - to_element_indices): - indices[i] = np.hstack([indices[i], - _element_node_range(resampler.to_discr.groups[igrp], j)]) - - ranges = to_device(queue, - np.cumsum([0] + [b.shape[0] for b in indices])) - indices = to_device(queue, np.hstack(indices)) - - return BlockIndexRanges(resampler.cl_context, - indices.with_queue(None), - ranges.with_queue(None)) + return BlockIndexRanges(actx.context, + actx.freeze(indices), actx.freeze(ranges)) # }}} @@ -340,7 +257,7 @@ class ProxyGenerator(object): """.format(radius_expr=radius_expr)], [ lp.GlobalArg("sources", None, - shape=(self.ambient_dim, "nsources")), + shape=(self.ambient_dim, "nsources"), dim_tags="sep,C"), lp.GlobalArg("center_int", None, shape=(self.ambient_dim, "nsources"), dim_tags="sep,C"), lp.GlobalArg("center_ext", None, @@ -367,11 +284,11 @@ class ProxyGenerator(object): return knl - def __call__(self, queue, source_dd, indices, **kwargs): + def __call__(self, actx, source_dd, indices, **kwargs): """Generate proxy points for each given range of source points in the discretization in *source_dd*. - :arg queue: a :class:`pyopencl.CommandQueue`. + :arg actx: a :class:`~meshmode.array_context.ArrayContext`. :arg source_dd: a :class:`~pytential.symbolic.primitives.DOFDescriptor` for the discretization on which the proxy points are to be generated. @@ -397,47 +314,51 @@ class ProxyGenerator(object): source_dd.geometry, source_dd.discr_stage) radii = bind(self.places, sym.expansion_radii( - self.ambient_dim, dofdesc=source_dd))(queue) + self.ambient_dim, dofdesc=source_dd))(actx) center_int = bind(self.places, sym.expansion_centers( - self.ambient_dim, -1, dofdesc=source_dd))(queue) + self.ambient_dim, -1, dofdesc=source_dd))(actx) center_ext = bind(self.places, sym.expansion_centers( - self.ambient_dim, +1, dofdesc=source_dd))(queue) + self.ambient_dim, +1, dofdesc=source_dd))(actx) + from meshmode.dof_array import flatten, thaw knl = self.get_kernel() - _, (centers_dev, radii_dev,) = knl(queue, - sources=discr.nodes(), - center_int=center_int, - center_ext=center_ext, - expansion_radii=radii, + _, (centers_dev, radii_dev,) = knl(actx.queue, + sources=flatten(thaw(actx, discr.nodes())), + center_int=flatten(center_int), + center_ext=flatten(center_ext), + expansion_radii=flatten(radii), srcindices=indices.indices, srcranges=indices.ranges, **kwargs) - centers = centers_dev.get() - radii = radii_dev.get() + from pytential.utils import flatten_to_numpy + centers = flatten_to_numpy(actx, centers_dev) + radii = flatten_to_numpy(actx, radii_dev) proxies = np.empty(indices.nblocks, dtype=np.object) for i in range(indices.nblocks): proxies[i] = _affine_map(self.ref_points, A=(radii[i] * np.eye(self.ambient_dim)), b=centers[:, i].reshape(-1, 1)) - pxyranges = cl.array.arange(queue, - 0, - proxies.shape[0] * proxies[0].shape[1] + 1, - proxies[0].shape[1], - dtype=indices.ranges.dtype) + pxyranges = actx.from_numpy(np.arange( + 0, + proxies.shape[0] * proxies[0].shape[1] + 1, + proxies[0].shape[1], + dtype=indices.ranges.dtype)) proxies = make_obj_array([ - cl.array.to_device(queue, np.hstack([p[idim] for p in proxies])) - for idim in range(self.ambient_dim)]) + actx.freeze(actx.from_numpy(np.hstack([p[idim] for p in proxies]))) + for idim in range(self.ambient_dim) + ]) centers = make_obj_array([ - centers_dev[idim].with_queue(queue).copy() - for idim in range(self.ambient_dim)]) + actx.freeze(centers_dev[idim]) + for idim in range(self.ambient_dim) + ]) assert pxyranges[-1] == proxies[0].shape[0] - return proxies, pxyranges, centers, radii_dev + return proxies, actx.freeze(pxyranges), centers, actx.freeze(radii_dev) -def gather_block_neighbor_points(discr, indices, pxycenters, pxyradii, - max_nodes_in_box=None): +def gather_block_neighbor_points(actx, discr, indices, pxycenters, pxyradii, + max_nodes_in_box=None): """Generate a set of neighboring points for each range of points in *discr*. Neighboring points of a range :math:`i` are defined as all the points inside the proxy ball :math:`i` that do not also @@ -455,79 +376,77 @@ def gather_block_neighbor_points(discr, indices, pxycenters, pxyradii, # FIXME: this is a fairly arbitrary value max_nodes_in_box = 32 - with cl.CommandQueue(discr.cl_context) as queue: - indices = indices.get(queue) - - # NOTE: this is constructed for multiple reasons: - # * TreeBuilder takes object arrays - # * `srcindices` can be a small subset of nodes, so this will save - # some work - # * `srcindices` may reorder the array returned by nodes(), so this - # makes sure that we have the same order in tree.user_source_ids - # and friends - sources = discr.nodes().get(queue) - sources = make_obj_array([ - cl.array.to_device(queue, sources[idim, indices.indices]) - for idim in range(discr.ambient_dim)]) - - # construct tree - from boxtree import TreeBuilder - builder = TreeBuilder(discr.cl_context) - tree, _ = builder(queue, sources, - max_particles_in_box=max_nodes_in_box) - - from boxtree.area_query import AreaQueryBuilder - builder = AreaQueryBuilder(discr.cl_context) - query, _ = builder(queue, tree, pxycenters, pxyradii) - - # find nodes inside each proxy ball - tree = tree.get(queue) - query = query.get(queue) - - if isinstance(pxycenters[0], cl.array.Array): - pxycenters = np.vstack([pxycenters[idim].get(queue) - for idim in range(discr.ambient_dim)]) - if isinstance(pxyradii, cl.array.Array): - pxyradii = pxyradii.get(queue) - - nbrindices = np.empty(indices.nblocks, dtype=np.object) - for iproxy in range(indices.nblocks): - # get list of boxes intersecting the current ball - istart = query.leaves_near_ball_starts[iproxy] - iend = query.leaves_near_ball_starts[iproxy + 1] - iboxes = query.leaves_near_ball_lists[istart:iend] - - # get nodes inside the boxes - istart = tree.box_source_starts[iboxes] - iend = istart + tree.box_source_counts_cumul[iboxes] - isources = np.hstack([np.arange(s, e) - for s, e in zip(istart, iend)]) - nodes = np.vstack([tree.sources[idim][isources] - for idim in range(discr.ambient_dim)]) - isources = tree.user_source_ids[isources] - - # get nodes inside the ball but outside the current range - center = pxycenters[:, iproxy].reshape(-1, 1) - radius = pxyradii[iproxy] - mask = ((la.norm(nodes - center, axis=0) < radius) - & ((isources < indices.ranges[iproxy]) - | (indices.ranges[iproxy + 1] <= isources))) - - nbrindices[iproxy] = indices.indices[isources[mask]] - - nbrranges = to_device(queue, - np.cumsum([0] + [n.shape[0] for n in nbrindices])) - nbrindices = to_device(queue, np.hstack(nbrindices)) - - return BlockIndexRanges(discr.cl_context, - nbrindices.with_queue(None), - nbrranges.with_queue(None)) - - -def gather_block_interaction_points(places, source_dd, indices, - radius_factor=None, - approx_nproxy=None, - max_nodes_in_box=None): + indices = indices.get(actx.queue) + + # NOTE: this is constructed for multiple reasons: + # * TreeBuilder takes object arrays + # * `srcindices` can be a small subset of nodes, so this will save + # some work + # * `srcindices` may reorder the array returned by nodes(), so this + # makes sure that we have the same order in tree.user_source_ids + # and friends + from pytential.utils import flatten_to_numpy + sources = flatten_to_numpy(actx, discr.nodes()) + sources = make_obj_array([ + actx.from_numpy(sources[idim][indices.indices]) + for idim in range(discr.ambient_dim)]) + + # construct tree + from boxtree import TreeBuilder + builder = TreeBuilder(actx.context) + tree, _ = builder(actx.queue, sources, + max_particles_in_box=max_nodes_in_box) + + from boxtree.area_query import AreaQueryBuilder + builder = AreaQueryBuilder(actx.context) + query, _ = builder(actx.queue, tree, pxycenters, pxyradii) + + # find nodes inside each proxy ball + tree = tree.get(actx.queue) + query = query.get(actx.queue) + + pxycenters = np.vstack([ + actx.to_numpy(pxycenters[idim]) + for idim in range(discr.ambient_dim) + ]) + pxyradii = actx.to_numpy(pxyradii) + + nbrindices = np.empty(indices.nblocks, dtype=np.object) + for iproxy in range(indices.nblocks): + # get list of boxes intersecting the current ball + istart = query.leaves_near_ball_starts[iproxy] + iend = query.leaves_near_ball_starts[iproxy + 1] + iboxes = query.leaves_near_ball_lists[istart:iend] + + # get nodes inside the boxes + istart = tree.box_source_starts[iboxes] + iend = istart + tree.box_source_counts_cumul[iboxes] + isources = np.hstack([np.arange(s, e) + for s, e in zip(istart, iend)]) + nodes = np.vstack([tree.sources[idim][isources] + for idim in range(discr.ambient_dim)]) + isources = tree.user_source_ids[isources] + + # get nodes inside the ball but outside the current range + center = pxycenters[:, iproxy].reshape(-1, 1) + radius = pxyradii[iproxy] + mask = ((la.norm(nodes - center, axis=0) < radius) + & ((isources < indices.ranges[iproxy]) + | (indices.ranges[iproxy + 1] <= isources))) + + nbrindices[iproxy] = indices.indices[isources[mask]] + + nbrranges = actx.from_numpy(np.cumsum([0] + [n.shape[0] for n in nbrindices])) + nbrindices = actx.from_numpy(np.hstack(nbrindices)) + + return BlockIndexRanges(actx.context, + actx.freeze(nbrindices), actx.freeze(nbrranges)) + + +def gather_block_interaction_points(actx, places, source_dd, indices, + radius_factor=None, + approx_nproxy=None, + max_nodes_in_box=None): """Generate sets of interaction points for each given range of indices in the *source* discretization. For each input range of indices, the corresponding output range of points is consists of: @@ -583,7 +502,7 @@ def gather_block_interaction_points(places, source_dd, indices, """, [ lp.GlobalArg("sources", None, - shape=(lpot_source.ambient_dim, "nsources")), + shape=(lpot_source.ambient_dim, "nsources"), dim_tags="sep,C"), lp.GlobalArg("proxies", None, shape=(lpot_source.ambient_dim, "nproxies"), dim_tags="sep,C"), lp.GlobalArg("nbrindices", None, @@ -607,28 +526,28 @@ def gather_block_interaction_points(places, source_dd, indices, return loopy_knl lpot_source = places.get_geometry(source_dd.geometry) - with cl.CommandQueue(lpot_source.cl_context) as queue: - generator = ProxyGenerator(places, - radius_factor=radius_factor, - approx_nproxy=approx_nproxy) - proxies, pxyranges, pxycenters, pxyradii = \ - generator(queue, source_dd, indices) - - discr = places.get_discretization(source_dd.geometry, source_dd.discr_stage) - neighbors = gather_block_neighbor_points(discr, - indices, pxycenters, pxyradii, - max_nodes_in_box=max_nodes_in_box) - - ranges = cl.array.zeros(queue, indices.nblocks + 1, dtype=np.int) - _, (nodes, ranges) = knl()(queue, - sources=discr.nodes(), - proxies=proxies, - pxyranges=pxyranges, - nbrindices=neighbors.indices, - nbrranges=neighbors.ranges, - ranges=ranges) - - return nodes.with_queue(None), ranges.with_queue(None) + generator = ProxyGenerator(places, + radius_factor=radius_factor, + approx_nproxy=approx_nproxy) + proxies, pxyranges, pxycenters, pxyradii = \ + generator(actx, source_dd, indices) + + discr = places.get_discretization(source_dd.geometry, source_dd.discr_stage) + neighbors = gather_block_neighbor_points(actx, discr, + indices, pxycenters, pxyradii, + max_nodes_in_box=max_nodes_in_box) + + from meshmode.dof_array import flatten, thaw + ranges = actx.zeros(indices.nblocks + 1, dtype=np.int) + _, (nodes, ranges) = knl()(actx.queue, + sources=flatten(thaw(actx, discr.nodes())), + proxies=proxies, + pxyranges=pxyranges, + nbrindices=neighbors.indices, + nbrranges=neighbors.ranges, + ranges=ranges) + + return actx.freeze(nodes), actx.freeze(ranges) # }}} diff --git a/pytential/qbx/__init__.py b/pytential/qbx/__init__.py index 67115241dbbf75b1adbc1abc4280d1cb8e89f5cf..b838764aea4c43f0bdc52bac6c2809cbfc234403 100644 --- a/pytential/qbx/__init__.py +++ b/pytential/qbx/__init__.py @@ -1,5 +1,4 @@ -# -*- coding: utf-8 -*- -from __future__ import division, absolute_import +from __future__ import annotations __copyright__ = "Copyright (C) 2013 Andreas Kloeckner" @@ -25,8 +24,11 @@ THE SOFTWARE. import six +from meshmode.array_context import PyOpenCLArrayContext +from meshmode.dof_array import flatten, unflatten, thaw import numpy as np -from pytools import memoize_method +from pytools import memoize_method, memoize_in + from pytential.qbx.target_assoc import QBXTargetAssociationFailedException from pytential.source import LayerPotentialSourceBase @@ -212,8 +214,8 @@ class QBXLayerPotentialSource(LayerPotentialSourceBase): if cost_model is None: from pytential.qbx.cost import QBXCostModel - queue = cl.CommandQueue(self.cl_context) - cost_model = QBXCostModel(queue) + # FIXME: Yuck + cost_model = QBXCostModel(self._setup_actx) self.cost_model = cost_model @@ -336,24 +338,57 @@ class QBXLayerPotentialSource(LayerPotentialSourceBase): # }}} + # {{{ code containers + @property - @memoize_method def tree_code_container(self): - from pytential.qbx.utils import TreeCodeContainer - return TreeCodeContainer(self.cl_context) + @memoize_in(self._setup_actx, ( + QBXLayerPotentialSource, "tree_code_container")) + def make_container(): + from pytential.qbx.utils import TreeCodeContainer + return TreeCodeContainer(self._setup_actx) + return make_container() @property - @memoize_method def refiner_code_container(self): - from pytential.qbx.refinement import RefinerCodeContainer - return RefinerCodeContainer(self.cl_context, self.tree_code_container) + @memoize_in(self._setup_actx, ( + QBXLayerPotentialSource, "refiner_code_container")) + def make_container(): + from pytential.qbx.refinement import RefinerCodeContainer + return RefinerCodeContainer( + self._setup_actx, self.tree_code_container) + return make_container() @property - @memoize_method def target_association_code_container(self): - from pytential.qbx.target_assoc import TargetAssociationCodeContainer - return TargetAssociationCodeContainer( - self.cl_context, self.tree_code_container) + @memoize_in(self._setup_actx, ( + QBXLayerPotentialSource, "target_association_code_container")) + def make_container(): + from pytential.qbx.target_assoc import TargetAssociationCodeContainer + return TargetAssociationCodeContainer( + self._setup_actx, self.tree_code_container) + return make_container() + + @property + def qbx_fmm_geometry_data_code_container(self): + @memoize_in(self._setup_actx, ( + QBXLayerPotentialSource, + "qbx_fmm_geometry_data_code_container")) + def make_container( + debug, ambient_dim, well_sep_is_n_away, + from_sep_smaller_crit): + from pytential.qbx.geometry import QBXFMMGeometryDataCodeContainer + return QBXFMMGeometryDataCodeContainer( + self._setup_actx, + ambient_dim, self.tree_code_container, debug, + _well_sep_is_n_away=well_sep_is_n_away, + _from_sep_smaller_crit=from_sep_smaller_crit) + + return make_container( + self.debug, self.ambient_dim, + self._well_sep_is_n_away, self._from_sep_smaller_crit) + + # }}} # {{{ internal API @@ -372,7 +407,7 @@ class QBXLayerPotentialSource(LayerPotentialSourceBase): from pytential.qbx.geometry import QBXFMMGeometryData return QBXFMMGeometryData(places, name, - self.qbx_fmm_code_getter, + self.qbx_fmm_geometry_data_code_container, target_discrs_and_qbx_sides, target_association_tolerance=self.target_association_tolerance, tree_kind=self._tree_kind, @@ -403,7 +438,7 @@ class QBXLayerPotentialSource(LayerPotentialSourceBase): # {{{ internal functionality for execution - def exec_compute_potential_insn(self, queue, insn, bound_expr, evaluate, + def exec_compute_potential_insn(self, actx, insn, bound_expr, evaluate, return_timing_data): extra_args = {} @@ -426,9 +461,9 @@ class QBXLayerPotentialSource(LayerPotentialSourceBase): extra_args["fmm_driver"] = drive_fmm return self._dispatch_compute_potential_insn( - queue, insn, bound_expr, evaluate, func, extra_args) + actx, insn, bound_expr, evaluate, func, extra_args) - def cost_model_compute_potential_insn(self, queue, insn, bound_expr, evaluate, + def cost_model_compute_potential_insn(self, actx, insn, bound_expr, evaluate, calibration_params, per_box): """Using :attr:`cost_model`, evaluate the cost of executing *insn*. Cost model results are gathered in @@ -453,15 +488,20 @@ class QBXLayerPotentialSource(LayerPotentialSourceBase): geo_data, kernel, kernel_arguments, calibration_params ) - return wrangler.full_output_zeros(), (cost_model_result, metadata) + from pytools.obj_array import obj_array_vectorize + return ( + obj_array_vectorize( + wrangler.finalize_potentials, + wrangler.full_output_zeros()), + (cost_model_result, metadata)) return self._dispatch_compute_potential_insn( - queue, insn, bound_expr, evaluate, + actx, insn, bound_expr, evaluate, self.exec_compute_potential_insn_fmm, extra_args={"fmm_driver": drive_cost_model} ) - def _dispatch_compute_potential_insn(self, queue, insn, bound_expr, + def _dispatch_compute_potential_insn(self, actx, insn, bound_expr, evaluate, func, extra_args=None): if self._disable_refinement: from warnings import warn @@ -472,16 +512,7 @@ class QBXLayerPotentialSource(LayerPotentialSourceBase): if extra_args is None: extra_args = {} - return func(queue, insn, bound_expr, evaluate, **extra_args) - - @property - @memoize_method - def qbx_fmm_code_getter(self): - from pytential.qbx.geometry import QBXFMMGeometryCodeGetter - return QBXFMMGeometryCodeGetter(self.cl_context, self.ambient_dim, - self.tree_code_container, debug=self.debug, - _well_sep_is_n_away=self._well_sep_is_n_away, - _from_sep_smaller_crit=self._from_sep_smaller_crit) + return func(actx, insn, bound_expr, evaluate, **extra_args) # {{{ fmm-based execution @@ -545,8 +576,8 @@ class QBXLayerPotentialSource(LayerPotentialSourceBase): return target_name_and_side_to_number, tuple(target_discrs_and_qbx_sides) - def exec_compute_potential_insn_fmm(self, queue, insn, bound_expr, evaluate, - fmm_driver): + def exec_compute_potential_insn_fmm(self, actx: PyOpenCLArrayContext, + insn, bound_expr, evaluate, fmm_driver): """ :arg fmm_driver: A function that accepts four arguments: *wrangler*, *strength*, *geo_data*, *kernel*, *kernel_arguments* @@ -575,21 +606,23 @@ class QBXLayerPotentialSource(LayerPotentialSourceBase): from pytential import bind, sym waa = bind(bound_expr.places, sym.weights_and_area_elements( - self.ambient_dim, dofdesc=insn.source))(queue) - strengths = waa * evaluate(insn.density).with_queue(queue) + self.ambient_dim, dofdesc=insn.source))(actx) + density = evaluate(insn.density) + strengths = waa * density + flat_strengths = flatten(strengths) out_kernels = tuple(knl for knl in insn.kernels) fmm_kernel = self.get_fmm_kernel(out_kernels) output_and_expansion_dtype = ( - self.get_fmm_output_and_expansion_dtype(fmm_kernel, strengths)) + self.get_fmm_output_and_expansion_dtype(fmm_kernel, flat_strengths)) kernel_extra_kwargs, source_extra_kwargs = ( self.get_fmm_expansion_wrangler_extra_kwargs( - queue, out_kernels, geo_data.tree().user_source_ids, + actx, out_kernels, geo_data.tree().user_source_ids, insn.kernel_arguments, evaluate)) wrangler = self.expansion_wrangler_code_container( fmm_kernel, out_kernels).get_wrangler( - queue, geo_data, output_and_expansion_dtype, + actx.queue, geo_data, output_and_expansion_dtype, self.qbx_order, self.fmm_level_to_order, source_extra_kwargs=source_extra_kwargs, @@ -597,7 +630,7 @@ class QBXLayerPotentialSource(LayerPotentialSourceBase): _use_target_specific_qbx=self._use_target_specific_qbx) from pytential.qbx.geometry import target_state - if (geo_data.user_target_to_center().with_queue(queue) + if (actx.thaw(geo_data.user_target_to_center()) == target_state.FAILED).any().get(): raise RuntimeError("geometry has failed targets") @@ -613,20 +646,28 @@ class QBXLayerPotentialSource(LayerPotentialSourceBase): # Execute global QBX. all_potentials_on_every_target, extra_outputs = ( fmm_driver( - wrangler, strengths, geo_data, fmm_kernel, kernel_extra_kwargs)) + wrangler, flat_strengths, geo_data, + fmm_kernel, kernel_extra_kwargs)) - result = [] + results = [] for o in insn.outputs: target_side_number = target_name_and_side_to_number[ o.target_name, o.qbx_forced_limit] + target_discr, _ = target_discrs_and_qbx_sides[target_side_number] target_slice = slice(*geo_data.target_info().target_discr_starts[ target_side_number:target_side_number+2]) - result.append((o.name, - all_potentials_on_every_target[o.kernel_index][target_slice])) + result = all_potentials_on_every_target[o.kernel_index][target_slice] + + from meshmode.discretization import Discretization + if isinstance(target_discr, Discretization): + from meshmode.dof_array import unflatten + result = unflatten(actx, target_discr, result) - return result, extra_outputs + results.append((o.name, result)) + + return results, extra_outputs # }}} @@ -684,7 +725,7 @@ class QBXLayerPotentialSource(LayerPotentialSourceBase): *count = item; """) - def exec_compute_potential_insn_direct(self, queue, insn, bound_expr, evaluate, + def exec_compute_potential_insn_direct(self, actx, insn, bound_expr, evaluate, return_timing_data): from pytential import bind, sym if return_timing_data: @@ -698,19 +739,23 @@ class QBXLayerPotentialSource(LayerPotentialSourceBase): p2p = None lpot_applier_on_tgt_subset = None + from pytential.utils import flatten_if_needed kernel_args = {} for arg_name, arg_expr in six.iteritems(insn.kernel_arguments): - kernel_args[arg_name] = evaluate(arg_expr) + kernel_args[arg_name] = flatten_if_needed(actx, evaluate(arg_expr)) waa = bind(bound_expr.places, sym.weights_and_area_elements( - self.ambient_dim, dofdesc=insn.source))(queue) - strengths = waa * evaluate(insn.density).with_queue(queue) + self.ambient_dim, dofdesc=insn.source))(actx) + strengths = waa * evaluate(insn.density) + + from meshmode.discretization import Discretization + flat_strengths = flatten(strengths) source_discr = bound_expr.places.get_discretization( insn.source.geometry, insn.source.discr_stage) # FIXME: Do this all at once - result = [] + results = [] for o in insn.outputs: source_dd = insn.source.copy(discr_stage=o.target_name.discr_stage) target_discr = bound_expr.places.get_discretization( @@ -725,31 +770,41 @@ class QBXLayerPotentialSource(LayerPotentialSourceBase): assert abs(o.qbx_forced_limit) > 0 expansion_radii = bind(bound_expr.places, sym.expansion_radii( - self.ambient_dim, dofdesc=o.target_name))(queue) + self.ambient_dim, dofdesc=o.target_name))(actx) centers = bind(bound_expr.places, sym.expansion_centers( self.ambient_dim, o.qbx_forced_limit, - dofdesc=o.target_name))(queue) + dofdesc=o.target_name))(actx) evt, output_for_each_kernel = lpot_applier( - queue, target_discr.nodes(), - source_discr.nodes(), - centers, - [strengths], - expansion_radii=expansion_radii, + actx.queue, + flatten(thaw(actx, target_discr.nodes())), + flatten(thaw(actx, source_discr.nodes())), + flatten(centers), + [flat_strengths], + expansion_radii=flatten(expansion_radii), **kernel_args) - result.append((o.name, output_for_each_kernel[o.kernel_index])) + + result = output_for_each_kernel[o.kernel_index] + if isinstance(target_discr, Discretization): + result = unflatten(actx, target_discr, result) + + results.append((o.name, result)) else: # no on-disk kernel caching if p2p is None: - p2p = self.get_p2p(insn.kernels) + p2p = self.get_p2p(actx, insn.kernels) if lpot_applier_on_tgt_subset is None: lpot_applier_on_tgt_subset = self.get_lpot_applier_on_tgt_subset( insn.kernels) + queue = actx.queue + + flat_targets = flatten_if_needed(actx, target_discr.nodes()) + flat_sources = flatten(thaw(actx, source_discr.nodes())) + evt, output_for_each_kernel = p2p(queue, - target_discr.nodes(), - source_discr.nodes(), - [strengths], **kernel_args) + flat_targets, flat_sources, + [flat_strengths], **kernel_args) qbx_forced_limit = o.qbx_forced_limit if qbx_forced_limit is None: @@ -782,7 +837,7 @@ class QBXLayerPotentialSource(LayerPotentialSourceBase): if (o.qbx_forced_limit is not None and abs(o.qbx_forced_limit) == 1 - and qbx_tgt_count < target_discr.nnodes): + and qbx_tgt_count < target_discr.ndofs): raise RuntimeError("Did not find a matching QBX center " "for some targets") @@ -797,19 +852,23 @@ class QBXLayerPotentialSource(LayerPotentialSourceBase): if qbx_tgt_count: lpot_applier_on_tgt_subset( queue, - targets=target_discr.nodes(), - sources=source_discr.nodes(), - centers=geo_data.centers(), - expansion_radii=geo_data.expansion_radii(), - strengths=[strengths], + targets=flat_targets, + sources=flat_sources, + centers=geo_data.flat_centers(), + expansion_radii=geo_data.flat_expansion_radii(), + strengths=[flat_strengths], qbx_tgt_numbers=qbx_tgt_numbers, qbx_center_numbers=qbx_center_numbers, **tgt_subset_kwargs) - result.append((o.name, output_for_each_kernel[o.kernel_index])) + result = output_for_each_kernel[o.kernel_index] + if isinstance(target_discr, Discretization): + result = unflatten(actx, target_discr, result) + + results.append((o.name, result)) timing_data = {} - return result, timing_data + return results, timing_data # }}} diff --git a/pytential/qbx/cost.py b/pytential/qbx/cost.py index 629be98e70627e08bbb77a378d741fecd79ed557..1280c0b3ffecb1e5211e013cd250d72b59363dab 100644 --- a/pytential/qbx/cost.py +++ b/pytential/qbx/cost.py @@ -39,6 +39,8 @@ from pytools import memoize_method from functools import partial import sys +from meshmode.array_context import PyOpenCLArrayContext + from boxtree.cost import ( FMMTranslationCostModel, AbstractFMMCostModel, FMMCostModel, _PythonFMMCostModel ) @@ -580,10 +582,10 @@ class AbstractQBXCostModel(AbstractFMMCostModel): class QBXCostModel(AbstractQBXCostModel, FMMCostModel): """This class is an implementation of interface :class:`AbstractQBXCostModel` - using PyOpenCL + using :mod:`pyopencl`. """ def __init__( - self, queue, + self, actx, translation_cost_model_factory=make_pde_aware_translation_cost_model): """ :arg queue: a :class:`pyopencl.CommandQueue` object on which the execution @@ -592,7 +594,12 @@ class QBXCostModel(AbstractQBXCostModel, FMMCostModel): and the number of tree levels as arguments, returns an object of :class:`QBXTranslationCostModel`. """ - FMMCostModel.__init__(self, queue, translation_cost_model_factory) + if not isinstance(actx, PyOpenCLArrayContext): + raise TypeError("actx must be a PyOpenCLArrayContext") + + # FIXME: Should the cost model own a queue? + self.array_context = actx + FMMCostModel.__init__(self, actx.queue, translation_cost_model_factory) @memoize_method def _fill_array_with_index_knl(self, idx_dtype, array_dtype): diff --git a/pytential/qbx/direct.py b/pytential/qbx/direct.py index eac3c7fc8d667ef6b48a7f3509ade5b2f21878e2..1bcee7c042a1e8758868d4718f68fc4e4e7da3a8 100644 --- a/pytential/qbx/direct.py +++ b/pytential/qbx/direct.py @@ -109,7 +109,11 @@ class LayerPotentialOnTargetAndCenterSubset(LayerPotentialBase): def __call__(self, queue, targets, sources, centers, strengths, expansion_radii, **kwargs): - knl = self.get_cached_optimized_kernel() + from sumpy.tools import is_obj_array_like + knl = self.get_cached_optimized_kernel( + targets_is_obj_array=is_obj_array_like(targets), + sources_is_obj_array=is_obj_array_like(sources), + centers_is_obj_array=is_obj_array_like(centers)) for i, dens in enumerate(strengths): kwargs["strength_%d" % i] = dens diff --git a/pytential/qbx/fmm.py b/pytential/qbx/fmm.py index 8b9ea4292868fc7c811f46d8fe8b966555b5784e..5185376e475f2f98a2a6e67bc1f33039ef14f42b 100644 --- a/pytential/qbx/fmm.py +++ b/pytential/qbx/fmm.py @@ -225,8 +225,8 @@ QBXFMMGeometryData.non_qbx_box_target_lists`), self.queue, global_qbx_centers=geo_data.global_qbx_centers(), qbx_center_to_target_box=geo_data.qbx_center_to_target_box(), - qbx_centers=geo_data.centers(), - qbx_expansion_radii=geo_data.expansion_radii(), + qbx_centers=geo_data.flat_centers(), + qbx_expansion_radii=geo_data.flat_expansion_radii(), source_box_starts=starts, source_box_lists=lists, @@ -268,8 +268,8 @@ QBXFMMGeometryData.non_qbx_box_target_lists`), ), centers=self.tree.box_centers, - qbx_centers=geo_data.centers(), - qbx_expansion_radii=geo_data.expansion_radii(), + qbx_centers=geo_data.flat_centers(), + qbx_expansion_radii=geo_data.flat_expansion_radii(), src_expansions=source_mpoles_view, src_base_ibox=source_level_start_ibox, @@ -321,8 +321,8 @@ QBXFMMGeometryData.non_qbx_box_target_lists`), target_base_ibox=target_level_start_ibox, centers=self.tree.box_centers, - qbx_centers=geo_data.centers(), - qbx_expansion_radii=geo_data.expansion_radii(), + qbx_centers=geo_data.flat_centers(), + qbx_expansion_radii=geo_data.flat_expansion_radii(), expansions=target_locals_view, qbx_expansions=qbx_expansions, @@ -356,8 +356,8 @@ QBXFMMGeometryData.non_qbx_box_target_lists`), qbxl2p = self.code.qbxl2p(self.qbx_order) evt, pot_res = qbxl2p(self.queue, - qbx_centers=geo_data.centers(), - qbx_expansion_radii=geo_data.expansion_radii(), + qbx_centers=geo_data.flat_centers(), + qbx_expansion_radii=geo_data.flat_expansion_radii(), global_qbx_centers=geo_data.global_qbx_centers(), @@ -582,8 +582,8 @@ def drive_fmm(expansion_wrangler, src_weights, timing_data=None, # potential back into a CL array. return wrangler.finalize_potentials(x[tree.sorted_target_ids]) - from pytools.obj_array import with_object_array_or_scalar - result = with_object_array_or_scalar( + from pytools.obj_array import obj_array_vectorize + result = obj_array_vectorize( reorder_and_finalize_potentials, all_potentials_in_tree_order) # }}} diff --git a/pytential/qbx/fmmlib.py b/pytential/qbx/fmmlib.py index 98df707e296fd8e0d128edb1dafcae7e7f6b7fa1..531b25616ffd4aae472c1ec26c7d69eae939f9ab 100644 --- a/pytential/qbx/fmmlib.py +++ b/pytential/qbx/fmmlib.py @@ -234,7 +234,7 @@ class QBXFMMLibExpansionWrangler(FMMLibExpansionWrangler): def reorder_potentials(self, potentials): raise NotImplementedError("reorder_potentials should not " - "be called on a QBXFMMLibHelmholtzExpansionWrangler") + "be called on a QBXFMMLibExpansionWrangler") # Because this is a multi-stage, more complicated process that combines # potentials from non-QBX targets and QBX targets. diff --git a/pytential/qbx/geometry.py b/pytential/qbx/geometry.py index 7a991ddfa2b17c6f5b6fa3dc81d1c8d9fdecab06..ace246b7a4b324652062eda1c8c619f85519c782 100644 --- a/pytential/qbx/geometry.py +++ b/pytential/qbx/geometry.py @@ -28,6 +28,9 @@ import numpy as np import pyopencl as cl import pyopencl.array # noqa from pytools import memoize_method +from pytools.obj_array import obj_array_vectorize +from meshmode.array_context import PyOpenCLArrayContext +from meshmode.dof_array import flatten, thaw from boxtree.tools import DeviceDataRecord from boxtree.pyfmmlib_integration import FMMLibRotationDataInterface import loopy as lp @@ -76,7 +79,7 @@ Enums of special values Geometry description code container ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: QBXFMMGeometryCodeGetter +.. autoclass:: QBXFMMGeometryDataCodeContainer :members: :undoc-members: @@ -109,16 +112,21 @@ class target_state(Enum): # noqa FAILED = -2 -class QBXFMMGeometryCodeGetter(TreeCodeContainerMixin): - def __init__(self, cl_context, ambient_dim, tree_code_container, debug, +class QBXFMMGeometryDataCodeContainer(TreeCodeContainerMixin): + def __init__(self, actx: PyOpenCLArrayContext, ambient_dim, + tree_code_container, debug, _well_sep_is_n_away, _from_sep_smaller_crit): - self.cl_context = cl_context + self.array_context = actx self.ambient_dim = ambient_dim self.tree_code_container = tree_code_container self.debug = debug self._well_sep_is_n_away = _well_sep_is_n_away self._from_sep_smaller_crit = _from_sep_smaller_crit + @property + def cl_context(self): + return self.array_context.context + @memoize_method def copy_targets_kernel(self): knl = lp.make_kernel( @@ -324,7 +332,7 @@ class QBXFMMGeometryData(FMMLibRotationDataInterface): .. attribute:: code_getter - The :class:`QBXFMMGeometryCodeGetter` for this object. + The :class:`QBXFMMGeometryDataCodeContainer` for this object. .. attribute:: target_discrs_and_qbx_sides @@ -348,7 +356,7 @@ class QBXFMMGeometryData(FMMLibRotationDataInterface): .. rubric :: Expansion centers .. attribute:: ncenters - .. automethod:: centers() + .. automethod:: flat_centers() .. rubric :: Methods @@ -401,49 +409,54 @@ class QBXFMMGeometryData(FMMLibRotationDataInterface): def ambient_dim(self): return self.lpot_source.ambient_dim - @property - def cl_context(self): - return self.lpot_source.cl_context - @property def coord_dtype(self): return self.lpot_source.density_discr.real_dtype + @property + def array_context(self): + return self.code_getter.array_context + + @property + def cl_context(self): + return self.code_getter.cl_context + # {{{ centers/radii @property def ncenters(self): - return len(self.centers()[0]) + return len(self.flat_centers()[0]) @memoize_method - def centers(self): - """ Return an object array of (interleaved) center coordinates. + def flat_centers(self): + """Return an object array of (interleaved) center coordinates. ``coord_t [ambient_dim][ncenters]`` """ from pytential import bind, sym - from pytools.obj_array import make_obj_array - with cl.CommandQueue(self.cl_context) as queue: - centers = bind(self.places, sym.interleaved_expansion_centers( + centers = bind(self.places, sym.interleaved_expansion_centers( self.ambient_dim, - dofdesc=self.source_dd.to_stage1()))(queue) - return make_obj_array([ax.with_queue(None) for ax in centers]) + dofdesc=self.source_dd.to_stage1()))(self.array_context) + return obj_array_vectorize(self.array_context.freeze, flatten(centers)) @memoize_method - def expansion_radii(self): - """Return an array of radii associated with the (interleaved) + def flat_expansion_radii(self): + """Return an array of radii associated with the (interleaved) expansion centers. ``coord_t [ncenters]`` """ from pytential import bind, sym - with cl.CommandQueue(self.cl_context) as queue: - return bind(self.places, sym.expansion_radii( - self.ambient_dim, - granularity=sym.GRANULARITY_CENTER, - dofdesc=self.source_dd.to_stage1()))(queue) + radii = bind(self.places, + sym.expansion_radii( + self.ambient_dim, + granularity=sym.GRANULARITY_CENTER, + dofdesc=self.source_dd.to_stage1()))( + self.array_context) + + return self.array_context.freeze(flatten(radii)) # }}} @@ -453,36 +466,41 @@ class QBXFMMGeometryData(FMMLibRotationDataInterface): def target_info(self): """Return a :class:`TargetInfo`. |cached|""" - code_getter = self.code_getter - with cl.CommandQueue(self.cl_context) as queue: - ntargets = self.ncenters - target_discr_starts = [] + from pytential.utils import flatten_if_needed - for target_discr, qbx_side in self.target_discrs_and_qbx_sides: - target_discr_starts.append(ntargets) - ntargets += target_discr.nnodes + code_getter = self.code_getter + queue = self.array_context.queue + ntargets = self.ncenters + target_discr_starts = [] + for target_discr, qbx_side in self.target_discrs_and_qbx_sides: target_discr_starts.append(ntargets) + ntargets += target_discr.ndofs - targets = cl.array.empty( - self.cl_context, (self.ambient_dim, ntargets), - self.coord_dtype) + target_discr_starts.append(ntargets) + + targets = cl.array.empty( + self.cl_context, (self.ambient_dim, ntargets), + self.coord_dtype) + code_getter.copy_targets_kernel()( + queue, + targets=targets[:, :self.ncenters], + points=self.flat_centers()) + + for start, (target_discr, _) in zip( + target_discr_starts, self.target_discrs_and_qbx_sides): code_getter.copy_targets_kernel()( queue, - targets=targets[:, :self.ncenters], - points=self.centers()) + targets=targets[:, + start:start+target_discr.ndofs], + points=flatten_if_needed( + self.array_context, + target_discr.nodes())) - for start, (target_discr, _) in zip( - target_discr_starts, self.target_discrs_and_qbx_sides): - code_getter.copy_targets_kernel()( - queue, - targets=targets[:, start:start+target_discr.nnodes], - points=target_discr.nodes()) - - return TargetInfo( - targets=targets, - target_discr_starts=target_discr_starts, - ntargets=ntargets).with_queue(None) + return TargetInfo( + targets=targets, + target_discr_starts=target_discr_starts, + ntargets=ntargets).with_queue(None) def target_side_preferences(self): """Return one big array combining all the data from @@ -492,7 +510,7 @@ class QBXFMMGeometryData(FMMLibRotationDataInterface): tgt_info = self.target_info() - with cl.CommandQueue(self.cl_context) as queue: + with cl.CommandQueue(self.array_context.context) as queue: target_side_preferences = cl.array.empty( queue, tgt_info.ntargets, np.int8) target_side_preferences[:self.ncenters] = 0 @@ -500,7 +518,7 @@ class QBXFMMGeometryData(FMMLibRotationDataInterface): for tdstart, (target_discr, qbx_side) in \ zip(tgt_info.target_discr_starts, self.target_discrs_and_qbx_sides): - target_side_preferences[tdstart:tdstart+target_discr.nnodes] \ + target_side_preferences[tdstart:tdstart+target_discr.ndofs] \ = qbx_side return target_side_preferences.with_queue(None) @@ -521,52 +539,54 @@ class QBXFMMGeometryData(FMMLibRotationDataInterface): lpot_source = self.lpot_source target_info = self.target_info() - with cl.CommandQueue(self.cl_context) as queue: - from pytential import sym - quad_stage2_discr = self.places.get_discretization( - self.source_dd.geometry, sym.QBX_SOURCE_QUAD_STAGE2) - - nsources = quad_stage2_discr.nnodes - nparticles = nsources + target_info.ntargets - - target_radii = None - if lpot_source._expansions_in_tree_have_extent: - target_radii = cl.array.zeros(queue, target_info.ntargets, - self.coord_dtype) - target_radii[:self.ncenters] = self.expansion_radii() - - refine_weights = cl.array.empty(queue, nparticles, dtype=np.int32) - - # Assign a weight of 1 to all sources, QBX centers, and conventional - # (non-QBX) targets. Assign a weight of 0 to all targets that need - # QBX centers. The potential at the latter targets is mediated - # entirely by the QBX center, so as a matter of evaluation cost, - # their location in the tree is irrelevant. - refine_weights[:-target_info.ntargets] = 1 - user_ttc = self.user_target_to_center().with_queue(queue) - refine_weights[-target_info.ntargets:] = ( - user_ttc == target_state.NO_QBX_NEEDED).astype(np.int32) - - refine_weights.finish() - - tree, _ = code_getter.build_tree()(queue, - particles=quad_stage2_discr.nodes(), - targets=target_info.targets, - target_radii=target_radii, - max_leaf_refine_weight=lpot_source._max_leaf_refine_weight, - refine_weights=refine_weights, - debug=self.debug, - stick_out_factor=lpot_source._expansion_stick_out_factor, - extent_norm=lpot_source._box_extent_norm, - kind=self.tree_kind) + queue = self.array_context.queue - if self.debug: - tgt_count_2 = cl.array.sum( - tree.box_target_counts_nonchild, queue=queue).get() + from pytential import sym + quad_stage2_discr = self.places.get_discretization( + self.source_dd.geometry, sym.QBX_SOURCE_QUAD_STAGE2) - assert (tree.ntargets == tgt_count_2), (tree.ntargets, tgt_count_2) + nsources = sum(grp.ndofs for grp in quad_stage2_discr.groups) + nparticles = nsources + target_info.ntargets - return tree + target_radii = None + if lpot_source._expansions_in_tree_have_extent: + target_radii = cl.array.zeros(queue, target_info.ntargets, + self.coord_dtype) + target_radii[:self.ncenters] = self.flat_expansion_radii() + + refine_weights = cl.array.empty(queue, nparticles, dtype=np.int32) + + # Assign a weight of 1 to all sources, QBX centers, and conventional + # (non-QBX) targets. Assign a weight of 0 to all targets that need + # QBX centers. The potential at the latter targets is mediated + # entirely by the QBX center, so as a matter of evaluation cost, + # their location in the tree is irrelevant. + refine_weights[:-target_info.ntargets] = 1 + user_ttc = self.user_target_to_center().with_queue(queue) + refine_weights[-target_info.ntargets:] = ( + user_ttc == target_state.NO_QBX_NEEDED).astype(np.int32) + + refine_weights.finish() + + tree, _ = code_getter.build_tree()(queue, + particles=flatten(thaw( + self.array_context, quad_stage2_discr.nodes())), + targets=target_info.targets, + target_radii=target_radii, + max_leaf_refine_weight=lpot_source._max_leaf_refine_weight, + refine_weights=refine_weights, + debug=self.debug, + stick_out_factor=lpot_source._expansion_stick_out_factor, + extent_norm=lpot_source._box_extent_norm, + kind=self.tree_kind) + + if self.debug: + tgt_count_2 = cl.array.sum( + tree.box_target_counts_nonchild, queue=queue).get() + + assert (tree.ntargets == tgt_count_2), (tree.ntargets, tgt_count_2) + + return tree # }}} @@ -761,31 +781,32 @@ class QBXFMMGeometryData(FMMLibRotationDataInterface): from pytential.target import PointsTarget - with cl.CommandQueue(self.cl_context) as queue: - target_side_prefs = (self - .target_side_preferences()[self.ncenters:].get(queue=queue)) - - target_discrs_and_qbx_sides = [( - PointsTarget(target_info.targets[:, self.ncenters:]), - target_side_prefs.astype(np.int32))] - - target_association_wrangler = ( - self.lpot_source.target_association_code_container - .get_wrangler(queue)) - - tgt_assoc_result = associate_targets_to_qbx_centers( - self.places, - self.source_dd, - target_association_wrangler, - target_discrs_and_qbx_sides, - target_association_tolerance=( - self.target_association_tolerance), - debug=self.debug) - - result = cl.array.empty(queue, target_info.ntargets, - tgt_assoc_result.target_to_center.dtype) - result[:self.ncenters].fill(target_state.NO_QBX_NEEDED) - result[self.ncenters:] = tgt_assoc_result.target_to_center + queue = self.array_context.queue + + target_side_prefs = (self + .target_side_preferences()[self.ncenters:].get(queue=queue)) + + target_discrs_and_qbx_sides = [( + PointsTarget(target_info.targets[:, self.ncenters:]), + target_side_prefs.astype(np.int32))] + + target_association_wrangler = ( + self.lpot_source.target_association_code_container + .get_wrangler(self.array_context)) + + tgt_assoc_result = associate_targets_to_qbx_centers( + self.places, + self.source_dd, + target_association_wrangler, + target_discrs_and_qbx_sides, + target_association_tolerance=( + self.target_association_tolerance), + debug=self.debug) + + result = cl.array.empty(queue, target_info.ntargets, + tgt_assoc_result.target_to_center.dtype) + result[:self.ncenters].fill(target_state.NO_QBX_NEEDED) + result[self.ncenters:] = tgt_assoc_result.target_to_center return result.with_queue(None) @@ -918,7 +939,7 @@ class QBXFMMGeometryData(FMMLibRotationDataInterface): # {{{ draw centers and circles - centers = self.centers() + centers = self.flat_centers() centers = [ centers[0].get(queue), centers[1].get(queue)] @@ -937,7 +958,7 @@ class QBXFMMGeometryData(FMMLibRotationDataInterface): if draw_circles: for icenter, (cx, cy, r) in enumerate(zip( centers[0], centers[1], - self.expansion_radii().get(queue))): + self.flat_expansion_radii().get(queue))): ax.add_artist( pt.Circle((cx, cy), r, fill=False, ls="dotted", lw=1)) diff --git a/pytential/qbx/refinement.py b/pytential/qbx/refinement.py index b3c28ee6e7c843ced0a39bc27da6e2a1b503d3f2..dbcfe657b650d46e9d407e30097a1339afc11bed 100644 --- a/pytential/qbx/refinement.py +++ b/pytential/qbx/refinement.py @@ -1,5 +1,5 @@ # -*- coding: utf-8 -*- -from __future__ import division, absolute_import, print_function +from __future__ import annotations __copyright__ = """ Copyright (C) 2013 Andreas Kloeckner @@ -29,6 +29,8 @@ THE SOFTWARE. import loopy as lp from loopy.version import MOST_RECENT_LANGUAGE_VERSION +from meshmode.array_context import PyOpenCLArrayContext +from meshmode.dof_array import flatten import numpy as np import pyopencl as cl @@ -219,8 +221,8 @@ SUFFICIENT_SOURCE_QUADRATURE_RESOLUTION_CHECKER = AreaQueryElementwiseTemplate( class RefinerCodeContainer(TreeCodeContainerMixin): - def __init__(self, cl_context, tree_code_container): - self.cl_context = cl_context + def __init__(self, actx: PyOpenCLArrayContext, tree_code_container): + self.array_context = actx self.tree_code_container = tree_code_container @memoize_method @@ -228,7 +230,7 @@ class RefinerCodeContainer(TreeCodeContainerMixin): self, dimensions, coord_dtype, box_id_dtype, peer_list_idx_dtype, particle_id_dtype, max_levels): return EXPANSION_DISK_UNDISTURBED_BY_SOURCES_CHECKER.generate( - self.cl_context, + self.array_context.context, dimensions, coord_dtype, box_id_dtype, peer_list_idx_dtype, max_levels, extra_type_aliases=(("particle_id_t", particle_id_dtype),)) @@ -238,7 +240,7 @@ class RefinerCodeContainer(TreeCodeContainerMixin): self, dimensions, coord_dtype, box_id_dtype, peer_list_idx_dtype, particle_id_dtype, max_levels): return SUFFICIENT_SOURCE_QUADRATURE_RESOLUTION_CHECKER.generate( - self.cl_context, + self.array_context.context, dimensions, coord_dtype, box_id_dtype, peer_list_idx_dtype, max_levels, extra_type_aliases=(("particle_id_t", particle_id_dtype),)) @@ -268,11 +270,11 @@ class RefinerCodeContainer(TreeCodeContainerMixin): knl = lp.split_iname(knl, "ielement", 128, inner_tag="l.0", outer_tag="g.0") return knl - def get_wrangler(self, queue): + def get_wrangler(self): """ :arg queue: """ - return RefinerWrangler(self, queue) + return RefinerWrangler(self.array_context, self) # }}} @@ -309,9 +311,10 @@ class RefinerWrangler(TreeWranglerBase): unwrap_args = AreaQueryElementwiseTemplate.unwrap_args from pytential import bind, sym - center_danger_zone_radii = bind(stage1_density_discr, + center_danger_zone_radii = flatten( + bind(stage1_density_discr, sym.expansion_radii(stage1_density_discr.ambient_dim, - granularity=sym.GRANULARITY_CENTER))(self.queue) + granularity=sym.GRANULARITY_CENTER))(self.array_context)) evt = knl( *unwrap_args( @@ -367,9 +370,11 @@ class RefinerWrangler(TreeWranglerBase): from pytential import bind, sym dd = sym.as_dofdesc(sym.GRANULARITY_ELEMENT).to_stage2() - source_danger_zone_radii_by_panel = bind(stage2_density_discr, - sym._source_danger_zone_radii( - stage2_density_discr.ambient_dim, dofdesc=dd))(self.queue) + source_danger_zone_radii_by_panel = flatten( + bind(stage2_density_discr, + sym._source_danger_zone_radii( + stage2_density_discr.ambient_dim, dofdesc=dd)) + (self.array_context)) unwrap_args = AreaQueryElementwiseTemplate.unwrap_args evt = knl( @@ -407,6 +412,10 @@ class RefinerWrangler(TreeWranglerBase): if debug: npanels_to_refine_prev = cl.array.sum(refine_flags).get() + from pytential.utils import flatten_if_needed + element_property = flatten_if_needed( + self.array_context, element_property) + evt, out = knl(self.queue, element_property=element_property, refine_flags=refine_flags, @@ -436,8 +445,10 @@ class RefinerWrangler(TreeWranglerBase): with ProcessLogger(logger, "refine mesh"): refiner.refine(refine_flags) - from meshmode.discretization.connection import make_refinement_connection - conn = make_refinement_connection(refiner, density_discr, factory) + from meshmode.discretization.connection import ( + make_refinement_connection) + conn = make_refinement_connection( + self.array_context, refiner, density_discr, factory) return conn @@ -485,7 +496,7 @@ def _warn_max_iterations(violated_criteria, expansion_disturbance_tolerance): RefinerNotConvergedWarning) -def _visualize_refinement(queue, discr, +def _visualize_refinement(actx: PyOpenCLArrayContext, discr, niter, stage_nr, stage_name, flags, visualize=False): if not visualize: return @@ -498,18 +509,18 @@ def _visualize_refinement(queue, discr, stage_name, np.sum(flags), discr.mesh.nelements, stage_nr) from meshmode.discretization.visualization import make_visualizer - vis = make_visualizer(queue, discr, 3) + vis = make_visualizer(actx, discr, 3) assert len(flags) == discr.mesh.nelements flags = flags.astype(np.bool) - nodes_flags = np.zeros(discr.nnodes) + nodes_flags = np.zeros(discr.ndofs) for grp in discr.groups: meg = grp.mesh_el_group grp.view(nodes_flags)[ flags[meg.element_nr_base:meg.nelements+meg.element_nr_base]] = 1 - nodes_flags = cl.array.to_device(queue, nodes_flags) + nodes_flags = actx.from_numpy(nodes_flags) vis_data = [ ("refine_flags", nodes_flags), ] @@ -517,7 +528,7 @@ def _visualize_refinement(queue, discr, if 0: from pytential import sym, bind bdry_normals = bind(discr, sym.normal(discr.ambient_dim))( - queue).as_vector(dtype=object) + actx).as_vector(dtype=object) vis_data.append(("bdry_normals", bdry_normals),) vis.write_vtk_file("refinement-%s-%03d.vtu" % (stage_name, niter), vis_data) @@ -529,7 +540,7 @@ def _make_quad_stage2_discr(lpot_source, stage2_density_discr): QuadratureSimplexGroupFactory return Discretization( - lpot_source.cl_context, + lpot_source._setup_actx, stage2_density_discr.mesh, QuadratureSimplexGroupFactory(lpot_source.fine_order), lpot_source.real_dtype) @@ -583,6 +594,8 @@ def _refine_qbx_stage1(lpot_source, density_discr, iter_violated_criteria = ["start"] niter = 0 + actx = wrangler.array_context + stage1_density_discr = density_discr while iter_violated_criteria: iter_violated_criteria = [] @@ -602,7 +615,7 @@ def _refine_qbx_stage1(lpot_source, density_discr, quad_resolution = bind(stage1_density_discr, sym._quad_resolution(stage1_density_discr.ambient_dim, - dofdesc=sym.GRANULARITY_ELEMENT))(wrangler.queue) + dofdesc=sym.GRANULARITY_ELEMENT))(actx) violates_kernel_length_scale = \ wrangler.check_element_prop_threshold( @@ -612,7 +625,7 @@ def _refine_qbx_stage1(lpot_source, density_discr, if violates_kernel_length_scale: iter_violated_criteria.append("kernel length scale") - _visualize_refinement(wrangler.queue, stage1_density_discr, + _visualize_refinement(actx, stage1_density_discr, niter, 1, "kernel-length-scale", refine_flags, visualize=visualize) @@ -622,7 +635,7 @@ def _refine_qbx_stage1(lpot_source, density_discr, scaled_max_curv = bind(stage1_density_discr, sym.ElementwiseMax(sym._scaled_max_curvature( stage1_density_discr.ambient_dim), - dofdesc=sym.GRANULARITY_ELEMENT))(wrangler.queue) + dofdesc=sym.GRANULARITY_ELEMENT))(actx) violates_scaled_max_curv = \ wrangler.check_element_prop_threshold( @@ -764,7 +777,8 @@ def _refine_qbx_stage2(lpot_source, stage1_density_discr, def _refine_qbx_quad_stage2(lpot_source, stage2_density_discr): from meshmode.discretization.connection import make_same_mesh_connection discr = _make_quad_stage2_discr(lpot_source, stage2_density_discr) - conn = make_same_mesh_connection(discr, stage2_density_discr) + conn = make_same_mesh_connection( + lpot_source._setup_actx, discr, stage2_density_discr) return discr, conn @@ -898,7 +912,7 @@ def _refine_for_global_qbx(places, dofdesc, wrangler, # {{{ refine_geometry_collection -def refine_geometry_collection(queue, places, +def refine_geometry_collection(places, group_factory=None, refine_discr_stage=None, kernel_length_scale=None, @@ -945,7 +959,7 @@ def refine_geometry_collection(queue, places, continue _refine_for_global_qbx(places, dofdesc, - lpot_source.refiner_code_container.get_wrangler(queue), + lpot_source.refiner_code_container.get_wrangler(), group_factory=group_factory, kernel_length_scale=kernel_length_scale, scaled_max_curvature_threshold=scaled_max_curvature_threshold, diff --git a/pytential/qbx/target_assoc.py b/pytential/qbx/target_assoc.py index 39b226ab491e72adfe1dba8ec714d73f090779f6..64aecae6de399c5bf8acf0d9fc7e78ee3f537938 100644 --- a/pytential/qbx/target_assoc.py +++ b/pytential/qbx/target_assoc.py @@ -36,6 +36,8 @@ from boxtree.tools import DeviceDataRecord from boxtree.area_query import AreaQueryElementwiseTemplate from boxtree.tools import InlineBinarySearch from cgen import Enum +from meshmode.array_context import PyOpenCLArrayContext +from meshmode.dof_array import flatten from pytential.qbx.utils import ( QBX_TREE_C_PREAMBLE, QBX_TREE_MAKO_DEFS, TreeWranglerBase, TreeCodeContainerMixin) @@ -444,10 +446,14 @@ class QBXTargetAssociation(DeviceDataRecord): class TargetAssociationCodeContainer(TreeCodeContainerMixin): - def __init__(self, cl_context, tree_code_container): - self.cl_context = cl_context + def __init__(self, actx: PyOpenCLArrayContext, tree_code_container): + self.array_context = actx self.tree_code_container = tree_code_container + @property + def cl_context(self): + return self.array_context.context + @memoize_method def target_marker(self, dimensions, coord_dtype, box_id_dtype, peer_list_idx_dtype, particle_id_dtype, max_levels): @@ -489,8 +495,8 @@ class TargetAssociationCodeContainer(TreeCodeContainerMixin): from boxtree.area_query import SpaceInvaderQueryBuilder return SpaceInvaderQueryBuilder(self.cl_context) - def get_wrangler(self, queue): - return TargetAssociationWrangler(self, queue) + def get_wrangler(self, actx: PyOpenCLArrayContext): + return TargetAssociationWrangler(actx, code_container=self) class TargetAssociationWrangler(TreeWranglerBase): @@ -521,9 +527,11 @@ class TargetAssociationWrangler(TreeWranglerBase): source_slice = tree.sorted_target_ids[tree.qbx_user_source_slice] sources = [ axis.with_queue(self.queue)[source_slice] for axis in tree.sources] - tunnel_radius_by_source = bind(places, - sym._close_target_tunnel_radii(ambient_dim, dofdesc=dofdesc))( - self.queue) + + tunnel_radius_by_source = flatten( + bind(places, + sym._close_target_tunnel_radii(ambient_dim, dofdesc=dofdesc)) + (self.array_context)) # Target-marking algorithm (TGTMARK): # @@ -620,9 +628,9 @@ class TargetAssociationWrangler(TreeWranglerBase): expansion_radii_by_center = bind(places, sym.expansion_radii( ambient_dim, granularity=sym.GRANULARITY_CENTER, - dofdesc=dofdesc))(self.queue) - expansion_radii_by_center_with_tolerance = \ - expansion_radii_by_center * (1 + target_association_tolerance) + dofdesc=dofdesc))(self.array_context) + expansion_radii_by_center_with_tolerance = flatten( + expansion_radii_by_center * (1 + target_association_tolerance)) # Idea: # @@ -716,11 +724,13 @@ class TargetAssociationWrangler(TreeWranglerBase): source_slice = tree.user_source_ids[tree.qbx_user_source_slice] sources = [ axis.with_queue(self.queue)[source_slice] for axis in tree.sources] - tunnel_radius_by_source = bind(places, - sym._close_target_tunnel_radii(ambient_dim, dofdesc=dofdesc))( - self.queue) - # See (TGTMARK) above for algorithm. + tunnel_radius_by_source = flatten( + bind(places, + sym._close_target_tunnel_radii(ambient_dim, dofdesc=dofdesc)) + (self.array_context)) + + # see (TGTMARK) above for algorithm. box_to_search_dist, evt = self.code_container.space_invader_query()( self.queue, @@ -731,10 +741,6 @@ class TargetAssociationWrangler(TreeWranglerBase): wait_for=wait_for) wait_for = [evt] - tunnel_radius_by_source = bind(places, - sym._close_target_tunnel_radii(ambient_dim, dofdesc=dofdesc))( - self.queue) - evt = knl( *unwrap_args( tree, peer_lists, @@ -767,17 +773,17 @@ class TargetAssociationWrangler(TreeWranglerBase): return (found_panel_to_refine == 1).all().get() def make_target_flags(self, target_discrs_and_qbx_sides): - ntargets = sum(discr.nnodes for discr, _ in target_discrs_and_qbx_sides) + ntargets = sum(discr.ndofs for discr, _ in target_discrs_and_qbx_sides) target_flags = cl.array.empty(self.queue, ntargets, dtype=np.int32) offset = 0 for discr, flags in target_discrs_and_qbx_sides: if np.isscalar(flags): - target_flags[offset:offset + discr.nnodes].fill(flags) + target_flags[offset:offset + discr.ndofs].fill(flags) else: - assert len(flags) == discr.nnodes - target_flags[offset:offset + discr.nnodes] = flags - offset += discr.nnodes + assert len(flags) == discr.ndofs + target_flags[offset:offset + discr.ndofs] = flags + offset += discr.ndofs target_flags.finish() return target_flags diff --git a/pytential/qbx/utils.py b/pytential/qbx/utils.py index b872152a2002ddec5678073e15255c25842db4ed..da804df5cf39219d8b4162fdbaaae1c115042d99 100644 --- a/pytential/qbx/utils.py +++ b/pytential/qbx/utils.py @@ -1,5 +1,5 @@ # -*- coding: utf-8 -*- -from __future__ import division, absolute_import, print_function +from __future__ import annotations __copyright__ = """ Copyright (C) 2016 Matt Wala @@ -28,6 +28,7 @@ THE SOFTWARE. import numpy as np from boxtree.tree import Tree +from meshmode.array_context import PyOpenCLArrayContext import pyopencl as cl import pyopencl.array # noqa from pytools import memoize_method @@ -72,23 +73,23 @@ QBX_TREE_MAKO_DEFS = r"""//CL:mako// class TreeCodeContainer(object): - def __init__(self, cl_context): - self.cl_context = cl_context + def __init__(self, actx: PyOpenCLArrayContext): + self.array_context = actx @memoize_method def build_tree(self): from boxtree.tree_build import TreeBuilder - return TreeBuilder(self.cl_context) + return TreeBuilder(self.array_context.context) @memoize_method def peer_list_finder(self): from boxtree.area_query import PeerListFinder - return PeerListFinder(self.cl_context) + return PeerListFinder(self.array_context.context) @memoize_method def particle_list_filter(self): from boxtree.tree import ParticleListFilter - return ParticleListFilter(self.cl_context) + return ParticleListFilter(self.array_context.context) # }}} @@ -116,9 +117,13 @@ class TreeCodeContainerMixin(object): class TreeWranglerBase(object): - def __init__(self, code_container, queue): + def __init__(self, array_context: PyOpenCLArrayContext, code_container): self.code_container = code_container - self.queue = queue + self.array_context = array_context + + @property + def queue(self): + return self.array_context.queue def build_tree(self, places, targets_list=(), sources_list=(), use_stage2_discr=False): @@ -126,7 +131,7 @@ class TreeWranglerBase(object): plfilt = self.code_container.particle_list_filter() return build_tree_with_qbx_metadata( - self.queue, places, tb, plfilt, + self.array_context, places, tb, plfilt, sources_list=sources_list, targets_list=targets_list, use_stage2_discr=use_stage2_discr) @@ -226,8 +231,8 @@ MAX_REFINE_WEIGHT = 64 @log_process(logger) -def build_tree_with_qbx_metadata(queue, places, - tree_builder, particle_list_filter, +def build_tree_with_qbx_metadata(actx: PyOpenCLArrayContext, + places, tree_builder, particle_list_filter, sources_list=(), targets_list=(), use_stage2_discr=False): """Return a :class:`TreeWithQBXMetadata` built from the given layer @@ -240,7 +245,7 @@ def build_tree_with_qbx_metadata(queue, places, :class:`~pytential.symbolic.primitives.QBX_SOURCE_STAGE1`. * targets from ``targets_list``. - :arg queue: An instance of :class:`pyopencl.CommandQueue` + :arg actx: A :class:`PyOpenCLArrayContext` :arg places: An instance of :class:`~pytential.symbolic.execution.GeometryCollection`. :arg targets_list: A list of :class:`pytential.target.TargetBase` @@ -274,15 +279,20 @@ def build_tree_with_qbx_metadata(queue, places, def _make_centers(discr): return bind(discr, sym.interleaved_expansion_centers( - discr.ambient_dim))(queue) + discr.ambient_dim))(actx) stage1_density_discr = stage1_density_discrs[0] density_discr = density_discrs[0] - sources = density_discr.nodes() - centers = _make_centers(stage1_density_discr) - targets = (tgt.nodes() for tgt in targets_list) + from meshmode.dof_array import flatten, thaw + from pytential.utils import flatten_if_needed + sources = flatten(thaw(actx, density_discr.nodes())) + centers = flatten(_make_centers(stage1_density_discr)) + targets = [ + flatten_if_needed(actx, tgt.nodes()) + for tgt in targets_list] + queue = actx.queue particles = tuple( cl.array.concatenate(dim_coords, queue=queue) for dim_coords in zip(sources, centers, *targets)) @@ -294,7 +304,7 @@ def build_tree_with_qbx_metadata(queue, places, ncenters = len(centers[0]) # Each source gets an interior / exterior center. assert 2 * nsources == ncenters or use_stage2_discr - ntargets = sum(tgt.nnodes for tgt in targets_list) + ntargets = sum(tgt.ndofs for tgt in targets_list) # Slices qbx_user_source_slice = slice(0, nsources) @@ -333,7 +343,7 @@ def build_tree_with_qbx_metadata(queue, places, box_to_class = ( particle_list_filter .filter_target_lists_in_user_order(queue, tree, flags) - .with_queue(queue)) + .with_queue(actx.queue)) if fixup: box_to_class.target_lists += fixup @@ -347,12 +357,14 @@ def build_tree_with_qbx_metadata(queue, places, qbx_panel_to_source_starts = cl.array.empty( queue, npanels + 1, dtype=tree.particle_id_dtype) el_offset = 0 + node_nr_base = 0 for group in density_discr.groups: qbx_panel_to_source_starts[el_offset:el_offset + group.nelements] = \ - cl.array.arange(queue, group.node_nr_base, - group.node_nr_base + group.nnodes, - group.nunit_nodes, + cl.array.arange(queue, node_nr_base, + node_nr_base + group.ndofs, + group.nunit_dofs, dtype=tree.particle_id_dtype) + node_nr_base += group.ndofs el_offset += group.nelements qbx_panel_to_source_starts[-1] = nsources @@ -418,11 +430,11 @@ class ToHostTransferredGeoDataWrapper(FMMLibRotationDataInterface): def centers(self): return np.array([ ci.get(queue=self.queue) - for ci in self.geo_data.centers()]) + for ci in self.geo_data.flat_centers()]) @memoize_method def expansion_radii(self): - return self.geo_data.expansion_radii().get(queue=self.queue) + return self.geo_data.flat_expansion_radii().get(queue=self.queue) @memoize_method def global_qbx_centers(self): diff --git a/pytential/solve.py b/pytential/solve.py index c2e932432b8db59a2734d0c8225f1edd500f84bc..9a12cff47b4a60c30b765e6ced3f6c05b6fd04b0 100644 --- a/pytential/solve.py +++ b/pytential/solve.py @@ -22,9 +22,6 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ - -from six.moves import range - __doc__ = """ .. autofunction:: gmres @@ -37,52 +34,24 @@ __doc__ = """ """ -def get_array_module(vec): - try: - from pyopencl.tools import array_module - from pytools.obj_array import is_obj_array - if is_obj_array(vec): - return array_module(vec[0]) - else: - return array_module(vec) - except ImportError: - return np - - -# {{{ block system support - -class VectorChopper(object): - def __init__(self, structured_vec): - from pytools.obj_array import is_obj_array - self.is_structured = is_obj_array(structured_vec) - self.array_module = get_array_module(structured_vec) - - if self.is_structured: - self.slices = [] - num_dofs = 0 - for entry in structured_vec: - if isinstance(entry, self.array_module.ndarray): - length = len(entry) - else: - length = 1 - - self.slices.append(slice(num_dofs, num_dofs+length)) - num_dofs += length - - def stack(self, vec): - if not self.is_structured: - return vec - - return self.array_module.hstack(vec) - - def chop(self, vec): - if not self.is_structured: - return vec - - from pytools.obj_array import make_obj_array - return make_obj_array([vec[slc] for slc in self.slices]) - -# }}} +import numpy as np +from numbers import Number +import pyopencl as cl +import pyopencl.array # noqa +from pytools.obj_array import obj_array_vectorize_n_args + + +def structured_vdot(x, y): + # vdot() implementation that is aware of scalars and host or + # PyOpenCL arrays. It also recurses down nested object arrays. + if (isinstance(x, Number) + or (isinstance(x, np.ndarray) and x.dtype.char != "O")): + return np.vdot(x, y) + elif isinstance(x, cl.array.Array): + return cl.array.vdot(x, y).get() + else: + assert isinstance(x, np.ndarray) and x.dtype.char == "O" + return sum(obj_array_vectorize_n_args(structured_vdot, x, y)) # {{{ gmres @@ -92,7 +61,6 @@ class VectorChopper(object): # Necessary because SciPy gmres is not reentrant and thus does # not allow recursive solves. -import numpy as np from pytools import Record @@ -274,11 +242,8 @@ def _gmres(A, b, restart=None, tol=None, x0=None, dot=None, # noqa # {{{ progress reporting class ResidualPrinter: - def __init__(self, inner_product=None): + def __init__(self, inner_product=structured_vdot): self.count = 0 - if inner_product is None: - inner_product = np.vdot - self.inner_product = inner_product def __call__(self, resid): @@ -298,7 +263,7 @@ class ResidualPrinter: # {{{ entrypoint def gmres(op, rhs, restart=None, tol=None, x0=None, - inner_product=None, + inner_product=structured_vdot, maxiter=None, hard_failure=None, no_progress_factor=None, stall_iterations=None, callback=None, progress=False, require_monotonicity=True): @@ -320,32 +285,20 @@ def gmres(op, rhs, restart=None, tol=None, x0=None, :return: a :class:`GMRESResult` """ - amod = get_array_module(rhs) - - chopper = VectorChopper(rhs) - stacked_rhs = chopper.stack(rhs) - - stacked_x0 = x0 - if stacked_x0 is not None: - stacked_x0 = chopper.stack(stacked_x0) - - if inner_product is None: - inner_product = amod.vdot - if callback is None: if progress: callback = ResidualPrinter(inner_product) else: callback = None - result = _gmres(op, stacked_rhs, restart=restart, tol=tol, x0=stacked_x0, + result = _gmres(op, rhs, restart=restart, tol=tol, x0=x0, dot=inner_product, maxiter=maxiter, hard_failure=hard_failure, no_progress_factor=no_progress_factor, stall_iterations=stall_iterations, callback=callback, require_monotonicity=require_monotonicity) - return result.copy(solution=chopper.chop(result.solution)) + return result # }}} @@ -367,10 +320,7 @@ def lu(op, rhs, show_spectrum=False): pt.plot(ev.real, ev.imag, "o") pt.show() - chopper = VectorChopper(rhs) - return chopper.chop( - la.solve(mat, - chopper.stack(rhs))) + return la.solve(mat, rhs) # }}} diff --git a/pytential/source.py b/pytential/source.py index 7ed794abfb2d1fa1ff32d21117e13ccfa3069a8c..19d90ae7a5daeafed5ee2d7ca71ac5345af12324 100644 --- a/pytential/source.py +++ b/pytential/source.py @@ -26,7 +26,7 @@ THE SOFTWARE. import numpy as np # noqa: F401 import pyopencl as cl # noqa: F401 import six -from pytools import memoize_method +from pytools import memoize_in from sumpy.fmm import UnableToCollectTimingData @@ -53,23 +53,51 @@ class PotentialSource(object): def preprocess_optemplate(self, name, discretizations, expr): return expr + @property + def real_dtype(self): + raise NotImplementedError + + @property + def complex_dtype(self): + raise NotImplementedError + + def get_p2p(self, actx, kernels): + raise NotImplementedError + + +class _SumpyP2PMixin(object): + + def get_p2p(self, actx, kernels): + @memoize_in(actx, (_SumpyP2PMixin, "p2p")) + def p2p(kernels): + from pytools import any + if any(knl.is_complex_valued for knl in kernels): + value_dtype = self.complex_dtype + else: + value_dtype = self.real_dtype + + from sumpy.p2p import P2P + return P2P(actx.context, + kernels, exclude_self=False, value_dtypes=value_dtype) + + return p2p(kernels) + # {{{ point potential source -class PointPotentialSource(PotentialSource): +class PointPotentialSource(_SumpyP2PMixin, PotentialSource): """ .. attribute:: nodes - An :class:`pyopencl.array.Array` of shape ``[ambient_dim, nnodes]``. + An :class:`pyopencl.array.Array` of shape ``[ambient_dim, ndofs]``. - .. attribute:: nnodes + .. attribute:: ndofs .. automethod:: cost_model_compute_potential_insn .. automethod:: exec_compute_potential_insn """ - def __init__(self, cl_context, nodes): - self.cl_context = cl_context + def __init__(self, nodes): self._nodes = nodes @property @@ -88,8 +116,9 @@ class PointPotentialSource(PotentialSource): return self._nodes.dtype @property - def nnodes(self): - return self._nodes.shape[-1] + def ndofs(self): + for coord_ary in self._nodes: + return coord_ary.shape[0] @property def complex_dtype(self): @@ -111,27 +140,11 @@ class PointPotentialSource(PotentialSource): return result - @memoize_method - def get_p2p(self, kernels): - # needs to be separate method for caching - - from pytools import any - if any(knl.is_complex_valued for knl in kernels): - value_dtype = self.complex_dtype - else: - value_dtype = self.real_dtype - - from sumpy.p2p import P2P - p2p = P2P(self.cl_context, - kernels, exclude_self=False, value_dtypes=value_dtype) - - return p2p - - def cost_model_compute_potential_insn(self, queue, insn, bound_expr, + def cost_model_compute_potential_insn(self, actx, insn, bound_expr, evaluate, costs): raise NotImplementedError - def exec_compute_potential_insn(self, queue, insn, bound_expr, evaluate, + def exec_compute_potential_insn(self, actx, insn, bound_expr, evaluate, return_timing_data): if return_timing_data: from warnings import warn @@ -145,33 +158,41 @@ class PointPotentialSource(PotentialSource): for arg_name, arg_expr in six.iteritems(insn.kernel_arguments): kernel_args[arg_name] = evaluate(arg_expr) - strengths = evaluate(insn.density).with_queue(queue).copy() + strengths = evaluate(insn.density) # FIXME: Do this all at once - result = [] + results = [] for o in insn.outputs: target_discr = bound_expr.places.get_discretization( o.target_name.geometry, o.target_name.discr_stage) # no on-disk kernel caching if p2p is None: - p2p = self.get_p2p(insn.kernels) + p2p = self.get_p2p(actx, insn.kernels) - evt, output_for_each_kernel = p2p(queue, - target_discr.nodes(), self._nodes, + from pytential.utils import flatten_if_needed + evt, output_for_each_kernel = p2p(actx.queue, + flatten_if_needed(actx, target_discr.nodes()), + self._nodes, [strengths], **kernel_args) - result.append((o.name, output_for_each_kernel[o.kernel_index])) + from meshmode.discretization import Discretization + result = output_for_each_kernel[o.kernel_index] + if isinstance(target_discr, Discretization): + from meshmode.dof_array import unflatten + result = unflatten(actx, target_discr, result) + + results.append((o.name, result)) timing_data = {} - return result, timing_data + return results, timing_data # }}} # {{{ layer potential source -class LayerPotentialSourceBase(PotentialSource): +class LayerPotentialSourceBase(_SumpyP2PMixin, PotentialSource): """A discretization of a layer potential using panel-based geometry, with support for refinement and upsampling. @@ -197,13 +218,17 @@ class LayerPotentialSourceBase(PotentialSource): def ambient_dim(self): return self.density_discr.ambient_dim + @property + def _setup_actx(self): + return self.density_discr._setup_actx + @property def dim(self): return self.density_discr.dim @property def cl_context(self): - return self.density_discr.cl_context + return self.density_discr._setup_actx.context @property def real_dtype(self): @@ -213,22 +238,6 @@ class LayerPotentialSourceBase(PotentialSource): def complex_dtype(self): return self.density_discr.complex_dtype - @memoize_method - def get_p2p(self, kernels): - # needs to be separate method for caching - - from pytools import any - if any(knl.is_complex_valued for knl in kernels): - value_dtype = self.density_discr.complex_dtype - else: - value_dtype = self.density_discr.real_dtype - - from sumpy.p2p import P2P - p2p = P2P(self.cl_context, - kernels, exclude_self=False, value_dtypes=value_dtype) - - return p2p - # {{{ fmm setup helpers def get_fmm_kernel(self, kernels): @@ -252,10 +261,12 @@ class LayerPotentialSourceBase(PotentialSource): return self.real_dtype def get_fmm_expansion_wrangler_extra_kwargs( - self, queue, out_kernels, tree_user_source_ids, arguments, evaluator): + self, actx, out_kernels, tree_user_source_ids, arguments, evaluator): # This contains things like the Helmholtz parameter k or # the normal directions for double layers. + queue = actx.queue + def reorder_sources(source_array): if isinstance(source_array, cl.array.Array): return (source_array @@ -269,15 +280,17 @@ class LayerPotentialSourceBase(PotentialSource): source_extra_kwargs = {} from sumpy.tools import gather_arguments, gather_source_arguments - from pytools.obj_array import with_object_array_or_scalar + from pytools.obj_array import obj_array_vectorize + from pytential.utils import flatten_if_needed + for func, var_dict in [ (gather_arguments, kernel_extra_kwargs), (gather_source_arguments, source_extra_kwargs), ]: for arg in func(out_kernels): - var_dict[arg.name] = with_object_array_or_scalar( + var_dict[arg.name] = obj_array_vectorize( reorder_sources, - evaluator(arguments[arg.name])) + flatten_if_needed(actx, evaluator(arguments[arg.name]))) return kernel_extra_kwargs, source_extra_kwargs diff --git a/pytential/symbolic/compiler.py b/pytential/symbolic/compiler.py index 3fcfbf2ec6eb4c42cb2a6d1af24bdb0bb1eca297..eb01532d96f71dbc7322fc6db0b23b32fc431ada 100644 --- a/pytential/symbolic/compiler.py +++ b/pytential/symbolic/compiler.py @@ -270,13 +270,13 @@ def dot_dataflow_graph(code, max_node_label_length=30, for dep in insn.get_dependencies(): gen_expr_arrow(dep, node_names[insn]) - from pytools.obj_array import is_obj_array + code_res = code.result - if is_obj_array(code.result): - for subexp in code.result: + if isinstance(code_res, np.ndarray) and code_res.dtype.char == "O": + for subexp in code_res: gen_expr_arrow(subexp, "result") else: - gen_expr_arrow(code.result, "result") + gen_expr_arrow(code_res, "result") return "digraph dataflow {\n%s\n}\n" % "\n".join(result) @@ -331,7 +331,7 @@ class Code(object): discardable_vars = set(available_names) - needed_vars # {{{ make sure results do not get discarded - from pytools.obj_array import with_object_array_or_scalar + from pytools.obj_array import obj_array_vectorize from pytential.symbolic.mappers import DependencyMapper dm = DependencyMapper(composite_leaves=False) @@ -347,7 +347,7 @@ class Code(object): assert isinstance(var, Variable) discardable_vars.discard(var.name) - with_object_array_or_scalar(remove_result_variable, self.result) + obj_array_vectorize(remove_result_variable, self.result) # }}} return argmax2(available_insns), discardable_vars @@ -387,9 +387,9 @@ class Code(object): done_insns.add(insn) assignments = ( - self.get_exec_function(insn, exec_mapper) - (exec_mapper.queue, insn, exec_mapper.bound_expr, - exec_mapper)) + self.get_exec_function(insn, exec_mapper)( + exec_mapper.array_context, + insn, exec_mapper.bound_expr, exec_mapper)) assignees = insn.get_assignees() for target, value in assignments: @@ -412,8 +412,8 @@ class Code(object): raise RuntimeError("not all instructions are reachable" "--did you forget to pass a value for a placeholder?") - from pytools.obj_array import with_object_array_or_scalar - return with_object_array_or_scalar(exec_mapper, self.result) + from pytools.obj_array import obj_array_vectorize + return obj_array_vectorize(exec_mapper, self.result) # }}} @@ -480,8 +480,8 @@ class OperatorCompiler(IdentityMapper): # Put the toplevel expressions into variables as well. - from pytools.obj_array import with_object_array_or_scalar - result = with_object_array_or_scalar(self.assign_to_new_var, result) + from pytools.obj_array import obj_array_vectorize + result = obj_array_vectorize(self.assign_to_new_var, result) return Code(self.code, result) diff --git a/pytential/symbolic/dof_connection.py b/pytential/symbolic/dof_connection.py index 9d23fb73d980e08b7bee5bece36c738a688f232d..4863652c6819de52b8d8d14f6758197a471797f1 100644 --- a/pytential/symbolic/dof_connection.py +++ b/pytential/symbolic/dof_connection.py @@ -27,12 +27,12 @@ THE SOFTWARE. """ import six -import pyopencl as cl -import pyopencl.array # noqa -from pytools import memoize +from meshmode.array_context import PyOpenCLArrayContext # noqa +from meshmode.dof_array import DOFArray +import numpy as np +from pytools import memoize_in import loopy as lp -from loopy.version import MOST_RECENT_LANGUAGE_VERSION __doc__ = """ @@ -69,7 +69,11 @@ class GranularityConnection(object): def to_discr(self): return self.discr - def __call__(self, queue, vec): + @property + def array_context(self): + return self.discr._setup_actx + + def __call__(self, ary): raise NotImplementedError() @@ -85,61 +89,66 @@ class CenterGranularityConnection(GranularityConnection): def __init__(self, discr): super(CenterGranularityConnection, self).__init__(discr) - @memoize - def kernel(self): - knl = lp.make_kernel( - "[srclen, dstlen] -> {[i]: 0 <= i < srclen}", - """ - dst[2*i] = src1[i] - dst[2*i + 1] = src2[i] - """, - [ - lp.GlobalArg("src1", shape="srclen"), - lp.GlobalArg("src2", shape="srclen"), - lp.GlobalArg("dst", shape="dstlen"), - "..." - ], - name="node_interleaver_knl", - assumptions="2*srclen = dstlen", - lang_version=MOST_RECENT_LANGUAGE_VERSION, - ) - - knl = lp.split_iname(knl, "i", 128, - inner_tag="l.0", outer_tag="g.0") - return knl - - def __call__(self, queue, vecs): + def _interleave_dof_arrays(self, ary1, ary2): + if not isinstance(ary1, DOFArray) or not isinstance(ary2, DOFArray): + raise TypeError("non-array passed to connection") + + @memoize_in(self.array_context, + (CenterGranularityConnection, "interleave")) + def prg(): + from meshmode.array_context import make_loopy_program + return make_loopy_program( + """{[iel, idof]: 0<=iel 1 + + def flatten(self, ary): + # Return a flat version of *ary*. The returned value is suitable for + # use with solvers whose API expects a one-dimensional array. + if not self._operator_uses_obj_array: + ary = [ary] + + result = self.array_context.empty(self.total_dofs, self.dtype) + from pytential.utils import flatten_if_needed + for res_i, (start, end) in zip(ary, self.starts_and_ends): + result[start:end] = flatten_if_needed(self.array_context, res_i) + return result - do_split = len(self.starts_and_ends) > 1 - from pytools.obj_array import make_obj_array + def unflatten(self, ary): + # Convert a flat version of *ary* into a structured version. + components = [] + for discr, (start, end) in zip(self.discrs, self.starts_and_ends): + component = ary[start:end] + from meshmode.discretization import Discretization + if isinstance(discr, Discretization): + from meshmode.dof_array import unflatten + component = unflatten(self.array_context, discr, component) + components.append(component) + + if self._operator_uses_obj_array: + from pytools.obj_array import make_obj_array + return make_obj_array(components) + else: + return components[0] - if do_split: - x = make_obj_array( - [x[start:end] for start, end in self.starts_and_ends]) + def matvec(self, x): + # Three types of inputs are supported: + # * flat NumPy arrays + # => output is a flat NumPy array + # * flat PyOpenCL arrays + # => output is a flat PyOpenCL array + # * structured arrays (object arrays/DOFArrays) + # => output has same structure as input + if isinstance(x, np.ndarray) and x.dtype.char != "O": + x = self.array_context.from_numpy(x) + flat = True + host = True + assert x.shape == (self.total_dofs,) + elif isinstance(x, cl.array.Array): + flat = True + host = False + assert x.shape == (self.total_dofs,) + elif isinstance(x, np.ndarray) and x.dtype.char == "O": + flat = False + host = False + else: + raise ValueError("unsupported input type") args = self.extra_args.copy() - args[self.arg_name] = x - result = self.bound_expr(self.queue, **args) - - if do_split: - # re-join what was split - joined_result = cl.array.empty(self.queue, self.total_dofs, - self.dtype) - for res_i, (start, end) in zip(result, self.starts_and_ends): - joined_result[start:end] = res_i - result = joined_result + args[self.arg_name] = self.unflatten(x) if flat else x + result = self.bound_expr(self.array_context, **args) - if out_host: - result = result.get() + if flat: + result = self.flatten(result) + if host: + result = self.array_context.to_numpy(result) return result @@ -650,25 +689,8 @@ class GeometryCollection(object): raise TypeError("Values in 'places' must be discretization, targets " "or layer potential sources.") - # check cl_context - from pytools import is_single_valued - cl_contexts = [] - for p in six.itervalues(self.places): - if isinstance(p, (PotentialSource, Discretization)): - cl_contexts.append(p.cl_context) - elif isinstance(p, TargetBase): - nodes = p.nodes()[0] - if isinstance(nodes, cl.array.Array) and nodes.queue is not None: - cl_contexts.append(nodes.queue.context) - else: - raise ValueError("unexpected value type in 'places'") - - if not is_single_valued(cl_contexts): - raise RuntimeError("All 'places' must have the same CL context.") - - self.cl_context = cl_contexts[0] - # check ambient_dim + from pytools import is_single_valued ambient_dims = [p.ambient_dim for p in six.itervalues(self.places)] if not is_single_valued(ambient_dims): raise RuntimeError("All 'places' must have the same ambient dimension.") @@ -737,12 +759,11 @@ class GeometryCollection(object): from pytential import sym from pytential.qbx.refinement import _refine_for_global_qbx - with cl.CommandQueue(lpot_source.cl_context) as queue: - # NOTE: this adds the required discretizations to the cache - dofdesc = sym.DOFDescriptor(geometry, discr_stage) - _refine_for_global_qbx(self, dofdesc, - lpot_source.refiner_code_container.get_wrangler(queue), - _copy_collection=False) + # NOTE: this adds the required discretizations to the cache + dofdesc = sym.DOFDescriptor(geometry, discr_stage) + _refine_for_global_qbx(self, dofdesc, + lpot_source.refiner_code_container.get_wrangler(), + _copy_collection=False) discr = self._get_discr_from_cache(geometry, discr_stage) @@ -819,6 +840,47 @@ class GeometryCollection(object): # {{{ bound expression +def _find_array_context_from_args_in_context(context, supplied_array_context=None): + array_contexts = [] + if supplied_array_context is not None: + if not isinstance(supplied_array_context, PyOpenCLArrayContext): + raise TypeError( + "first argument (if supplied) must be a " + "PyOpenCLArrayContext") + + array_contexts.append(supplied_array_context) + del supplied_array_context + + def look_for_array_contexts(ary): + if isinstance(ary, DOFArray): + if ary.array_context is not None: + array_contexts.append(ary.array_context) + elif isinstance(ary, np.ndarray) and ary.dtype.char == "O": + for idx in np.ndindex(ary.shape): + look_for_array_contexts(ary[idx]) + else: + pass + + for key, val in context.items(): + look_for_array_contexts(val) + + if array_contexts: + from pytools import is_single_valued + if not is_single_valued(array_contexts): + raise ValueError("arguments do not agree on an array context") + + array_context = array_contexts[0] + else: + array_context = None + + if not isinstance(array_context, PyOpenCLArrayContext): + raise TypeError( + "array context (derived from arguments) is not a " + "PyOpenCLArrayContext") + + return array_context + + class BoundExpression(object): """An expression readied for evaluation by binding it to a :class:`~pytential.symbolic.execution.GeometryCollection`. @@ -843,7 +905,7 @@ class BoundExpression(object): def _get_cache(self, name): return self.caches.setdefault(name, {}) - def cost_per_stage(self, queue, calibration_params, **args): + def cost_per_stage(self, calibration_params, **kwargs): """ :arg queue: a :class:`pyopencl.CommandQueue` object. :arg calibration_params: either a :class:`dict` returned by @@ -853,13 +915,18 @@ class BoundExpression(object): per-stage cost is represented by a :class:`dict` mapping from the stage name to the predicted time. """ + array_context = _find_array_context_from_args_in_context(kwargs) + + if array_context is None: + raise ValueError("unable to figure array context from arguments") + cost_model_mapper = CostModelMapper( - self, queue, calibration_params, False, args + self, array_context, calibration_params, False, kwargs ) self.code.execute(cost_model_mapper) return cost_model_mapper.get_modeled_cost() - def cost_per_box(self, queue, calibration_params, **args): + def cost_per_box(self, calibration_params, **kwargs): """ :arg queue: a :class:`pyopencl.CommandQueue` object. :arg calibration_params: either a :class:`dict` returned by @@ -870,13 +937,16 @@ class BoundExpression(object): :class:`pyopencl.array.Array` of shape (nboxes,), where the ith entry represents the cost of all stages for box i. """ + array_context = _find_array_context_from_args_in_context(kwargs) + cost_model_mapper = CostModelMapper( - self, queue, calibration_params, True, args - ) + self, array_context, calibration_params, True, kwargs) self.code.execute(cost_model_mapper) return cost_model_mapper.get_modeled_cost() - def scipy_op(self, queue, arg_name, dtype, domains=None, **extra_args): + def scipy_op( + self, actx: PyOpenCLArrayContext, arg_name, dtype, + domains=None, **extra_args): """ :arg domains: a list of discretization identifiers or *None* values indicating the domains on which each component of the @@ -889,8 +959,7 @@ class BoundExpression(object): and returning :class:`pyopencl.array.Array` arrays. """ - from pytools.obj_array import is_obj_array - if is_obj_array(self.code.result): + if isinstance(self.code.result, np.ndarray): nresults = len(self.code.result) else: nresults = 1 @@ -899,15 +968,18 @@ class BoundExpression(object): self.places, domains, self.places.auto_target) total_dofs = 0 + discrs = [] starts_and_ends = [] for dom_name in domains: if dom_name is None: + discr = None size = 1 else: discr = self.places.get_discretization( dom_name.geometry, dom_name.discr_stage) - size = discr.nnodes + size = discr.ndofs + discrs.append(discr) starts_and_ends.append((total_dofs, total_dofs+size)) total_dofs += size @@ -916,10 +988,11 @@ class BoundExpression(object): # fair, since these operators are usually only used # for linear system solving, in which case the assumption # has to be true. - return MatVecOp(self, queue, - arg_name, dtype, total_dofs, starts_and_ends, extra_args) + return MatVecOp(self, actx, + arg_name, dtype, total_dofs, discrs, starts_and_ends, extra_args) - def eval(self, queue, context=None, timing_data=None): + def eval(self, context=None, timing_data=None, + array_context: Optional[PyOpenCLArrayContext] = None): """Evaluate the expression in *self*, using the :class:`pyopencl.CommandQueue` *queue* and the input variables given in the dictionary *context*. @@ -927,25 +1000,48 @@ class BoundExpression(object): :arg timing_data: A dictionary into which timing data will be inserted during evaluation. (experimental) + :arg array_context: only needs to be supplied if no instances of + :class:`~meshmode.dof_array.DOFArray` with a + :class:`~meshmode.array_context.PyOpenCLArrayContext` + are supplied as part of *context*. :returns: the value of the expression, as a scalar, :class:`pyopencl.array.Array`, or an object array of these. """ if context is None: context = {} + + array_context = _find_array_context_from_args_in_context( + context, array_context) + exec_mapper = EvaluationMapper( - self, queue, context, timing_data=timing_data) + self, array_context, context, timing_data=timing_data) return self.code.execute(exec_mapper) - def __call__(self, queue, **args): + def __call__(self, *args, **kwargs): """Evaluate the expression in *self*, using the :class:`pyopencl.CommandQueue` *queue* and the input variables given in the dictionary *context*. :returns: the value of the expression, as a scalar, - :class:`pyopencl.array.Array`, or an object array of these. + :class:`meshmode.dof_array.DOFArray`, or an object array of + these. """ - return self.eval(queue, args) + array_context = None + if len(args) == 1: + array_context, = args + if not isinstance(array_context, PyOpenCLArrayContext): + raise TypeError("first positional argument (if given) " + "must be of type PyOpenCLArrayContext") + + elif not args: + pass + + else: + raise TypeError("More than one positional argument supplied. " + "None or an ArrayContext expected.") + + return self.eval(kwargs, array_context=array_context) def bind(places, expr, auto_where=None): @@ -1003,10 +1099,10 @@ def _bmat(blocks, dtypes): return result -def build_matrix(queue, places, exprs, input_exprs, domains=None, +def build_matrix(actx, places, exprs, input_exprs, domains=None, auto_where=None, context=None): """ - :arg queue: a :class:`pyopencl.CommandQueue`. + :arg actx: a :class:`~meshmode.array_context.ArrayContext`. :arg places: a :class:`~pytential.symbolic.execution.GeometryCollection`. Alternatively, any list or mapping that is a valid argument for its constructor can also be used. @@ -1028,13 +1124,14 @@ def build_matrix(queue, places, exprs, input_exprs, domains=None, context = {} from pytential import GeometryCollection - from pytools.obj_array import is_obj_array, make_obj_array if not isinstance(places, GeometryCollection): places = GeometryCollection(places, auto_where=auto_where) exprs = _prepare_expr(places, exprs, auto_where=auto_where) - if not is_obj_array(exprs): + if not (isinstance(exprs, np.ndarray) and exprs.dtype.char == "O"): + from pytools.obj_array import make_obj_array exprs = make_obj_array([exprs]) + try: input_exprs = list(input_exprs) except TypeError: @@ -1056,7 +1153,7 @@ def build_matrix(queue, places, exprs, input_exprs, domains=None, domains[ibcol].geometry, domains[ibcol].discr_stage) mbuilder = MatrixBuilder( - queue, + actx, dep_expr=input_exprs[ibcol], other_dep_exprs=(input_exprs[:ibcol] + input_exprs[ibcol + 1:]), @@ -1073,7 +1170,7 @@ def build_matrix(queue, places, exprs, input_exprs, domains=None, if isinstance(block, np.ndarray): dtypes.append(block.dtype) - return cl.array.to_device(queue, _bmat(blocks, dtypes)) + return actx.from_numpy(_bmat(blocks, dtypes)) # }}} diff --git a/pytential/symbolic/matrix.py b/pytential/symbolic/matrix.py index d63f30549fdada50a0e7a64e55dd5f0e111f8a15..dc0eb3734d112a39d41de654e4258b52d05fa813 100644 --- a/pytential/symbolic/matrix.py +++ b/pytential/symbolic/matrix.py @@ -26,14 +26,14 @@ THE SOFTWARE. """ import numpy as np -import pyopencl as cl # noqa -import pyopencl.array # noqa import six from six.moves import intern from pytools import memoize_method from pytential.symbolic.mappers import EvaluationMapperBase +from pytential.utils import ( + flatten_if_needed, flatten_to_numpy, unflatten_from_numpy) # {{{ helpers @@ -56,7 +56,9 @@ def _get_layer_potential_args(mapper, expr, include_args=None): and arg_name not in include_args): continue - kernel_args[arg_name] = mapper.rec(arg_expr) + kernel_args[arg_name] = flatten_if_needed(mapper.array_context, + mapper.rec(arg_expr) + ) return kernel_args @@ -66,7 +68,7 @@ def _get_layer_potential_args(mapper, expr, include_args=None): # {{{ base classes for matrix builders class MatrixBuilderBase(EvaluationMapperBase): - def __init__(self, queue, dep_expr, other_dep_exprs, + def __init__(self, actx, dep_expr, other_dep_exprs, dep_source, dep_discr, places, context): """ :arg queue: a :class:`pyopencl.CommandQueue`. @@ -84,7 +86,7 @@ class MatrixBuilderBase(EvaluationMapperBase): """ super(MatrixBuilderBase, self).__init__(context=context) - self.queue = queue + self.array_context = actx self.dep_expr = dep_expr self.other_dep_exprs = other_dep_exprs self.dep_source = dep_source @@ -94,7 +96,7 @@ class MatrixBuilderBase(EvaluationMapperBase): # {{{ def get_dep_variable(self): - return np.eye(self.dep_discr.nnodes, dtype=np.float64) + return np.eye(self.dep_discr.ndofs, dtype=np.float64) def is_kind_vector(self, x): return len(x.shape) == 1 @@ -196,17 +198,25 @@ class MatrixBuilderBase(EvaluationMapperBase): if self.is_kind_matrix(rec_operand): raise NotImplementedError("derivatives") - rec_operand = cl.array.to_device(self.queue, rec_operand) + dofdesc = expr.dofdesc op = sym.NumReferenceDerivative( ref_axes=expr.ref_axes, operand=sym.var("u"), - dofdesc=expr.dofdesc) - return bind(self.places, op)(self.queue, u=rec_operand).get() + dofdesc=dofdesc) + + discr = self.places.get_discretization(dofdesc.geometry, dofdesc.discr_stage) + rec_operand = unflatten_from_numpy(self.array_context, discr, rec_operand) + + return flatten_to_numpy(self.array_context, + bind(self.places, op)(self.array_context, u=rec_operand) + ) def map_node_coordinate_component(self, expr): from pytential import bind, sym op = sym.NodeCoordinateComponent(expr.ambient_axis, dofdesc=expr.dofdesc) - return bind(self.places, op)(self.queue).get() + return flatten_to_numpy(self.array_context, + bind(self.places, op)(self.array_context) + ) def map_call(self, expr): arg, = expr.parameters @@ -215,17 +225,13 @@ class MatrixBuilderBase(EvaluationMapperBase): if isinstance(rec_arg, np.ndarray) and self.is_kind_matrix(rec_arg): raise RuntimeError("expression is nonlinear in variable") - if isinstance(rec_arg, np.ndarray): - rec_arg = cl.array.to_device(self.queue, rec_arg) - - from pytential import bind, sym - op = expr.function(sym.var("u")) - result = bind(self.places, op)(self.queue, u=rec_arg) - - if isinstance(result, cl.array.Array): - result = result.get() - - return result + from numbers import Number + if isinstance(rec_arg, Number): + return getattr(np, expr.function.name)(rec_arg) + else: + rec_arg = unflatten_from_numpy(self.array_context, None, rec_arg) + result = getattr(self.array_context.np, expr.function.name)(rec_arg) + return flatten_to_numpy(self.array_context, result) # }}} @@ -240,14 +246,14 @@ class MatrixBlockBuilderBase(MatrixBuilderBase): assume that each operator acts directly on the density. """ - def __init__(self, queue, dep_expr, other_dep_exprs, + def __init__(self, actx, dep_expr, other_dep_exprs, dep_source, dep_discr, places, index_set, context): """ :arg index_set: a :class:`sumpy.tools.MatrixBlockIndexRanges` class describing which blocks are going to be evaluated. """ - super(MatrixBlockBuilderBase, self).__init__(queue, + super(MatrixBlockBuilderBase, self).__init__(actx, dep_expr, other_dep_exprs, dep_source, dep_discr, places, context) self.index_set = index_set @@ -259,7 +265,7 @@ class MatrixBlockBuilderBase(MatrixBuilderBase): # be computed on the full discretization, ignoring our index_set, # e.g the normal in a double layer potential - return MatrixBuilderBase(self.queue, + return MatrixBuilderBase(self.array_context, self.dep_expr, self.other_dep_exprs, self.dep_source, @@ -272,7 +278,7 @@ class MatrixBlockBuilderBase(MatrixBuilderBase): # blk_mapper is used to recursively compute the density to # a layer potential operator to ensure there is no composition - return MatrixBlockBuilderBase(self.queue, + return MatrixBlockBuilderBase(self.array_context, self.dep_expr, self.other_dep_exprs, self.dep_source, @@ -302,9 +308,10 @@ class MatrixBlockBuilderBase(MatrixBuilderBase): # We'll cheat and build the matrix on the host. class MatrixBuilder(MatrixBuilderBase): - def __init__(self, queue, dep_expr, other_dep_exprs, + def __init__(self, actx, dep_expr, other_dep_exprs, dep_source, dep_discr, places, context): - super(MatrixBuilder, self).__init__(queue, dep_expr, other_dep_exprs, + super(MatrixBuilder, self).__init__( + actx, dep_expr, other_dep_exprs, dep_source, dep_discr, places, context) def map_interpolation(self, expr): @@ -313,13 +320,17 @@ class MatrixBuilder(MatrixBuilderBase): if expr.to_dd.discr_stage != sym.QBX_SOURCE_QUAD_STAGE2: raise RuntimeError("can only interpolate to QBX_SOURCE_QUAD_STAGE2") operand = self.rec(expr.operand) + actx = self.array_context if isinstance(operand, (int, float, complex, np.number)): return operand elif isinstance(operand, np.ndarray) and operand.ndim == 1: conn = self.places.get_connection(expr.from_dd, expr.to_dd) - return conn(self.queue, - cl.array.to_device(self.queue, operand)).get(self.queue) + discr = self.places.get_discretization( + expr.from_dd.geometry, expr.from_dd.discr_stage) + + operand = unflatten_from_numpy(actx, discr, operand) + return flatten_to_numpy(actx, conn(operand)) elif isinstance(operand, np.ndarray) and operand.ndim == 2: cache = self.places._get_cache("direct_resampler") key = (expr.from_dd.geometry, @@ -333,8 +344,8 @@ class MatrixBuilder(MatrixBuilderBase): flatten_chained_connection conn = self.places.get_connection(expr.from_dd, expr.to_dd) - conn = flatten_chained_connection(self.queue, conn) - mat = conn.full_resample_matrix(self.queue).get(self.queue) + conn = flatten_chained_connection(actx, conn) + mat = actx.to_numpy(conn.full_resample_matrix(actx)) # FIXME: the resample matrix is slow to compute and very big # to store, so caching it may not be the best idea @@ -359,6 +370,7 @@ class MatrixBuilder(MatrixBuilderBase): if not self.is_kind_matrix(rec_density): raise NotImplementedError("layer potentials on non-variables") + actx = self.array_context kernel = expr.kernel kernel_args = _get_layer_potential_args(self, expr) @@ -366,31 +378,31 @@ class MatrixBuilder(MatrixBuilderBase): local_expn = LineTaylorLocalExpansion(kernel, lpot_source.qbx_order) from sumpy.qbx import LayerPotentialMatrixGenerator - mat_gen = LayerPotentialMatrixGenerator( - self.queue.context, (local_expn,)) + mat_gen = LayerPotentialMatrixGenerator(actx.context, (local_expn,)) assert abs(expr.qbx_forced_limit) > 0 from pytential import bind, sym radii = bind(self.places, sym.expansion_radii( source_discr.ambient_dim, - dofdesc=expr.target))(self.queue) + dofdesc=expr.target))(actx) centers = bind(self.places, sym.expansion_centers( source_discr.ambient_dim, expr.qbx_forced_limit, - dofdesc=expr.target))(self.queue) - - _, (mat,) = mat_gen(self.queue, - targets=target_discr.nodes(), - sources=source_discr.nodes(), - centers=centers, - expansion_radii=radii, + dofdesc=expr.target))(actx) + + from meshmode.dof_array import flatten, thaw + _, (mat,) = mat_gen(actx.queue, + targets=flatten(thaw(actx, target_discr.nodes())), + sources=flatten(thaw(actx, source_discr.nodes())), + centers=flatten(centers), + expansion_radii=flatten(radii), **kernel_args) - mat = mat.get() + mat = actx.to_numpy(mat) waa = bind(self.places, sym.weights_and_area_elements( source_discr.ambient_dim, - dofdesc=expr.source))(self.queue) - mat[:, :] *= waa.get(self.queue) + dofdesc=expr.source))(actx) + mat[:, :] *= actx.to_numpy(flatten(waa)) mat = mat.dot(rec_density) return mat @@ -401,9 +413,9 @@ class MatrixBuilder(MatrixBuilderBase): # {{{ p2p matrix builder class P2PMatrixBuilder(MatrixBuilderBase): - def __init__(self, queue, dep_expr, other_dep_exprs, + def __init__(self, actx, dep_expr, other_dep_exprs, dep_source, dep_discr, places, context, exclude_self=True): - super(P2PMatrixBuilder, self).__init__(queue, + super(P2PMatrixBuilder, self).__init__(actx, dep_expr, other_dep_exprs, dep_source, dep_discr, places, context) @@ -430,25 +442,26 @@ class P2PMatrixBuilder(MatrixBuilderBase): kernel_args = kernel.get_args() + kernel.get_source_args() kernel_args = set(arg.loopy_arg.name for arg in kernel_args) + actx = self.array_context kernel_args = _get_layer_potential_args(self, expr, include_args=kernel_args) if self.exclude_self: - kernel_args["target_to_source"] = \ - cl.array.arange(self.queue, 0, target_discr.nnodes, dtype=np.int) + kernel_args["target_to_source"] = actx.from_numpy( + np.arange(0, target_discr.ndofs, dtype=np.int) + ) from sumpy.p2p import P2PMatrixGenerator - mat_gen = P2PMatrixGenerator( - self.queue.context, (kernel,), exclude_self=self.exclude_self) + mat_gen = P2PMatrixGenerator(actx.context, (kernel,), + exclude_self=self.exclude_self) - _, (mat,) = mat_gen(self.queue, - targets=target_discr.nodes(), - sources=source_discr.nodes(), + from meshmode.dof_array import flatten, thaw + _, (mat,) = mat_gen(actx.queue, + targets=flatten(thaw(actx, target_discr.nodes())), + sources=flatten(thaw(actx, source_discr.nodes())), **kernel_args) - mat = mat.get() - mat = mat.dot(rec_density) + return actx.to_numpy(mat).dot(rec_density) - return mat # }}} @@ -462,8 +475,9 @@ class NearFieldBlockBuilder(MatrixBlockBuilderBase): places, index_set, context) def get_dep_variable(self): - tgtindices = self.index_set.linear_row_indices.get(self.queue) - srcindices = self.index_set.linear_col_indices.get(self.queue) + queue = self.array_context.queue + tgtindices = self.index_set.linear_row_indices.get(queue) + srcindices = self.index_set.linear_col_indices.get(queue) return np.equal(tgtindices, srcindices).astype(np.float64) @@ -484,6 +498,7 @@ class NearFieldBlockBuilder(MatrixBlockBuilderBase): if not np.isscalar(rec_density): raise NotImplementedError + actx = self.array_context kernel = expr.kernel kernel_args = _get_layer_potential_args(self._mat_mapper, expr) @@ -491,34 +506,34 @@ class NearFieldBlockBuilder(MatrixBlockBuilderBase): local_expn = LineTaylorLocalExpansion(kernel, lpot_source.qbx_order) from sumpy.qbx import LayerPotentialMatrixBlockGenerator - mat_gen = LayerPotentialMatrixBlockGenerator( - self.queue.context, (local_expn,)) + mat_gen = LayerPotentialMatrixBlockGenerator(actx.context, (local_expn,)) assert abs(expr.qbx_forced_limit) > 0 from pytential import bind, sym radii = bind(self.places, sym.expansion_radii( source_discr.ambient_dim, - dofdesc=expr.target))(self.queue) + dofdesc=expr.target))(actx) centers = bind(self.places, sym.expansion_centers( source_discr.ambient_dim, expr.qbx_forced_limit, - dofdesc=expr.target))(self.queue) - - _, (mat,) = mat_gen(self.queue, - targets=target_discr.nodes(), - sources=source_discr.nodes(), - centers=centers, - expansion_radii=radii, + dofdesc=expr.target))(actx) + + from meshmode.dof_array import flatten, thaw + _, (mat,) = mat_gen(actx.queue, + targets=flatten(thaw(actx, target_discr.nodes())), + sources=flatten(thaw(actx, source_discr.nodes())), + centers=flatten(centers), + expansion_radii=flatten(radii), index_set=self.index_set, **kernel_args) waa = bind(self.places, sym.weights_and_area_elements( source_discr.ambient_dim, - dofdesc=expr.source))(self.queue) - mat *= waa[self.index_set.linear_col_indices] - mat = rec_density * mat.get(self.queue) + dofdesc=expr.source))(actx) + waa = flatten(waa) - return mat + mat *= waa[self.index_set.linear_col_indices] + return rec_density * actx.to_numpy(mat) class FarFieldBlockBuilder(MatrixBlockBuilderBase): @@ -530,8 +545,9 @@ class FarFieldBlockBuilder(MatrixBlockBuilderBase): self.exclude_self = exclude_self def get_dep_variable(self): - tgtindices = self.index_set.linear_row_indices.get(self.queue) - srcindices = self.index_set.linear_col_indices.get(self.queue) + queue = self.array_context.queue + tgtindices = self.index_set.linear_row_indices.get(queue) + srcindices = self.index_set.linear_col_indices.get(queue) return np.equal(tgtindices, srcindices).astype(np.float64) @@ -558,24 +574,26 @@ class FarFieldBlockBuilder(MatrixBlockBuilderBase): kernel_args = kernel.get_args() + kernel.get_source_args() kernel_args = set(arg.loopy_arg.name for arg in kernel_args) + actx = self.array_context kernel_args = _get_layer_potential_args(self._mat_mapper, expr, include_args=kernel_args) if self.exclude_self: - kernel_args["target_to_source"] = \ - cl.array.arange(self.queue, 0, target_discr.nnodes, dtype=np.int) + kernel_args["target_to_source"] = actx.from_numpy( + np.arange(0, target_discr.ndofs, dtype=np.int) + ) from sumpy.p2p import P2PMatrixBlockGenerator - mat_gen = P2PMatrixBlockGenerator( - self.queue.context, (kernel,), exclude_self=self.exclude_self) + mat_gen = P2PMatrixBlockGenerator(actx.context, (kernel,), + exclude_self=self.exclude_self) - _, (mat,) = mat_gen(self.queue, - targets=target_discr.nodes(), - sources=source_discr.nodes(), + from meshmode.dof_array import flatten, thaw + _, (mat,) = mat_gen(actx.queue, + targets=flatten(thaw(actx, target_discr.nodes())), + sources=flatten(thaw(actx, source_discr.nodes())), index_set=self.index_set, **kernel_args) - mat = rec_density * mat.get(self.queue) - return mat + return rec_density * actx.to_numpy(mat) # }}} diff --git a/pytential/symbolic/pde/maxwell/__init__.py b/pytential/symbolic/pde/maxwell/__init__.py index d89d393c4948a2944d4efa4da2b89db65e55d4b1..f7148cfd0156872ee4dbb36029e2bc1f00a1a032 100644 --- a/pytential/symbolic/pde/maxwell/__init__.py +++ b/pytential/symbolic/pde/maxwell/__init__.py @@ -61,7 +61,7 @@ def get_sym_maxwell_point_source(kernel, jxyz, k): # https://en.wikipedia.org/w/index.php?title=Maxwell%27s_equations&oldid=798940325#Alternative_formulations # (Vector calculus/Potentials/Any Gauge) # assumed time dependence exp(-1j*omega*t) - return sym.join_fields( + return sym.flat_obj_array( 1j*k*A, sym.curl(A)) @@ -108,7 +108,7 @@ def get_sym_maxwell_plane_wave(amplitude_vec, v, omega, e = amplitude_vec * sym.exp(1j*np.dot(n*omega, x)) - return sym.join_fields(e, c_inv * sym.cross(n, e)) + return sym.flat_obj_array(e, c_inv * sym.cross(n, e)) # }}} @@ -180,7 +180,7 @@ class PECChargeCurrentMFIEOperator: E_scat = 1j*self.k*A - sym.grad(3, phi) H_scat = sym.curl(A) - return sym.join_fields(E_scat, H_scat) + return sym.flat_obj_array(E_scat, H_scat) # }}} @@ -248,13 +248,13 @@ class MuellerAugmentedMFIEOperator(object): # sign flip included F4 = -sym.n_dot(mu1*H1-mu0*H0) + 0.5*(mu1+mu0)*u.rho_m # noqa pylint:disable=invalid-unary-operand-type - return sym.join_fields(F1, F2, F3, F4) + return sym.flat_obj_array(F1, F2, F3, F4) def rhs(self, Einc_xyz, Hinc_xyz): mu1 = self.mus[1] eps1 = self.epss[1] - return sym.join_fields( + return sym.flat_obj_array( xyz_to_tangential(sym.n_cross(Hinc_xyz)), sym.n_dot(eps1*Einc_xyz), xyz_to_tangential(sym.n_cross(Einc_xyz)), @@ -280,7 +280,7 @@ class MuellerAugmentedMFIEOperator(object): E0 = 1j*k*eps*S(Jxyz) + mu*curl_S(Mxyz) - grad(S(u.rho_e)) H0 = -1j*k*mu*S(Mxyz) + eps*curl_S(Jxyz) + grad(S(u.rho_m)) - return sym.join_fields(E0, H0) + return sym.flat_obj_array(E0, H0) # }}} diff --git a/pytential/symbolic/pde/maxwell/generalized_debye.py b/pytential/symbolic/pde/maxwell/generalized_debye.py index 1b6ea8ee9dab508237973f1861ecb77405c495ab..df7fd9f50ea23167fb415ae7c41ab428214dd28f 100644 --- a/pytential/symbolic/pde/maxwell/generalized_debye.py +++ b/pytential/symbolic/pde/maxwell/generalized_debye.py @@ -153,8 +153,8 @@ class DebyeOperatorBase(object): E = 1j*k*A - grad_phi - curl_S_volume(k, m) H = curl_S_volume(k, j) + 1j*k*Q - grad_psi - from pytools.obj_array import join_fields - return join_fields(E, H) + from pytools.obj_array import flat_obj_array + return flat_obj_array(E, H) def integral_equation(self, *args, **kwargs): nxnxE, ndotH = self.boundary_field(*args) @@ -178,8 +178,8 @@ class DebyeOperatorBase(object): E_minus_grad_phi = 1j*k*A - curl_S_volume(k, m) from hellskitchen.fmm import DifferenceKernel - from pytools.obj_array import join_fields - return join_fields( + from pytools.obj_array import flat_obj_array + return flat_obj_array( eh_op, # FIXME: These are inefficient. They compute a full volume field, # but only actually use the line part of it. @@ -262,10 +262,10 @@ class InvertingDebyeOperatorBase(DebyeOperatorBase): r_coeff = inv_rank_one_coeff(r_tilde) q_coeff = inv_rank_one_coeff(q_tilde) - from pytools.obj_array import join_fields + from pytools.obj_array import flat_obj_array factors = self.cluster_points() - fix = join_fields( + fix = flat_obj_array( factors[0]*s_ones*r_coeff, factors[1]*Ones()*q_coeff, ) @@ -376,10 +376,10 @@ class NonInvertingDebyeOperator(DebyeOperatorBase): r_coeff = inv_rank_one_coeff(r_tilde) q_coeff = inv_rank_one_coeff(q_tilde) - from pytools.obj_array import join_fields + from pytools.obj_array import flat_obj_array factors = self.cluster_points() - fix = join_fields( + fix = flat_obj_array( factors[0]*s_ones*(r_coeff), factors[1]*Ones()*(q_coeff), ) diff --git a/pytential/symbolic/pde/maxwell/waveguide.py b/pytential/symbolic/pde/maxwell/waveguide.py index 3a1d3a63dfa932969c2363ac2c09da0a98e3b89f..aab6e94268e957b31b48083bbaef7f25cce3e741 100644 --- a/pytential/symbolic/pde/maxwell/waveguide.py +++ b/pytential/symbolic/pde/maxwell/waveguide.py @@ -28,16 +28,9 @@ Second-Kind Waveguide ^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: SecondKindInfZMuellerOperator - -2D Dielectric (old-style) -^^^^^^^^^^^^^^^^^^^^^^^^^ - -.. autoclass:: DielectricSRep2DBoundaryOperator -.. autoclass:: DielectricSDRep2DBoundaryOperator """ import numpy as np -from collections import namedtuple from six.moves import range from pytential import sym @@ -353,632 +346,4 @@ class SecondKindInfZMuellerOperator(L2WeightedPDEOperator): # }}} - -# {{{ old-style waveguide - -class Dielectric2DBoundaryOperatorBase(L2WeightedPDEOperator): - r""" - Solves the following system of BVPs on :math:`\mathbb{R}^2`, in which - a disjoint family of domains :math:`\Omega_i` is embedded: - - .. math:: - - \triangle E + (k_i^2-\beta^2) E = 0\quad \text{on $\Omega_i$}\\ - \triangle H + (k_i^2-\beta^2) H = 0\quad \text{on $\Omega_i$}\\ - [H] = 0 \text{ on $\partial \Omega_i$},\quad - [E] = 0 \text{ on $\partial \Omega_i$}\\ - \left[ \frac{k_0}{k^2-\beta^2} \partial_{\hat n}H\right] = 0 - \quad\text{on $\partial \Omega_i$},\quad\\ - \left[ \frac{k_0}{k^2-\beta^2} \partial_{\hat n}E\right] = 0 - \quad\text{on $\partial \Omega_i$} - - :math:`E` and :math:`H` are assumed to be of the form - - .. math:: - - E(x,y,z,t)=E(x,y)e^{i(\beta z-\omega t) - H(x,y,z,t)=H(x,y)e^{i(\beta z-\omega t) - - where :math:`[\cdot]` denotes the jump across an interface, and :math:`k` - (without an index) denotes the value of :math:`k` on either side of the - interface, for the purpose of computing the jump. :math:`\hat n` denotes - the unit normal of the interface. - - .. automethod:: make_unknown - .. automethod:: representation_outer - .. automethod:: representation_inner - .. automethod:: operator - """ - - field_kind_e = 0 - field_kind_h = 1 - field_kinds = [field_kind_e, field_kind_h] - - side_in = 0 - side_out = 1 - sides = [side_in, side_out] - side_to_sign = { - side_in: -1, - side_out: 1, - } - - dir_none = 0 - dir_normal = 1 - dir_tangential = 2 - - BCTermDescriptor = namedtuple("BCDescriptor", - "i_interface direction field_kind coeff_inner coeff_outer".split()) - - # {{{ constructor - - def __init__(self, mode, k_vacuum, domain_k_exprs, beta, - interfaces, use_l2_weighting=None): - """ - :attr mode: one of 'te', 'tm', 'tem' - :attr k_vacuum: A symbolic expression for the wave number in vacuum. - May be a string, which will be interpreted as a variable name. - :attr interfaces: a tuple of tuples - ``(outer_domain, inner_domain, interface_id)``, - where *outer_domain* and *inner_domain* are indices into - *domain_k_names*, - and *interface_id* is a symbolic name for the discretization of the - interface. 'outer' designates the side of the interface to which - the normal points. - :attr domain_k_exprs: a tuple of variable names of the Helmholtz - parameter *k*, to be used inside each part of the source geometry. - May also be a tuple of strings, which will be transformed into - variable references of the corresponding names. - :attr beta: A symbolic expression for the wave number in the :math:`z` - direction. May be a string, which will be interpreted as a variable - name. - """ - - if use_l2_weighting is None: - use_l2_weighting = False - - from sumpy.kernel import HelmholtzKernel - self.kernel = HelmholtzKernel(2, allow_evanescent=True) - - super(Dielectric2DBoundaryOperatorBase, self).__init__( - self.kernel, - use_l2_weighting=use_l2_weighting) - - if mode == "te": - self.ez_enabled = False - self.hz_enabled = True - elif mode == "tm": - self.ez_enabled = True - self.hz_enabled = False - elif mode == "tem": - self.ez_enabled = True - self.hz_enabled = True - else: - raise ValueError("invalid mode '%s'" % mode) - - self.interfaces = interfaces - - fk_e = self.field_kind_e - fk_h = self.field_kind_h - - dir_none = self.dir_none - dir_normal = self.dir_normal - dir_tangential = self.dir_tangential - - if isinstance(beta, str): - beta = sym.var(beta) - beta = sym.cse(beta, "beta") - - if isinstance(k_vacuum, str): - k_vacuum = sym.var(k_vacuum) - k_vacuum = sym.cse(k_vacuum, "k_vac") - - self.domain_k_exprs = [ - sym.var(k_expr) - if isinstance(k_expr, str) - else sym.cse(k_expr, "k%d" % idom) - for idom, k_expr in enumerate(domain_k_exprs)] - del domain_k_exprs - - # Note the case of k/K! - # "K" is the 2D Helmholtz parameter. - # "k" is the 3D Helmholtz parameter. - - self.domain_K_exprs = [ - sym.cse((k_expr**2-beta**2)**0.5, "K%d" % i) - for i, k_expr in enumerate(self.domain_k_exprs)] - - # {{{ build bc list - - # list of tuples, where each tuple consists of BCTermDescriptor instances - - all_bcs = [] - for i_interface, (outer_domain, inner_domain, _) in ( - enumerate(self.interfaces)): - k_outer = self.domain_k_exprs[outer_domain] - k_inner = self.domain_k_exprs[inner_domain] - - all_bcs += [ - ( # [E] = 0 - self.BCTermDescriptor( - i_interface=i_interface, - direction=dir_none, - field_kind=fk_e, - coeff_outer=1, - coeff_inner=-1), - ), - ( # [H] = 0 - self.BCTermDescriptor( - i_interface=i_interface, - direction=dir_none, - field_kind=fk_h, - coeff_outer=1, - coeff_inner=-1), - ), - ( - self.BCTermDescriptor( - i_interface=i_interface, - direction=dir_tangential, - field_kind=fk_e, - coeff_outer=beta/(k_outer**2-beta**2), - coeff_inner=-beta/(k_inner**2-beta**2)), - self.BCTermDescriptor( - i_interface=i_interface, - direction=dir_normal, - field_kind=fk_h, - coeff_outer=sym.cse(-k_vacuum/(k_outer**2-beta**2)), - coeff_inner=sym.cse(k_vacuum/(k_inner**2-beta**2))), - ), - ( - self.BCTermDescriptor( - i_interface=i_interface, - direction=dir_tangential, - field_kind=fk_h, - coeff_outer=beta/(k_outer**2-beta**2), - coeff_inner=-beta/(k_inner**2-beta**2)), - self.BCTermDescriptor( - i_interface=i_interface, - direction=dir_normal, - field_kind=fk_e, - coeff_outer=sym.cse( - (k_outer**2/k_vacuum)/(k_outer**2-beta**2)), - coeff_inner=sym.cse( - -(k_inner**2/k_vacuum) - / (k_inner**2-beta**2))) - ), - ] - - del k_outer - del k_inner - - self.bcs = [] - for bc in all_bcs: - any_significant_e = any( - term.field_kind == fk_e - and term.direction in [dir_normal, dir_none] - for term in bc) - any_significant_h = any( - term.field_kind == fk_h - and term.direction in [dir_normal, dir_none] - for term in bc) - is_necessary = ( - (self.ez_enabled and any_significant_e) - or (self.hz_enabled and any_significant_h)) - - # Only keep tangential modes for TEM. Otherwise, - # no jump in H already implies jump condition on - # tangential derivative. - is_tem = self.ez_enabled and self.hz_enabled - terms = tuple( - term - for term in bc - if term.direction != dir_tangential - or is_tem) - - if is_necessary: - self.bcs.append(terms) - - assert (len(all_bcs) - * (int(self.ez_enabled) + int(self.hz_enabled)) // 2 - == len(self.bcs)) - - # }}} - - # }}} - - def is_field_present(self, field_kind): - return ( - (field_kind == self.field_kind_e and self.ez_enabled) - or (field_kind == self.field_kind_h and self.hz_enabled)) - - def make_unknown(self, name): - num_densities = ( - 2 - * (int(self.ez_enabled) + int(self.hz_enabled)) - * len(self.interfaces)) - - assert num_densities == len(self.bcs) - - return sym.make_sym_vector(name, num_densities) - - def bc_term_to_operator_contrib(self, term, side, raw_potential_op, - density, discrete): - potential_op = raw_potential_op - - side_sign = self.side_to_sign[side] - - domain_outer, domain_inner, interface_id = \ - self.interfaces[term.i_interface] - if side == self.side_in: - K_expr = self.domain_K_exprs[domain_inner] # noqa - bc_coeff = term.coeff_inner - elif side == self.side_out: - K_expr = self.domain_K_exprs[domain_outer] # noqa - bc_coeff = term.coeff_outer - else: - raise ValueError("invalid value of 'side'") - - potential_op = potential_op( - self.kernel, density, source=interface_id, - k=K_expr) - - if term.direction == self.dir_none: - if raw_potential_op is sym.S: - jump_term = 0 - elif raw_potential_op is sym.D: - jump_term = (side_sign*0.5) * discrete - else: - assert False, raw_potential_op - elif term.direction == self.dir_normal: - potential_op = sym.normal_derivative( - 2, potential_op, dofdesc=interface_id) - - if raw_potential_op is sym.S: - # S' - jump_term = (-side_sign*0.5) * discrete - elif raw_potential_op is sym.D: - jump_term = 0 - else: - assert False, raw_potential_op - - elif term.direction == self.dir_tangential: - potential_op = sym.tangential_derivative( - raw_potential_op( - self.kernel, density, source=interface_id, - k=K_expr, qbx_forced_limit=side_sign), - interface_id).a.as_scalar() - - # Some of these may have jumps, but QBX does the dirty - # work here by directly computing the limit. - jump_term = 0 - - else: - raise ValueError("invalid direction") - - potential_op = ( - jump_term - + self.get_sqrt_weight(interface_id)*potential_op) - - del jump_term - - contrib = bc_coeff * potential_op - - if (raw_potential_op is sym.D - and term.direction == self.dir_normal): - # FIXME The hypersingular part should perhaps be - # treated specially to avoid cancellation. - pass - - return contrib - - -# {{{ single-layer representation - -class DielectricSRep2DBoundaryOperator(Dielectric2DBoundaryOperatorBase): - def _structured_unknown(self, unknown, with_l2_weights): - """ - :arg with_l2_weights: If True, return the 'bare' unknowns - that do not have the :math:`L^2` weights divided out. - Note: Those unknowns should *not* be interpreted as - point values of a density. - :returns: an array of unknowns, with the following index axes: - ``[side, field_kind, i_interface]``, where - ``side`` is o for the outside part and i for the interior part, - ``field_kind`` is 0 for the E-field and 1 for the H-field part, - ``i_interface`` is the number of the enclosed domain, starting from 0. - """ - result = np.zeros((2, 2, len(self.interfaces)), dtype=np.object) - sides = { - self.side_out: "o", - self.side_in: "i" - } - fields = { - self.field_kind_e: "E", - self.field_kind_h: "H" - } - - i_unknown = 0 - for side in self.sides: - for field_kind in self.field_kinds: - for i_interface in range(len(self.interfaces)): - - if self.is_field_present(field_kind): - dens = unknown[i_unknown] - i_unknown += 1 - else: - dens = 0 - - _, _, interface_id = self.interfaces[i_interface] - - if not with_l2_weights: - dens = sym.cse( - dens/self.get_sqrt_weight(interface_id), - "dens_{side}_{field}_{dom}".format( - side=sides[side], - field=fields[field_kind], - dom=i_interface)) - - result[side, field_kind, i_interface] = dens - - assert i_unknown == len(unknown) - return result - - def representation(self, unknown, i_domain, qbx_forced_limit=None): - """ - :return: a symbolic expression for the representation of the PDE solution - in domain number *i_domain*. - """ - unk = self._structured_unknown(unknown, with_l2_weights=False) - - result = [] - - for field_kind in self.field_kinds: - if not self.is_field_present(field_kind): - continue - - field_result = 0 - for i_interface, (i_domain_outer, i_domain_inner, interface_id) in ( - enumerate(self.interfaces)): - if i_domain_outer == i_domain: - side = self.side_out - elif i_domain_inner == i_domain: - side = self.side_in - else: - continue - - my_unk = unk[side, field_kind, i_interface] - if my_unk: - field_result += sym.S( - self.kernel, - my_unk, - source=interface_id, - k=self.domain_K_exprs[i_domain], - qbx_forced_limit=qbx_forced_limit) - - result.append(field_result) - - from pytools.obj_array import make_obj_array - return make_obj_array(result) - - def operator(self, unknown): - density_unk = self._structured_unknown(unknown, with_l2_weights=False) - discrete_unk = self._structured_unknown(unknown, with_l2_weights=True) - - result = [] - for bc in self.bcs: - op = 0 - - for side in self.sides: - for term in bc: - unk_index = (side, term.field_kind, term.i_interface) - density = density_unk[unk_index] - discrete = discrete_unk[unk_index] - - op += self.bc_term_to_operator_contrib( - term, side, sym.S, density, discrete) - - result.append(op) - - return np.array(result, dtype=np.object) - -# }}} - - -# {{{ single + double layer representation - -class DielectricSDRep2DBoundaryOperator(Dielectric2DBoundaryOperatorBase): - pot_kind_S = 0 # noqa: N815 - pot_kind_D = 1 # noqa: N815 - pot_kinds = [pot_kind_S, pot_kind_D] - potential_ops = { - pot_kind_S: sym.S, - pot_kind_D: sym.D, - } - - def __init__(self, mode, k_vacuum, domain_k_exprs, beta, - interfaces, use_l2_weighting=None): - - super(DielectricSDRep2DBoundaryOperator, self).__init__( - mode, k_vacuum, domain_k_exprs, beta, - interfaces, use_l2_weighting=use_l2_weighting) - - side_in = self.side_in - side_out = self.side_out - - def find_normal_derivative_bc_coeff(field_kind, i_interface, side): - result = 0 - for bc in self.bcs: - for term in bc: - if term.field_kind != field_kind: - continue - if term.i_interface != i_interface: - continue - if term.direction != self.dir_normal: - continue - - if side == side_in: - result += term.coeff_inner - elif side == side_out: - result += term.coeff_outer - else: - raise ValueError("invalid side") - - return result - - self.density_coeffs = np.zeros( - (len(self.pot_kinds), len(self.field_kinds), - len(self.interfaces), len(self.sides)), - dtype=np.object) - for field_kind in self.field_kinds: - for i_interface in range(len(self.interfaces)): - self.density_coeffs[ - self.pot_kind_S, field_kind, i_interface, side_in] = 1 - self.density_coeffs[ - self.pot_kind_S, field_kind, i_interface, side_out] = 1 - - # These need to satisfy - # - # [dens_coeff_D * bc_coeff * dn D] - # = dens_coeff_D_out * bc_coeff_out * (dn D) - # + dens_coeff_D_in * bc_coeff_in * dn D - # = 0 - # - # (because dn D is hypersingular, which we'd like to cancel out) - # - # NB: bc_coeff_{in,out} already contain the signs to realize - # the subtraction for the jump. (So the "+" above is as it - # should be.) - - dens_coeff_D_in = find_normal_derivative_bc_coeff( # noqa - field_kind, i_interface, side_out) - dens_coeff_D_out = - find_normal_derivative_bc_coeff( # noqa - field_kind, i_interface, side_in) - - self.density_coeffs[ - self.pot_kind_D, field_kind, i_interface, side_in] \ - = dens_coeff_D_in - self.density_coeffs[ - self.pot_kind_D, field_kind, i_interface, side_out] \ - = dens_coeff_D_out - - def _structured_unknown(self, unknown, with_l2_weights): - """ - :arg with_l2_weights: If True, return the 'bare' unknowns - that do not have the :math:`L^2` weights divided out. - Note: Those unknowns should *not* be interpreted as - point values of a density. - :returns: an array of unknowns, with the following index axes: - ``[pot_kind, field_kind, i_interface]``, where - ``pot_kind`` is 0 for the single-layer part and 1 for the double-layer - part, - ``field_kind`` is 0 for the E-field and 1 for the H-field part, - ``i_interface`` is the number of the enclosed domain, starting from 0. - """ - result = np.zeros((2, 2, len(self.interfaces)), dtype=np.object) - - i_unknown = 0 - for pot_kind in self.pot_kinds: - for field_kind in self.field_kinds: - for i_interface in range(len(self.interfaces)): - - if self.is_field_present(field_kind): - dens = unknown[i_unknown] - i_unknown += 1 - else: - dens = 0 - - _, _, interface_id = self.interfaces[i_interface] - - if not with_l2_weights: - dens = sym.cse( - dens/self.get_sqrt_weight(interface_id), - "dens_{pot}_{field}_{intf}".format( - pot={0: "S", 1: "D"}[pot_kind], - field={ - self.field_kind_e: "E", - self.field_kind_h: "H" - } - [field_kind], - intf=i_interface)) - - result[pot_kind, field_kind, i_interface] = dens - - assert i_unknown == len(unknown) - return result - - def representation(self, unknown, i_domain): - """ - :return: a symbolic expression for the representation of the PDE solution - in domain number *i_domain*. - """ - unk = self._structured_unknown(unknown, with_l2_weights=False) - - result = [] - - for field_kind in self.field_kinds: - if not self.is_field_present(field_kind): - continue - - field_result = 0 - for pot_kind in self.pot_kinds: - for i_interface, (i_domain_outer, i_domain_inner, interface_id) in ( - enumerate(self.interfaces)): - if i_domain_outer == i_domain: - side = self.side_out - elif i_domain_inner == i_domain: - side = self.side_in - else: - continue - - my_unk = unk[pot_kind, field_kind, i_interface] - if my_unk: - field_result += ( - self.density_coeffs[ - pot_kind, field_kind, i_interface, side] - * self.potential_ops[pot_kind]( - self.kernel, - my_unk, - source=interface_id, - k=self.domain_K_exprs[i_domain] - )) - - result.append(field_result) - - from pytools.obj_array import make_obj_array - return make_obj_array(result) - - def operator(self, unknown): - density_unk = self._structured_unknown(unknown, with_l2_weights=False) - discrete_unk = self._structured_unknown(unknown, with_l2_weights=True) - - result = [] - for bc in self.bcs: - op = 0 - - for pot_kind in self.pot_kinds: - for term in bc: - - for side in self.sides: - raw_potential_op = \ - self.potential_ops[pot_kind] - - unk_index = (pot_kind, term.field_kind, term.i_interface) - density = density_unk[unk_index] - discrete = discrete_unk[unk_index] - - op += ( - self.density_coeffs[ - pot_kind, term.field_kind, term.i_interface, - side] - * self.bc_term_to_operator_contrib( - term, side, raw_potential_op, density, discrete) - ) - - result.append(op) - - return np.array(result, dtype=np.object) - -# }}} - -# }}} - # vim: foldmethod=marker diff --git a/pytential/symbolic/primitives.py b/pytential/symbolic/primitives.py index 1799e09b491105927f8f35d608603d32c1ef1ecd..65c18bbe896d90433c439a9677a496f07e09dcf2 100644 --- a/pytential/symbolic/primitives.py +++ b/pytential/symbolic/primitives.py @@ -36,7 +36,7 @@ from pymbolic.geometric_algebra import MultiVector, componentwise from pymbolic.geometric_algebra.primitives import ( # noqa: F401 NablaComponent, DerivativeSource, Derivative as DerivativeBase) from pymbolic.primitives import make_sym_vector # noqa: F401 -from pytools.obj_array import make_obj_array, join_fields # noqa: F401 +from pytools.obj_array import make_obj_array, flat_obj_array # noqa: F401 from functools import partial @@ -53,28 +53,33 @@ Object types Based on the mathematical quantity being represented, the following types of objects occur as part of a symbolic operator representation: -* If a quantity is a scalar, it is just a symbolic expression--that is, a nested - combination of placeholders (see below), arithmetic on them (see - :mod:`pymbolic.primitives`. These objects are created simply by doing - arithmetic on placeholders. +* If a quantity is a scalar, it is just a symbolic expression--that is, an + element of the set of formal expressions recursively generated by the + placeholders (see :ref:`placeholders`), constants, and arithmetic operations + on them (see :mod:`pymbolic.primitives`). Objects of this type are created + simply by doing arithmetic on placeholders and scalar constants. * If the quantity is "just a bunch of scalars" (like, say, rows in a system - of integral equations), the symbolic representation an object array. Each + of integral equations), the symbolic representation is an object array. Each element of the object array contains a symbolic expression. :func:`pytools.obj_array.make_obj_array` and - :func:`pytools.obj_array.join_fields` + :func:`pytools.obj_array.flat_obj_array` can help create those. * If it is a geometric quantity (that makes sense without explicit reference to coordinates), it is a :class:`pymbolic.geometric_algebra.MultiVector`. - This can be converted to an object array by calling : + This can be converted to an object array by calling: :meth:`pymbolic.geometric_algebra.MultiVector.as_vector`. -:mod:`pyopencl.array.Array` instances do not occur on the symbolic of -:mod:`pymbolic` at all. Those hold per-node degrees of freedom (and only -those), which is not visible as an array axis in symbolic code. (They're -visible only once evaluated.) +:class:`pyopencl.array.Array` and :class:`meshmode.dof_array.DOFArray` instances +hold per-node degrees of freedom (and only those). Such instances do *not* occur +on the symbolic side of :mod:`pytential` at all. They're only visible either as +bound inputs (see :func:`pytential.bind`) or outputs of evaluation. Which one is +used depends on the meaning of the data being represented. If the data is +associated with a :class:`~meshmode.discretization.Discretization`, then +:class:`~meshmode.dof_array.DOFArray` is used and otherwise +:class:`~pyopencl.array.Array` is used. DOF Description ^^^^^^^^^^^^^^^ @@ -93,6 +98,8 @@ DOF Description .. autoclass:: DOFDescriptor .. autofunction:: as_dofdesc +.. _placeholders: + Placeholders ^^^^^^^^^^^^ @@ -472,12 +479,12 @@ class Function(var): # return an object array of the operator applied to each of the # operands. - from pytools.obj_array import is_obj_array, with_object_array_or_scalar - if is_obj_array(operand): + from pytools.obj_array import obj_array_vectorize + if isinstance(operand, np.ndarray) and operand.dtype.char == "O": def make_op(operand_i): return self(operand_i, *args, **kwargs) - return with_object_array_or_scalar(make_op, operand) + return obj_array_vectorize(make_op, operand) else: return var.__call__(self, operand, *args, **kwargs) @@ -759,7 +766,7 @@ def first_fundamental_form(ambient_dim, dim=None, dofdesc=None): if dim is None: dim = ambient_dim - 1 - if ambient_dim != 3 and dim != 2: + if not (ambient_dim == 3 and dim == 2): raise NotImplementedError("only available for surfaces in 3D") pd_mat = parametrization_derivative_matrix(ambient_dim, dim, dofdesc) @@ -782,7 +789,7 @@ def second_fundamental_form(ambient_dim, dim=None, dofdesc=None): if dim is None: dim = ambient_dim - 1 - if ambient_dim != 3 and dim != 2: + if not (ambient_dim == 3 and dim == 2): raise NotImplementedError("only available for surfaces in 3D") r = nodes(ambient_dim, dofdesc=dofdesc).as_vector() @@ -814,7 +821,7 @@ def shape_operator(ambient_dim, dim=None, dofdesc=None): if dim is None: dim = ambient_dim - 1 - if ambient_dim != 3 and dim != 2: + if not (ambient_dim == 3 and dim == 2): raise NotImplementedError("only available for surfaces in 3D") # https://en.wikipedia.org/w/index.php?title=Differential_geometry_of_surfaces&oldid=833587563 @@ -1333,12 +1340,12 @@ def dd_axis(axis, ambient_dim, operand): """Return the derivative along (XYZ) axis *axis* (in *ambient_dim*-dimensional space) of *operand*. """ - from pytools.obj_array import is_obj_array, with_object_array_or_scalar - if is_obj_array(operand): + from pytools.obj_array import obj_array_vectorize + if isinstance(operand, np.ndarray) and operand.dtype.char == "O": def dd_axis_comp(operand_i): return dd_axis(axis, ambient_dim, operand_i) - return with_object_array_or_scalar(dd_axis_comp, operand) + return obj_array_vectorize(dd_axis_comp, operand) d = Derivative() diff --git a/pytential/target.py b/pytential/target.py index e677bdb7a2f5a1494d87faef74800292bce7fdad..98f876b51d505ecb55d4cc2ec1800b65505d03be 100644 --- a/pytential/target.py +++ b/pytential/target.py @@ -41,8 +41,8 @@ class TargetBase(object): .. attribute:: ambient_dim .. method:: nodes - Shape: ``[ambient_dim, nnodes]`` - .. attribute:: nnodes + Shape: ``[ambient_dim, ndofs]`` + .. attribute:: ndofs """ @@ -63,13 +63,13 @@ class PointsTarget(TargetBase): return expr def nodes(self): - """Shape: ``[ambient_dim, nnodes]`` + """Shape: ``[ambient_dim, ndofs]`` """ return self._nodes @property - def nnodes(self): + def ndofs(self): for coord_ary in self._nodes: return coord_ary.shape[0] diff --git a/pytential/unregularized.py b/pytential/unregularized.py index 6f0125cd56d6af95bf9576fea3f24e08d114c65f..b7cae228ab10ad979dbe46838aa47e24c6b3ba38 100644 --- a/pytential/unregularized.py +++ b/pytential/unregularized.py @@ -27,6 +27,7 @@ THE SOFTWARE. import six +from meshmode.array_context import PyOpenCLArrayContext import numpy as np import loopy as lp @@ -99,8 +100,8 @@ class UnregularizedLayerPotentialSource(LayerPotentialSourceBase): density_discr=density_discr or self.density_discr, debug=debug if debug is not None else self.debug) - def exec_compute_potential_insn(self, queue, insn, bound_expr, evaluate, - return_timing_data): + def exec_compute_potential_insn(self, actx: PyOpenCLArrayContext, + insn, bound_expr, evaluate, return_timing_data): if return_timing_data: from warnings import warn from pytential.source import UnableToCollectTimingData @@ -108,18 +109,18 @@ class UnregularizedLayerPotentialSource(LayerPotentialSourceBase): "Timing data collection not supported.", category=UnableToCollectTimingData) - from pytools.obj_array import with_object_array_or_scalar + from pytools.obj_array import obj_array_vectorize def evaluate_wrapper(expr): value = evaluate(expr) - return with_object_array_or_scalar(lambda x: x, value) + return obj_array_vectorize(lambda x: x, value) if self.fmm_level_to_order is False: func = self.exec_compute_potential_insn_direct else: func = self.exec_compute_potential_insn_fmm - return func(queue, insn, bound_expr, evaluate_wrapper) + return func(actx, insn, bound_expr, evaluate_wrapper) def op_group_features(self, expr): from sumpy.kernel import AxisTargetDerivativeRemover @@ -138,18 +139,23 @@ class UnregularizedLayerPotentialSource(LayerPotentialSourceBase): from pytential.symbolic.mappers import UnregularizedPreprocessor return UnregularizedPreprocessor(name, discretizations)(expr) - def exec_compute_potential_insn_direct(self, queue, insn, bound_expr, evaluate): + def exec_compute_potential_insn_direct(self, actx: PyOpenCLArrayContext, + insn, bound_expr, evaluate): kernel_args = {} + from pytential.utils import flatten_if_needed + from meshmode.dof_array import flatten, thaw, unflatten + for arg_name, arg_expr in six.iteritems(insn.kernel_arguments): - kernel_args[arg_name] = evaluate(arg_expr) + kernel_args[arg_name] = flatten_if_needed(actx, evaluate(arg_expr)) from pytential import bind, sym waa = bind(bound_expr.places, sym.weights_and_area_elements( - self.ambient_dim, dofdesc=insn.source))(queue) - strengths = waa * evaluate(insn.density).with_queue(queue) + self.ambient_dim, dofdesc=insn.source))(actx) + strengths = waa * evaluate(insn.density) + flat_strengths = flatten(strengths) - result = [] + results = [] p2p = None for o in insn.outputs: @@ -157,17 +163,22 @@ class UnregularizedLayerPotentialSource(LayerPotentialSourceBase): o.target_name.geometry, o.target_name.discr_stage) if p2p is None: - p2p = self.get_p2p(insn.kernels) + p2p = self.get_p2p(actx, insn.kernels) + + evt, output_for_each_kernel = p2p(actx.queue, + flatten_if_needed(actx, target_discr.nodes()), + flatten(thaw(actx, self.density_discr.nodes())), + [flat_strengths], **kernel_args) - evt, output_for_each_kernel = p2p(queue, - target_discr.nodes(), - self.density_discr.nodes(), - [strengths], **kernel_args) + from meshmode.discretization import Discretization + result = output_for_each_kernel[o.kernel_index] + if isinstance(target_discr, Discretization): + result = unflatten(actx, target_discr, result) - result.append((o.name, output_for_each_kernel[o.kernel_index])) + results.append((o.name, result)) timing_data = {} - return result, timing_data + return results, timing_data # {{{ fmm-based execution @@ -190,10 +201,9 @@ class UnregularizedLayerPotentialSource(LayerPotentialSourceBase): out_kernels) @property - @memoize_method def fmm_geometry_code_container(self): - return _FMMGeometryCodeContainer( - self.cl_context, self.ambient_dim, self.debug) + return _FMMGeometryDataCodeContainer( + self._setup_actx, self.ambient_dim, self.debug) def fmm_geometry_data(self, targets): return _FMMGeometryData( @@ -202,7 +212,8 @@ class UnregularizedLayerPotentialSource(LayerPotentialSourceBase): targets, self.debug) - def exec_compute_potential_insn_fmm(self, queue, insn, bound_expr, evaluate): + def exec_compute_potential_insn_fmm(self, actx: PyOpenCLArrayContext, + insn, bound_expr, evaluate): # {{{ gather unique target discretizations used target_name_to_index = {} @@ -227,8 +238,11 @@ class UnregularizedLayerPotentialSource(LayerPotentialSourceBase): from pytential import bind, sym waa = bind(bound_expr.places, sym.weights_and_area_elements( - self.ambient_dim, dofdesc=insn.source))(queue) - strengths = waa * evaluate(insn.density).with_queue(queue) + self.ambient_dim, dofdesc=insn.source))(actx) + strengths = waa * evaluate(insn.density) + + from meshmode.dof_array import flatten + flat_strengths = flatten(strengths) out_kernels = tuple(knl for knl in insn.kernels) fmm_kernel = self.get_fmm_kernel(out_kernels) @@ -236,12 +250,12 @@ class UnregularizedLayerPotentialSource(LayerPotentialSourceBase): self.get_fmm_output_and_expansion_dtype(fmm_kernel, strengths)) kernel_extra_kwargs, source_extra_kwargs = ( self.get_fmm_expansion_wrangler_extra_kwargs( - queue, out_kernels, geo_data.tree().user_source_ids, + actx, out_kernels, geo_data.tree().user_source_ids, insn.kernel_arguments, evaluate)) wrangler = self.expansion_wrangler_code_container( fmm_kernel, out_kernels).get_wrangler( - queue, + actx.queue, geo_data.tree(), output_and_expansion_dtype, self.fmm_level_to_order, @@ -252,25 +266,32 @@ class UnregularizedLayerPotentialSource(LayerPotentialSourceBase): from boxtree.fmm import drive_fmm all_potentials_on_every_tgt = drive_fmm( - geo_data.traversal(), wrangler, strengths, timing_data=None) + geo_data.traversal(), wrangler, flat_strengths, + timing_data=None) # {{{ postprocess fmm - result = [] + results = [] for o in insn.outputs: target_index = target_name_to_index[o.target_name] target_slice = slice(*geo_data.target_info().target_discr_starts[ target_index:target_index+2]) + target_discr = targets[target_index] + + result = all_potentials_on_every_tgt[o.kernel_index][target_slice] - result.append( - (o.name, - all_potentials_on_every_tgt[o.kernel_index][target_slice])) + from meshmode.discretization import Discretization + if isinstance(target_discr, Discretization): + from meshmode.dof_array import unflatten + result = unflatten(actx, target_discr, result) + + results.append((o.name, result)) # }}} timing_data = {} - return result, timing_data + return results, timing_data # }}} @@ -279,13 +300,17 @@ class UnregularizedLayerPotentialSource(LayerPotentialSourceBase): # {{{ fmm tools -class _FMMGeometryCodeContainer(object): +class _FMMGeometryDataCodeContainer(object): - def __init__(self, cl_context, ambient_dim, debug): - self.cl_context = cl_context + def __init__(self, actx, ambient_dim, debug): + self.array_context = actx self.ambient_dim = ambient_dim self.debug = debug + @property + def cl_context(self): + return self.array_context.context + @memoize_method def copy_targets_kernel(self): knl = lp.make_kernel( @@ -343,11 +368,15 @@ class _FMMGeometryData(object): @property def cl_context(self): - return self.lpot_source.cl_context + return self.code_getter.cl_context + + @property + def array_context(self): + return self.code_getter.array_context @property def coord_dtype(self): - return self.lpot_source.density_discr.nodes().dtype + return self.lpot_source.density_discr.real_dtype @property def ambient_dim(self): @@ -373,25 +402,29 @@ class _FMMGeometryData(object): lpot_src = self.lpot_source target_info = self.target_info() - with cl.CommandQueue(self.cl_context) as queue: - nsources = lpot_src.density_discr.nnodes - nparticles = nsources + target_info.ntargets + queue = self.array_context.queue + + nsources = lpot_src.density_discr.ndofs + nparticles = nsources + target_info.ntargets - refine_weights = cl.array.zeros(queue, nparticles, dtype=np.int32) - refine_weights[:nsources] = 1 - refine_weights.finish() + refine_weights = cl.array.zeros(queue, nparticles, dtype=np.int32) + refine_weights[:nsources] = 1 + refine_weights.finish() - MAX_LEAF_REFINE_WEIGHT = 32 # noqa + MAX_LEAF_REFINE_WEIGHT = 32 # noqa - tree, _ = code_getter.build_tree(queue, - particles=lpot_src.density_discr.nodes(), - targets=target_info.targets, - max_leaf_refine_weight=MAX_LEAF_REFINE_WEIGHT, - refine_weights=refine_weights, - debug=self.debug, - kind="adaptive") + from meshmode.dof_array import thaw, flatten - return tree + tree, _ = code_getter.build_tree(queue, + particles=flatten( + thaw(self.array_context, lpot_src.density_discr.nodes())), + targets=target_info.targets, + max_leaf_refine_weight=MAX_LEAF_REFINE_WEIGHT, + refine_weights=refine_weights, + debug=self.debug, + kind="adaptive") + + return tree @memoize_method def target_info(self): @@ -399,31 +432,31 @@ class _FMMGeometryData(object): lpot_src = self.lpot_source target_discrs = self.target_discrs - with cl.CommandQueue(self.cl_context) as queue: - ntargets = 0 - target_discr_starts = [] - - for target_discr in target_discrs: - target_discr_starts.append(ntargets) - ntargets += target_discr.nnodes + ntargets = 0 + target_discr_starts = [] + for target_discr in target_discrs: target_discr_starts.append(ntargets) - - targets = cl.array.empty( - self.cl_context, - (lpot_src.ambient_dim, ntargets), - self.coord_dtype) - - for start, target_discr in zip(target_discr_starts, target_discrs): - code_getter.copy_targets_kernel()( - queue, - targets=targets[:, start:start+target_discr.nnodes], - points=target_discr.nodes()) - - return _TargetInfo( - targets=targets, - target_discr_starts=target_discr_starts, - ntargets=ntargets).with_queue(None) + ntargets += target_discr.ndofs + + target_discr_starts.append(ntargets) + + targets = self.array_context.empty( + (lpot_src.ambient_dim, ntargets), + self.coord_dtype) + + from pytential.utils import flatten_if_needed + for start, target_discr in zip(target_discr_starts, target_discrs): + code_getter.copy_targets_kernel()( + self.array_context.queue, + targets=targets[:, start:start+target_discr.ndofs], + points=flatten_if_needed( + self.array_context, target_discr.nodes())) + + return _TargetInfo( + targets=targets, + target_discr_starts=target_discr_starts, + ntargets=ntargets).with_queue(None) # }}} diff --git a/pytential/utils.py b/pytential/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f1e9f0d109069d54b34090b1b0103b012693e454 --- /dev/null +++ b/pytential/utils.py @@ -0,0 +1,64 @@ +__copyright__ = """ +Copyright (C) 2020 Matt Wala +""" + +__license__ = """ +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +THE SOFTWARE. +""" + +import numpy as np +from meshmode.array_context import PyOpenCLArrayContext + + +def flatten_if_needed(actx: PyOpenCLArrayContext, ary: np.ndarray): + from pytools.obj_array import obj_array_vectorize_n_args + from meshmode.dof_array import DOFArray, thaw, flatten + + if (isinstance(ary, np.ndarray) + and ary.dtype.char == "O" + and not isinstance(ary, DOFArray)): + return obj_array_vectorize_n_args(flatten_if_needed, actx, ary) + + if not isinstance(ary, DOFArray): + return ary + + if ary.array_context is None: + ary = thaw(actx, ary) + + return flatten(ary) + + +def unflatten_from_numpy(actx, discr, ary): + from pytools.obj_array import obj_array_vectorize + from meshmode.dof_array import unflatten + + ary = obj_array_vectorize(actx.from_numpy, ary) + if discr is None: + return ary + else: + return unflatten(actx, discr, ary) + + +def flatten_to_numpy(actx, ary): + result = flatten_if_needed(actx, ary) + + from pytools.obj_array import obj_array_vectorize + return obj_array_vectorize(actx.to_numpy, result) + +# vim: foldmethod=marker diff --git a/pytential/version.py b/pytential/version.py index 5cb9cc61161073b0cd9e9a5fad471dcc6620763d..aafdf685fbe56dae62b2af7d2179f547ccc577e3 100644 --- a/pytential/version.py +++ b/pytential/version.py @@ -43,7 +43,7 @@ else: # }}} -VERSION = (2016, 1) +VERSION = (2020, 2) VERSION_TEXT = ".".join(str(i) for i in VERSION) PYTENTIAL_KERNEL_VERSION = (VERSION, _git_rev, 0) diff --git a/requirements.txt b/requirements.txt index 625deb28d7e5a04253bc3ffb5c12b2ba263362b5..b3a861ca2b07e8f372b1ff58d4aeedcc1a192f05 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,12 +1,12 @@ numpy -git+git://github.com/inducer/pytools -git+git://github.com/inducer/pymbolic +git+git://github.com/inducer/pytools#egg=pytools +git+git://github.com/inducer/pymbolic#egg=pymbolic sympy==1.1.1 -git+https://github.com/inducer/modepy -git+https://github.com/inducer/pyopencl -git+https://github.com/inducer/islpy -git+https://github.com/inducer/loopy -git+https://gitlab.tiker.net/inducer/boxtree -git+https://github.com/inducer/meshmode -git+https://gitlab.tiker.net/inducer/sumpy -git+https://gitlab.tiker.net/inducer/pyfmmlib +git+https://github.com/inducer/modepy#egg=modepy +git+https://github.com/inducer/pyopencl#egg=pyopencl +git+https://github.com/inducer/islpy#egg=islpy +git+https://github.com/inducer/loopy#egg=loo.py +git+https://github.com/inducer/boxtree#egg=boxtree +git+https://github.com/inducer/meshmode#egg=meshmode +git+https://github.com/inducer/sumpy#egg=sumpy +git+https://github.com/inducer/pyfmmlib#egg=pyfmmlib diff --git a/setup.cfg b/setup.cfg index a353f3f7242d42f689d5721b8f4a104aaf3e4e6b..444d02d0d654bdb034caed50f35f429ee4dd58cf 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,4 +1,3 @@ - [flake8] ignore = E126,E127,E128,E123,E226,E241,E242,E265,E402,W503,N803,N806,N802,D102,D103 max-line-length=85 diff --git a/setup.py b/setup.py index d3095519c12a6dadcee3fb2ac431becc69d5e9fb..8a3bd5f872fa76af0763bc98094c72ab5c9c077d 100644 --- a/setup.py +++ b/setup.py @@ -94,10 +94,7 @@ setup(name="pytential", 'License :: OSI Approved :: MIT License', 'Natural Language :: English', 'Programming Language :: Python', - - 'Programming Language :: Python :: 2.6', - 'Programming Language :: Python :: 2.7', - # 3.x has not yet been tested. + 'Programming Language :: Python :: 3', 'Topic :: Scientific/Engineering', 'Topic :: Scientific/Engineering :: Information Analysis', 'Topic :: Scientific/Engineering :: Mathematics', @@ -110,6 +107,7 @@ setup(name="pytential", ext_modules=cythonize(ext_modules), + python_requires="~=3.6", install_requires=[ "pytest>=2.3", # FIXME leave out for now @@ -122,7 +120,7 @@ setup(name="pytential", "boxtree>=2019.1", "pymbolic>=2013.2", "loo.py>=2017.2", - "sumpy>=2013.1", + "sumpy>=2020.2beta1", "cgen>=2013.1.2", "pyfmmlib>=2019.1.1", diff --git a/test/betterplane.brep b/test/betterplane.brep new file mode 100644 index 0000000000000000000000000000000000000000..b575bb489e720c149d34c2ec58847d43dd62656d --- /dev/null +++ b/test/betterplane.brep @@ -0,0 +1,2348 @@ +DBRep_DrawableShape + +CASCADE Topology V1, (c) Matra-Datavision +Locations 85 +1 + 1 0 0 0 + 0 1 0 0 + 0 0 1 0 +1 + 1 0 0 0 + 0 1 0 0 + 0 0 1 0 +1 + 1 0 0 0 + 0 1 0 0 + 0 0 1 0 +1 + 1 0 0 0 + 0 1 0 0 + 0 0 1 0 +1 + 1 0 0 0 + 0 1 0 0 + 0 0 1 0 +1 + 1 0 0 0 + 0 1 0 0 + 0 0 1 0 +1 + 1 0 0 0 + 0 1 0 0 + 0 0 1 0 +2 3 1 4 1 5 1 6 1 7 1 0 +2 2 -1 0 +2 3 1 4 1 5 1 6 1 7 1 2 1 0 +2 5 1 6 1 7 1 0 +2 3 1 4 1 0 +2 7 -1 6 -1 5 -1 0 +2 7 -1 6 -1 5 -1 4 -1 3 -1 0 +1 + 1 0 0 0 + 0 1 0 0 + 0 0 1 0 +1 + 1 0 0 0 + 0 1 0 0 + 0 0 1 0 +1 + 1 0 0 0 + 0 1 0 0 + 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3116 3113 1960 1896 1931 3222 3113 3220 1960 1919 1896 731 668 603 3223 3184 3202 731 603 669 3223 3205 3184 1961 1851 1936 3224 55 3186 3224 3186 3200 732 641 690 1961 1867 1851 3225 3207 63 732 681 641 1962 1920 1911 3226 3215 3193 1962 1911 1951 733 700 688 733 688 718 3226 3193 3201 3227 3072 3181 3227 3123 3072 1963 1908 1940 734 698 687 3227 3181 19 1963 1927 1908 734 687 723 3228 3213 3147 735 686 728 735 690 686 3228 3147 3159 3229 3214 3190 1964 1921 1898 1964 1898 1937 3229 3190 3206 736 681 732 1965 1865 1943 3230 3191 3068 736 682 681 1965 1881 1865 1966 1954 1928 3230 3068 3117 737 660 642 1966 1928 1938 737 642 717 3231 3187 3207 3231 3211 3187 3232 3118 3099 1967 1934 1947 738 703 727 3232 3099 3214 1967 142 1934 738 709 703 1967 143 142 739 721 705 739 705 719 1968 1930 1946 3233 3212 3197 740 737 717 3233 3197 3218 740 717 722 3234 19 20 1968 1955 1930 3234 3123 3227 3234 3134 3123 1969 1957 70 741 664 721 3234 3227 19 1970 1932 1919 3235 3218 3196 3235 3196 3211 1970 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1902 1958 3250 3118 3232 1981 1942 1920 3250 3139 3118 752 708 715 3251 3218 3235 1981 1920 1962 752 747 708 3251 3233 3218 1982 1921 1964 753 676 670 753 670 747 754 737 740 3252 3230 3117 1982 1944 1921 3252 3117 3139 754 750 737 1983 1969 70 755 721 739 3253 3231 3207 1984 143 1967 3253 3207 3225 755 741 721 54 55 3224 756 709 738 3254 3237 3223 756 726 709 1985 1900 1881 3254 3223 3238 1985 1881 1965 757 697 742 3255 3191 3230 757 713 697 3255 3210 3191 3256 3235 3211 3256 3211 3231 1986 1977 1954 758 702 689 1986 1954 1966 3257 3219 3236 1987 1932 1970 758 689 746 3257 3241 3219 3258 3244 63 1987 1947 1932 759 725 744 1988 1849 1933 759 746 725 3259 3174 3192 3259 3243 3174 760 730 685 760 685 711 1988 1891 1849 761 684 748 1989 1970 1960 761 696 684 3260 3212 3233 1989 1960 1974 3260 3239 3212 1990 1979 1957 3261 3254 3238 1990 1957 1969 762 747 752 3261 3238 3245 762 753 747 3262 199 3176 3263 3245 3226 1991 1955 1968 3263 3226 3242 763 682 736 3264 3242 3216 763 710 682 1991 1976 1955 764 741 755 3264 3216 3241 1992 1983 70 3265 54 3224 3265 3224 3239 764 748 741 3265 53 54 3266 3228 3243 765 693 668 1993 1942 1981 3266 109 3249 1993 1952 1942 3266 3249 3228 765 668 731 3267 3232 3247 1994 1899 1891 3267 3250 3232 3268 3258 63 766 700 733 1994 1891 1988 766 728 700 3269 3225 3244 767 710 763 3269 3253 3225 767 711 710 1995 1943 1857 3270 3259 3192 1995 1857 1897 3270 3192 3204 768 658 698 3271 3235 3256 1996 1958 1944 768 724 658 1996 1944 1982 769 723 713 3271 3251 3235 769 713 757 1997 1982 1964 3272 3230 3252 1997 1964 1977 3272 3255 3230 1998 1966 1976 3273 3231 3253 3273 3256 3231 770 714 699 1998 1986 1966 770 699 751 771 735 728 771 728 766 3274 3245 3263 3274 3261 3245 3275 3233 3251 1999 1991 1968 1999 1968 1979 772 746 759 3275 3260 3233 772 758 746 3276 3210 3255 3276 3236 3210 2000 1947 1987 2000 1967 1947 773 696 761 2001 1922 1901 773 707 696 774 695 82 3277 3176 3194 2001 1901 1978 3277 3262 3176 3278 3241 3257 775 716 702 3278 3264 3241 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1982 785 695 774 2013 1999 1979 3292 3275 3251 786 95 743 3292 3251 3271 2013 1979 1990 3293 3194 3116 3293 3116 3222 787 707 773 2014 2004 1983 787 718 707 2014 1983 1992 3294 3276 3255 788 758 772 3294 3255 3272 788 775 758 789 716 775 789 729 716 3295 3257 3236 2015 1998 1976 3295 3236 3276 2015 1976 1991 3296 3263 3283 2016 1988 2009 790 761 777 3296 3274 3263 790 773 761 2016 1994 1988 2017 1965 1943 3297 3234 20 2017 1943 1995 3297 3134 3234 3297 3203 3134 791 673 750 3298 3264 3278 791 706 673 3298 3283 3264 2018 1952 1993 3299 3284 3260 2018 1978 1952 3299 3260 3275 3300 3217 3237 792 666 730 2019 1941 1922 792 715 666 2019 1922 2001 3300 3281 3217 3301 20 21 3301 3297 20 793 750 754 3301 3203 3297 3301 3208 3203 2020 2011 70 3302 3287 3273 3302 3273 3288 793 791 750 2021 1987 2005 794 774 82 794 82 81 3303 109 3291 2021 2000 1987 3303 3270 3281 795 742 720 3303 3291 3270 2022 1980 1958 795 720 779 3304 3288 3269 3304 3269 3282 2022 1958 1996 796 729 789 2023 1967 2000 3305 3247 3229 796 743 729 2023 1984 1967 3305 3289 224 3305 3229 3289 3306 3268 3280 2024 1961 1971 797 770 751 797 751 782 3306 3290 3268 3307 3265 3284 2024 2010 1961 798 759 744 3307 3286 3265 2025 1997 2008 3308 3280 3285 798 744 784 3308 3285 61 3308 3306 3280 799 95 786 3309 3304 3282 800 722 704 3309 3282 3290 2025 2012 1997 2026 2008 1986 800 704 785 2026 1986 1998 3310 3271 3287 801 789 775 2027 1923 1899 3310 3292 3271 801 775 788 802 787 773 3311 3296 3283 3311 3283 3298 2027 1899 1994 802 773 790 2028 1907 1834 3312 3276 3294 3312 3295 3276 2028 1834 1925 803 733 718 3313 108 3300 803 718 787 2029 1895 2003 3313 3300 3237 3313 3237 3254 2029 1914 1895 804 706 791 3314 199 3262 804 719 706 3315 3278 3257 2030 2015 1991 3315 3257 3295 2030 1991 1999 2031 1940 1918 3316 3275 3292 3316 3299 3275 2031 1918 2002 805 727 693 805 693 765 2032 1971 1948 3317 3288 3304 806 774 794 806 785 774 3317 3302 3288 2032 1948 2006 3318 3222 3248 807 770 797 2033 2013 1990 3318 3293 3222 2033 1990 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3243 47 3228 3243 3155 3174 818 736 730 3244 3230 3216 818 730 810 3244 3216 3233 1991 1889 1868 1992 1972 1955 819 748 802 3245 46 3235 3245 3219 3229 3245 3235 3219 1992 1955 1975 3246 3232 3044 819 762 748 3246 3220 3232 1993 1970 1943 3246 3044 6 820 818 810 3246 3234 3220 820 810 814 1993 1943 1964 3247 72 3214 1994 1921 1989 1994 1923 1921 821 806 797 3247 3214 3231 3248 3159 3222 1995 1961 1936 1995 1936 1966 821 797 815 3248 187 3159 822 812 772 1996 1807 1817 3249 3243 3174 822 772 787 3249 47 3243 3249 3174 3180 3250 3244 3233 1996 1985 1807 3250 3233 3237 823 696 744 3251 3242 3227 1997 1976 1968 823 776 696 3251 3227 3236 1997 1968 1990 824 780 24 3252 3239 3223 825 776 823 3252 3223 3234 3253 187 3248 1998 1953 1969 825 777 776 3253 3196 187 1998 1978 1953 3254 3225 3239 826 804 790 3254 3237 3225 1999 1913 1951 826 790 808 1999 1956 1913 2000 1920 1974 3255 3236 3230 2000 1941 1920 3255 3230 3244 827 783 809 827 799 783 3256 3245 3229 2001 1901 1801 3256 3229 204 2001 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2126 2114 958 873 937 958 891 873 2149 178 2117 2149 2117 2128 2150 2127 2138 959 916 902 2150 2140 2127 959 902 939 2151 2133 2134 960 867 913 960 914 867 2151 2148 2133 2152 2132 2141 961 927 936 2152 2146 2132 961 947 927 2153 2016 2044 962 930 941 962 951 930 2153 2134 2016 963 952 21 964 954 931 964 931 940 2154 2044 2045 2154 2153 2044 965 928 920 2155 2138 150 965 920 948 2155 2150 2138 966 947 961 2155 150 149 2156 2126 2148 2156 2141 2126 966 957 947 967 887 876 967 876 954 2157 2101 2081 2157 2081 2143 968 922 900 968 900 929 2158 2026 2094 2158 2090 2026 969 895 944 969 910 895 2159 2121 2092 2159 2092 2125 970 916 959 2160 2148 2151 970 919 916 2160 2156 2148 971 896 955 971 906 896 2161 2130 2145 2161 2147 2130 972 915 860 972 860 914 973 883 951 2162 2099 2078 2162 2078 2147 2163 2149 2128 2163 2128 2146 973 899 883 974 936 952 2164 2123 2121 974 961 936 975 925 956 2164 2121 2159 2165 2143 10 975 937 925 2166 178 2149 976 905 858 2167 149 148 976 858 918 2167 2155 149 2168 2144 2119 977 967 954 977 954 964 978 966 961 2169 2154 2045 2169 2045 2053 2170 2151 2134 978 961 974 2170 2134 2153 979 891 958 979 903 891 2171 2170 2153 2171 2153 2154 980 963 21 2172 2163 2146 2172 2146 2152 2173 2094 2105 981 905 976 2173 2158 2094 981 911 905 982 944 928 982 928 965 2174 2141 2156 2174 2152 2141 983 973 951 983 951 962 2175 2168 2119 2175 2119 2123 984 952 963 984 974 952 2176 2125 2142 2176 2159 2125 2177 2156 2160 985 934 922 985 922 968 2177 2174 2156 986 929 919 2178 2104 2003 2178 2003 2074 986 919 970 2179 2105 2104 987 909 899 987 899 973 2179 2104 2178 988 923 910 988 910 969 2180 2101 2157 989 974 984 2180 2112 2101 2181 2099 2162 2181 2109 2099 2181 54 2109 989 978 974 2182 2164 2159 2182 2159 2176 990 912 903 990 903 979 2183 2147 2161 2183 54 2181 2183 2162 2147 2183 2181 2162 2184 2157 2143 2184 2143 2165 991 958 937 991 937 975 2185 2166 2149 2185 2149 2163 992 918 933 992 976 918 2186 132 2139 2187 2167 148 2187 147 146 2187 148 147 993 980 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2227 2202 2208 2227 2215 2202 1029 932 1010 1029 948 932 1030 946 950 2228 2217 2186 2229 2158 2173 2229 2211 2158 1030 1025 946 1031 985 968 2230 2218 2150 1031 968 1012 2230 2150 2155 1032 1011 981 1032 981 996 2231 2208 2193 2231 2193 2209 1033 975 956 2232 2144 2168 2232 2206 2144 1033 956 1014 2233 2231 2209 2233 2209 2210 1034 1019 20 1035 1018 1028 2234 2216 2164 2234 2164 2182 2235 2232 2168 1035 1023 1018 2235 2168 2175 2236 2211 2229 1036 1017 1011 1036 1011 1032 2236 2220 2211 1037 1003 988 2237 2183 2161 2237 53 54 1037 988 1015 2237 2161 2212 2237 54 2183 1038 1004 990 1038 990 1016 2238 132 2217 2239 2204 2214 2239 2222 2204 1039 941 887 1039 887 967 2240 2225 2215 2240 2215 2227 2241 2207 2218 1040 1034 20 2241 2226 2207 1041 949 854 2242 142 141 1041 854 926 2242 2224 142 2243 2210 2195 1042 950 949 2243 2195 2221 1042 949 1041 1043 1006 1019 2244 2221 2222 2244 2243 2221 2245 2155 2167 1043 1028 1006 2245 2230 2155 1044 948 1029 2246 178 2166 2246 2166 2225 2246 179 178 1044 965 948 2247 2190 2087 1045 1035 1028 2247 2087 2135 2248 2190 2247 1045 1028 1043 2248 2191 2190 1046 986 970 2249 2228 2186 1046 970 1026 2249 2186 2197 1047 940 1024 2250 2136 207 2250 2247 2135 1047 964 940 2250 2135 2136 2251 207 206 2251 2250 207 2251 2248 2247 2251 2247 2250 1048 1010 1027 1048 1029 1010 2252 2242 141 2253 2237 2212 1049 1039 967 1049 967 977 2253 53 2237 2253 2212 2226 2254 2217 2228 1050 985 1031 2254 2238 2217 1050 997 985 2255 2241 2218 2255 2218 2230 1051 991 975 2256 2169 2053 1051 975 1033 2256 2053 2113 1052 1040 20 1053 1019 1034 2257 2208 2231 2257 2227 2208 1053 1043 1019 1054 1020 1003 2258 2231 2233 1054 1003 1037 2258 2257 2231 2259 2249 2197 1055 962 941 2259 2197 2206 1055 941 1039 1056 1043 1053 2260 134 133 1056 1045 1043 2261 132 2238 1057 1024 1004 1057 1004 1038 2262 2225 2240 2262 2246 2225 1058 977 964 1058 964 1047 2263 2167 2187 2263 2245 2167 1059 1053 1034 1059 1034 1040 2264 2173 2105 1060 982 965 2264 2105 2179 2265 2226 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833 881 833 855 882 791 867 2157 1918 1931 882 803 791 2157 2147 1918 2158 2140 2153 883 876 872 883 872 880 2158 2145 2140 884 857 838 884 838 870 2159 2143 2131 2159 2131 2146 885 875 16 2160 2148 2115 2161 2069 2149 886 817 814 2161 2086 2069 886 814 879 887 834 821 2162 2117 2085 2162 2085 2129 887 821 873 2163 2134 2142 2163 2150 2134 888 840 831 2163 49 2150 888 831 878 2164 2097 2119 889 867 874 2164 2141 2097 889 882 867 890 863 875 2165 2080 2066 2165 2066 2152 890 880 863 2166 2128 2111 891 828 868 2166 2111 2147 891 850 828 2167 2152 2087 892 864 877 2167 2087 2099 892 879 864 2168 2012 2026 893 815 803 2168 2155 2012 893 803 882 894 837 806 2169 2136 2148 894 806 857 2169 2153 2136 895 885 16 2170 1931 1949 2170 2157 1931 896 883 880 896 880 890 897 818 770 2171 2115 3 2171 2160 2115 2172 2137 2128 897 770 839 2172 2128 2166 898 879 892 898 886 879 899 830 817 2173 2158 2153 2173 2153 2169 899 817 886 2174 2149 2130 900 869 824 2174 2130 2151 900 824 854 2175 2156 2143 2175 2143 2159 901 897 839 901 839 842 2176 2100 2086 902 853 840 2176 2086 2161 902 840 888 2177 2165 2152 2177 2152 2167 903 890 875 903 875 885 2178 1949 1958 2178 2170 1949 904 893 882 904 882 889 905 834 887 905 847 834 2179 2163 2142 2179 2142 2155 906 861 813 2179 49 2163 906 813 849 2180 2162 2129 907 890 903 2180 2129 2144 907 896 890 2181 2147 2157 908 16 15 2181 2166 2147 908 895 16 909 837 894 909 849 837 2182 2070 2156 2182 2095 2070 2183 2141 2164 2183 2154 2141 910 895 908 911 827 815 911 815 893 911 60 827 912 868 861 2184 2168 2026 912 861 906 2184 2026 2039 913 885 895 913 903 885 2185 2148 2160 2185 2169 2148 2186 2171 3 914 795 851 2187 2166 2181 914 844 795 915 878 869 915 869 900 2187 2172 2166 2188 2161 2149 2188 2149 2174 916 886 898 2189 49 2179 916 899 886 2189 2179 2155 2189 2155 2168 917 799 850 2190 2169 2185 2190 2173 2169 917 855 799 2191 2181 2157 918 843 830 918 830 899 2191 2157 2170 2192 1958 1977 919 907 903 2192 2178 1958 919 903 913 920 911 893 920 893 904 920 60 911 921 853 902 921 873 853 2193 129 2117 922 908 15 2194 2137 2172 2194 2151 2137 923 844 914 2195 2160 2171 923 854 844 924 842 789 2195 2185 2160 924 789 860 2196 2182 2156 925 857 884 2196 2156 2175 925 894 857 2197 2184 2039 926 910 908 2197 2039 2062 2198 2119 2100 2198 2100 2176 927 888 878 927 878 915 2199 2186 3 928 913 895 928 895 910 2200 2170 2178 2200 2191 2170 929 858 843 929 843 918 2201 2187 2181 2201 2181 2191 2202 2168 2184 930 851 871 2202 2189 2168 2202 49 2189 930 914 851 2203 2190 2185 2203 2185 2195 931 913 928 931 919 913 2204 2113 2095 2204 2095 2182 932 922 15 2205 2176 2161 933 859 762 2205 2161 2188 2206 2191 2200 933 762 826 2206 2201 2191 934 899 916 934 918 899 2207 2199 3 935 887 873 935 873 921 2208 2172 2187 2208 2194 2172 936 908 922 936 926 908 2209 2192 1977 937 888 927 2209 1977 1994 937 902 888 938 894 925 938 909 894 939 914 930 2210 2195 2171 2210 2171 2186 939 923 914 2211 2197 2062 940 868 912 2211 2062 2072 940 891 868 2212 2200 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997 59 60 997 920 904 997 904 980 997 60 920 2268 2227 2241 998 988 14 2268 2250 2227 999 967 949 999 949 978 2269 2240 2259 1000 940 912 1000 912 970 2269 2249 2240 1001 973 981 2270 2167 2099 2270 2099 2094 1001 992 973 2271 2183 2164 1002 970 965 1002 965 994 2271 2164 2254 2272 2224 2205 2272 2205 2248 1003 925 884 2273 2261 2237 2273 2237 2249 1003 884 959 2274 2180 2144 1004 983 971 2274 2144 2234 1004 971 987 2275 2258 2243 2275 2243 2257 1005 968 811 2276 2259 2236 1005 811 836 2276 2236 2251 2277 2245 2256 2277 2263 2245 1006 916 898 1006 898 989 2278 2159 2258 2278 2175 2159 2279 2121 2132 1007 992 1001 1007 996 992 2279 2262 2121 1008 993 976 2280 2239 2255 2280 2265 2239 2280 49 2265 2281 2266 2250 1008 976 988 1009 891 940 2281 2250 2268 1009 960 891 2282 2264 2053 1010 1006 989 1010 989 991 2282 2053 2073 1011 985 924 1011 924 946 1012 937 927 1012 927 995 2283 2177 2167 1013 967 999 2283 2167 2270 1013 980 967 2284 2254 2198 1014 1001 981 2284 2198 2224 2285 2277 2256 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69 70 2301 2297 69 1028 1014 993 2301 2289 2297 2301 70 2299 2302 2274 2234 2302 2234 2260 1029 1006 1010 2303 2175 2278 1029 1026 1006 2303 2196 2175 1030 1012 995 1030 995 1015 2304 2254 2284 2304 2271 2254 2305 2291 2264 1031 997 980 2305 2264 2282 1031 980 1013 1032 939 930 2306 2288 2246 2306 2246 2267 1032 930 1021 2307 2269 2286 2307 66 2294 2307 2294 2269 1033 937 1012 1033 955 937 2308 2277 2285 2308 2298 2277 2308 70 71 2308 2285 2299 2308 2299 70 1034 984 1022 1034 991 984 2309 2295 2090 1035 1014 1028 1035 1019 1014 1036 970 1002 2309 2090 2101 1036 1000 970 2310 2291 2305 1037 1032 1021 2310 2296 2291 1037 1021 1024 2311 2303 2278 1038 1017 1003 1038 1003 1020 2311 2278 2293 2312 66 2307 2312 65 66 2312 2286 2296 2312 2307 2286 2312 93 65 1039 944 934 1039 934 1026 2313 2282 2295 2313 2305 2282 2314 2292 144 2314 2300 2292 1040 856 866 2315 130 129 2315 129 2228 1040 1027 856 2316 2218 2196 2316 2196 2303 1041 1025 14 2317 2305 2313 2317 2310 2305 1042 1008 1018 1042 1028 1008 2318 2309 2101 1043 986 960 1043 960 1009 1044 1026 1029 1044 1039 1026 2318 2101 2114 1045 901 985 2319 2224 2272 2319 2284 2224 2320 2295 2309 1045 951 901 1046 1041 14 2320 2313 2295 2321 93 2312 2321 2312 2296 2321 2296 2310 1047 978 963 1047 963 1023 1048 1028 1042 2322 2316 2303 2322 2303 2311 1048 1035 1028 2323 2317 2313 2323 2313 2320 1049 1018 998 2324 2319 2272 1049 998 1025 2324 2272 2290 1050 1005 1027 1050 1022 1005 2325 2318 2114 1051 951 1045 2325 2114 2126 1051 958 951 1052 1033 1012 1052 1012 1030 2326 2284 2319 2326 2304 2284 1053 1010 991 2327 2242 2218 1053 991 1034 1054 997 1031 2327 2218 2316 1054 59 997 2328 2183 2271 1055 1040 866 2328 2252 2183 2329 2177 2283 1055 866 876 2329 2238 2177 1056 955 1033 2330 2321 2310 1056 974 955 2330 2310 2317 2331 2309 2318 2331 2320 2309 1057 944 1039 1057 957 944 2332 2325 2126 1058 1034 1022 1058 1022 1050 2332 2126 2135 1059 14 13 1059 1046 14 2333 2260 2252 1060 1039 1044 1060 1057 1039 2333 2252 2328 2334 2308 71 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1120 1015 1004 1120 1004 1108 2402 2392 2203 1121 1111 1101 2403 2373 2390 2403 2382 2373 2404 2389 2372 2404 2372 2383 1121 1101 1117 1122 1100 1112 1122 1113 1100 2405 2322 2311 2405 2311 2386 1123 1112 1089 1123 1089 1102 2406 2393 2387 2406 2387 2399 1124 1109 919 1124 919 931 2407 2380 2394 2407 2396 2380 2408 2378 2392 2408 2399 2378 1125 1060 1104 2409 2377 2393 2409 2397 2377 1125 1080 1060 1126 986 1043 2410 130 2315 1126 1065 986 2410 2315 2347 1127 1120 1108 1127 1108 1114 2411 2402 2213 2411 2213 2226 1128 1088 1113 1128 1104 1088 1129 1036 1002 2412 2400 2376 1129 1002 1094 2412 2376 2395 1130 1047 1107 1130 1066 1047 2413 2390 142 2413 2403 2390 89 90 2396 1131 975 1076 1131 1020 975 2414 2394 2401 2414 2407 2394 2415 2389 2404 1132 1096 1109 2415 2401 2389 1132 1117 1096 2416 2399 2408 2416 2406 2399 1133 958 1051 1133 1024 958 2417 2395 2398 2417 2412 2395 1134 1116 1095 2418 2404 2383 2418 2383 2397 1134 1095 1111 2419 2398 2382 1135 952 990 1135 1085 952 2419 2382 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2057 2069 833 742 744 2087 2013 2005 834 812 819 834 824 812 2087 2005 2079 835 800 790 835 790 818 2088 2086 2069 836 831 816 2088 2069 2070 836 816 820 2090 2078 2060 2090 2060 2075 837 783 729 837 729 789 2091 2062 2033 838 771 766 2092 2081 2078 2092 2078 2090 838 766 831 839 778 823 839 792 778 2093 2083 5 2094 2079 2065 840 777 768 840 768 824 841 794 786 2094 2065 2082 2095 2089 74 2095 74 2062 2096 2088 2070 841 786 828 2096 2070 2083 842 838 831 842 831 836 843 780 807 2097 2063 2028 2097 2028 2072 843 810 780 2098 2013 2087 844 815 803 2098 2031 2013 844 803 827 2099 2090 2075 845 772 826 2099 2075 2086 845 784 772 846 822 808 2100 2033 2044 2100 2091 2033 846 808 829 847 824 834 2101 2079 2094 847 840 824 2101 2087 2079 848 774 810 2102 2086 2088 848 805 774 849 815 844 2102 2099 2086 849 823 815 2103 1891 1916 850 784 845 850 788 784 851 789 798 2103 2082 1891 851 837 789 2104 2090 2099 852 777 840 852 61 777 2104 2092 2090 2105 5 4 853 802 794 2105 2093 5 853 794 841 854 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2009 2034 3287 3222 3257 2055 2040 2013 845 760 834 3287 3263 3222 845 771 760 2055 2013 2032 3288 3282 3248 3288 3248 3261 2056 2010 1956 3289 3276 3242 3289 3242 3263 846 772 745 846 745 815 2056 1956 1996 847 809 831 847 832 809 2057 1924 2015 3290 3235 3266 848 820 802 2057 1957 1924 3290 3271 3235 3291 3246 3268 848 802 835 849 792 720 849 720 782 3291 3274 3246 2058 2044 2017 2058 2017 2036 3292 3270 3237 3292 109 3270 3292 3237 3267 2059 2031 1931 850 800 81 3293 3239 3283 2059 1931 1963 3293 197 3239 2060 2043 70 851 795 837 3294 3283 3285 851 807 795 3294 3293 3283 852 783 824 3295 3278 223 3295 3260 3278 852 804 783 2061 2028 2002 2061 2002 2040 3295 3275 3260 853 757 840 3296 3265 3279 853 773 757 3296 3281 3265 2062 1989 1968 3297 3262 3280 3297 3277 3262 2062 1968 2038 2063 2049 2023 854 815 829 2063 2023 2039 3298 3253 3271 854 846 815 3298 3279 3253 3299 3220 3244 855 775 823 2064 2038 2041 3299 3280 3220 855 803 775 2064 2062 2038 3300 3257 3272 144 143 2045 3300 3287 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2097 2037 2012 891 855 823 891 823 859 3340 3313 3298 3340 3298 3321 2098 1995 2069 892 808 784 3341 3319 3289 3341 3289 3316 2098 2018 1995 892 784 863 893 789 857 3342 3327 3301 2099 2078 2056 3342 3301 3325 2099 2056 2080 893 828 789 3342 3325 19 3343 3297 3314 2100 2069 2072 894 799 878 3343 3328 3297 894 816 799 3344 3333 3304 3344 109 3333 2100 2098 2069 895 95 825 3344 3304 3320 3345 3339 3310 895 124 95 2101 1980 1965 895 825 886 896 886 871 3345 3310 3322 2101 1965 2081 2102 2058 2077 896 871 882 3346 3302 3319 2102 2086 2058 897 837 883 3346 3331 3302 897 851 837 3347 3250 3284 3347 3315 3250 2103 2076 2057 898 855 891 2103 2057 2084 898 864 855 3348 3332 3300 3348 3300 3317 2104 1969 2073 2104 1990 1969 3349 55 56 899 817 884 899 836 817 3349 56 3323 2105 2085 70 3350 3306 3326 3350 3326 223 3350 3335 3306 900 781 862 2106 2091 2063 3351 3191 3327 2106 2063 2074 3351 3227 3191 900 812 781 2107 2029 2078 3352 3314 3324 901 735 818 2107 2051 2029 3352 3343 3314 3353 3321 3339 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3102 1981 1920 1962 754 750 737 1982 1921 1964 3248 3218 3229 755 721 739 1982 1943 1921 3248 3240 3218 755 741 721 3249 3223 3181 1983 1969 70 3249 3181 3211 756 725 709 1984 143 1967 3250 3225 3223 756 709 738 3250 3223 3249 1985 1881 1965 3251 3216 3231 1985 1900 1881 3251 109 3242 3251 3242 3216 757 697 742 757 713 697 3252 3143 3131 758 702 688 3252 3131 3247 758 688 746 3253 3235 3221 1986 1977 1954 759 746 724 3253 3221 3237 1986 1954 1966 759 724 744 1987 1946 1932 760 730 685 3254 3233 3121 1987 1932 1970 3254 3121 3143 1988 1891 1849 760 685 711 1988 1849 1933 3255 3210 3228 761 696 684 3255 3234 3210 761 684 748 54 55 3227 1989 1970 1960 762 753 747 762 747 752 3256 3239 3226 3256 3226 3240 1989 1960 1974 3257 3214 3194 763 682 736 1990 1979 1957 3257 3194 3233 1990 1957 1969 3258 3237 3213 3258 3213 3234 763 710 682 1991 1955 1968 764 741 755 764 748 741 3259 3243 3222 1991 1976 1955 3259 3222 3238 3260 3247 3217 765 693 668 3260 3217 3232 1992 1983 70 3261 3245 63 765 668 731 1993 1941 1981 3262 3177 3195 3262 3246 3177 766 727 700 1993 1952 1941 3263 3241 3215 766 700 733 767 710 763 3263 3215 3235 1994 1898 1891 767 711 710 3264 3240 3248 768 658 698 3264 3256 3240 1994 1891 1988 1995 1857 1897 1995 1942 1857 3265 199 3179 768 723 658 3266 3248 3229 3266 3229 3244 769 714 699 1996 1943 1982 769 699 751 1996 1958 1943 3267 3244 3219 770 735 727 1997 1982 1964 3267 3219 3243 1997 1964 1977 770 727 766 3268 3227 3241 3268 53 54 3268 54 3227 771 758 746 3269 3231 3246 3269 3251 3231 771 746 759 3269 109 3251 3270 3247 3260 1998 1966 1976 1998 1986 1966 3270 3252 3247 772 707 696 3271 3261 63 772 696 761 3272 3228 3245 1999 1991 1968 1999 1968 1979 773 695 82 3272 3255 3228 2000 1967 1946 3273 3262 3195 2000 1946 1987 774 702 758 3273 3195 3207 2001 1901 1978 774 716 702 3274 3237 3258 2001 1923 1901 3274 3253 3237 775 765 731 775 731 745 776 713 757 776 728 713 3275 3233 3254 2002 1918 1900 3275 3257 3233 2002 1900 1985 777 761 748 3276 3258 3234 3276 3234 3255 777 748 764 2003 1847 1944 778 749 725 3277 3264 3248 2003 1895 1847 3277 3248 3266 2004 1969 1983 2004 1990 1969 778 725 756 3278 3263 3235 779 720 714 3278 3235 3253 2005 1987 1970 3279 3214 3257 3279 3238 3214 779 714 769 2005 1970 1989 780 734 728 780 728 776 2006 1924 1980 3280 3265 3179 3280 3179 3197 2006 1948 1924 781 689 735 3281 3243 3259 2007 143 1984 3281 3267 3243 781 732 689 3282 109 3269 3282 3246 3262 3282 3269 3246 2008 1977 1986 2008 1997 1977 782 749 778 2009 1988 1933 782 751 749 3283 3271 63 2009 1933 1953 2010 1867 1961 3284 3207 3220 783 732 781 3284 3273 3207 783 736 732 3285 3245 3261 2010 1904 1867 3285 3272 3245 784 723 768 784 744 723 785 704 695 3286 3266 3244 2011 1992 70 3286 3244 3267 785 695 773 3287 3241 3263 3287 3268 3241 786 95 743 2012 1982 1997 2012 1996 1982 3288 53 3268 3289 3283 63 787 707 772 3289 63 61 787 718 707 2013 1979 1990 2013 1999 1979 3290 3258 3276 788 774 758 2014 2004 1983 788 758 771 3290 3274 3258 789 729 716 3291 3255 3272 2014 1983 1992 789 716 774 3291 3276 3255 2015 1998 1976 3292 3144 3133 3292 3167 3144 3292 3180 3167 790 761 777 3292 225 224 790 772 761 3292 3232 3209 3292 3133 3100 3292 3100 225 2015 1976 1991 3292 3209 3180 3293 3285 3261 2016 1994 1988 791 706 673 791 673 750 2016 1988 2009 3293 3261 3271 2017 1965 1942 3294 3262 3273 3294 3282 3262 792 666 730 3294 109 3282 2017 1942 1995 3295 3278 3253 2018 1978 1952 3295 3253 3274 792 715 666 2018 1952 1993 793 750 754 2019 2011 70 793 791 750 3296 3120 3225 3296 3197 3120 794 773 82 3297 3279 3257 2020 2000 1987 794 82 81 3297 3257 3275 2020 1987 2005 3298 3238 3279 795 720 779 3298 3259 3238 2021 1980 1958 795 742 720 3299 3277 3266 3299 3266 3286 2021 1958 1996 796 729 789 2022 1967 2000 2022 1984 1967 796 743 729 3300 3236 20 797 751 782 3300 3138 3236 3300 3206 3138 2023 2010 1961 797 769 751 3301 3286 3267 3301 3267 3281 3302 3263 3278 2023 1961 1971 798 759 744 2024 1997 2008 3302 3287 3263 798 744 784 3303 3284 3220 2024 2012 1997 799 95 786 800 722 704 3303 3220 3239 2025 2008 1986 3304 3211 3206 3304 20 21 800 704 785 3304 3300 20 3304 3206 3300 2025 1986 1998 801 774 788 3305 3276 3291 801 789 774 802 772 790 3305 3290 3276 2026 1922 1898 802 787 772 3306 109 3294 2026 1898 1994 3306 3294 3273 2027 1907 1834 3306 3273 3284 3307 3291 3272 803 718 787 2027 1834 1925 3307 3272 3285 803 733 718 2028 1895 2003 3308 3271 3283 3308 3293 3271 804 719 706 2028 1914 1895 804 706 791 3309 3268 3287 2029 1991 1999 2029 2015 1991 3309 3288 3268 3310 3289 61 3310 3308 3283 3310 3283 3289 2030 1940 1918 3311 3307 3285 805 726 693 3311 3285 3293 2030 1918 2002 805 693 765 3312 3274 3290 3312 3295 3274 2031 1971 1948 806 773 794 2031 1948 2006 806 785 773 2032 1923 2001 3313 3299 3286 807 769 797 3313 3286 3301 807 779 769 3314 3232 3292 3314 3292 224 3314 3260 3232 2032 1947 1923 808 791 793 3315 3298 3279 2033 2013 1990 808 804 791 3315 3279 3297 2033 1990 2004 2034 1926 1904 3316 3239 3256 809 768 698 2034 1904 2010 809 698 734 3316 108 3303 3316 3303 3239 3317 199 3265 810 726 805 810 738 726 2035 1925 1929 3318 3259 3298 2035 1929 172 2035 2027 1925 3318 3281 3259 2036 1944 1973 3319 3278 3295 811 789 801 3319 3302 3278 2036 2003 1944 811 796 789 812 763 736 2037 1947 2032 812 736 783 3320 3305 3291 2037 1959 1947 3320 3291 3307 2038 1992 2011 813 767 763 2038 2014 1992 3321 3225 3250 813 763 812 3321 3296 3225 3322 3314 224 3322 3260 3314 3322 3270 3260 2039 1998 2015 814 757 742 3323 3309 3287 3323 3287 3302 814 742 795 3324 108 109 2039 2025 1998 3324 109 3306 3324 3303 108 3324 3306 3284 2040 2024 2008 3324 3284 3303 3325 3316 3256 815 771 759 3325 3256 3264 2040 2008 2025 815 759 798 3325 108 3316 2041 1999 2013 816 805 765 3326 3320 3307 816 765 775 2041 2029 1999 3326 3307 3311 2042 1996 2012 3327 199 3317 2042 2021 1996 3327 200 199 817 760 711 3328 3311 3293 817 711 767 818 735 770 3328 3293 3308 2043 2004 2014 818 781 735 2043 2033 2004 3329 3290 3305 3329 3312 3290 819 95 799 2044 2019 70 820 787 802 2045 1985 1965 3330 3328 3308 820 803 787 3330 3308 3310 3330 3310 61 2045 1965 2017 3331 3317 3265 2046 2000 2020 821 792 730 821 730 760 3331 3265 3280 2046 2022 2000 3332 3298 3315 822 781 818 3332 3318 3298 822 783 781 3333 3295 3312 2047 2038 2011 3333 3319 3295 2047 2011 2019 823 722 800 823 740 722 2048 1978 2018 2048 2001 1978 3334 108 3325 3334 3325 3264 824 719 804 3334 3264 3277 3335 3281 3318 2049 1994 2016 824 739 719 3335 3301 3281 2049 2026 1994 2050 2034 2010 3336 3323 3302 3336 3302 3319 2050 2010 2023 825 810 805 3337 3249 3211 2051 1984 2022 825 805 816 3337 3211 3304 2051 2007 1984 3337 3304 21 826 804 808 2052 1980 2021 3338 3326 3311 2052 2006 1980 826 824 804 3338 3311 3328 827 800 785 3339 3249 3337 827 785 806 3339 3250 3249 2053 1963 1940 3339 3337 21 2053 1940 2030 828 784 768 828 768 809 3340 3329 3305 3340 3305 3320 2054 2042 2012 829 795 779 3341 3317 3331 2054 2012 2024 829 779 807 3341 3327 3317 3342 3330 61 3342 3328 3330 830 676 753 3342 3338 3328 2055 2015 2029 830 745 676 2055 2039 2015 3343 3313 3301 3343 3301 3335 2056 143 2007 2057 1939 1914 3344 3320 3326 2057 1914 2028 831 715 792 3344 3340 3320 831 752 715 2058 2025 2039 3345 3312 3329 832 771 815 832 788 771 3345 3333 3312 2058 2040 2025 3346 3143 3252 833 756 738 2059 1951 1922 833 738 810 2059 1922 2026 3346 3254 3143 3347 3335 3318 3347 3318 3332 834 776 757 3348 53 3288 834 757 814 3348 52 53 2060 1949 1926 3348 3288 3309 2060 1926 2034 2061 2041 2013 835 766 733 3349 108 3334 835 733 803 3349 3277 3299 3349 3334 3277 2061 2013 2033 3350 3319 3333 836 740 823 3350 3336 3319 2062 2044 70 836 754 740 3351 3326 3338 2063 144 143 2063 143 2056 837 830 753 2064 2028 2003 3351 3344 3326 3352 3346 3252 2064 2003 2036 837 753 762 2065 1951 2059 838 776 834 3352 3252 3270 2065 1962 1951 3353 3345 3329 838 780 776 3353 3329 3340 839 752 831 3354 3335 3347 839 762 752 2066 1963 2053 3354 3343 3335 840 770 766 3355 3342 61 2066 1973 1963 2067 1995 1897 3355 3351 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b/test/test_cost_model.py index 4b667548ecc54535ddda32c308a2a78b1822748f..1aa5e1572fb21969f1774223a304551e57828f3c 100644 --- a/test/test_cost_model.py +++ b/test/test_cost_model.py @@ -29,6 +29,8 @@ import pytest from pyopencl.tools import ( # noqa pytest_generate_tests_for_pyopencl as pytest_generate_tests) +from meshmode.array_context import PyOpenCLArrayContext + import numpy as np import pyopencl as cl @@ -62,6 +64,7 @@ def test_compare_cl_and_py_cost_model(ctx_factory): ctx = ctx_factory() queue = cl.CommandQueue(ctx) + actx = PyOpenCLArrayContext(queue) # {{{ Construct geometry @@ -72,7 +75,7 @@ def test_compare_cl_and_py_cost_model(ctx_factory): from meshmode.discretization.poly_element import \ InterpolatoryQuadratureSimplexGroupFactory pre_density_discr = Discretization( - ctx, mesh, + actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order) ) @@ -84,7 +87,7 @@ def test_compare_cl_and_py_cost_model(ctx_factory): places = GeometryCollection(qbx) from pytential.qbx.refinement import refine_geometry_collection - places = refine_geometry_collection(queue, places) + places = refine_geometry_collection(places) target_discrs_and_qbx_sides = tuple([(qbx.density_discr, 0)]) geo_data_dev = qbx.qbx_fmm_geometry_data( @@ -98,7 +101,7 @@ def test_compare_cl_and_py_cost_model(ctx_factory): # {{{ Construct cost models - cl_cost_model = QBXCostModel(queue) + cl_cost_model = QBXCostModel(actx) python_cost_model = _PythonQBXCostModel() tree = geo_data.tree() @@ -291,7 +294,7 @@ DEFAULT_LPOT_KWARGS = { } -def get_lpot_source(queue, dim): +def get_lpot_source(actx: PyOpenCLArrayContext, dim): from meshmode.discretization import Discretization from meshmode.discretization.poly_element import ( InterpolatoryQuadratureSimplexGroupFactory) @@ -308,7 +311,7 @@ def get_lpot_source(queue, dim): raise ValueError("unsupported dimension: %d" % dim) pre_density_discr = Discretization( - queue.context, mesh, + actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order)) lpot_kwargs = DEFAULT_LPOT_KWARGS.copy() @@ -327,9 +330,10 @@ def get_lpot_source(queue, dim): return lpot_source -def get_density(queue, discr): - nodes = discr.nodes().with_queue(queue) - return cl.clmath.sin(10 * nodes[0]) +def get_density(actx, discr): + from meshmode.dof_array import thaw + nodes = thaw(actx, discr.nodes()) + return actx.np.sin(10 * nodes[0]) # }}} @@ -344,13 +348,14 @@ def test_timing_data_gathering(ctx_factory): cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx, properties=cl.command_queue_properties.PROFILING_ENABLE) + actx = PyOpenCLArrayContext(queue) - lpot_source = get_lpot_source(queue, 2) + lpot_source = get_lpot_source(actx, 2) places = GeometryCollection(lpot_source) dofdesc = places.auto_source.to_stage1() density_discr = places.get_discretization(dofdesc.geometry) - sigma = get_density(queue, density_discr) + sigma = get_density(actx, density_discr) sigma_sym = sym.var("sigma") k_sym = LaplaceKernel(lpot_source.ambient_dim) @@ -359,7 +364,7 @@ def test_timing_data_gathering(ctx_factory): op_S = bind(places, sym_op_S) timing_data = {} - op_S.eval(queue, dict(sigma=sigma), timing_data=timing_data) + op_S.eval(dict(sigma=sigma), timing_data=timing_data, array_context=actx) assert timing_data print(timing_data) @@ -379,14 +384,15 @@ def test_cost_model(ctx_factory, dim, use_target_specific_qbx, per_box): """Test that cost model gathering can execute successfully.""" cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) - lpot_source = get_lpot_source(queue, dim).copy( + lpot_source = get_lpot_source(actx, dim).copy( _use_target_specific_qbx=use_target_specific_qbx, - cost_model=QBXCostModel(queue)) + cost_model=QBXCostModel(actx)) places = GeometryCollection(lpot_source) density_discr = places.get_discretization(places.auto_source.geometry) - sigma = get_density(queue, density_discr) + sigma = get_density(actx, density_discr) sigma_sym = sym.var("sigma") k_sym = LaplaceKernel(lpot_source.ambient_dim) @@ -395,9 +401,9 @@ def test_cost_model(ctx_factory, dim, use_target_specific_qbx, per_box): op_S = bind(places, sym_op_S) if per_box: - cost_S, _ = op_S.cost_per_box(queue, "constant_one", sigma=sigma) + cost_S, _ = op_S.cost_per_box("constant_one", sigma=sigma) else: - cost_S, _ = op_S.cost_per_stage(queue, "constant_one", sigma=sigma) + cost_S, _ = op_S.cost_per_stage("constant_one", sigma=sigma) assert len(cost_S) == 1 @@ -408,11 +414,11 @@ def test_cost_model(ctx_factory, dim, use_target_specific_qbx, per_box): if per_box: cost_S_plus_D, _ = op_S_plus_D.cost_per_box( - queue, "constant_one", sigma=sigma + "constant_one", sigma=sigma ) else: cost_S_plus_D, _ = op_S_plus_D.cost_per_stage( - queue, "constant_one", sigma=sigma + "constant_one", sigma=sigma ) assert len(cost_S_plus_D) == 2 @@ -426,17 +432,18 @@ def test_cost_model_metadata_gathering(ctx_factory): """Test that the cost model correctly gathers metadata.""" cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) from sumpy.expansion.level_to_order import SimpleExpansionOrderFinder fmm_level_to_order = SimpleExpansionOrderFinder(tol=1e-5) - lpot_source = get_lpot_source(queue, 2).copy( + lpot_source = get_lpot_source(actx, 2).copy( fmm_level_to_order=fmm_level_to_order) places = GeometryCollection(lpot_source) density_discr = places.get_discretization(places.auto_source.geometry) - sigma = get_density(queue, density_discr) + sigma = get_density(actx, density_discr) sigma_sym = sym.var("sigma") k_sym = HelmholtzKernel(2, "k") @@ -446,7 +453,7 @@ def test_cost_model_metadata_gathering(ctx_factory): op_S = bind(places, sym_op_S) _, metadata = op_S.cost_per_stage( - queue, "constant_one", sigma=sigma, k=k, return_metadata=True + "constant_one", sigma=sigma, k=k, return_metadata=True ) metadata = one(metadata.values()) @@ -693,12 +700,12 @@ def test_cost_model_correctness(ctx_factory, dim, off_surface, """Check that computed cost matches that of a constant-one FMM.""" cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) cost_model = QBXCostModel( - queue, translation_cost_model_factory=OpCountingTranslationCostModel - ) + actx, translation_cost_model_factory=OpCountingTranslationCostModel) - lpot_source = get_lpot_source(queue, dim).copy( + lpot_source = get_lpot_source(actx, dim).copy( cost_model=cost_model, _use_target_specific_qbx=use_target_specific_qbx) @@ -726,10 +733,10 @@ def test_cost_model_correctness(ctx_factory, dim, off_surface, sym_op_S = sym.S(k_sym, sigma_sym, qbx_forced_limit=qbx_forced_limit) op_S = bind(places, sym_op_S) - sigma = get_density(queue, density_discr) + sigma = get_density(actx, density_discr) from pytools import one - modeled_time, _ = op_S.cost_per_stage(queue, "constant_one", sigma=sigma) + modeled_time, _ = op_S.cost_per_stage("constant_one", sigma=sigma) modeled_time = one(modeled_time.values()) # Run FMM with ConstantOneWrangler. This can't be done with pytential's @@ -744,15 +751,15 @@ def test_cost_model_correctness(ctx_factory, dim, off_surface, quad_stage2_density_discr = places.get_discretization( source_dd.geometry, sym.QBX_SOURCE_QUAD_STAGE2) - nnodes = quad_stage2_density_discr.nnodes - src_weights = np.ones(nnodes) + ndofs = quad_stage2_density_discr.ndofs + src_weights = np.ones(ndofs) timing_data = {} potential = drive_fmm(wrangler, src_weights, timing_data, traversal=wrangler.trav)[0][geo_data.ncenters:] # Check constant one wrangler for correctness. - assert (potential == nnodes).all() + assert (potential == ndofs).all() # Check that the cost model matches the timing data returned by the # constant one wrangler. @@ -773,7 +780,7 @@ def test_cost_model_correctness(ctx_factory, dim, off_surface, for stage in timing_data: total_cost += timing_data[stage]["ops_elapsed"] - per_box_cost, _ = op_S.cost_per_box(queue, "constant_one", sigma=sigma) + per_box_cost, _ = op_S.cost_per_box("constant_one", sigma=sigma) print(per_box_cost) per_box_cost = one(per_box_cost.values()) @@ -798,14 +805,15 @@ def test_cost_model_order_varying_by_level(ctx_factory): cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) # {{{ constant level to order def level_to_order_constant(kernel, kernel_args, tree, level): return 1 - lpot_source = get_lpot_source(queue, 2).copy( - cost_model=QBXCostModel(queue), + lpot_source = get_lpot_source(actx, 2).copy( + cost_model=QBXCostModel(actx), fmm_level_to_order=level_to_order_constant) places = GeometryCollection(lpot_source) @@ -815,11 +823,10 @@ def test_cost_model_order_varying_by_level(ctx_factory): k_sym = LaplaceKernel(2) sym_op = sym.S(k_sym, sigma_sym, qbx_forced_limit=+1) - sigma = get_density(queue, density_discr) + sigma = get_density(actx, density_discr) cost_constant, metadata = bind(places, sym_op).cost_per_stage( - queue, "constant_one", sigma=sigma - ) + "constant_one", sigma=sigma) cost_constant = one(cost_constant.values()) metadata = one(metadata.values()) @@ -831,18 +838,17 @@ def test_cost_model_order_varying_by_level(ctx_factory): def level_to_order_varying(kernel, kernel_args, tree, level): return metadata["nlevels"] - level - lpot_source = get_lpot_source(queue, 2).copy( - cost_model=QBXCostModel(queue), + lpot_source = get_lpot_source(actx, 2).copy( + cost_model=QBXCostModel(actx), fmm_level_to_order=level_to_order_varying) places = GeometryCollection(lpot_source) density_discr = places.get_discretization(places.auto_source.geometry) - sigma = get_density(queue, density_discr) + sigma = get_density(actx, density_discr) cost_varying, _ = bind(lpot_source, sym_op).cost_per_stage( - queue, "constant_one", sigma=sigma - ) + "constant_one", sigma=sigma) cost_varying = one(cost_varying.values()) diff --git a/test/test_global_qbx.py b/test/test_global_qbx.py index 9ab5b6926e2f88767bd0b01594f1a05c686174e9..6b8047b36b4197f9d5b840ee2009f2a16587d75d 100644 --- a/test/test_global_qbx.py +++ b/test/test_global_qbx.py @@ -37,6 +37,7 @@ from pyopencl.tools import pytest_generate_tests_for_pyopencl \ from pytential.qbx import QBXLayerPotentialSource from functools import partial +from meshmode.array_context import PyOpenCLArrayContext from meshmode.mesh.generation import ( # noqa ellipse, cloverleaf, starfish, drop, n_gon, qbx_peanut, make_curve_mesh, generate_icosphere, generate_torus) @@ -55,6 +56,22 @@ RNG_SEED = 10 FAR_TARGET_DIST_FROM_SOURCE = 10 +# {{{ utils + +def dof_array_to_numpy(actx, ary): + """Converts DOFArrays (or object arrays of DOFArrays) to NumPy arrays. + Object arrays get turned into multidimensional arrays. + """ + from pytools.obj_array import obj_array_vectorize + from meshmode.dof_array import flatten + arr = obj_array_vectorize(actx.to_numpy, flatten(ary)) + if arr.dtype.char == "O": + arr = np.array(list(arr)) + return arr + +# }}} + + # {{{ source refinement checker class ElementInfo(RecordWithoutPickling): @@ -76,22 +93,23 @@ def iter_elements(discr): yield ElementInfo( element_nr=element_nr, discr_slice=slice(discr_nodes_idx, - discr_nodes_idx + discr_group.nunit_nodes)) + discr_nodes_idx + discr_group.nunit_dofs)) - discr_nodes_idx += discr_group.nunit_nodes + discr_nodes_idx += discr_group.nunit_dofs def run_source_refinement_test(ctx_factory, mesh, order, helmholtz_k=None, visualize=False): cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) # {{{ initial geometry from meshmode.discretization import Discretization from meshmode.discretization.poly_element import ( InterpolatoryQuadratureSimplexGroupFactory) - discr = Discretization(cl_ctx, mesh, + discr = Discretization(actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(order)) lpot_source = QBXLayerPotentialSource(discr, @@ -107,7 +125,7 @@ def run_source_refinement_test(ctx_factory, mesh, order, expansion_disturbance_tolerance = 0.025 from pytential.qbx.refinement import refine_geometry_collection - places = refine_geometry_collection(queue, places, + places = refine_geometry_collection(places, kernel_length_scale=kernel_length_scale, expansion_disturbance_tolerance=expansion_disturbance_tolerance, visualize=visualize) @@ -116,27 +134,34 @@ def run_source_refinement_test(ctx_factory, mesh, order, dd = places.auto_source stage1_density_discr = places.get_discretization(dd.geometry) - stage1_density_nodes = stage1_density_discr.nodes().get(queue) + from meshmode.dof_array import thaw + + stage1_density_nodes = dof_array_to_numpy(actx, + thaw(actx, stage1_density_discr.nodes())) quad_stage2_density_discr = places.get_discretization( dd.geometry, sym.QBX_SOURCE_QUAD_STAGE2) - quad_stage2_density_nodes = quad_stage2_density_discr.nodes().get(queue) - - int_centers = bind(places, - sym.expansion_centers(lpot_source.ambient_dim, -1))(queue) - int_centers = np.array([axis.get(queue) for axis in int_centers]) - ext_centers = bind(places, - sym.expansion_centers(lpot_source.ambient_dim, +1))(queue) - ext_centers = np.array([axis.get(queue) for axis in ext_centers]) - - expansion_radii = bind(places, - sym.expansion_radii(lpot_source.ambient_dim))(queue).get() + quad_stage2_density_nodes = dof_array_to_numpy(actx, + thaw(actx, quad_stage2_density_discr.nodes())) + + int_centers = dof_array_to_numpy(actx, + bind(places, + sym.expansion_centers(lpot_source.ambient_dim, -1))(actx)) + ext_centers = dof_array_to_numpy(actx, + bind(places, + sym.expansion_centers(lpot_source.ambient_dim, +1))(actx)) + expansion_radii = dof_array_to_numpy(actx, + bind(places, sym.expansion_radii(lpot_source.ambient_dim))(actx)) dd = dd.copy(granularity=sym.GRANULARITY_ELEMENT) - source_danger_zone_radii = bind(places, sym._source_danger_zone_radii( - lpot_source.ambient_dim, dofdesc=dd.to_stage2()))(queue).get() - quad_res = bind(places, sym._quad_resolution( - lpot_source.ambient_dim, dofdesc=dd))(queue) + source_danger_zone_radii = dof_array_to_numpy(actx, + bind(places, + sym._source_danger_zone_radii( + lpot_source.ambient_dim, dofdesc=dd.to_stage2()))(actx)) + quad_res = dof_array_to_numpy(actx, + bind(places, + sym._quad_resolution( + lpot_source.ambient_dim, dofdesc=dd))(actx)) # {{{ check if satisfying criteria @@ -236,6 +261,7 @@ def test_target_association(ctx_factory, curve_name, curve_f, nelements, visualize=False): cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) # {{{ generate lpot source @@ -248,7 +274,7 @@ def test_target_association(ctx_factory, curve_name, curve_f, nelements, from meshmode.discretization.poly_element import \ InterpolatoryQuadratureSimplexGroupFactory factory = InterpolatoryQuadratureSimplexGroupFactory(order) - discr = Discretization(cl_ctx, mesh, factory) + discr = Discretization(actx, mesh, factory) lpot_source = QBXLayerPotentialSource(discr, qbx_order=order, # not used in target association @@ -263,20 +289,25 @@ def test_target_association(ctx_factory, curve_name, curve_f, nelements, rng = PhiloxGenerator(cl_ctx, seed=RNG_SEED) dd = places.auto_source.to_stage1() - centers = bind(places, sym.interleaved_expansion_centers( - lpot_source.ambient_dim, dofdesc=dd))(queue) - centers = np.array([ax.get(queue) for ax in centers]) - - tunnel_radius = bind(places, sym._close_target_tunnel_radii( - lpot_source.ambient_dim, dofdesc=dd))(queue) + centers = dof_array_to_numpy(actx, + bind(places, sym.interleaved_expansion_centers( + lpot_source.ambient_dim, dofdesc=dd))(actx)) density_discr = places.get_discretization(dd.geometry) - noise = rng.uniform(queue, density_discr.nnodes, dtype=np.float, a=0.01, b=1.0) + + noise = actx.to_numpy( + rng.uniform(queue, density_discr.ndofs, dtype=np.float, a=0.01, b=1.0)) + + tunnel_radius = dof_array_to_numpy(actx, + bind(places, sym._close_target_tunnel_radii( + lpot_source.ambient_dim, dofdesc=dd))(actx)) def targets_from_sources(sign, dist, dim=2): - nodes = bind(places, sym.nodes(dim, dofdesc=dd))(queue) - normals = bind(places, sym.normal(dim, dofdesc=dd))(queue) - return (nodes + normals * sign * dist).as_vector(np.object) + nodes = dof_array_to_numpy(actx, + bind(places, sym.nodes(dim, dofdesc=dd))(actx).as_vector(np.object)) + normals = dof_array_to_numpy(actx, + bind(places, sym.normal(dim, dofdesc=dd))(actx).as_vector(np.object)) + return actx.from_numpy(nodes + normals * sign * dist) from pytential.target import PointsTarget int_targets = PointsTarget(targets_from_sources(-1, noise * tunnel_radius)) @@ -297,7 +328,7 @@ def test_target_association(ctx_factory, curve_name, curve_f, nelements, (far_targets, 0), ) - sizes = np.cumsum([discr.nnodes for discr, _ in target_discrs]) + sizes = np.cumsum([discr.ndofs for discr, _ in target_discrs]) (surf_int_slice, surf_ext_slice, @@ -315,23 +346,24 @@ def test_target_association(ctx_factory, curve_name, curve_f, nelements, from pytential.qbx.utils import TreeCodeContainer code_container = TargetAssociationCodeContainer( - cl_ctx, TreeCodeContainer(cl_ctx)) + actx, TreeCodeContainer(actx)) target_assoc = (associate_targets_to_qbx_centers( places, places.auto_source, - code_container.get_wrangler(queue), + code_container.get_wrangler(actx), target_discrs, target_association_tolerance=1e-10) .get(queue=queue)) - expansion_radii = bind(places, sym.expansion_radii( - lpot_source.ambient_dim, - granularity=sym.GRANULARITY_CENTER))(queue).get() - surf_targets = np.array( - [axis.get(queue) for axis in density_discr.nodes()]) - int_targets = np.array([axis.get(queue) for axis in int_targets.nodes()]) - ext_targets = np.array([axis.get(queue) for axis in ext_targets.nodes()]) + expansion_radii = dof_array_to_numpy(actx, + bind(places, sym.expansion_radii( + lpot_source.ambient_dim, + granularity=sym.GRANULARITY_CENTER))(actx)) + from meshmode.dof_array import thaw + surf_targets = dof_array_to_numpy(actx, thaw(actx, density_discr.nodes())) + int_targets = actx.to_numpy(int_targets.nodes()) + ext_targets = actx.to_numpy(ext_targets.nodes()) def visualize_curve_and_assoc(): import matplotlib.pyplot as plt @@ -416,6 +448,7 @@ def test_target_association(ctx_factory, curve_name, curve_f, nelements, def test_target_association_failure(ctx_factory): cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) # {{{ generate circle @@ -430,7 +463,7 @@ def test_target_association_failure(ctx_factory): from meshmode.discretization.poly_element import \ InterpolatoryQuadratureSimplexGroupFactory factory = InterpolatoryQuadratureSimplexGroupFactory(order) - discr = Discretization(cl_ctx, mesh, factory) + discr = Discretization(actx, mesh, factory) lpot_source = QBXLayerPotentialSource(discr, qbx_order=order, # not used in target association fine_order=order) @@ -444,8 +477,9 @@ def test_target_association_failure(ctx_factory): 2j * np.pi * np.linspace(0, 1, 500, endpoint=False)) from pytential.target import PointsTarget close_circle_target = ( - PointsTarget(cl.array.to_device( - queue, np.array([close_circle.real, close_circle.imag])))) + PointsTarget( + actx.from_numpy( + np.array([close_circle.real, close_circle.imag])))) targets = ( (close_circle_target, 0), @@ -458,13 +492,13 @@ def test_target_association_failure(ctx_factory): from pytential.qbx.utils import TreeCodeContainer code_container = TargetAssociationCodeContainer( - cl_ctx, TreeCodeContainer(cl_ctx)) + actx, TreeCodeContainer(actx)) with pytest.raises(QBXTargetAssociationFailedException): associate_targets_to_qbx_centers( places, places.auto_source, - code_container.get_wrangler(queue), + code_container.get_wrangler(actx), targets, target_association_tolerance=1e-10) diff --git a/test/test_layer_pot.py b/test/test_layer_pot.py index c7eeb200c97195a51881a28fcb4092d94d257599..9fa844355b2699baa26511b3efbf1e4a1d1de45b 100644 --- a/test/test_layer_pot.py +++ b/test/test_layer_pot.py @@ -26,12 +26,12 @@ THE SOFTWARE. import numpy as np import numpy.linalg as la # noqa import pyopencl as cl -import pyopencl.clmath # noqa import pytest from pyopencl.tools import ( # noqa pytest_generate_tests_for_pyopencl as pytest_generate_tests) from functools import partial +from meshmode.array_context import PyOpenCLArrayContext from meshmode.mesh.generation import ( # noqa ellipse, cloverleaf, starfish, drop, n_gon, qbx_peanut, WobblyCircle, make_curve_mesh, NArmedStarfish) @@ -51,6 +51,7 @@ circle = partial(ellipse, 1) def test_geometry(ctx_factory): cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) nelements = 30 order = 5 @@ -63,13 +64,13 @@ def test_geometry(ctx_factory): from meshmode.discretization.poly_element import \ InterpolatoryQuadratureSimplexGroupFactory - discr = Discretization(cl_ctx, mesh, + discr = Discretization(actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(order)) import pytential.symbolic.primitives as prim area_sym = prim.integral(2, 1, 1) - area = bind(discr, area_sym)(queue) + area = bind(discr, area_sym)(actx) err = abs(area-2*np.pi) print(err) @@ -86,6 +87,7 @@ def test_off_surface_eval(ctx_factory, use_fmm, visualize=False): cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) # prevent cache 'splosion from sympy.core.cache import clear_cache @@ -109,7 +111,7 @@ def test_off_surface_eval(ctx_factory, use_fmm, visualize=False): InterpolatoryQuadratureSimplexGroupFactory pre_density_discr = Discretization( - cl_ctx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order)) + actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order)) qbx = QBXLayerPotentialSource( pre_density_discr, 4*target_order, @@ -127,16 +129,16 @@ def test_off_surface_eval(ctx_factory, use_fmm, visualize=False): from sumpy.kernel import LaplaceKernel op = sym.D(LaplaceKernel(2), sym.var("sigma"), qbx_forced_limit=-2) - sigma = density_discr.zeros(queue) + 1 - fld_in_vol = bind(places, op)(queue, sigma=sigma) + sigma = density_discr.zeros(actx) + 1 + fld_in_vol = bind(places, op)(actx, sigma=sigma) fld_in_vol_exact = -1 - err = cl.clmath.fabs(fld_in_vol - fld_in_vol_exact) - linf_err = cl.array.max(err).get() + err = actx.np.fabs(fld_in_vol - fld_in_vol_exact) + linf_err = actx.to_numpy(err).max() print("l_inf error:", linf_err) if visualize: - fplot.show_scalar_in_matplotlib(fld_in_vol.get()) + fplot.show_scalar_in_matplotlib(actx.to_numpy(fld_in_vol)) import matplotlib.pyplot as pt pt.colorbar() pt.show() @@ -153,6 +155,7 @@ def test_off_surface_eval_vs_direct(ctx_factory, do_plot=False): cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) # prevent cache 'splosion from sympy.core.cache import clear_cache @@ -172,7 +175,7 @@ def test_off_surface_eval_vs_direct(ctx_factory, do_plot=False): InterpolatoryQuadratureSimplexGroupFactory pre_density_discr = Discretization( - cl_ctx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order)) + actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order)) direct_qbx = QBXLayerPotentialSource( pre_density_discr, 4*target_order, qbx_order, fmm_order=False, @@ -201,31 +204,31 @@ def test_off_surface_eval_vs_direct(ctx_factory, do_plot=False): from pytential.qbx import QBXTargetAssociationFailedException op = sym.D(LaplaceKernel(2), sym.var("sigma"), qbx_forced_limit=None) try: - direct_sigma = direct_density_discr.zeros(queue) + 1 + direct_sigma = direct_density_discr.zeros(actx) + 1 direct_fld_in_vol = bind(places, op, auto_where=("direct_qbx", "target"))( - queue, sigma=direct_sigma) + actx, sigma=direct_sigma) except QBXTargetAssociationFailedException as e: - fplot.show_scalar_in_matplotlib(e.failed_target_flags.get(queue)) + fplot.show_scalar_in_matplotlib( + actx.to_numpy(actx.thaw(e.failed_target_flags))) import matplotlib.pyplot as pt pt.show() raise - fmm_sigma = fmm_density_discr.zeros(queue) + 1 + fmm_sigma = fmm_density_discr.zeros(actx) + 1 fmm_fld_in_vol = bind(places, op, auto_where=("fmm_qbx", "target"))( - queue, sigma=fmm_sigma) + actx, sigma=fmm_sigma) - err = cl.clmath.fabs(fmm_fld_in_vol - direct_fld_in_vol) - - linf_err = cl.array.max(err).get() + err = actx.np.fabs(fmm_fld_in_vol - direct_fld_in_vol) + linf_err = actx.to_numpy(err).max() print("l_inf error:", linf_err) if do_plot: #fplot.show_scalar_in_mayavi(0.1*.get(queue)) fplot.write_vtk_file("potential.vts", [ - ("fmm_fld_in_vol", fmm_fld_in_vol.get(queue)), - ("direct_fld_in_vol", direct_fld_in_vol.get(queue)) + ("fmm_fld_in_vol", actx.to_numpy(fmm_fld_in_vol)), + ("direct_fld_in_vol", actx.to_numpy(direct_fld_in_vol)) ]) assert linf_err < 1e-3 @@ -235,10 +238,10 @@ def test_off_surface_eval_vs_direct(ctx_factory, do_plot=False): # {{{ unregularized tests - def test_unregularized_with_ones_kernel(ctx_factory): cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) nelements = 10 order = 8 @@ -251,7 +254,7 @@ def test_unregularized_with_ones_kernel(ctx_factory): from meshmode.discretization.poly_element import \ InterpolatoryQuadratureSimplexGroupFactory - discr = Discretization(cl_ctx, mesh, + discr = Discretization(actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(order)) from pytential.unregularized import UnregularizedLayerPotentialSource @@ -268,24 +271,24 @@ def test_unregularized_with_ones_kernel(ctx_factory): sigma_sym = sym.var("sigma") op = sym.IntG(one_kernel_2d, sigma_sym, qbx_forced_limit=None) - sigma = cl.array.zeros(queue, discr.nnodes, dtype=float) - sigma.fill(1) - sigma.finish() + sigma = discr.zeros(actx) + 1 result_self = bind(places, op, auto_where=places.auto_where)( - queue, sigma=sigma) + actx, sigma=sigma) result_nonself = bind(places, op, auto_where=(places.auto_source, "target_non_self"))( - queue, sigma=sigma) + actx, sigma=sigma) - assert np.allclose(result_self.get(), 2 * np.pi) - assert np.allclose(result_nonself.get(), 2 * np.pi) + from meshmode.dof_array import flatten + assert np.allclose(actx.to_numpy(flatten(result_self)), 2 * np.pi) + assert np.allclose(actx.to_numpy(result_nonself), 2 * np.pi) def test_unregularized_off_surface_fmm_vs_direct(ctx_factory): cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) nelements = 300 target_order = 8 @@ -303,7 +306,7 @@ def test_unregularized_off_surface_fmm_vs_direct(ctx_factory): InterpolatoryQuadratureSimplexGroupFactory density_discr = Discretization( - cl_ctx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order)) + actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order)) direct = UnregularizedLayerPotentialSource( density_discr, fmm_order=False, @@ -311,7 +314,7 @@ def test_unregularized_off_surface_fmm_vs_direct(ctx_factory): fmm = direct.copy( fmm_level_to_order=lambda kernel, kernel_args, tree, level: fmm_order) - sigma = density_discr.zeros(queue) + 1 + sigma = density_discr.zeros(actx) + 1 fplot = FieldPlotter(np.zeros(2), extent=5, npoints=100) from pytential.target import PointsTarget @@ -332,13 +335,12 @@ def test_unregularized_off_surface_fmm_vs_direct(ctx_factory): direct_fld_in_vol = bind(places, op, auto_where=("unregularized_direct", "targets"))( - queue, sigma=sigma) + actx, sigma=sigma) fmm_fld_in_vol = bind(places, op, - auto_where=("unregularized_fmm", "targets"))(queue, sigma=sigma) - - err = cl.clmath.fabs(fmm_fld_in_vol - direct_fld_in_vol) + auto_where=("unregularized_fmm", "targets"))(actx, sigma=sigma) - linf_err = cl.array.max(err).get() + err = actx.np.fabs(fmm_fld_in_vol - direct_fld_in_vol) + linf_err = actx.to_numpy(err).max() print("l_inf error:", linf_err) assert linf_err < 5e-3 @@ -356,6 +358,7 @@ def test_3d_jump_relations(ctx_factory, relation, visualize=False): cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) if relation == "div_s": target_order = 3 @@ -377,7 +380,7 @@ def test_3d_jump_relations(ctx_factory, relation, visualize=False): from meshmode.discretization.poly_element import \ InterpolatoryQuadratureSimplexGroupFactory pre_discr = Discretization( - cl_ctx, mesh, + actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(3)) from pytential.qbx import QBXLayerPotentialSource @@ -401,8 +404,9 @@ def test_3d_jump_relations(ctx_factory, relation, visualize=False): sym.cse(sym.tangential_to_xyz(density_sym), "jxyz"), qbx_forced_limit=qbx_forced_limit))) - x, y, z = density_discr.nodes().with_queue(queue) - m = cl.clmath + from meshmode.dof_array import thaw + x, y, z = thaw(actx, density_discr.nodes()) + m = actx.np if relation == "nxcurls": density_sym = sym.make_sym_vector("density", 2) @@ -417,7 +421,7 @@ def test_3d_jump_relations(ctx_factory, relation, visualize=False): # an XYZ function and project it. density = bind(places, sym.xyz_to_tangential(sym.make_sym_vector("jxyz", 3)))( - queue, + actx, jxyz=sym.make_obj_array([ m.cos(0.5*x) * m.cos(0.5*y) * m.cos(0.5*z), m.sin(0.5*x) * m.cos(0.5*y) * m.sin(0.5*z), @@ -448,12 +452,12 @@ def test_3d_jump_relations(ctx_factory, relation, visualize=False): raise ValueError("unexpected value of 'relation': %s" % relation) bound_jump_identity = bind(places, jump_identity_sym) - jump_identity = bound_jump_identity(queue, density=density) + jump_identity = bound_jump_identity(actx, density=density) - h_max = bind(places, sym.h_max(qbx.ambient_dim))(queue) + h_max = bind(places, sym.h_max(qbx.ambient_dim))(actx) err = ( - norm(density_discr, queue, jump_identity, np.inf) - / norm(density_discr, queue, density, np.inf)) + norm(density_discr, jump_identity, np.inf) + / norm(density_discr, density, np.inf)) print("ERROR", h_max, err) eoc_rec.add_data_point(h_max, err) @@ -461,15 +465,15 @@ def test_3d_jump_relations(ctx_factory, relation, visualize=False): # {{{ visualization if visualize and relation == "nxcurls": - nxcurlS_ext = bind(places, nxcurlS(+1))(queue, density=density) - nxcurlS_avg = bind(places, nxcurlS("avg"))(queue, density=density) + nxcurlS_ext = bind(places, nxcurlS(+1))(actx, density=density) + nxcurlS_avg = bind(places, nxcurlS("avg"))(actx, density=density) jtxyz = bind(places, sym.tangential_to_xyz(density_sym))( - queue, density=density) + actx, density=density) from meshmode.discretization.visualization import make_visualizer - bdry_vis = make_visualizer(queue, qbx.density_discr, target_order+3) + bdry_vis = make_visualizer(actx, qbx.density_discr, target_order+3) - bdry_normals = bind(places, sym.normal(3))(queue)\ + bdry_normals = bind(places, sym.normal(3))(actx)\ .as_vector(dtype=object) bdry_vis.write_vtk_file("source-%s.vtu" % nel_factor, [ @@ -481,15 +485,15 @@ def test_3d_jump_relations(ctx_factory, relation, visualize=False): if visualize and relation == "sp": op = sym.Sp(knl, density_sym, qbx_forced_limit=+1) - sp_ext = bind(places, op)(queue, density=density) + sp_ext = bind(places, op)(actx, density=density) op = sym.Sp(knl, density_sym, qbx_forced_limit="avg") - sp_avg = bind(places, op)(queue, density=density) + sp_avg = bind(places, op)(actx, density=density) from meshmode.discretization.visualization import make_visualizer - bdry_vis = make_visualizer(queue, qbx.density_discr, target_order+3) + bdry_vis = make_visualizer(actx, qbx.density_discr, target_order+3) bdry_normals = bind(places, - sym.normal(3))(queue).as_vector(dtype=object) + sym.normal(3))(actx).as_vector(dtype=object) bdry_vis.write_vtk_file("source-%s.vtu" % nel_factor, [ ("density", density), diff --git a/test/test_layer_pot_eigenvalues.py b/test/test_layer_pot_eigenvalues.py index af2278ebeb402190cd921255404bd06ed35a0d26..68baf63396b329914b4e7cba87c4abfabdfb1f6d 100644 --- a/test/test_layer_pot_eigenvalues.py +++ b/test/test_layer_pot_eigenvalues.py @@ -26,12 +26,12 @@ THE SOFTWARE. import numpy as np import numpy.linalg as la # noqa import pyopencl as cl -import pyopencl.clmath # noqa import pytest from pyopencl.tools import ( # noqa pytest_generate_tests_for_pyopencl as pytest_generate_tests) from functools import partial +from meshmode.array_context import PyOpenCLArrayContext from meshmode.mesh.generation import ( # noqa ellipse, cloverleaf, starfish, drop, n_gon, qbx_peanut, WobblyCircle, make_curve_mesh, NArmedStarfish) @@ -72,6 +72,7 @@ def test_ellipse_eigenvalues(ctx_factory, ellipse_aspect, mode_nr, qbx_order, cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) target_order = 8 @@ -107,7 +108,7 @@ def test_ellipse_eigenvalues(ctx_factory, ellipse_aspect, mode_nr, qbx_order, fmm_order = False pre_density_discr = Discretization( - cl_ctx, mesh, + actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order)) qbx = QBXLayerPotentialSource( pre_density_discr, 4*target_order, @@ -117,18 +118,19 @@ def test_ellipse_eigenvalues(ctx_factory, ellipse_aspect, mode_nr, qbx_order, places = GeometryCollection(qbx) density_discr = places.get_discretization(places.auto_source.geometry) - nodes = density_discr.nodes().with_queue(queue) + from meshmode.dof_array import thaw, flatten + nodes = thaw(actx, density_discr.nodes()) if visualize: # plot geometry, centers, normals centers = bind(places, - sym.expansion_centers(qbx.ambient_dim, +1))(queue) - normal = bind(places, - sym.normal(qbx.ambient_dim))(queue).as_vector(np.object) + sym.expansion_centers(qbx.ambient_dim, +1))(actx) + normals = bind(places, + sym.normal(qbx.ambient_dim))(actx).as_vector(np.object) - nodes_h = nodes.get() - centers_h = [centers[0].get(), centers[1].get()] - normals_h = [normal[0].get(), normal[1].get()] + nodes_h = np.array([actx.to_numpy(axis) for axis in flatten(nodes)]) + centers_h = np.array([actx.to_numpy(axis) for axis in flatten(centers)]) + normals_h = np.array([actx.to_numpy(axis) for axis in flatten(normals)]) pt.plot(nodes_h[0], nodes_h[1], "x-") pt.plot(centers_h[0], centers_h[1], "o") @@ -136,14 +138,14 @@ def test_ellipse_eigenvalues(ctx_factory, ellipse_aspect, mode_nr, qbx_order, pt.gca().set_aspect("equal") pt.show() - angle = cl.clmath.atan2(nodes[1]*ellipse_aspect, nodes[0]) + angle = actx.np.arctan2(nodes[1]*ellipse_aspect, nodes[0]) ellipse_fraction = ((1-ellipse_aspect)/(1+ellipse_aspect))**mode_nr # (2.6) in [1] - J = cl.clmath.sqrt( # noqa - cl.clmath.sin(angle)**2 - + (1/ellipse_aspect)**2 * cl.clmath.cos(angle)**2) + J = actx.np.sqrt( # noqa + actx.np.sin(angle)**2 + + (1/ellipse_aspect)**2 * actx.np.cos(angle)**2) from sumpy.kernel import LaplaceKernel lap_knl = LaplaceKernel(2) @@ -153,8 +155,8 @@ def test_ellipse_eigenvalues(ctx_factory, ellipse_aspect, mode_nr, qbx_order, sigma_sym = sym.var("sigma") s_sigma_op = sym.S(lap_knl, sigma_sym, qbx_forced_limit=+1) - sigma = cl.clmath.cos(mode_nr*angle)/J - s_sigma = bind(places, s_sigma_op)(queue=queue, sigma=sigma) + sigma = actx.np.cos(mode_nr*angle)/J + s_sigma = bind(places, s_sigma_op)(actx, sigma=sigma) # SIGN BINGO! :) s_eigval = 1/(2*mode_nr) * (1 + (-1)**mode_nr * ellipse_fraction) @@ -165,14 +167,14 @@ def test_ellipse_eigenvalues(ctx_factory, ellipse_aspect, mode_nr, qbx_order, if 0: #pt.plot(s_sigma.get(), label="result") #pt.plot(s_sigma_ref.get(), label="ref") - pt.plot((s_sigma_ref - s_sigma).get(), label="err") + pt.plot(actx.to_numpy(flatten(s_sigma_ref - s_sigma)), label="err") pt.legend() pt.show() - h_max = bind(places, sym.h_max(qbx.ambient_dim))(queue) + h_max = bind(places, sym.h_max(qbx.ambient_dim))(actx) s_err = ( - norm(density_discr, queue, s_sigma - s_sigma_ref) - / norm(density_discr, queue, s_sigma_ref)) + norm(density_discr, s_sigma - s_sigma_ref) + / norm(density_discr, s_sigma_ref)) s_eoc_rec.add_data_point(h_max, s_err) # }}} @@ -181,8 +183,8 @@ def test_ellipse_eigenvalues(ctx_factory, ellipse_aspect, mode_nr, qbx_order, d_sigma_op = sym.D(lap_knl, sigma_sym, qbx_forced_limit="avg") - sigma = cl.clmath.cos(mode_nr*angle) - d_sigma = bind(places, d_sigma_op)(queue=queue, sigma=sigma) + sigma = actx.np.cos(mode_nr*angle) + d_sigma = bind(places, d_sigma_op)(actx, sigma=sigma) # SIGN BINGO! :) d_eigval = -(-1)**mode_nr * 1/2*ellipse_fraction @@ -190,18 +192,18 @@ def test_ellipse_eigenvalues(ctx_factory, ellipse_aspect, mode_nr, qbx_order, d_sigma_ref = d_eigval*sigma if 0: - pt.plot(d_sigma.get(), label="result") - pt.plot(d_sigma_ref.get(), label="ref") + pt.plot(actx.to_numpy(flatten(d_sigma)), label="result") + pt.plot(actx.to_numpy(flatten(d_sigma_ref)), label="ref") pt.legend() pt.show() if ellipse_aspect == 1: - d_ref_norm = norm(density_discr, queue, sigma) + d_ref_norm = norm(density_discr, sigma) else: - d_ref_norm = norm(density_discr, queue, d_sigma_ref) + d_ref_norm = norm(density_discr, d_sigma_ref) d_err = ( - norm(density_discr, queue, d_sigma - d_sigma_ref) + norm(density_discr, d_sigma - d_sigma_ref) / d_ref_norm) d_eoc_rec.add_data_point(h_max, d_err) @@ -212,15 +214,15 @@ def test_ellipse_eigenvalues(ctx_factory, ellipse_aspect, mode_nr, qbx_order, sp_sigma_op = sym.Sp(lap_knl, sym.var("sigma"), qbx_forced_limit="avg") - sigma = cl.clmath.cos(mode_nr*angle) - sp_sigma = bind(places, sp_sigma_op)(queue=queue, sigma=sigma) + sigma = actx.np.cos(mode_nr*angle) + sp_sigma = bind(places, sp_sigma_op)(actx, sigma=sigma) sp_eigval = 0 sp_sigma_ref = sp_eigval*sigma sp_err = ( - norm(density_discr, queue, sp_sigma - sp_sigma_ref) - / norm(density_discr, queue, sigma)) + norm(density_discr, sp_sigma - sp_sigma_ref) + / norm(density_discr, sigma)) sp_eoc_rec.add_data_point(h_max, sp_err) # }}} @@ -261,6 +263,7 @@ def test_sphere_eigenvalues(ctx_factory, mode_m, mode_n, qbx_order, cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) target_order = 8 @@ -277,8 +280,8 @@ def test_sphere_eigenvalues(ctx_factory, mode_m, mode_n, qbx_order, def rel_err(comp, ref): return ( - norm(density_discr, queue, comp - ref) - / norm(density_discr, queue, ref)) + norm(density_discr, comp - ref) + / norm(density_discr, ref)) for nrefinements in [0, 1]: from meshmode.mesh.generation import generate_icosphere @@ -292,7 +295,7 @@ def test_sphere_eigenvalues(ctx_factory, mode_m, mode_n, qbx_order, mesh = refiner.get_current_mesh() pre_density_discr = Discretization( - cl_ctx, mesh, + actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order)) qbx = QBXLayerPotentialSource( pre_density_discr, 4*target_order, @@ -301,14 +304,20 @@ def test_sphere_eigenvalues(ctx_factory, mode_m, mode_n, qbx_order, ) places = GeometryCollection(qbx) - density_discr = places.get_discretization(places.auto_source.geometry) - nodes = density_discr.nodes().with_queue(queue) - r = cl.clmath.sqrt(nodes[0]**2 + nodes[1]**2 + nodes[2]**2) - phi = cl.clmath.acos(nodes[2]/r) - theta = cl.clmath.atan2(nodes[0], nodes[1]) + from meshmode.dof_array import flatten, unflatten, thaw - ymn = cl.array.to_device(queue, - special.sph_harm(mode_m, mode_n, theta.get(), phi.get())) + density_discr = places.get_discretization(places.auto_source.geometry) + nodes = thaw(actx, density_discr.nodes()) + r = actx.np.sqrt(nodes[0]*nodes[0] + nodes[1]*nodes[1] + nodes[2]*nodes[2]) + phi = actx.np.acos(nodes[2]/r) + theta = actx.np.arctan2(nodes[0], nodes[1]) + + ymn = unflatten(actx, density_discr, + actx.from_numpy( + special.sph_harm( + mode_m, mode_n, + actx.to_numpy(flatten(theta)), + actx.to_numpy(flatten(phi))))) from sumpy.kernel import LaplaceKernel lap_knl = LaplaceKernel(3) @@ -317,10 +326,10 @@ def test_sphere_eigenvalues(ctx_factory, mode_m, mode_n, qbx_order, s_sigma_op = bind(places, sym.S(lap_knl, sym.var("sigma"), qbx_forced_limit=+1)) - s_sigma = s_sigma_op(queue=queue, sigma=ymn) + s_sigma = s_sigma_op(actx, sigma=ymn) s_eigval = 1/(2*mode_n + 1) - h_max = bind(places, sym.h_max(qbx.ambient_dim))(queue) + h_max = bind(places, sym.h_max(qbx.ambient_dim))(actx) s_eoc_rec.add_data_point(h_max, rel_err(s_sigma, s_eigval*ymn)) # }}} @@ -329,7 +338,7 @@ def test_sphere_eigenvalues(ctx_factory, mode_m, mode_n, qbx_order, d_sigma_op = bind(places, sym.D(lap_knl, sym.var("sigma"), qbx_forced_limit="avg")) - d_sigma = d_sigma_op(queue=queue, sigma=ymn) + d_sigma = d_sigma_op(actx, sigma=ymn) d_eigval = -1/(2*(2*mode_n + 1)) d_eoc_rec.add_data_point(h_max, rel_err(d_sigma, d_eigval*ymn)) @@ -339,7 +348,7 @@ def test_sphere_eigenvalues(ctx_factory, mode_m, mode_n, qbx_order, sp_sigma_op = bind(places, sym.Sp(lap_knl, sym.var("sigma"), qbx_forced_limit="avg")) - sp_sigma = sp_sigma_op(queue=queue, sigma=ymn) + sp_sigma = sp_sigma_op(actx, sigma=ymn) sp_eigval = -1/(2*(2*mode_n + 1)) sp_eoc_rec.add_data_point(h_max, rel_err(sp_sigma, sp_eigval*ymn)) @@ -350,7 +359,7 @@ def test_sphere_eigenvalues(ctx_factory, mode_m, mode_n, qbx_order, dp_sigma_op = bind(places, sym.Dp(lap_knl, sym.var("sigma"), qbx_forced_limit="avg")) - dp_sigma = dp_sigma_op(queue=queue, sigma=ymn) + dp_sigma = dp_sigma_op(actx, sigma=ymn) dp_eigval = -(mode_n*(mode_n+1))/(2*mode_n + 1) dp_eoc_rec.add_data_point(h_max, rel_err(dp_sigma, dp_eigval*ymn)) diff --git a/test/test_layer_pot_identity.py b/test/test_layer_pot_identity.py index 7b0cbc65cd87a2061d06894cb405b4341967ba7c..b206e78de063c1f56e3f1f1a2f277e337e2d2414 100644 --- a/test/test_layer_pot_identity.py +++ b/test/test_layer_pot_identity.py @@ -32,6 +32,7 @@ from pyopencl.tools import ( # noqa pytest_generate_tests_for_pyopencl as pytest_generate_tests) from functools import partial +from meshmode.array_context import PyOpenCLArrayContext from meshmode.mesh.generation import ( # noqa ellipse, cloverleaf, starfish, drop, n_gon, qbx_peanut, WobblyCircle, NArmedStarfish, @@ -280,6 +281,7 @@ def test_identity_convergence(ctx_factory, case, visualize=False): cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) # prevent cache 'splosion from sympy.core.cache import clear_cache @@ -314,7 +316,7 @@ def test_identity_convergence(ctx_factory, case, visualize=False): InterpolatoryQuadratureSimplexGroupFactory from pytential.qbx import QBXLayerPotentialSource pre_density_discr = Discretization( - cl_ctx, mesh, + actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order)) qbx = QBXLayerPotentialSource( @@ -331,16 +333,18 @@ def test_identity_convergence(ctx_factory, case, visualize=False): from pytential.qbx.refinement import refine_geometry_collection kernel_length_scale = 5 / case.k if case.k else None - places = refine_geometry_collection(queue, places, + places = refine_geometry_collection(places, kernel_length_scale=kernel_length_scale) # {{{ compute values of a solution to the PDE density_discr = places.get_discretization(places.auto_source.geometry) - nodes_host = density_discr.nodes().get(queue) - normal = bind(places, sym.normal(d))(queue).as_vector(np.object) - normal_host = [normal[j].get() for j in range(d)] + from meshmode.dof_array import thaw, flatten, unflatten + nodes_host = [actx.to_numpy(axis) + for axis in flatten(thaw(actx, density_discr.nodes()))] + normal = bind(places, sym.normal(d))(actx).as_vector(np.object) + normal_host = [actx.to_numpy(axis)for axis in flatten(normal)] if k != 0: if d == 2: @@ -376,31 +380,33 @@ def test_identity_convergence(ctx_factory, case, visualize=False): # }}} - u_dev = cl.array.to_device(queue, u) - dn_u_dev = cl.array.to_device(queue, dn_u) - grad_u_dev = cl.array.to_device(queue, grad_u) + u_dev = unflatten(actx, density_discr, actx.from_numpy(u)) + dn_u_dev = unflatten(actx, density_discr, actx.from_numpy(dn_u)) + from pytools.obj_array import make_obj_array, obj_array_vectorize + grad_u_dev = unflatten(actx, density_discr, + obj_array_vectorize(actx.from_numpy, make_obj_array(grad_u))) key = (case.qbx_order, case.geometry.mesh_name, resolution, case.expr.zero_op_name) bound_op = bind(places, case.expr.get_zero_op(k_sym, **knl_kwargs)) error = bound_op( - queue, u=u_dev, dn_u=dn_u_dev, grad_u=grad_u_dev, k=case.k) + actx, u=u_dev, dn_u=dn_u_dev, grad_u=grad_u_dev, k=case.k) if 0: pt.plot(error) pt.show() - linf_error_norm = norm(density_discr, queue, error, p=np.inf) + linf_error_norm = norm(density_discr, error, p=np.inf) print("--->", key, linf_error_norm) - h_max = bind(places, sym.h_max(qbx.ambient_dim))(queue) + h_max = bind(places, sym.h_max(qbx.ambient_dim))(actx) eoc_rec.add_data_point(h_max, linf_error_norm) if visualize: from meshmode.discretization.visualization import make_visualizer - bdry_vis = make_visualizer(queue, density_discr, target_order) + bdry_vis = make_visualizer(actx, density_discr, target_order) - bdry_normals = bind(places, sym.normal(mesh.ambient_dim))(queue)\ + bdry_normals = bind(places, sym.normal(mesh.ambient_dim))(actx)\ .as_vector(dtype=np.object) bdry_vis.write_vtk_file("source-%s.vtu" % resolution, [ diff --git a/test/test_linalg_proxy.py b/test/test_linalg_proxy.py index a44872906c08f68e9c387b0006da34933de859ad..8e485d6e9fb3e3cb8fc6f541971796bc8fcf4f63 100644 --- a/test/test_linalg_proxy.py +++ b/test/test_linalg_proxy.py @@ -29,6 +29,8 @@ import pyopencl as cl import pyopencl.array # noqa from pytential import bind, sym + +from meshmode.array_context import PyOpenCLArrayContext from meshmode.mesh.generation import ( # noqa ellipse, NArmedStarfish, generate_torus, make_curve_mesh) @@ -41,9 +43,9 @@ from pyopencl.tools import ( # noqa from test_matrix import _build_geometry, _build_block_index -def _plot_partition_indices(queue, discr, indices, **kwargs): +def _plot_partition_indices(actx, discr, indices, **kwargs): import matplotlib.pyplot as pt - indices = indices.get(queue) + indices = indices.get(actx.queue) args = [ kwargs.get("method", "unknown"), @@ -57,12 +59,13 @@ def _plot_partition_indices(queue, discr, indices, **kwargs): pt.savefig("test_partition_{0}_{1}_{3}d_ranges_{2}.png".format(*args)) pt.clf() + from pytential.utils import flatten_to_numpy if discr.ambient_dim == 2: - sources = discr.nodes().get(queue) + sources = flatten_to_numpy(actx, discr.nodes()) pt.figure(figsize=(10, 8), dpi=300) - if indices.indices.shape[0] != discr.nnodes: + if indices.indices.shape[0] != discr.ndofs: pt.plot(sources[0], sources[1], 'ko', alpha=0.5) for i in range(indices.nblocks): isrc = indices.block_indices(i) @@ -80,17 +83,20 @@ def _plot_partition_indices(queue, discr, indices, **kwargs): return from meshmode.discretization.visualization import make_visualizer - marker = -42.0 * np.ones(discr.nnodes) + marker = -42.0 * np.ones(discr.ndofs) for i in range(indices.nblocks): isrc = indices.block_indices(i) marker[isrc] = 10.0 * (i + 1.0) - vis = make_visualizer(queue, discr, 10) + from meshmode.dof_array import unflatten + marker = unflatten(actx, discr, actx.from_numpy(marker)) + + vis = make_visualizer(actx, discr, 10) - filename = "test_partition_{0}_{1}_{3}d_{2}.png".format(*args) + filename = "test_partition_{0}_{1}_{3}d_{2}.vtu".format(*args) vis.write_vtk_file(filename, [ - ("marker", cl.array.to_device(queue, marker)) + ("marker", marker) ]) @@ -99,12 +105,14 @@ def _plot_partition_indices(queue, discr, indices, **kwargs): def test_partition_points(ctx_factory, use_tree, ambient_dim, visualize=False): ctx = ctx_factory() queue = cl.CommandQueue(ctx) + actx = PyOpenCLArrayContext(queue) - places, dofdesc = _build_geometry(queue, ambient_dim=ambient_dim) - _build_block_index(queue, - places.get_discretization(dofdesc.geometry, dofdesc.discr_stage), - use_tree=use_tree, - factor=0.6) + places, dofdesc = _build_geometry(actx, ambient_dim=ambient_dim) + discr = places.get_discretization(dofdesc.geometry, dofdesc.discr_stage) + indices = _build_block_index(actx, discr, use_tree=use_tree, factor=0.6) + + if visualize: + _plot_partition_indices(actx, discr, indices, use_tree=use_tree) @pytest.mark.parametrize("ambient_dim", [2, 3]) @@ -112,24 +120,23 @@ def test_partition_points(ctx_factory, use_tree, ambient_dim, visualize=False): def test_proxy_generator(ctx_factory, ambient_dim, factor, visualize=False): ctx = ctx_factory() queue = cl.CommandQueue(ctx) + actx = PyOpenCLArrayContext(queue) - places, dofdesc = _build_geometry(queue, ambient_dim=ambient_dim) + places, dofdesc = _build_geometry(actx, ambient_dim=ambient_dim) dofdesc = dofdesc.to_stage1() density_discr = places.get_discretization(dofdesc.geometry, dofdesc.discr_stage) - srcindices = _build_block_index(queue, - density_discr, - factor=factor) + srcindices = _build_block_index(actx, density_discr, factor=factor) from pytential.linalg.proxy import ProxyGenerator generator = ProxyGenerator(places) proxies, pxyranges, pxycenters, pxyradii = \ - generator(queue, dofdesc, srcindices) + generator(actx, dofdesc, srcindices) - proxies = np.vstack([p.get() for p in proxies]) - pxyranges = pxyranges.get() - pxycenters = np.vstack([c.get() for c in pxycenters]) - pxyradii = pxyradii.get() + proxies = np.vstack([actx.to_numpy(p) for p in proxies]) + pxyranges = actx.to_numpy(pxyranges) + pxycenters = np.vstack([actx.to_numpy(c) for c in pxycenters]) + pxyradii = actx.to_numpy(pxyradii) for i in range(srcindices.nblocks): ipxy = np.s_[pxyranges[i]:pxyranges[i + 1]] @@ -142,12 +149,14 @@ def test_proxy_generator(ctx_factory, ambient_dim, factor, visualize=False): if ambient_dim == 2: import matplotlib.pyplot as pt - density_nodes = density_discr.nodes().get(queue) - ci = bind(places, sym.expansion_centers(ambient_dim, -1))(queue) - ci = np.vstack([c.get(queue) for c in ci]) - ce = bind(places, sym.expansion_centers(ambient_dim, +1))(queue) - ce = np.vstack([c.get(queue) for c in ce]) - r = bind(places, sym.expansion_radii(ambient_dim))(queue).get() + from pytential.utils import flatten_to_numpy + density_nodes = np.vstack(flatten_to_numpy(actx, density_discr.nodes())) + ci = bind(places, sym.expansion_centers(ambient_dim, -1))(actx) + ci = np.vstack(flatten_to_numpy(actx, ci)) + ce = bind(places, sym.expansion_centers(ambient_dim, +1))(actx) + ce = np.vstack(flatten_to_numpy(actx, ce)) + r = bind(places, sym.expansion_radii(ambient_dim))(actx) + r = flatten_to_numpy(actx, r) for i in range(srcindices.nblocks): isrc = srcindices.block_indices(i) @@ -195,10 +204,10 @@ def test_proxy_generator(ctx_factory, ambient_dim, factor, visualize=False): b=pxycenters[:, i].reshape(-1)) mesh = merge_disjoint_meshes([mesh, density_discr.mesh]) - discr = Discretization(ctx, mesh, + discr = Discretization(actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(10)) - vis = make_visualizer(queue, discr, 10) + vis = make_visualizer(actx, discr, 10) filename = "test_proxy_generator_{}d_{:04}.vtu".format( ambient_dim, i) vis.write_vtk_file(filename, []) @@ -209,26 +218,25 @@ def test_proxy_generator(ctx_factory, ambient_dim, factor, visualize=False): def test_interaction_points(ctx_factory, ambient_dim, factor, visualize=False): ctx = ctx_factory() queue = cl.CommandQueue(ctx) + actx = PyOpenCLArrayContext(queue) - places, dofdesc = _build_geometry(queue, ambient_dim=ambient_dim) + places, dofdesc = _build_geometry(actx, ambient_dim=ambient_dim) dofdesc = dofdesc.to_stage1() density_discr = places.get_discretization(dofdesc.geometry, dofdesc.discr_stage) - srcindices = _build_block_index(queue, - density_discr, - factor=factor) + srcindices = _build_block_index(actx, density_discr, factor=factor) # generate proxy points from pytential.linalg.proxy import ProxyGenerator generator = ProxyGenerator(places) - _, _, pxycenters, pxyradii = generator(queue, dofdesc, srcindices) + _, _, pxycenters, pxyradii = generator(actx, dofdesc, srcindices) from pytential.linalg.proxy import ( # noqa gather_block_neighbor_points, gather_block_interaction_points) - nbrindices = gather_block_neighbor_points(density_discr, + nbrindices = gather_block_neighbor_points(actx, density_discr, srcindices, pxycenters, pxyradii) - nodes, ranges = gather_block_interaction_points( + nodes, ranges = gather_block_interaction_points(actx, places, dofdesc, srcindices) srcindices = srcindices.get(queue) @@ -240,12 +248,13 @@ def test_interaction_points(ctx_factory, ambient_dim, factor, visualize=False): assert not np.any(np.isin(inbr, isrc)) + from pytential.utils import flatten_to_numpy if visualize: if ambient_dim == 2: import matplotlib.pyplot as pt - density_nodes = density_discr.nodes().get(queue) - nodes = nodes.get(queue) - ranges = ranges.get(queue) + density_nodes = flatten_to_numpy(actx, density_discr.nodes()) + nodes = flatten_to_numpy(actx, nodes) + ranges = actx.to_numpy(ranges) for i in range(srcindices.nblocks): isrc = srcindices.block_indices(i) @@ -255,14 +264,14 @@ def test_interaction_points(ctx_factory, ambient_dim, factor, visualize=False): pt.figure(figsize=(10, 8)) pt.plot(density_nodes[0], density_nodes[1], 'ko', ms=2.0, alpha=0.5) - pt.plot(density_nodes[0, srcindices.indices], - density_nodes[1, srcindices.indices], + pt.plot(density_nodes[0][srcindices.indices], + density_nodes[1][srcindices.indices], 'o', ms=2.0) - pt.plot(density_nodes[0, isrc], density_nodes[1, isrc], + pt.plot(density_nodes[0][isrc], density_nodes[1][isrc], 'o', ms=2.0) - pt.plot(density_nodes[0, inbr], density_nodes[1, inbr], + pt.plot(density_nodes[0][inbr], density_nodes[1][inbr], 'o', ms=2.0) - pt.plot(nodes[0, iall], nodes[1, iall], + pt.plot(nodes[0][iall], nodes[1][iall], 'x', ms=2.0) pt.xlim([-1.5, 1.5]) pt.ylim([-1.5, 1.5]) @@ -272,7 +281,7 @@ def test_interaction_points(ctx_factory, ambient_dim, factor, visualize=False): pt.clf() elif ambient_dim == 3: from meshmode.discretization.visualization import make_visualizer - marker = np.empty(density_discr.nnodes) + marker = np.empty(density_discr.ndofs) for i in range(srcindices.nblocks): isrc = srcindices.block_indices(i) @@ -282,9 +291,11 @@ def test_interaction_points(ctx_factory, ambient_dim, factor, visualize=False): marker[srcindices.indices] = 0.0 marker[isrc] = -42.0 marker[inbr] = +42.0 - marker_dev = cl.array.to_device(queue, marker) - vis = make_visualizer(queue, density_discr, 10) + from meshmode.dof_array import unflatten + marker_dev = unflatten(actx, density_discr, actx.from_numpy(marker)) + + vis = make_visualizer(actx, density_discr, 10) filename = "test_area_query_{}d_{:04}.vtu".format(ambient_dim, i) vis.write_vtk_file(filename, [ ("marker", marker_dev), diff --git a/test/test_matrix.py b/test/test_matrix.py index 12be496c5b1d50af7fb72d39c458dbbe3fce4d27..ce429642df74870fa2553fffac0e548ee5bb4061 100644 --- a/test/test_matrix.py +++ b/test/test_matrix.py @@ -31,16 +31,17 @@ import numpy as np import numpy.linalg as la import pyopencl as cl -import pyopencl.array # noqa +import pyopencl.array from pytools.obj_array import make_obj_array, is_obj_array from sumpy.tools import BlockIndexRanges, MatrixBlockIndexRanges from sumpy.symbolic import USE_SYMENGINE -from pytential import sym +from pytential import bind, sym from pytential import GeometryCollection +from meshmode.array_context import PyOpenCLArrayContext from meshmode.mesh.generation import ( # noqa ellipse, NArmedStarfish, make_curve_mesh, generate_torus) @@ -55,7 +56,7 @@ except ImportError: pass -def _build_geometry(queue, +def _build_geometry(actx, ambient_dim=2, nelements=30, target_order=7, @@ -79,8 +80,7 @@ def _build_geometry(queue, from meshmode.discretization.poly_element import \ InterpolatoryQuadratureSimplexGroupFactory from pytential.qbx import QBXLayerPotentialSource - density_discr = Discretization( - queue.context, mesh, + density_discr = Discretization(actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order)) qbx = QBXLayerPotentialSource(density_discr, @@ -92,24 +92,24 @@ def _build_geometry(queue, return places, places.auto_source -def _build_block_index(queue, - discr, +def _build_block_index(actx, discr, nblks=10, factor=1.0, use_tree=True): - nnodes = discr.nnodes - max_particles_in_box = nnodes // nblks + max_particles_in_box = discr.ndofs // nblks # create index ranges from pytential.linalg.proxy import partition_by_nodes - indices = partition_by_nodes(discr, - use_tree=use_tree, max_nodes_in_box=max_particles_in_box) + indices = partition_by_nodes(actx, discr, + use_tree=use_tree, + max_nodes_in_box=max_particles_in_box) if abs(factor - 1.0) < 1.0e-14: return indices # randomly pick a subset of points - indices = indices.get(queue) + # FIXME: this needs porting in sumpy.tools.BlockIndexRanges + indices = indices.get(actx.queue) indices_ = np.empty(indices.nblocks, dtype=np.object) for i in range(indices.nblocks): @@ -120,13 +120,11 @@ def _build_block_index(queue, indices_[i] = np.sort( np.random.choice(iidx, size=isize, replace=False)) - ranges_ = cl.array.to_device(queue, - np.cumsum([0] + [r.shape[0] for r in indices_])) - indices_ = cl.array.to_device(queue, np.hstack(indices_)) + ranges_ = actx.from_numpy(np.cumsum([0] + [r.shape[0] for r in indices_])) + indices_ = actx.from_numpy(np.hstack(indices_)) - indices = BlockIndexRanges(discr.cl_context, - indices_.with_queue(None), - ranges_.with_queue(None)) + indices = BlockIndexRanges(actx.context, + actx.freeze(indices_), actx.freeze(ranges_)) return indices @@ -137,8 +135,8 @@ def _build_op(lpot_id, source=sym.DEFAULT_SOURCE, target=sym.DEFAULT_TARGET, qbx_forced_limit="avg"): - from sumpy.kernel import LaplaceKernel, HelmholtzKernel + if k: knl = HelmholtzKernel(ambient_dim) knl_kwargs = {"k": k} @@ -200,6 +198,7 @@ def _max_block_error(mat, blk, index_set): def test_matrix_build(ctx_factory, k, curve_f, lpot_id, visualize=False): cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) # prevent cache 'splosion from sympy.core.cache import clear_cache @@ -215,8 +214,7 @@ def test_matrix_build(ctx_factory, k, curve_f, lpot_id, visualize=False): from meshmode.discretization import Discretization from meshmode.discretization.poly_element import \ InterpolatoryQuadratureSimplexGroupFactory - pre_density_discr = Discretization( - cl_ctx, mesh, + pre_density_discr = Discretization(actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order)) from pytential.qbx import QBXLayerPotentialSource @@ -228,7 +226,7 @@ def test_matrix_build(ctx_factory, k, curve_f, lpot_id, visualize=False): from pytential.qbx.refinement import refine_geometry_collection places = GeometryCollection(qbx) - places = refine_geometry_collection(queue, places, + places = refine_geometry_collection(places, kernel_length_scale=(5 / k if k else None)) source = places.auto_source.to_stage1() @@ -237,15 +235,14 @@ def test_matrix_build(ctx_factory, k, curve_f, lpot_id, visualize=False): op, u_sym, knl_kwargs = _build_op(lpot_id, k=k, source=places.auto_source, target=places.auto_target) - from pytential import bind bound_op = bind(places, op) from pytential.symbolic.execution import build_matrix - mat = build_matrix(queue, places, op, u_sym).get() + mat = build_matrix(actx, places, op, u_sym).get() if visualize: from sumpy.tools import build_matrix as build_matrix_via_matvec - mat2 = bound_op.scipy_op(queue, "u", dtype=mat.dtype, **knl_kwargs) + mat2 = bound_op.scipy_op(actx, "u", dtype=mat.dtype, **knl_kwargs) mat2 = build_matrix_via_matvec(mat2) print(la.norm((mat - mat2).real, "fro") / la.norm(mat2.real, "fro"), la.norm((mat - mat2).imag, "fro") / la.norm(mat2.imag, "fro")) @@ -267,23 +264,22 @@ def test_matrix_build(ctx_factory, k, curve_f, lpot_id, visualize=False): pt.colorbar() pt.show() - from sumpy.tools import vector_to_device, vector_from_device + from pytential.utils import unflatten_from_numpy, flatten_to_numpy np.random.seed(12) for i in range(5): if is_obj_array(u_sym): u = make_obj_array([ - np.random.randn(density_discr.nnodes) + np.random.randn(density_discr.ndofs) for _ in range(len(u_sym)) ]) else: - u = np.random.randn(density_discr.nnodes) + u = np.random.randn(density_discr.ndofs) + u_dev = unflatten_from_numpy(actx, density_discr, u) - u_dev = vector_to_device(queue, u) res_matvec = np.hstack( - list(vector_from_device( - queue, bound_op(queue, u=u_dev)))) - - res_mat = mat.dot(np.hstack(list(u))) + flatten_to_numpy(actx, bound_op(actx, u=u_dev)) + ) + res_mat = mat.dot(np.hstack(u)) abs_err = la.norm(res_mat - res_matvec, np.inf) rel_err = abs_err / la.norm(res_matvec, np.inf) @@ -299,6 +295,7 @@ def test_p2p_block_builder(ctx_factory, factor, ambient_dim, lpot_id, visualize=False): ctx = ctx_factory() queue = cl.CommandQueue(ctx) + actx = PyOpenCLArrayContext(queue) # prevent cache explosion from sympy.core.cache import clear_cache @@ -312,7 +309,7 @@ def test_p2p_block_builder(ctx_factory, factor, ambient_dim, lpot_id, ) target_order = 2 if ambient_dim == 3 else 7 - places, dofdesc = _build_geometry(queue, + places, dofdesc = _build_geometry(actx, target_order=target_order, ambient_dim=ambient_dim, auto_where=place_ids) @@ -323,14 +320,14 @@ def test_p2p_block_builder(ctx_factory, factor, ambient_dim, lpot_id, dd = places.auto_source density_discr = places.get_discretization(dd.geometry, dd.discr_stage) - index_set = _build_block_index(queue, density_discr, factor=factor) + index_set = _build_block_index(actx, density_discr, factor=factor) index_set = MatrixBlockIndexRanges(ctx, index_set, index_set) from pytential.symbolic.execution import _prepare_expr expr = _prepare_expr(places, op) from pytential.symbolic.matrix import P2PMatrixBuilder - mbuilder = P2PMatrixBuilder(queue, + mbuilder = P2PMatrixBuilder(actx, dep_expr=u_sym, other_dep_exprs=[], dep_source=places.get_geometry(dd.geometry), @@ -341,7 +338,7 @@ def test_p2p_block_builder(ctx_factory, factor, ambient_dim, lpot_id, mat = mbuilder(expr) from pytential.symbolic.matrix import FarFieldBlockBuilder - mbuilder = FarFieldBlockBuilder(queue, + mbuilder = FarFieldBlockBuilder(actx, dep_expr=u_sym, other_dep_exprs=[], dep_source=places.get_geometry(dd.geometry), @@ -352,7 +349,7 @@ def test_p2p_block_builder(ctx_factory, factor, ambient_dim, lpot_id, exclude_self=True) blk = mbuilder(expr) - index_set = index_set.get(queue) + index_set = index_set.get(actx.queue) if visualize and ambient_dim == 2: blk_full = np.zeros_like(mat) mat_full = np.zeros_like(mat) @@ -381,6 +378,7 @@ def test_qbx_block_builder(ctx_factory, factor, ambient_dim, lpot_id, visualize=False): ctx = ctx_factory() queue = cl.CommandQueue(ctx) + actx = PyOpenCLArrayContext(queue) # prevent cache explosion from sympy.core.cache import clear_cache @@ -394,7 +392,7 @@ def test_qbx_block_builder(ctx_factory, factor, ambient_dim, lpot_id, ) target_order = 2 if ambient_dim == 3 else 7 - places, _ = _build_geometry(queue, + places, _ = _build_geometry(actx, target_order=target_order, ambient_dim=ambient_dim, auto_where=place_ids) @@ -409,11 +407,11 @@ def test_qbx_block_builder(ctx_factory, factor, ambient_dim, lpot_id, dd = places.auto_source density_discr = places.get_discretization(dd.geometry, dd.discr_stage) - index_set = _build_block_index(queue, density_discr, factor=factor) + index_set = _build_block_index(actx, density_discr, factor=factor) index_set = MatrixBlockIndexRanges(ctx, index_set, index_set) from pytential.symbolic.matrix import NearFieldBlockBuilder - mbuilder = NearFieldBlockBuilder(queue, + mbuilder = NearFieldBlockBuilder(actx, dep_expr=u_sym, other_dep_exprs=[], dep_source=places.get_geometry(dd.geometry), @@ -424,7 +422,7 @@ def test_qbx_block_builder(ctx_factory, factor, ambient_dim, lpot_id, blk = mbuilder(expr) from pytential.symbolic.matrix import MatrixBuilder - mbuilder = MatrixBuilder(queue, + mbuilder = MatrixBuilder(actx, dep_expr=u_sym, other_dep_exprs=[], dep_source=places.get_geometry(dd.geometry), @@ -462,6 +460,7 @@ def test_build_matrix_places(ctx_factory, source_discr_stage, target_discr_stage, visualize=False): ctx = ctx_factory() queue = cl.CommandQueue(ctx) + actx = PyOpenCLArrayContext(queue) # prevent cache explosion from sympy.core.cache import clear_cache @@ -476,7 +475,7 @@ def test_build_matrix_places(ctx_factory, ) # build test operators - places, _ = _build_geometry(queue, + places, _ = _build_geometry(actx, nelements=8, target_order=2, ambient_dim=2, @@ -493,7 +492,7 @@ def test_build_matrix_places(ctx_factory, dd = places.auto_source source_discr = places.get_discretization(dd.geometry, dd.discr_stage) - index_set = _build_block_index(queue, source_discr, factor=0.6) + index_set = _build_block_index(actx, source_discr, factor=0.6) index_set = MatrixBlockIndexRanges(ctx, index_set, index_set) from pytential.symbolic.execution import _prepare_expr @@ -501,7 +500,7 @@ def test_build_matrix_places(ctx_factory, # build full QBX matrix from pytential.symbolic.matrix import MatrixBuilder - mbuilder = MatrixBuilder(queue, + mbuilder = MatrixBuilder(actx, dep_expr=u_sym, other_dep_exprs=[], dep_source=places.get_geometry(dd.geometry), @@ -512,7 +511,7 @@ def test_build_matrix_places(ctx_factory, # build full p2p matrix from pytential.symbolic.matrix import P2PMatrixBuilder - mbuilder = P2PMatrixBuilder(queue, + mbuilder = P2PMatrixBuilder(actx, dep_expr=u_sym, other_dep_exprs=[], dep_source=places.get_geometry(dd.geometry), @@ -521,11 +520,11 @@ def test_build_matrix_places(ctx_factory, context={}) p2p_mat = mbuilder(op) - assert p2p_mat.shape == (target_discr.nnodes, source_discr.nnodes) + assert p2p_mat.shape == (target_discr.ndofs, source_discr.ndofs) # build block qbx and p2p matrices from pytential.symbolic.matrix import NearFieldBlockBuilder - mbuilder = NearFieldBlockBuilder(queue, + mbuilder = NearFieldBlockBuilder(actx, dep_expr=u_sym, other_dep_exprs=[], dep_source=places.get_geometry(dd.geometry), @@ -538,7 +537,7 @@ def test_build_matrix_places(ctx_factory, assert _max_block_error(qbx_mat, mat, index_set.get(queue)) < 1.0e-14 from pytential.symbolic.matrix import FarFieldBlockBuilder - mbuilder = FarFieldBlockBuilder(queue, + mbuilder = FarFieldBlockBuilder(actx, dep_expr=u_sym, other_dep_exprs=[], dep_source=places.get_geometry(dd.geometry), diff --git a/test/test_maxwell.py b/test/test_maxwell.py index 2e950193e17daf410cf8ef9c4d4fa74cae915670..0fc67b0bf9fa321d8a94d2e254e60f4ad0983f33 100644 --- a/test/test_maxwell.py +++ b/test/test_maxwell.py @@ -31,6 +31,7 @@ import pytest from pytential import bind, sym, norm +from meshmode.array_context import PyOpenCLArrayContext from sumpy.visualization import make_field_plotter_from_bbox # noqa from sumpy.point_calculus import CalculusPatch, frequency_domain_maxwell from sumpy.tools import vector_from_device @@ -77,7 +78,7 @@ class SphereTestCase(MaxwellTestCase): else: return generate_icosphere(0.5, target_order) - def get_source(self, queue): + def get_source(self, actx): if self.is_interior: source_ctr = np.array([[0.35, 0.1, 0.15]]).T else: @@ -87,9 +88,7 @@ class SphereTestCase(MaxwellTestCase): sources = source_ctr + source_rad*2*(np.random.rand(3, 10)-0.5) from pytential.source import PointPotentialSource - return PointPotentialSource( - queue.context, - cl.array.to_device(queue, sources)) + return PointPotentialSource(actx.from_numpy(sources)) class RoundedCubeTestCase(MaxwellTestCase): @@ -121,7 +120,7 @@ class RoundedCubeTestCase(MaxwellTestCase): else: return generate_icosphere(0.5, target_order) - def get_source(self, queue): + def get_source(self, actx): if self.is_interior: source_ctr = np.array([[0.35, 0.1, 0.15]]).T else: @@ -131,9 +130,7 @@ class RoundedCubeTestCase(MaxwellTestCase): sources = source_ctr + source_rad*2*(np.random.rand(3, 10)-0.5) from pytential.source import PointPotentialSource - return PointPotentialSource( - queue.context, - cl.array.to_device(queue, sources)) + return PointPotentialSource(actx.from_numpy(sources)) class ElliptiPlaneTestCase(MaxwellTestCase): @@ -168,7 +165,7 @@ class ElliptiPlaneTestCase(MaxwellTestCase): else: return generate_icosphere(0.5, target_order) - def get_source(self, queue): + def get_source(self, actx): if self.is_interior: source_ctr = np.array([[0.35, 0.1, 0.15]]).T else: @@ -178,9 +175,7 @@ class ElliptiPlaneTestCase(MaxwellTestCase): sources = source_ctr + source_rad*2*(np.random.rand(3, 10)-0.5) from pytential.source import PointPotentialSource - return PointPotentialSource( - queue.context, - cl.array.to_device(queue, sources)) + return PointPotentialSource(actx.from_numpy(sources)) # }}} @@ -228,6 +223,7 @@ def test_pec_mfie_extinction(ctx_factory, case, cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) np.random.seed(12) @@ -245,13 +241,16 @@ def test_pec_mfie_extinction(ctx_factory, case, get_sym_maxwell_plane_wave) mfie = PECChargeCurrentMFIEOperator() - test_source = case.get_source(queue) + test_source = case.get_source(actx) calc_patch = CalculusPatch(np.array([-3, 0, 0]), h=0.01) - calc_patch_tgt = PointsTarget(cl.array.to_device(queue, calc_patch.points)) + calc_patch_tgt = PointsTarget(actx.from_numpy(calc_patch.points)) rng = cl.clrandom.PhiloxGenerator(cl_ctx, seed=12) - src_j = rng.normal(queue, (3, test_source.nnodes), dtype=np.float64) + from pytools.obj_array import make_obj_array + src_j = make_obj_array([ + rng.normal(actx.queue, (test_source.ndofs), dtype=np.float64) + for _ in range(3)]) def eval_inc_field_at(places, source=None, target=None): if source is None: @@ -264,12 +263,12 @@ def test_pec_mfie_extinction(ctx_factory, case, amplitude_vec=np.array([1, 1, 1]), v=np.array([1, 0, 0]), omega=case.k), - auto_where=target)(queue) + auto_where=target)(actx) else: # point source return bind(places, get_sym_maxwell_point_source(mfie.kernel, j_sym, mfie.k), - auto_where=(source, target))(queue, j=src_j, k=case.k) + auto_where=(source, target))(actx, j=src_j, k=case.k) # }}} @@ -294,7 +293,7 @@ def test_pec_mfie_extinction(ctx_factory, case, observation_mesh = case.get_observation_mesh(case.target_order) pre_scat_discr = Discretization( - cl_ctx, scat_mesh, + actx, scat_mesh, InterpolatoryQuadratureSimplexGroupFactory(case.target_order)) qbx = QBXLayerPotentialSource( pre_scat_discr, fine_order=4*case.target_order, @@ -306,7 +305,7 @@ def test_pec_mfie_extinction(ctx_factory, case, scat_discr = qbx.density_discr obs_discr = Discretization( - cl_ctx, observation_mesh, + actx, observation_mesh, InterpolatoryQuadratureSimplexGroupFactory(case.target_order)) places.update({ @@ -324,7 +323,7 @@ def test_pec_mfie_extinction(ctx_factory, case, fplot = make_field_plotter_from_bbox( find_bounding_box(scat_discr.mesh), h=(0.05, 0.05, 0.3), extend_factor=0.3) - fplot_tgt = PointsTarget(cl.array.to_device(queue, fplot.points)) + fplot_tgt = PointsTarget(actx.from_numpy(fplot.points)) places.update({ "qbx_target_tol": qbx_tgt_tol, @@ -337,9 +336,9 @@ def test_pec_mfie_extinction(ctx_factory, case, # {{{ system solve - h_max = bind(places, sym.h_max(qbx.ambient_dim))(queue) + h_max = bind(places, sym.h_max(qbx.ambient_dim))(actx) - pde_test_inc = EHField(vector_from_device(queue, + pde_test_inc = EHField(vector_from_device(actx.queue, eval_inc_field_at(places, target="patch_target"))) source_maxwell_resids = [ @@ -356,7 +355,7 @@ def test_pec_mfie_extinction(ctx_factory, case, bound_j_op = bind(places, mfie.j_operator(loc_sign, jt_sym)) j_rhs = bind(places, mfie.j_rhs(inc_xyz_sym.h))( - queue, inc_fld=inc_field_scat.field, **knl_kwargs) + actx, inc_fld=inc_field_scat.field, **knl_kwargs) gmres_settings = dict( tol=case.gmres_tol, @@ -365,24 +364,24 @@ def test_pec_mfie_extinction(ctx_factory, case, stall_iterations=50, no_progress_factor=1.05) from pytential.solve import gmres gmres_result = gmres( - bound_j_op.scipy_op(queue, "jt", np.complex128, **knl_kwargs), + bound_j_op.scipy_op(actx, "jt", np.complex128, **knl_kwargs), j_rhs, **gmres_settings) jt = gmres_result.solution bound_rho_op = bind(places, mfie.rho_operator(loc_sign, rho_sym)) rho_rhs = bind(places, mfie.rho_rhs(jt_sym, inc_xyz_sym.e))( - queue, jt=jt, inc_fld=inc_field_scat.field, **knl_kwargs) + actx, jt=jt, inc_fld=inc_field_scat.field, **knl_kwargs) gmres_result = gmres( - bound_rho_op.scipy_op(queue, "rho", np.complex128, **knl_kwargs), + bound_rho_op.scipy_op(actx, "rho", np.complex128, **knl_kwargs), rho_rhs, **gmres_settings) rho = gmres_result.solution # }}} - jxyz = bind(places, sym.tangential_to_xyz(jt_sym))(queue, jt=jt) + jxyz = bind(places, sym.tangential_to_xyz(jt_sym))(actx, jt=jt) # {{{ volume eval @@ -393,9 +392,9 @@ def test_pec_mfie_extinction(ctx_factory, case, source = sym.DEFAULT_SOURCE return bind(places, sym_repr, auto_where=(source, target))( - queue, jt=jt, rho=rho, **knl_kwargs) + actx, jt=jt, rho=rho, **knl_kwargs) - pde_test_repr = EHField(vector_from_device(queue, + pde_test_repr = EHField(vector_from_device(actx.queue, eval_repr_at(places, target="patch_target"))) maxwell_residuals = [ @@ -415,12 +414,12 @@ def test_pec_mfie_extinction(ctx_factory, case, pec_bc_e = sym.n_cross(bc_repr.e + inc_xyz_sym.e) pec_bc_h = sym.normal(3).as_vector().dot(bc_repr.h + inc_xyz_sym.h) - eh_bc_values = bind(places, sym.join_fields(pec_bc_e, pec_bc_h))( - queue, jt=jt, rho=rho, inc_fld=inc_field_scat.field, + eh_bc_values = bind(places, sym.flat_obj_array(pec_bc_e, pec_bc_h))( + actx, jt=jt, rho=rho, inc_fld=inc_field_scat.field, **knl_kwargs) def scat_norm(f): - return norm(density_discr, queue, f, p=np.inf) + return norm(density_discr, f, p=np.inf) e_bc_residual = scat_norm(eh_bc_values[:3]) / scat_norm(inc_field_scat.e) h_bc_residual = scat_norm(eh_bc_values[3]) / scat_norm(inc_field_scat.h) @@ -435,11 +434,11 @@ def test_pec_mfie_extinction(ctx_factory, case, if visualize: from meshmode.discretization.visualization import make_visualizer - bdry_vis = make_visualizer(queue, scat_discr, case.target_order+3) + bdry_vis = make_visualizer(actx, scat_discr, case.target_order+3) bdry_normals = bind(places, sym.normal(3, dofdesc="scat_discr") - )(queue).as_vector(dtype=object) + )(actx).as_vector(dtype=object) bdry_vis.write_vtk_file("source-%s.vtu" % resolution, [ ("j", jxyz), @@ -459,12 +458,13 @@ def test_pec_mfie_extinction(ctx_factory, case, fplot.write_vtk_file( "failed-targets.vts", [ - ("failed_targets", e.failed_target_flags.get(queue)) + ("failed_targets", actx.to_numpy( + actx.thaw(e.failed_target_flags))), ]) raise - fplot_repr = EHField(vector_from_device(queue, fplot_repr)) - fplot_inc = EHField(vector_from_device(queue, + fplot_repr = EHField(vector_from_device(actx.queue, fplot_repr)) + fplot_inc = EHField(vector_from_device(actx.queue, eval_inc_field_at(places, target="plot_targets"))) fplot.write_vtk_file( @@ -484,7 +484,7 @@ def test_pec_mfie_extinction(ctx_factory, case, obs_repr = EHField(eval_repr_at(places, target="obs_discr")) def obs_norm(f): - return norm(obs_discr, queue, f, p=np.inf) + return norm(obs_discr, f, p=np.inf) rel_err_e = (obs_norm(inc_field_obs.e + obs_repr.e) / obs_norm(inc_field_obs.e)) diff --git a/test/test_scalar_int_eq.py b/test/test_scalar_int_eq.py index bc197395771f2ed3e82b709bbe9ef00fb4ca7839..82fca9ae12adcce48b2e5a562f96be709ad2f2d8 100644 --- a/test/test_scalar_int_eq.py +++ b/test/test_scalar_int_eq.py @@ -32,6 +32,7 @@ import pytest from pyopencl.tools import ( # noqa pytest_generate_tests_for_pyopencl as pytest_generate_tests) +from meshmode.array_context import PyOpenCLArrayContext from meshmode.mesh.generation import ( # noqa ellipse, cloverleaf, starfish, drop, n_gon, qbx_peanut, WobblyCircle, make_curve_mesh) @@ -436,7 +437,8 @@ class BetterplaneIntEqTestCase(IntEqTestCase): # {{{ test backend -def run_int_eq_test(cl_ctx, queue, case, resolution, visualize=False): +def run_int_eq_test(actx: PyOpenCLArrayContext, + case, resolution, visualize=False): mesh = case.get_mesh(resolution, case.target_order) print("%d elements" % mesh.nelements) @@ -445,7 +447,7 @@ def run_int_eq_test(cl_ctx, queue, case, resolution, visualize=False): from meshmode.discretization.poly_element import \ InterpolatoryQuadratureSimplexGroupFactory pre_density_discr = Discretization( - cl_ctx, mesh, + actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(case.target_order)) source_order = 4*case.target_order @@ -495,7 +497,7 @@ def run_int_eq_test(cl_ctx, queue, case, resolution, visualize=False): point_sources = make_circular_point_group( mesh.ambient_dim, 10, test_src_geo_radius, func=lambda x: x**1.5) - point_source = PointPotentialSource(cl_ctx, point_sources) + point_source = PointPotentialSource(point_sources) from pytential.target import PointsTarget test_targets = make_circular_point_group( @@ -554,7 +556,7 @@ def run_int_eq_test(cl_ctx, queue, case, resolution, visualize=False): places = GeometryCollection(places) if case.use_refinement: from pytential.qbx.refinement import refine_geometry_collection - places = refine_geometry_collection(queue, places, + places = refine_geometry_collection(places, **refiner_extra_kwargs) dd = sym.as_dofdesc(sym.DEFAULT_SOURCE).to_stage1() @@ -573,15 +575,15 @@ def run_int_eq_test(cl_ctx, queue, case, resolution, visualize=False): discr = places.get_discretization(dd.geometry, sym.QBX_SOURCE_QUAD_STAGE2) print("quad stage-2 elements have %d nodes" - % discr.groups[0].nunit_nodes) + % discr.groups[0].nunit_dofs) # }}} if hasattr(case, "visualize_geometry") and case.visualize_geometry: bdry_normals = bind(places, sym.normal(mesh.ambient_dim))( - queue).as_vector(dtype=np.object) + actx).as_vector(dtype=np.object) - bdry_vis = make_visualizer(queue, density_discr, case.target_order) + bdry_vis = make_visualizer(actx, density_discr, case.target_order) bdry_vis.write_vtk_file("geometry.vtu", [ ("normals", bdry_normals) ]) @@ -591,19 +593,23 @@ def run_int_eq_test(cl_ctx, queue, case, resolution, visualize=False): if visualize: if mesh.ambient_dim == 2: # show geometry, centers, normals - nodes_h = density_discr.nodes().get(queue=queue) - normal = bind(places, sym.normal(2))(queue).as_vector(np.object) + from meshmode.dof_array import thaw, flatten + nodes_h = [actx.to_numpy(axis) for axis in + flatten(thaw(actx, density_discr.nodes()))] + normal_h = [actx.to_numpy(axis) for axis in + flatten( + bind(places, sym.normal(2))(actx) + .as_vector(np.object))] pt.plot(nodes_h[0], nodes_h[1], "x-") - pt.quiver(nodes_h[0], nodes_h[1], - normal[0].get(queue), normal[1].get(queue)) + pt.quiver(nodes_h[0], nodes_h[1], normal_h[0], normal_h[1]) pt.gca().set_aspect("equal") pt.show() elif mesh.ambient_dim == 3: bdry_normals = bind(places, sym.normal(3))( - queue).as_vector(dtype=object) + actx).as_vector(dtype=object) - bdry_vis = make_visualizer(queue, density_discr, case.target_order+3) + bdry_vis = make_visualizer(actx, density_discr, case.target_order+3) bdry_vis.write_vtk_file("pre-solve-source-%s.vtu" % resolution, [ ("bdry_normals", bdry_normals), ]) @@ -654,7 +660,7 @@ def run_int_eq_test(cl_ctx, queue, case, resolution, visualize=False): source_charges = source_charges.astype(dtype) assert np.sum(source_charges) < 1e-15 - source_charges_dev = cl.array.to_device(queue, source_charges) + source_charges_dev = actx.from_numpy(source_charges) # }}} @@ -666,27 +672,27 @@ def run_int_eq_test(cl_ctx, queue, case, resolution, visualize=False): test_direct = bind(places, pot_src, auto_where=("point_source", "point_target"))( - queue, charges=source_charges_dev, **concrete_knl_kwargs) + actx, charges=source_charges_dev, **concrete_knl_kwargs) if case.bc_type == "dirichlet": bc = bind(places, pot_src, auto_where=("point_source", sym.DEFAULT_TARGET))( - queue, charges=source_charges_dev, **concrete_knl_kwargs) + actx, charges=source_charges_dev, **concrete_knl_kwargs) elif case.bc_type == "neumann": bc = bind(places, sym.normal_derivative( qbx.ambient_dim, pot_src, dofdesc=sym.DEFAULT_TARGET), auto_where=("point_source", sym.DEFAULT_TARGET))( - queue, charges=source_charges_dev, **concrete_knl_kwargs) + actx, charges=source_charges_dev, **concrete_knl_kwargs) elif case.bc_type == "clamped_plate": bc_u = bind((point_source, density_discr), pot_src)( - queue, charges=source_charges_dev, **concrete_knl_kwargs) + actx, charges=source_charges_dev, **concrete_knl_kwargs) bc_du = bind( (point_source, density_discr), sym.normal_derivative( qbx.ambient_dim, pot_src, dofdesc=sym.DEFAULT_TARGET) - )(queue, charges=source_charges_dev, **concrete_knl_kwargs) + )(actx, charges=source_charges_dev, **concrete_knl_kwargs) bc = [bc_u, bc_du] # }}} @@ -694,22 +700,22 @@ def run_int_eq_test(cl_ctx, queue, case, resolution, visualize=False): # {{{ solve bound_op = bind(places, op_u) - rhs = bind(places, op.prepare_rhs(op.get_density_var("bc")))(queue, bc=bc) + rhs = bind(places, op.prepare_rhs(op.get_density_var("bc")))(actx, bc=bc) try: from pytential.solve import gmres gmres_result = gmres( - bound_op.scipy_op(queue, "u", dtype, **concrete_knl_kwargs), + bound_op.scipy_op(actx, "u", dtype, **concrete_knl_kwargs), rhs, tol=case.gmres_tol, progress=True, hard_failure=True, stall_iterations=50, no_progress_factor=1.05) except QBXTargetAssociationFailedException as e: - bdry_vis = make_visualizer(queue, density_discr, case.target_order+3) + bdry_vis = make_visualizer(actx, density_discr, case.target_order+3) bdry_vis.write_vtk_file("failed-targets-%s.vtu" % resolution, [ - ("failed_targets", e.failed_target_flags), + ("failed_targets", actx.thaw(e.failed_target_flags)), ]) raise @@ -724,7 +730,7 @@ def run_int_eq_test(cl_ctx, queue, case, resolution, visualize=False): from sumpy.tools import build_matrix mat = build_matrix( bound_op.scipy_op( - queue, arg_name="u", dtype=dtype, **concrete_knl_kwargs)) + actx, arg_name="u", dtype=dtype, **concrete_knl_kwargs)) w, v = la.eig(mat) if visualize: pt.imshow(np.log10(1e-20+np.abs(mat))) @@ -740,7 +746,7 @@ def run_int_eq_test(cl_ctx, queue, case, resolution, visualize=False): op.representation(op.get_density_var("u")), auto_where=(sym.DEFAULT_SOURCE, "point_target")) - test_via_bdry = bound_tgt_op(queue, u=weighted_u, **concrete_knl_kwargs) + test_via_bdry = bound_tgt_op(actx, u=weighted_u, **concrete_knl_kwargs) err = test_via_bdry - test_direct @@ -784,11 +790,11 @@ def run_int_eq_test(cl_ctx, queue, case, resolution, visualize=False): #print(bound_t_deriv_op.code) grad_from_src = bound_grad_op( - queue, u=weighted_u, **concrete_knl_kwargs) + actx, u=weighted_u, **concrete_knl_kwargs) grad_ref = bind(places, sym.grad(mesh.ambient_dim, pot_src), - auto_where=("point_source", "point_target"))(queue, + auto_where=("point_source", "point_target"))(actx, charges=source_charges_dev, **concrete_knl_kwargs) @@ -812,14 +818,16 @@ def run_int_eq_test(cl_ctx, queue, case, resolution, visualize=False): sym.tangential_derivative(qbx.ambient_dim, pot), qbx_forced_limit=loc_sign)) - tang_deriv_from_src = bound_t_deriv_op( - queue, u=weighted_u, **concrete_knl_kwargs).as_scalar().get() + from meshmode.dof_array import flatten + tang_deriv_from_src = actx.to_numpy( + flatten(bound_t_deriv_op( + actx, u=weighted_u, **concrete_knl_kwargs).as_scalar())) - tang_deriv_ref = bind(places, + tang_deriv_ref = actx.to_numpy(flatten(bind(places, sym.tangential_derivative(qbx.ambient_dim, pot_src), - auto_where=("point_source", sym.DEFAULT_TARGET))(queue, + auto_where=("point_source", sym.DEFAULT_TARGET))(actx, charges=source_charges_dev, - **concrete_knl_kwargs).as_scalar().get() + **concrete_knl_kwargs).as_scalar())) if visualize: pt.plot(tang_deriv_ref.real) @@ -841,12 +849,12 @@ def run_int_eq_test(cl_ctx, queue, case, resolution, visualize=False): if visualize: bdry_normals = bind(places, sym.normal(qbx.ambient_dim))( - queue).as_vector(dtype=np.object) + actx).as_vector(dtype=np.object) sym_sqrt_j = sym.sqrt_jac_q_weight(density_discr.ambient_dim) - u = bind(places, op.get_density_var("u") / sym_sqrt_j)(queue, u=weighted_u) + u = bind(places, op.get_density_var("u") / sym_sqrt_j)(actx, u=weighted_u) - bdry_vis = make_visualizer(queue, density_discr, case.target_order+3) + bdry_vis = make_visualizer(actx, density_discr, case.target_order+3) bdry_vis.write_vtk_file("source-%s.vtu" % resolution, [ ("u", u), ("bc", bc), @@ -857,32 +865,34 @@ def run_int_eq_test(cl_ctx, queue, case, resolution, visualize=False): solved_pot = bind(places, op.representation(op.get_density_var("u")), auto_where=("qbx_target_tol", "plot_targets"))( - queue, u=weighted_u, k=case.k) + actx, u=weighted_u, k=getattr(case, "k", None)) except QBXTargetAssociationFailedException as e: fplot.write_vtk_file( "failed-targets.vts", [ - ("failed_targets", e.failed_target_flags.get(queue)) + ("failed_targets", actx.to_numpy( + actx.thaw(e.failed_target_flags))) ]) raise - ones_density = density_discr.zeros(queue) - ones_density.fill(1) + ones_density = density_discr.zeros(actx) + 1 indicator = -sym.D(LaplaceKernel(qbx.ambient_dim), op.get_density_var("sigma"), qbx_forced_limit=None) indicator = bind(places, indicator, auto_where=("qbx_target_tol", "plot_targets"))( - queue, sigma=ones_density).get() - - solved_pot = solved_pot.get() + actx, sigma=ones_density) true_pot = bind(places, pot_src, auto_where=("point_source", "plot_targets"))( - queue, + actx, charges=source_charges_dev, - **concrete_knl_kwargs).get() + **concrete_knl_kwargs) + + solved_pot = actx.to_numpy(solved_pot) + true_pot = actx.to_numpy(true_pot) + indicator = actx.to_numpy(indicator) #fplot.show_scalar_in_mayavi(solved_pot.real, max_val=5) if case.prob_side == "scat": @@ -906,7 +916,7 @@ def run_int_eq_test(cl_ctx, queue, case, resolution, visualize=False): # }}} - h_max = bind(places, sym.h_max(qbx.ambient_dim))(queue) + h_max = bind(places, sym.h_max(qbx.ambient_dim))(actx) return dict( h_max=h_max, rel_err_2=rel_err_2, @@ -943,6 +953,7 @@ def test_integral_equation(ctx_factory, case, visualize=False): cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) if USE_SYMENGINE and case.fmm_backend is None: pytest.skip("https://gitlab.tiker.net/inducer/sumpy/issues/25") @@ -959,8 +970,7 @@ def test_integral_equation(ctx_factory, case, visualize=False): have_error_data = False for resolution in case.resolutions: - result = run_int_eq_test(cl_ctx, queue, case, resolution, - visualize=visualize) + result = run_int_eq_test(actx, case, resolution, visualize=visualize) if result["rel_err_2"] is not None: have_error_data = True diff --git a/test/test_stokes.py b/test/test_stokes.py index 19167efe102673c5a5350b89002b9b6f28b01509..5ac2ab47a8ced6dfbd91b612ca37c1ea88fcb8ca 100644 --- a/test/test_stokes.py +++ b/test/test_stokes.py @@ -25,9 +25,9 @@ THE SOFTWARE. import numpy as np import pyopencl as cl -import pyopencl.clmath # noqa import pytest +from meshmode.array_context import PyOpenCLArrayContext from meshmode.discretization import Discretization from meshmode.discretization.poly_element import \ InterpolatoryQuadratureSimplexGroupFactory @@ -46,7 +46,8 @@ import logging def run_exterior_stokes_2d(ctx_factory, nelements, mesh_order=4, target_order=4, qbx_order=4, - fmm_order=10, mu=1, circle_rad=1.5, visualize=False): + fmm_order=False, # FIXME: FMM is slower than direct eval + mu=1, circle_rad=1.5, visualize=False): # This program tests an exterior Stokes flow in 2D using the # compound representation given in Hsiao & Kress, @@ -57,6 +58,7 @@ def run_exterior_stokes_2d(ctx_factory, nelements, cl_ctx = cl.create_some_context() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) ovsmp_target_order = 4*target_order @@ -68,8 +70,7 @@ def run_exterior_stokes_2d(ctx_factory, nelements, lambda t: circle_rad * ellipse(1, t), np.linspace(0, 1, nelements+1), target_order) - coarse_density_discr = Discretization( - cl_ctx, mesh, + coarse_density_discr = Discretization(actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order)) from pytential.qbx import QBXLayerPotentialSource @@ -111,8 +112,8 @@ def run_exterior_stokes_2d(ctx_factory, nelements, density_discr = places.get_discretization(sym.DEFAULT_SOURCE) - normal = bind(places, sym.normal(2).as_vector())(queue) - path_length = bind(places, sym.integral(2, 1, 1))(queue) + normal = bind(places, sym.normal(2).as_vector())(actx) + path_length = bind(places, sym.integral(2, 1, 1))(actx) # }}} @@ -150,47 +151,52 @@ def run_exterior_stokes_2d(ctx_factory, nelements, def fund_soln(x, y, loc, strength): #with direction (1,0) for point source - r = cl.clmath.sqrt((x - loc[0])**2 + (y - loc[1])**2) + r = actx.np.sqrt((x - loc[0])**2 + (y - loc[1])**2) scaling = strength/(4*np.pi*mu) - xcomp = (-cl.clmath.log(r) + (x - loc[0])**2/r**2) * scaling + xcomp = (-actx.np.log(r) + (x - loc[0])**2/r**2) * scaling ycomp = ((x - loc[0])*(y - loc[1])/r**2) * scaling return [xcomp, ycomp] def rotlet_soln(x, y, loc): - r = cl.clmath.sqrt((x - loc[0])**2 + (y - loc[1])**2) + r = actx.np.sqrt((x - loc[0])**2 + (y - loc[1])**2) xcomp = -(y - loc[1])/r**2 ycomp = (x - loc[0])/r**2 return [xcomp, ycomp] def fund_and_rot_soln(x, y, loc, strength): #with direction (1,0) for point source - r = cl.clmath.sqrt((x - loc[0])**2 + (y - loc[1])**2) + r = actx.np.sqrt((x - loc[0])**2 + (y - loc[1])**2) scaling = strength/(4*np.pi*mu) xcomp = ( - (-cl.clmath.log(r) + (x - loc[0])**2/r**2) * scaling + (-actx.np.log(r) + (x - loc[0])**2/r**2) * scaling - (y - loc[1])*strength*0.125/r**2 + 3.3) ycomp = ( ((x - loc[0])*(y - loc[1])/r**2) * scaling + (x - loc[0])*strength*0.125/r**2 + 1.5) - return [xcomp, ycomp] + return make_obj_array([xcomp, ycomp]) - nodes = density_discr.nodes().with_queue(queue) + from meshmode.dof_array import unflatten, flatten, thaw + nodes = flatten(thaw(actx, density_discr.nodes())) fund_soln_loc = np.array([0.5, -0.2]) strength = 100. - bc = fund_and_rot_soln(nodes[0], nodes[1], fund_soln_loc, strength) + bc = unflatten(actx, density_discr, + fund_and_rot_soln(nodes[0], nodes[1], fund_soln_loc, strength)) omega_sym = sym.make_sym_vector("omega", dim) u_A_sym_bdry = stokeslet_obj.apply( # noqa: N806 omega_sym, mu_sym, qbx_forced_limit=1) - omega = [ - cl.array.to_device(queue, (strength/path_length)*np.ones(len(nodes[0]))), - cl.array.to_device(queue, np.zeros(len(nodes[0])))] + from pytential.utils import unflatten_from_numpy + omega = unflatten_from_numpy(actx, density_discr, make_obj_array([ + (strength/path_length)*np.ones(len(nodes[0])), + np.zeros(len(nodes[0])) + ])) + bvp_rhs = bind(places, - sym.make_sym_vector("bc", dim) + u_A_sym_bdry)(queue, + sym.make_sym_vector("bc", dim) + u_A_sym_bdry)(actx, bc=bc, mu=mu, omega=omega) gmres_result = gmres( - bound_op.scipy_op(queue, "sigma", np.float64, mu=mu, normal=normal), + bound_op.scipy_op(actx, "sigma", np.float64, mu=mu, normal=normal), bvp_rhs, x0=bvp_rhs, tol=1e-9, progress=True, @@ -203,7 +209,7 @@ def run_exterior_stokes_2d(ctx_factory, nelements, sigma = gmres_result.solution sigma_int_val_sym = sym.make_sym_vector("sigma_int_val", 2) - int_val = bind(places, sym.integral(2, 1, sigma_sym))(queue, sigma=sigma) + int_val = bind(places, sym.integral(2, 1, sigma_sym))(actx, sigma=sigma) int_val = -int_val/(2 * np.pi) print("int_val = ", int_val) @@ -217,7 +223,7 @@ def run_exterior_stokes_2d(ctx_factory, nelements, - u_A_sym_vol + sigma_int_val_sym) where = (sym.DEFAULT_SOURCE, "point_target") - vel = bind(places, representation_sym, auto_where=where)(queue, + vel = bind(places, representation_sym, auto_where=where)(actx, sigma=sigma, mu=mu, normal=normal, @@ -226,7 +232,7 @@ def run_exterior_stokes_2d(ctx_factory, nelements, print("@@@@@@@@") plot_vel = bind(places, representation_sym, - auto_where=(sym.DEFAULT_SOURCE, "plot_target"))(queue, + auto_where=(sym.DEFAULT_SOURCE, "plot_target"))(actx, sigma=sigma, mu=mu, normal=normal, @@ -240,8 +246,10 @@ def run_exterior_stokes_2d(ctx_factory, nelements, ]) exact_soln = fund_and_rot_soln( - cl.array.to_device(queue, eval_points[0]), cl.array.to_device( - queue, eval_points[1]), fund_soln_loc, strength) + actx.from_numpy(eval_points[0]), + actx.from_numpy(eval_points[1]), + fund_soln_loc, + strength) vel = get_obj_array(vel) err = vel-get_obj_array(exact_soln) @@ -289,7 +297,7 @@ def run_exterior_stokes_2d(ctx_factory, nelements, # }}} - h_max = bind(places, sym.h_max(qbx.ambient_dim))(queue) + h_max = bind(places, sym.h_max(qbx.ambient_dim))(actx) return h_max, l2_err diff --git a/test/test_symbolic.py b/test/test_symbolic.py index 37b86c12a485b419b05f8b6878923870f1169a41..c5f8fbc1f16e0192995f44b090531b638a96901b 100644 --- a/test/test_symbolic.py +++ b/test/test_symbolic.py @@ -39,6 +39,7 @@ logger = logging.getLogger(__name__) from pyopencl.tools import ( # noqa pytest_generate_tests_for_pyopencl as pytest_generate_tests) +from meshmode.array_context import PyOpenCLArrayContext from meshmode.mesh.generation import ( # noqa ellipse, cloverleaf, starfish, drop, n_gon, qbx_peanut, WobblyCircle, make_curve_mesh) @@ -51,27 +52,27 @@ from meshmode.discretization.poly_element import \ # {{{ discretization getters -def get_ellipse_with_ref_mean_curvature(cl_ctx, nelements, aspect=1): +def get_ellipse_with_ref_mean_curvature(actx, nelements, aspect=1): order = 4 mesh = make_curve_mesh( partial(ellipse, aspect), np.linspace(0, 1, nelements+1), order) - discr = Discretization(cl_ctx, mesh, + discr = Discretization(actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(order)) - with cl.CommandQueue(cl_ctx) as queue: - nodes = discr.nodes().get(queue=queue) + from meshmode.dof_array import thaw + nodes = thaw(actx, discr.nodes()) a = 1 b = 1/aspect - t = np.arctan2(nodes[1] * aspect, nodes[0]) + t = actx.np.arctan2(nodes[1] * aspect, nodes[0]) - return discr, a*b / ((a*np.sin(t))**2 + (b*np.cos(t))**2)**(3/2) + return discr, a*b / ((a*actx.np.sin(t))**2 + (b*actx.np.cos(t))**2)**(3/2) -def get_torus_with_ref_mean_curvature(cl_ctx, h): +def get_torus_with_ref_mean_curvature(actx, h): order = 4 r_minor = 1.0 r_major = 3.0 @@ -79,20 +80,21 @@ def get_torus_with_ref_mean_curvature(cl_ctx, h): from meshmode.mesh.generation import generate_torus mesh = generate_torus(r_major, r_minor, n_major=h, n_minor=h, order=order) - discr = Discretization(cl_ctx, mesh, + discr = Discretization(actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(order)) - with cl.CommandQueue(cl_ctx) as queue: - nodes = discr.nodes().get(queue=queue) + from meshmode.dof_array import thaw + nodes = thaw(actx, discr.nodes()) # copied from meshmode.mesh.generation.generate_torus a = r_major b = r_minor - u = np.arctan2(nodes[1], nodes[0]) - rvec = np.array([np.cos(u), np.sin(u), np.zeros_like(u)]) - rvec = np.sum(nodes * rvec, axis=0) - a - cosv = np.cos(np.arctan2(nodes[2], rvec)) + u = actx.np.arctan2(nodes[1], nodes[0]) + from pytools.obj_array import flat_obj_array + rvec = flat_obj_array(actx.np.cos(u), actx.np.sin(u), 0*u) + rvec = sum(nodes * rvec) - a + cosv = actx.np.cos(actx.np.arctan2(nodes[2], rvec)) return discr, (a + 2.0 * b * cosv) / (2 * b * (a + b * cosv)) @@ -115,19 +117,20 @@ def test_mean_curvature(ctx_factory, discr_name, resolutions, discr_and_ref_mean_curvature_getter, visualize=False): ctx = ctx_factory() queue = cl.CommandQueue(ctx) + actx = PyOpenCLArrayContext(queue) from pytools.convergence import EOCRecorder eoc = EOCRecorder() for r in resolutions: discr, ref_mean_curvature = \ - discr_and_ref_mean_curvature_getter(ctx, r) + discr_and_ref_mean_curvature_getter(actx, r) mean_curvature = bind( - discr, - sym.mean_curvature(discr.ambient_dim))(queue).get(queue) + discr, sym.mean_curvature(discr.ambient_dim))(actx) h = 1.0 / r - h_error = la.norm(mean_curvature - ref_mean_curvature, np.inf) + from meshmode.dof_array import flat_norm + h_error = flat_norm(mean_curvature - ref_mean_curvature, np.inf) eoc.add_data_point(h, h_error) print(eoc) @@ -142,13 +145,13 @@ def test_mean_curvature(ctx_factory, discr_name, resolutions, def test_tangential_onb(ctx_factory): cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) from meshmode.mesh.generation import generate_torus mesh = generate_torus(5, 2, order=3) discr = Discretization( - cl_ctx, mesh, - InterpolatoryQuadratureSimplexGroupFactory(3)) + actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(3)) tob = sym.tangential_onb(mesh.ambient_dim) nvecs = tob.shape[1] @@ -157,8 +160,10 @@ def test_tangential_onb(ctx_factory): orth_check = bind(discr, sym.make_obj_array([ np.dot(tob[:, i], tob[:, j]) - (1 if i == j else 0) for i in range(nvecs) for j in range(nvecs)]) - )(queue) + )(actx) + from meshmode.dof_array import flatten + orth_check = flatten(orth_check) for i, orth_i in enumerate(orth_check): assert (cl.clmath.fabs(orth_i) < 1e-13).get().all() @@ -166,8 +171,9 @@ def test_tangential_onb(ctx_factory): orth_check = bind(discr, sym.make_obj_array([ np.dot(tob[:, i], sym.normal(mesh.ambient_dim).as_vector()) for i in range(nvecs)]) - )(queue) + )(actx) + orth_check = flatten(orth_check) for i, orth_i in enumerate(orth_check): assert (cl.clmath.fabs(orth_i) < 1e-13).get().all() @@ -227,6 +233,7 @@ def test_layer_potential_construction(lpot_class, ambient_dim=2): def test_interpolation(ctx_factory, name, source_discr_stage, target_granularity): ctx = ctx_factory() queue = cl.CommandQueue(ctx) + actx = PyOpenCLArrayContext(queue) nelements = 32 target_order = 7 @@ -245,7 +252,7 @@ def test_interpolation(ctx_factory, name, source_discr_stage, target_granularity mesh = make_curve_mesh(starfish, np.linspace(0.0, 1.0, nelements + 1), target_order) - discr = Discretization(ctx, mesh, + discr = Discretization(actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order)) from pytential.qbx import QBXLayerPotentialSource @@ -261,16 +268,22 @@ def test_interpolation(ctx_factory, name, source_discr_stage, target_granularity op_sym = sym.sin(sym.interp(from_dd, to_dd, sigma_sym)) bound_op = bind(places, op_sym, auto_where=where) - def nodes(stage): + from meshmode.dof_array import thaw, flatten, unflatten + + def discr_and_nodes(stage): density_discr = places.get_discretization(where.geometry, stage) - return density_discr.nodes().get(queue) + return density_discr, np.array([ + actx.to_numpy(flatten(axis)) + for axis in thaw(actx, density_discr.nodes())]) - target_nodes = nodes(sym.QBX_SOURCE_QUAD_STAGE2) - source_nodes = nodes(source_discr_stage) + _, target_nodes = discr_and_nodes(sym.QBX_SOURCE_QUAD_STAGE2) + source_discr, source_nodes = discr_and_nodes(source_discr_stage) - sigma_dev = cl.array.to_device(queue, la.norm(source_nodes, axis=0)) sigma_target = np.sin(la.norm(target_nodes, axis=0)) - sigma_target_interp = bound_op(queue, sigma=sigma_dev).get(queue) + sigma_dev = unflatten( + actx, source_discr, + actx.from_numpy(la.norm(source_nodes, axis=0))) + sigma_target_interp = actx.to_numpy(flatten(bound_op(actx, sigma=sigma_dev))) if name in ("default", "default_explicit", "stage2", "quad"): error = la.norm(sigma_target_interp - sigma_target) / la.norm(sigma_target) diff --git a/test/test_target_specific_qbx.py b/test/test_target_specific_qbx.py index 551bb38c5a5e73a30f4ed72124c866b89349330e..440e74862c7e452029ad7df93ac73aaff9ae9a43 100644 --- a/test/test_target_specific_qbx.py +++ b/test/test_target_specific_qbx.py @@ -23,10 +23,10 @@ THE SOFTWARE. """ +from meshmode.array_context import PyOpenCLArrayContext import numpy as np import numpy.linalg as la # noqa import pyopencl as cl -import pyopencl.clmath as clmath import pytest from pyopencl.tools import ( # noqa pytest_generate_tests_for_pyopencl as pytest_generate_tests) @@ -140,6 +140,7 @@ def test_target_specific_qbx(ctx_factory, op, helmholtz_k, qbx_order): cl_ctx = ctx_factory() queue = cl.CommandQueue(cl_ctx) + actx = PyOpenCLArrayContext(queue) target_order = 4 fmm_tol = 1e-3 @@ -152,7 +153,7 @@ def test_target_specific_qbx(ctx_factory, op, helmholtz_k, qbx_order): InterpolatoryQuadratureSimplexGroupFactory from pytential.qbx import QBXLayerPotentialSource pre_density_discr = Discretization( - cl_ctx, mesh, + actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order)) from sumpy.expansion.level_to_order import SimpleExpansionOrderFinder @@ -174,12 +175,13 @@ def test_target_specific_qbx(ctx_factory, op, helmholtz_k, qbx_order): from pytential.qbx.refinement import refine_geometry_collection places = GeometryCollection(places, auto_where="qbx") - places = refine_geometry_collection(queue, places, + places = refine_geometry_collection(places, kernel_length_scale=kernel_length_scale) density_discr = places.get_discretization("qbx") - nodes = density_discr.nodes().with_queue(queue) - u_dev = clmath.sin(nodes[0]) + from meshmode.dof_array import thaw + nodes = thaw(actx, density_discr.nodes()) + u_dev = actx.np.sin(nodes[0]) if helmholtz_k == 0: kernel = LaplaceKernel(3) @@ -201,11 +203,12 @@ def test_target_specific_qbx(ctx_factory, op, helmholtz_k, qbx_order): expr = op(kernel, u_sym, qbx_forced_limit=-1, **kernel_kwargs) + from meshmode.dof_array import flatten bound_op = bind(places, expr) - pot_ref = bound_op(queue, u=u_dev, k=helmholtz_k).get() + pot_ref = actx.to_numpy(flatten(bound_op(actx, u=u_dev, k=helmholtz_k))) bound_op = bind(places, expr, auto_where="qbx_target_specific") - pot_tsqbx = bound_op(queue, u=u_dev, k=helmholtz_k).get() + pot_tsqbx = actx.to_numpy(flatten(bound_op(actx, u=u_dev, k=helmholtz_k))) assert np.allclose(pot_tsqbx, pot_ref, atol=1e-13, rtol=1e-13) diff --git a/test/test_tools.py b/test/test_tools.py index d2f107848cb9785a74528e64e1bd1ea0cbee6582..89bfcd7857cc803255548c05b9553022295e0f1e 100644 --- a/test/test_tools.py +++ b/test/test_tools.py @@ -24,6 +24,7 @@ THE SOFTWARE. from functools import partial +from meshmode.array_context import PyOpenCLArrayContext import pytest import numpy as np @@ -63,6 +64,7 @@ def test_interpolatory_error_reporting(ctx_factory): ctx = ctx_factory() queue = cl.CommandQueue(ctx) + actx = PyOpenCLArrayContext(queue) h = 0.2 from meshmode.mesh.io import generate_gmsh, FileSource @@ -80,20 +82,19 @@ def test_interpolatory_error_reporting(ctx_factory): from meshmode.discretization.poly_element import \ QuadratureSimplexGroupFactory - vol_discr = Discretization(ctx, mesh, + vol_discr = Discretization(actx, mesh, QuadratureSimplexGroupFactory(5)) - vol_x = vol_discr.nodes().with_queue(queue) + from meshmode.dof_array import thaw + vol_x = thaw(actx, vol_discr.nodes()) # }}} from pytential import integral - rhs = 1 + 0*vol_x[0] - - one = rhs.copy() - one.fill(1) - with pytest.raises(TypeError): - print("AREA", integral(vol_discr, queue, one), 0.25**2*np.pi) + one = 1 + 0*vol_x[0] + from meshmode.discretization import NoninterpolatoryElementGroupError + with pytest.raises(NoninterpolatoryElementGroupError): + print("AREA", integral(vol_discr, one), 0.25**2*np.pi) def test_geometry_collection_caching(ctx_factory): @@ -103,6 +104,7 @@ def test_geometry_collection_caching(ctx_factory): # the `nodes` on each `discr_stage`. ctx = ctx_factory() queue = cl.CommandQueue(ctx) + actx = PyOpenCLArrayContext(queue) ndim = 2 nelements = 1024 @@ -128,7 +130,7 @@ def test_geometry_collection_caching(ctx_factory): mesh = affine_map(discrs[0].mesh, b=np.array([3 * k * radius, 0])) - discr = Discretization(ctx, mesh, + discr = Discretization(actx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order)) discrs.append(discr) @@ -161,7 +163,7 @@ def test_geometry_collection_caching(ctx_factory): discr = places._get_discr_from_cache(sources[k], discr_stage) dofdesc = sym.DOFDescriptor(sources[k], discr_stage=discr_stage) - bind(places, sym.nodes(ndim, dofdesc=dofdesc))(queue) + bind(places, sym.nodes(ndim, dofdesc=dofdesc))(actx) discr = places._get_discr_from_cache(sources[k], discr_stage) assert discr is not None diff --git a/test/too_slow_test_helmholtz.py b/test/too_slow_test_helmholtz.py deleted file mode 100644 index 9e9df21aa78d137d0fdb1c6a7521f37c486aad68..0000000000000000000000000000000000000000 --- a/test/too_slow_test_helmholtz.py +++ /dev/null @@ -1,455 +0,0 @@ -from __future__ import division, absolute_import, print_function - -__copyright__ = "Copyright (C) 2014 Shidong Jiang, Andreas Kloeckner" - -__license__ = """ -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in -all copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -THE SOFTWARE. -""" - - -import numpy as np -import numpy.linalg as la -import pyopencl as cl -import pyopencl.array # noqa -import pyopencl.clmath # noqa - -import pytest - -from pytools.obj_array import make_obj_array - -from meshmode.discretization import Discretization -from meshmode.discretization.poly_element import \ - InterpolatoryQuadratureSimplexGroupFactory - -from six.moves import range - -from pytential import bind, sym, norm # noqa -from pytential.symbolic.pde.maxwell.waveguide import ( # noqa - DielectricSRep2DBoundaryOperator as SRep, - DielectricSDRep2DBoundaryOperator as SDRep) - - -from pyopencl.tools import ( # noqa - pytest_generate_tests_for_pyopencl as pytest_generate_tests) - -import logging -logger = logging.getLogger(__name__) - - -def run_dielectric_test(cl_ctx, queue, nelements, qbx_order, - op_class, mode, - k0=3, k1=2.9, mesh_order=10, - bdry_quad_order=None, bdry_ovsmp_quad_order=None, - use_l2_weighting=False, - fmm_order=None, visualize=False): - - if fmm_order is None: - fmm_order = qbx_order * 2 - if bdry_quad_order is None: - bdry_quad_order = mesh_order - if bdry_ovsmp_quad_order is None: - bdry_ovsmp_quad_order = 4*bdry_quad_order - - # {{{ geometries - - from meshmode.mesh.generation import ellipse, make_curve_mesh - from functools import partial - mesh = make_curve_mesh( - partial(ellipse, 3), - np.linspace(0, 1, nelements+1), - mesh_order) - - density_discr = Discretization( - cl_ctx, mesh, - InterpolatoryQuadratureSimplexGroupFactory(bdry_quad_order)) - - logger.info("%d elements" % mesh.nelements) - - from pytential.qbx import QBXLayerPotentialSource - qbx = QBXLayerPotentialSource( - density_discr, fine_order=bdry_ovsmp_quad_order, qbx_order=qbx_order, - fmm_order=fmm_order - ) - - from pytential.target import PointsTarget - targets_0 = PointsTarget(make_obj_array(list(np.array([ - [3.2 + t, -4] - for t in [0, 0.5, 1] - ]).T.copy()))) - targets_1 = PointsTarget(make_obj_array(list(np.array([ - [-0.3 * t, -0.2 * t] - for t in [0, 0.5, 1] - ]).T.copy()))) - - if visualize: - low_order_qbx, _ = QBXLayerPotentialSource( - density_discr, - fine_order=bdry_ovsmp_quad_order, qbx_order=2, - fmm_order=3, - ) - - from sumpy.visualization import FieldPlotter - fplot = FieldPlotter(np.zeros(2), extent=5, npoints=300) - targets_plot = PointsTarget(fplot.points) - - places = { - sym.DEFAULT_SOURCE: qbx, - sym.DEFAULT_TARGET: qbx.density_discr, - "targets0": targets_0, - "targets1": targets_1 - } - if visualize: - places.update({ - "qbx-low-order": low_order_qbx, - "targets-plot": targets_plot - }) - - from pytential import GeometryCollection - places = GeometryCollection(places) - - # }}} - - # from meshmode.discretization.visualization import make_visualizer - # bdry_vis = make_visualizer(queue, density_discr, 20) - - # {{{ solve bvp - - from sumpy.kernel import HelmholtzKernel, AxisTargetDerivative - kernel = HelmholtzKernel(2) - - beta = 2.5 - K0 = np.sqrt(k0**2-beta**2) # noqa - K1 = np.sqrt(k1**2-beta**2) # noqa - - pde_op = op_class( - mode, - k_vacuum=1, - domain_k_exprs=(k0, k1), - beta=beta, - interfaces=((0, 1, sym.DEFAULT_SOURCE),), - use_l2_weighting=use_l2_weighting) - - op_unknown_sym = pde_op.make_unknown("unknown") - - representation0_sym = pde_op.representation(op_unknown_sym, 0) - representation1_sym = pde_op.representation(op_unknown_sym, 1) - bound_pde_op = bind(places, pde_op.operator(op_unknown_sym)) - - e_factor = float(pde_op.ez_enabled) - h_factor = float(pde_op.hz_enabled) - - e_sources_0 = make_obj_array(list(np.array([ - [0.1, 0.2] - ]).T.copy())) - e_strengths_0 = np.array([1*e_factor]) - e_sources_1 = make_obj_array(list(np.array([ - [4, 4] - ]).T.copy())) - e_strengths_1 = np.array([1*e_factor]) - - h_sources_0 = make_obj_array(list(np.array([ - [0.2, 0.1] - ]).T.copy())) - h_strengths_0 = np.array([1*h_factor]) - h_sources_1 = make_obj_array(list(np.array([ - [4, 5] - ]).T.copy())) - h_strengths_1 = np.array([1*h_factor]) - - kernel_grad = [ - AxisTargetDerivative(i, kernel) for i in range(density_discr.ambient_dim)] - - from sumpy.p2p import P2P - pot_p2p = P2P(cl_ctx, [kernel], exclude_self=False) - pot_p2p_grad = P2P(cl_ctx, kernel_grad, exclude_self=False) - - normal = bind(places, sym.normal(qbx.ambient_dim))( - queue).as_vector(np.object) - tangent = bind(places, - sym.pseudoscalar(qbx.ambient_dim)/sym.area_element(qbx.ambient_dim))( - queue).as_vector(np.object) - - _, (E0,) = pot_p2p(queue, density_discr.nodes(), e_sources_0, [e_strengths_0], - out_host=False, k=K0) - _, (E1,) = pot_p2p(queue, density_discr.nodes(), e_sources_1, [e_strengths_1], - out_host=False, k=K1) - _, (grad0_E0, grad1_E0) = pot_p2p_grad( - queue, density_discr.nodes(), e_sources_0, [e_strengths_0], - out_host=False, k=K0) - _, (grad0_E1, grad1_E1) = pot_p2p_grad( - queue, density_discr.nodes(), e_sources_1, [e_strengths_1], - out_host=False, k=K1) - - _, (H0,) = pot_p2p(queue, density_discr.nodes(), h_sources_0, [h_strengths_0], - out_host=False, k=K0) - _, (H1,) = pot_p2p(queue, density_discr.nodes(), h_sources_1, [h_strengths_1], - out_host=False, k=K1) - _, (grad0_H0, grad1_H0) = pot_p2p_grad( - queue, density_discr.nodes(), h_sources_0, [h_strengths_0], - out_host=False, k=K0) - _, (grad0_H1, grad1_H1) = pot_p2p_grad( - queue, density_discr.nodes(), h_sources_1, [h_strengths_1], - out_host=False, k=K1) - - E0_dntarget = (grad0_E0*normal[0] + grad1_E0*normal[1]) # noqa - E1_dntarget = (grad0_E1*normal[0] + grad1_E1*normal[1]) # noqa - - H0_dntarget = (grad0_H0*normal[0] + grad1_H0*normal[1]) # noqa - H1_dntarget = (grad0_H1*normal[0] + grad1_H1*normal[1]) # noqa - - E0_dttarget = (grad0_E0*tangent[0] + grad1_E0*tangent[1]) # noqa - E1_dttarget = (grad0_E1*tangent[0] + grad1_E1*tangent[1]) # noqa - - H0_dttarget = (grad0_H0*tangent[0] + grad1_H0*tangent[1]) # noqa - H1_dttarget = (grad0_H1*tangent[0] + grad1_H1*tangent[1]) # noqa - - sqrt_w = bind(places, sym.sqrt_jac_q_weight(qbx.ambient_dim))(queue) - - bvp_rhs = np.zeros(len(pde_op.bcs), dtype=np.object) - for i_bc, terms in enumerate(pde_op.bcs): - for term in terms: - assert term.i_interface == 0 - if term.field_kind == pde_op.field_kind_e: - - if term.direction == pde_op.dir_none: - bvp_rhs[i_bc] += ( - term.coeff_outer * E0 - + term.coeff_inner * E1) - elif term.direction == pde_op.dir_normal: - bvp_rhs[i_bc] += ( - term.coeff_outer * E0_dntarget - + term.coeff_inner * E1_dntarget) - elif term.direction == pde_op.dir_tangential: - bvp_rhs[i_bc] += ( - term.coeff_outer * E0_dttarget - + term.coeff_inner * E1_dttarget) - else: - raise NotImplementedError("direction spec in RHS") - - elif term.field_kind == pde_op.field_kind_h: - if term.direction == pde_op.dir_none: - bvp_rhs[i_bc] += ( - term.coeff_outer * H0 - + term.coeff_inner * H1) - elif term.direction == pde_op.dir_normal: - bvp_rhs[i_bc] += ( - term.coeff_outer * H0_dntarget - + term.coeff_inner * H1_dntarget) - elif term.direction == pde_op.dir_tangential: - bvp_rhs[i_bc] += ( - term.coeff_outer * H0_dttarget - + term.coeff_inner * H1_dttarget) - else: - raise NotImplementedError("direction spec in RHS") - - if use_l2_weighting: - bvp_rhs[i_bc] *= sqrt_w - - scipy_op = bound_pde_op.scipy_op(queue, "unknown", - domains=[sym.DEFAULT_TARGET]*len(pde_op.bcs), K0=K0, K1=K1, - dtype=np.complex128) - - if mode == "tem" or op_class is SRep: - from sumpy.tools import vector_from_device, vector_to_device - from pytential.solve import lu - unknown = lu(scipy_op, vector_from_device(queue, bvp_rhs)) - unknown = vector_to_device(queue, unknown) - - else: - from pytential.solve import gmres - gmres_result = gmres(scipy_op, - bvp_rhs, tol=1e-14, progress=True, - hard_failure=True, stall_iterations=0) - - unknown = gmres_result.solution - - # }}} - - from sumpy.tools import vector_from_device - F0_tgt = bind(places, representation0_sym, - auto_where=(sym.DEFAULT_SOURCE, "targets0"))( - queue, unknown=unknown, K0=K0, K1=K1) - F0_tgt = vector_from_device(queue, F0_tgt) - - F1_tgt = bind(places, representation1_sym, - auto_where=(sym.DEFAULT_SOURCE, "targets1"))( - queue, unknown=unknown, K0=K0, K1=K1) - F1_tgt = vector_from_device(queue, F1_tgt) - - _, (E0_tgt_true,) = pot_p2p(queue, - targets_0.nodes(), e_sources_0, [e_strengths_0], - out_host=True, k=K0) - _, (E1_tgt_true,) = pot_p2p(queue, - targets_1.nodes(), e_sources_1, [e_strengths_1], - out_host=True, k=K1) - - _, (H0_tgt_true,) = pot_p2p(queue, - targets_0.nodes(), h_sources_0, [h_strengths_0], - out_host=True, k=K0) - _, (H1_tgt_true,) = pot_p2p(queue, - targets_1.nodes(), h_sources_1, [h_strengths_1], - out_host=True, k=K1) - - err_F0_total = 0 # noqa - err_F1_total = 0 # noqa - - i_field = 0 - - def vec_norm(ary): - return la.norm(ary.reshape(-1)) - - def field_kind_to_string(field_kind): - return {pde_op.field_kind_e: "E", pde_op.field_kind_h: "H"}[field_kind] - - for field_kind in pde_op.field_kinds: - if not pde_op.is_field_present(field_kind): - continue - - if field_kind == pde_op.field_kind_e: - F0_tgt_true = E0_tgt_true # noqa - F1_tgt_true = E1_tgt_true # noqa - elif field_kind == pde_op.field_kind_h: - F0_tgt_true = H0_tgt_true # noqa - F1_tgt_true = H1_tgt_true # noqa - else: - assert False - - abs_err_F0 = vec_norm(F0_tgt[i_field] - F0_tgt_true) # noqa - abs_err_F1 = vec_norm(F1_tgt[i_field] - F1_tgt_true) # noqa - - rel_err_F0 = abs_err_F0/vec_norm(F0_tgt_true) # noqa - rel_err_F1 = abs_err_F1/vec_norm(F1_tgt_true) # noqa - - err_F0_total = max(rel_err_F0, err_F0_total) # noqa - err_F1_total = max(rel_err_F1, err_F1_total) # noqa - - print("Abs Err %s0" % field_kind_to_string(field_kind), abs_err_F0) - print("Abs Err %s1" % field_kind_to_string(field_kind), abs_err_F1) - - print("Rel Err %s0" % field_kind_to_string(field_kind), rel_err_F0) - print("Rel Err %s1" % field_kind_to_string(field_kind), rel_err_F1) - - i_field += 1 - - if visualize: - fld0 = bind(places, representation0_sym, - auto_where=(sym.DEFAULT_SOURCE, "targets-plot"))( - queue, unknown=unknown, K0=K0) - fld1 = bind(places, representation1_sym, - auto_where=(sym.DEFAULT_SOURCE, "targets-plot"))( - queue, unknown=unknown, K1=K1) - - comp_fields = [] - i_field = 0 - for field_kind in pde_op.field_kinds: - if not pde_op.is_field_present(field_kind): - continue - - fld_str = field_kind_to_string(field_kind) - comp_fields.extend([ - ("%s_fld0" % fld_str, fld0[i_field].get()), - ("%s_fld1" % fld_str, fld1[i_field].get()), - ]) - - i_field += 0 - - from sumpy.kernel import LaplaceKernel - ones = (cl.array.empty(queue, (density_discr.nnodes,), dtype=np.float64) - .fill(1)) - ind_func = - bind(places, sym.D(LaplaceKernel(2), sym.var("u")), - auto_where=("qbx-low-order", "targets-plot"))( - queue, u=ones).get() - - _, (e_fld0_true,) = pot_p2p( - queue, fplot.points, e_sources_0, [e_strengths_0], - out_host=True, k=K0) - _, (e_fld1_true,) = pot_p2p( - queue, fplot.points, e_sources_1, [e_strengths_1], - out_host=True, k=K1) - _, (h_fld0_true,) = pot_p2p( - queue, fplot.points, h_sources_0, [h_strengths_0], - out_host=True, k=K0) - _, (h_fld1_true,) = pot_p2p( - queue, fplot.points, h_sources_1, [h_strengths_1], - out_host=True, k=K1) - - #fplot.show_scalar_in_mayavi(fld_in_vol.real, max_val=5) - fplot.write_vtk_file( - "potential-n%d.vts" % nelements, - [ - ("e_fld0_true", e_fld0_true), - ("e_fld1_true", e_fld1_true), - ("h_fld0_true", h_fld0_true), - ("h_fld1_true", h_fld1_true), - ("ind", ind_func), - ] + comp_fields - ) - - return err_F0_total, err_F1_total - - -@pytest.mark.parametrize("qbx_order", [4]) -@pytest.mark.parametrize("op_class", [ - SRep, - SDRep, - ]) -@pytest.mark.parametrize("mode", [ - "te", - "tm", - "tem", - ]) -def test_dielectric(ctx_factory, qbx_order, op_class, mode, visualize=False): - cl_ctx = ctx_factory() - queue = cl.CommandQueue(cl_ctx) - - import logging - logging.basicConfig(level=logging.INFO) - - from pytools.convergence import EOCRecorder - eoc_rec = EOCRecorder() - - for nelements in [30, 50, 70]: - # prevent sympy cache 'splosion - from sympy.core.cache import clear_cache - clear_cache() - - errs = run_dielectric_test( - cl_ctx, queue, - nelements=nelements, qbx_order=qbx_order, - op_class=op_class, mode=mode, - visualize=visualize) - - eoc_rec.add_data_point(1/nelements, la.norm(list(errs), np.inf)) - - print(eoc_rec) - assert eoc_rec.order_estimate() > qbx_order - 0.5 - - -# You can test individual routines by typing -# $ python test_layer_pot.py 'test_routine()' - -if __name__ == "__main__": - import sys - if len(sys.argv) > 1: - exec(sys.argv[1]) - else: - from pytest import main - main([__file__]) - -# vim: fdm=marker diff --git a/test/yoink.geo b/test/yoink.geo new file mode 100644 index 0000000000000000000000000000000000000000..aa59601094a999102c8bf19c39dae76a16baff22 --- /dev/null +++ b/test/yoink.geo @@ -0,0 +1,54 @@ +Merge "betterplane.brep"; + +Mesh.CharacteristicLengthMax = 0.; +Mesh.ElementOrder = 2; +Mesh.CharacteristicLengthExtendFromBoundary = 0; + +// 2D mesh optimization +// Mesh.Lloyd = 1; + +l_superfine() = Unique(Abs(Boundary{ Surface{ + 27, 25, 17, 13, 18 }; })); +l_fine() = Unique(Abs(Boundary{ Surface{ 2, 6, 7}; })); +l_coarse() = Unique(Abs(Boundary{ Surface{ 14, 16 }; })); + +// p() = Unique(Abs(Boundary{ Line{l_fine()}; })); +// Characteristic Length{p()} = 0.05; + +Field[1] = Distance; +Field[1].NNodesByEdge = 100; +Field[1].EdgesList = {l_superfine()}; + +Field[2] = Threshold; +Field[2].IField = 1; +Field[2].LcMin = 0.075; +Field[2].LcMax = 0.2; +Field[2].DistMin = 0.1; +Field[2].DistMax = 0.4; + +Field[3] = Distance; +Field[3].NNodesByEdge = 100; +Field[3].EdgesList = {l_fine()}; + +Field[4] = Threshold; +Field[4].IField = 3; +Field[4].LcMin = 0.1; +Field[4].LcMax = 0.2; +Field[4].DistMin = 0.15; +Field[4].DistMax = 0.4; + +Field[5] = Distance; +Field[5].NNodesByEdge = 100; +Field[5].EdgesList = {l_coarse()}; + +Field[6] = Threshold; +Field[6].IField = 5; +Field[6].LcMin = 0.15; +Field[6].LcMax = 0.2; +Field[6].DistMin = 0.2; +Field[6].DistMax = 0.4; + +Field[7] = Min; +Field[7].FieldsList = {2, 4, 6}; + +Background Field = 7;