from __future__ import division __copyright__ = "Copyright (C) 2013 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 pytest from pyopencl.tools import ( # noqa pytest_generate_tests_for_pyopencl as pytest_generate_tests) from boxtree.tools import ( make_normal_particle_array as p_normal, make_surface_particle_array as p_surface, make_uniform_particle_array as p_uniform, particle_array_to_host) import logging logger = logging.getLogger(__name__) # {{{ fmm interaction completeness test class ConstantOneExpansionWrangler(object): """This implements the 'analytical routines' for a Green's function that is constant 1 everywhere. For 'charges' of 'ones', this should get every particle a copy of the particle count. """ def __init__(self, tree): self.tree = tree def multipole_expansion_zeros(self): return np.zeros(self.tree.nboxes, dtype=np.float64) local_expansion_zeros = multipole_expansion_zeros def potential_zeros(self): return np.zeros(self.tree.ntargets, dtype=np.float64) def _get_source_slice(self, ibox): pstart = self.tree.box_source_starts[ibox] return slice( pstart, pstart + self.tree.box_source_counts_nonchild[ibox]) def _get_target_slice(self, ibox): pstart = self.tree.box_target_starts[ibox] return slice( pstart, pstart + self.tree.box_target_counts_nonchild[ibox]) def reorder_src_weights(self, src_weights): return src_weights[self.tree.user_source_ids] def reorder_potentials(self, potentials): return potentials[self.tree.sorted_target_ids] def form_multipoles(self, source_boxes, src_weights): mpoles = self.multipole_expansion_zeros() for ibox in source_boxes: pslice = self._get_source_slice(ibox) mpoles[ibox] += np.sum(src_weights[pslice]) return mpoles def coarsen_multipoles(self, parent_boxes, mpoles): tree = self.tree for ibox in parent_boxes: for child in tree.box_child_ids[:, ibox]: if child: mpoles[ibox] += mpoles[child] def eval_direct(self, target_boxes, neighbor_sources_starts, neighbor_sources_lists, src_weights): pot = self.potential_zeros() for itgt_box, tgt_ibox in enumerate(target_boxes): tgt_pslice = self._get_target_slice(tgt_ibox) src_sum = 0 start, end = neighbor_sources_starts[itgt_box:itgt_box+2] #print "DIR: %s <- %s" % (tgt_ibox, neighbor_sources_lists[start:end]) for src_ibox in neighbor_sources_lists[start:end]: src_pslice = self._get_source_slice(src_ibox) src_sum += np.sum(src_weights[src_pslice]) pot[tgt_pslice] = src_sum return pot def multipole_to_local(self, target_or_target_parent_boxes, starts, lists, mpole_exps): local_exps = self.local_expansion_zeros() for itgt_box, tgt_ibox in enumerate(target_or_target_parent_boxes): start, end = starts[itgt_box:itgt_box+2] contrib = 0 #print tgt_ibox, "<-", lists[start:end] for src_ibox in lists[start:end]: contrib += mpole_exps[src_ibox] local_exps[tgt_ibox] += contrib return local_exps def eval_multipoles(self, target_boxes, sep_smaller_nonsiblings_starts, sep_smaller_nonsiblings_lists, mpole_exps): pot = self.potential_zeros() for itgt_box, tgt_ibox in enumerate(target_boxes): tgt_pslice = self._get_target_slice(tgt_ibox) contrib = 0 start, end = sep_smaller_nonsiblings_starts[itgt_box:itgt_box+2] for src_ibox in sep_smaller_nonsiblings_lists[start:end]: contrib += mpole_exps[src_ibox] pot[tgt_pslice] += contrib return pot def form_locals(self, target_or_target_parent_boxes, starts, lists, src_weights): local_exps = self.local_expansion_zeros() for itgt_box, tgt_ibox in enumerate(target_or_target_parent_boxes): start, end = starts[itgt_box:itgt_box+2] #print "LIST 4", tgt_ibox, "<-", lists[start:end] contrib = 0 for src_ibox in lists[start:end]: src_pslice = self._get_source_slice(src_ibox) contrib += np.sum(src_weights[src_pslice]) local_exps[tgt_ibox] += contrib return local_exps def refine_locals(self, child_boxes, local_exps): for ibox in child_boxes: local_exps[ibox] += local_exps[self.tree.box_parent_ids[ibox]] return local_exps def eval_locals(self, target_boxes, local_exps): pot = self.potential_zeros() for ibox in target_boxes: tgt_pslice = self._get_target_slice(ibox) pot[tgt_pslice] += local_exps[ibox] return pot class ConstantOneExpansionWranglerWithFilteredTargetsInTreeOrder( ConstantOneExpansionWrangler): def __init__(self, tree, filtered_targets): ConstantOneExpansionWrangler.__init__(self, tree) self.filtered_targets = filtered_targets def potential_zeros(self): return np.zeros(self.filtered_targets.nfiltered_targets, dtype=np.float64) def _get_target_slice(self, ibox): pstart = self.filtered_targets.box_target_starts[ibox] return slice( pstart, pstart + self.filtered_targets.box_target_counts_nonchild[ibox]) def reorder_potentials(self, potentials): tree_order_all_potentials = np.zeros(self.tree.ntargets, potentials.dtype) tree_order_all_potentials[ self.filtered_targets.unfiltered_from_filtered_target_indices] \ = potentials return tree_order_all_potentials[self.tree.sorted_target_ids] class ConstantOneExpansionWranglerWithFilteredTargetsInUserOrder( ConstantOneExpansionWrangler): def __init__(self, tree, filtered_targets): ConstantOneExpansionWrangler.__init__(self, tree) self.filtered_targets = filtered_targets def _get_target_slice(self, ibox): user_target_ids = self.filtered_targets.target_lists[ self.filtered_targets.target_starts[ibox]: self.filtered_targets.target_starts[ibox+1]] return self.tree.sorted_target_ids[user_target_ids] @pytest.mark.parametrize(("dims", "nsources_req", "ntargets_req", "who_has_extent", "source_gen", "target_gen", "filter_kind"), [ (2, 10**5, None, "", p_normal, p_normal, None), (3, 5 * 10**4, 4*10**4, "", p_normal, p_normal, None), (2, 5 * 10**5, 4*10**4, "s", p_normal, p_normal, None), (2, 5 * 10**5, 4*10**4, "st", p_normal, p_normal, None), (2, 5 * 10**5, 4*10**4, "t", p_normal, p_normal, None), (2, 5 * 10**5, 4*10**4, "st", p_surface, p_uniform, None), (3, 10**5, None, "", p_normal, p_normal, None), (3, 5 * 10**4, 4*10**4, "", p_normal, p_normal, None), (3, 5 * 10**5, 4*10**4, "s", p_normal, p_normal, None), (3, 5 * 10**5, 4*10**4, "st", p_normal, p_normal, None), (3, 5 * 10**5, 4*10**4, "t", p_normal, p_normal, None), (3, 5 * 10**5, 4*10**4, "st", p_surface, p_uniform, None), (2, 10**5, None, "", p_normal, p_normal, "user"), (3, 5 * 10**4, 4*10**4, "", p_normal, p_normal, "user"), (2, 5 * 10**5, 4*10**4, "s", p_normal, p_normal, "user"), (2, 5 * 10**5, 4*10**4, "st", p_normal, p_normal, "user"), (2, 5 * 10**5, 4*10**4, "t", p_normal, p_normal, "user"), (2, 5 * 10**5, 4*10**4, "st", p_surface, p_uniform, "user"), (2, 10**5, None, "", p_normal, p_normal, "tree"), (3, 5 * 10**4, 4*10**4, "", p_normal, p_normal, "tree"), (2, 5 * 10**5, 4*10**4, "s", p_normal, p_normal, "tree"), (2, 5 * 10**5, 4*10**4, "st", p_normal, p_normal, "tree"), (2, 5 * 10**5, 4*10**4, "t", p_normal, p_normal, "tree"), (2, 5 * 10**5, 4*10**4, "st", p_surface, p_uniform, "tree"), ]) def test_fmm_completeness(ctx_getter, dims, nsources_req, ntargets_req, who_has_extent, source_gen, target_gen, filter_kind): """Tests whether the built FMM traversal structures and driver completely capture all interactions. """ sources_have_extent = "s" in who_has_extent targets_have_extent = "t" in who_has_extent logging.basicConfig(level=logging.INFO) ctx = ctx_getter() queue = cl.CommandQueue(ctx) dtype = np.float64 try: sources = source_gen(queue, nsources_req, dims, dtype, seed=15) nsources = len(sources[0]) if ntargets_req is None: # This says "same as sources" to the tree builder. targets = None ntargets = ntargets_req else: targets = target_gen(queue, ntargets_req, dims, dtype, seed=16) ntargets = len(targets[0]) except ImportError: pytest.skip("loo.py not available, but needed for particle array " "generation") from pyopencl.clrandom import RanluxGenerator rng = RanluxGenerator(queue, seed=13) if sources_have_extent: source_radii = 2**rng.uniform(queue, nsources, dtype=dtype, a=-10, b=0) else: source_radii = None if targets_have_extent: target_radii = 2**rng.uniform(queue, ntargets, dtype=dtype, a=-10, b=0) else: target_radii = None from boxtree import TreeBuilder tb = TreeBuilder(ctx) tree, _ = tb(queue, sources, targets=targets, max_particles_in_box=30, source_radii=source_radii, target_radii=target_radii, debug=True) if 0: tree.get().plot() import matplotlib.pyplot as pt pt.show() from boxtree.traversal import FMMTraversalBuilder tbuild = FMMTraversalBuilder(ctx) trav, _ = tbuild(queue, tree, debug=True) if trav.sep_close_smaller_starts is not None: trav = trav.merge_close_lists(queue) weights = np.random.randn(nsources) #weights = np.ones(nsources) weights_sum = np.sum(weights) host_trav = trav.get(queue=queue) host_tree = host_trav.tree if filter_kind: flags = rng.uniform(queue, ntargets, np.int32, a=0, b=2).astype(np.int8) if filter_kind == "user": from boxtree.tree import filter_target_lists_in_user_order filtered_targets = filter_target_lists_in_user_order(queue, tree, flags) wrangler = ConstantOneExpansionWranglerWithFilteredTargetsInUserOrder( host_tree, filtered_targets.get(queue=queue)) elif filter_kind == "tree": from boxtree.tree import filter_target_lists_in_tree_order filtered_targets = filter_target_lists_in_tree_order(queue, tree, flags) wrangler = ConstantOneExpansionWranglerWithFilteredTargetsInTreeOrder( host_tree, filtered_targets.get(queue=queue)) else: raise ValueError("unsupported value of 'filter_kind'") else: wrangler = ConstantOneExpansionWrangler(host_tree) if ntargets is None: # This check only works for targets == sources. assert (wrangler.reorder_potentials( wrangler.reorder_src_weights(weights)) == weights).all() from boxtree.fmm import drive_fmm pot = drive_fmm(host_trav, wrangler, weights) # {{{ build, evaluate matrix (and identify missing interactions) if 0: mat = np.zeros((ntargets, nsources), dtype) from pytools import ProgressBar logging.getLogger().setLevel(logging.WARNING) pb = ProgressBar("matrix", nsources) for i in xrange(nsources): unit_vec = np.zeros(nsources, dtype=dtype) unit_vec[i] = 1 mat[:, i] = drive_fmm(host_trav, wrangler, unit_vec) pb.progress() pb.finished() logging.getLogger().setLevel(logging.INFO) import matplotlib.pyplot as pt if 1: pt.spy(mat) pt.show() missing_tgts, missing_srcs = np.where(mat == 0) if 1 and len(missing_tgts): from boxtree.visualization import TreePlotter plotter = TreePlotter(host_tree) plotter.draw_tree(fill=False, edgecolor="black") plotter.draw_box_numbers() plotter.set_bounding_box() tree_order_missing_tgts = \ host_tree.indices_to_tree_target_order(missing_tgts) tree_order_missing_srcs = \ host_tree.indices_to_tree_source_order(missing_srcs) src_boxes = [ host_tree.find_box_nr_for_source(i) for i in tree_order_missing_srcs] tgt_boxes = [ host_tree.find_box_nr_for_target(i) for i in tree_order_missing_tgts] print src_boxes print tgt_boxes pt.plot( host_tree.targets[0][tree_order_missing_tgts], host_tree.targets[1][tree_order_missing_tgts], "rv") pt.plot( host_tree.sources[0][tree_order_missing_srcs], host_tree.sources[1][tree_order_missing_srcs], "go") pt.gca().set_aspect("equal") pt.show() # }}} if filter_kind: pot = pot[flags.get() > 0] rel_err = la.norm((pot - weights_sum) / nsources) good = rel_err < 1e-8 if 0 and not good: import matplotlib.pyplot as pt pt.plot(pot-weights_sum) pt.show() if 0 and not good: import matplotlib.pyplot as pt filt_targets = [ host_tree.targets[0][flags.get() > 0], host_tree.targets[1][flags.get() > 0], ] host_tree.plot() bad = np.abs(pot - weights_sum) >= 1e-3 bad_targets = [ filt_targets[0][bad], filt_targets[1][bad], ] print bad_targets[0].shape pt.plot(filt_targets[0], filt_targets[1], "x") pt.plot(bad_targets[0], bad_targets[1], "v") pt.show() assert good # }}} # {{{ test Helmholtz fmm with pyfmmlib def test_pyfmmlib_fmm(ctx_getter): logging.basicConfig(level=logging.INFO) from pytest import importorskip importorskip("pyfmmlib") ctx = ctx_getter() queue = cl.CommandQueue(ctx) nsources = 3000 ntargets = 1000 dims = 2 dtype = np.float64 helmholtz_k = 2 sources = p_normal(queue, nsources, dims, dtype, seed=15) targets = ( p_normal(queue, ntargets, dims, dtype, seed=18) + np.array([2, 0])) sources_host = particle_array_to_host(sources) targets_host = particle_array_to_host(targets) from boxtree import TreeBuilder tb = TreeBuilder(ctx) tree, _ = tb(queue, sources, targets=targets, max_particles_in_box=30, debug=True) from boxtree.traversal import FMMTraversalBuilder tbuild = FMMTraversalBuilder(ctx) trav, _ = tbuild(queue, tree, debug=True) trav = trav.get(queue=queue) from pyopencl.clrandom import RanluxGenerator rng = RanluxGenerator(queue, seed=20) weights = rng.uniform(queue, nsources, dtype=np.float64).get() #weights = np.ones(nsources) logger.info("computing direct (reference) result") from pyfmmlib import hpotgrad2dall_vec ref_pot, _, _ = hpotgrad2dall_vec(ifgrad=False, ifhess=False, sources=sources_host.T, charge=weights, targets=targets_host.T, zk=helmholtz_k) from boxtree.pyfmmlib_integration import Helmholtz2DExpansionWrangler wrangler = Helmholtz2DExpansionWrangler(trav.tree, helmholtz_k, nterms=10) from boxtree.fmm import drive_fmm pot = drive_fmm(trav, wrangler, weights) rel_err = la.norm(pot - ref_pot) / la.norm(ref_pot) logger.info("relative l2 error: %g" % rel_err) assert rel_err < 1e-5 # }}} # You can test individual routines by typing # $ python test_fmm.py 'test_routine(cl.create_some_context)' if __name__ == "__main__": import sys if len(sys.argv) > 1: exec(sys.argv[1]) else: from py.test.cmdline import main main([__file__]) # vim: fdm=marker