ClaudeandClaude Opus 5.5
2bbf82121e
FixedPatchStepper(fine_block=..., fine_objects=[circle]) now accepts passive stationary circles; any surface actuation is rejected before CUDA (dense fine path unchanged). prepare_fine_boundary still decides legality on the dense fine grid; its SoA is mapped by the new array-based storage adapter blocks.blocked_link_tables (map_cut_links: unique receiver owner and local writer, row order kept; blocked flags sized nblocks*L), which PeriodicBlockStepper._static_circles now also uses (single-level legality builder unchanged). Per fine hop on the same buffer: halo -> existing BlockCurvedBoundary -> BlockStep(flags, kind). No CUDA changes. Tests: fixture 76 links / 68 q<.5 and 8 seam writes + 8 seam donors per decomposed layout (both modes); adapter flags sizing; mocked launch-order contract; GPU: periodic and slot_reservoir, 8 coarse steps, dense fine body vs 1x1/2x2/4x2 fine blocks, exact values + NaN masks on masked downloads and raw final arrays, ACTIVE fluid finite, solid/OUTER NaN. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
265 lines
14 KiB
Python
265 lines
14 KiB
Python
"""Opt-in blocked fine storage for FixedPatchStepper: CPU tables plus GPU gate.
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Frozen GPU gate (same stream, 8 coarse steps, nonequilibrium start), in both
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periodic and slot_reservoir modes: dense fine storage vs 1x1, 2x2 and 4x2
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fine blocks give bit-identical masked downloads AND bit-identical raw coarse
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and unpacked owner-core fine arrays, including patch-ghost values/NaN masks.
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"""
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from contextlib import nullcontext
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import unittest
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from unittest.mock import Mock, patch
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import numpy as np
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import pycuda.driver as cuda
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from CelerisLab.body.geometry.circle import CircleGeometry
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from CelerisLab.body.objects import SimObject, SurfaceActuation
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from CelerisLab.config import LBMConfig
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from CelerisLab.cuda import compiler_v2 as compiler
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from CelerisLab.lbm.blocks import NO_DONOR, BlockLayout2D, blocked_link_tables
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from CelerisLab.lbm.descriptors import D2Q9_EX, D2Q9_EY
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from CelerisLab.lbm.descriptors import SOLID
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from CelerisLab.lbm.refinement import (ACTIVE, INNER_GHOST, OUTER_GHOST, FixedPatch2D,
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prepare_fine_boundary)
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from CelerisLab.lbm.refinement_stepper import FixedPatchStepper, blocked_fine_tables
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NX, NY, BOUNDS, STEPS = 32, 24, (8, 6, 24, 18), 8
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FINE_BLOCKS = ((32, 40), (16, 20), (16, 10)) # fine window 32x40: 1x1, 2x2, 4x2
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W = np.array([4 / 9] + [1 / 9] * 4 + [1 / 36] * 4)
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CX, CY = np.array(D2Q9_EX, float), np.array(D2Q9_EY, float)
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def config(mode):
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if mode == "periodic":
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return LBMConfig(nx=NX, ny=NY, viscosity=.1)
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return LBMConfig(nx=NX, ny=NY, viscosity=.1, velocity=0, wall_normal_velocity=.001,
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wall_slot_width=8, y_wall_bc="on_site_velocity", top_wall_bc="reservoir")
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def state(ny, nx, seed):
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"""Positive low-Mach equilibrium plus a seeded nonequilibrium part."""
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y, x = np.mgrid[0:ny, 0:nx]
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rho = 1 + 1e-3 * np.cos(2 * np.pi * x / nx)
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ux, uy = .02 * np.sin(2 * np.pi * y / ny), .01 * np.cos(2 * np.pi * x / nx)
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cu = CX[:, None, None] * ux + CY[:, None, None] * uy
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feq = W[:, None, None] * rho * (1 + 3 * cu + 4.5 * cu * cu - 1.5 * (ux * ux + uy * uy))
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noise = np.random.default_rng(seed).uniform(-1, 1, feq.shape) * 1e-3 * W[:, None, None]
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return (feq + noise).astype(np.float32)
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def circle(actuation=None):
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"""Astra fixture: coarse cell-edge C=(16,12), R=2.15 -> fine C=(19.5,15.5), R=4.3."""
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return SimObject(0, geometry=CircleGeometry(16.0, 12.0, 2.15), center=(16.0, 12.0),
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radius=2.15, surface_actuation=actuation)
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def fine_circle(mode):
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cfg = config(mode)
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patch = FixedPatch2D.from_config(cfg, BOUNDS)
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return patch, prepare_fine_boundary(cfg, patch, (circle(),), wall_aware=mode == "slot_reservoir")
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def seam_cells(layout, mapped):
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"""Distinct non-core cells written / read as q<.5 donors (receiver-local halos)."""
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L = int(np.prod(layout.local_shape))
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core = set(layout.owner.ravel().tolist())
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writes = {int(w) // (9 * L) * L + int(w) % L for w in mapped.write}
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donors = {int(d) for d in mapped.donor if d != NO_DONOR}
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return len(writes - core), len(donors - core)
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def raw(backend):
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"""Unmasked device arrays; blocked fine storage unpacked from owner cores."""
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coarse, fine = backend._levels
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out = [np.empty(coarse.shape, np.float32)]
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out.append(np.empty(fine.shape if backend.fine_layout is None
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else backend.fine_layout.blocked_shape, np.float32))
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cuda.memcpy_dtoh(out[0], coarse.current)
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cuda.memcpy_dtoh(out[1], fine.current)
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if backend.fine_layout is not None:
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out[1] = backend.fine_layout.to_dense(out[1])
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return out
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class BlockedFineCPU(unittest.TestCase):
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def forbidden_context(self):
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return Mock(activate=Mock(side_effect=AssertionError("CUDA reached")), sm_arch="sm_70")
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def test_rejections_before_cuda(self):
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actuated = circle(SurfaceActuation(0.0, 0.5, 0.002))
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for kw in ({"fine_block": (7, 7)}, {"fine_block": (16,)},
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{"fine_block": (16, 20), "fine_objects": [actuated]}):
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with self.subTest(kw=kw), self.assertRaises(ValueError):
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FixedPatchStepper(config("periodic"), BOUNDS, context=self.forbidden_context(), **kw)
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def test_single_block_layout_allocates_no_empty_halo_tables(self):
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# A 1x1 non-periodic fine layout has zero halo pairs; CUDA rejects mem_alloc(0).
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context = Mock(activate=nullcontext, sm_arch="sm_70")
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module = Mock()
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module.get_global.return_value = (Mock(), 20)
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sizes = []
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def alloc(size):
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if size <= 0:
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raise AssertionError("zero-byte device allocation")
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sizes.append(size)
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return Mock()
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with patch.object(cuda, "Stream"), patch.object(compiler, "generate_config"), \
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patch.object(compiler, "compile_kernel"), patch.object(compiler, "cleanup_build_dir"), \
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patch.object(compiler, "load_module", return_value=module), \
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patch.object(cuda, "mem_alloc", side_effect=alloc), patch.object(cuda, "memcpy_htod"):
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backend = FixedPatchStepper(config("periodic"), BOUNDS, context=context,
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fine_block=FINE_BLOCKS[0])
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self.assertEqual(len(backend.fine_layout.halo_dst), 0)
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self.assertIsNone(backend._halo_dst)
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backend._fine_halo(Mock()) # no launch without pairs
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module.get_function.return_value.assert_not_called()
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backend.close()
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self.assertTrue(sizes)
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def test_circle_fixture_counts_and_seam_staging(self):
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for mode in ("periodic", "slot_reservoir"):
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patch, boundary = fine_circle(mode)
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fluid_idx, dirs, q, _, _, _, _, fallback = boundary.curved_host
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self.assertEqual(len(fluid_idx), 76, mode)
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self.assertEqual(int(np.count_nonzero(q < .5)), 68, mode)
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for by, bx in FINE_BLOCKS[1:]:
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layout = BlockLayout2D.build(*patch.fine_shape, by, bx, periodic=(False, False))
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_, mapped = blocked_link_tables(layout, boundary.flags, fluid_idx, dirs, q, fallback)
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self.assertEqual(seam_cells(layout, mapped), (8, 8), (mode, by, bx))
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def test_storage_adapter_flags_sizing(self):
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patch, boundary = fine_circle("periodic")
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columns = [boundary.curved_host[i] for i in (0, 1, 2, 7)]
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layout = BlockLayout2D.build(*patch.fine_shape, 16, 20, periodic=(False, False))
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tables, mapped = blocked_link_tables(layout, boundary.flags, *columns)
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L = int(np.prod(layout.local_shape))
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self.assertEqual(tables["flags"].shape, (layout.nblocks * L,))
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np.testing.assert_array_equal(tables["flags"][layout.owner.ravel().astype(np.intp)],
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boundary.flags)
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np.testing.assert_array_equal(tables["cell"], mapped.receiver % np.uint64(L))
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for bad in (boundary.flags[:-1], boundary.flags.astype(np.uint32)):
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with self.assertRaises(ValueError):
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blocked_link_tables(layout, bad, *columns)
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def test_same_buffer_halo_bfl_step_launch_order(self):
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context = Mock(activate=nullcontext, sm_arch="sm_70")
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calls, functions = [], {}
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def get_function(name):
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return functions.setdefault(name, Mock(side_effect=lambda *a, **k: calls.append((name, a))))
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module = Mock(get_function=Mock(side_effect=get_function))
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module.get_global.return_value = (Mock(), 20)
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with patch.object(cuda, "Stream"), patch.object(compiler, "generate_config"), \
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patch.object(compiler, "compile_kernel"), patch.object(compiler, "cleanup_build_dir"), \
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patch.object(compiler, "load_module", return_value=module), \
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patch.object(cuda, "mem_alloc", side_effect=lambda size: Mock()), \
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patch.object(cuda, "memcpy_htod"):
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backend = FixedPatchStepper(config("periodic"), BOUNDS, context=context,
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fine_objects=[circle()], fine_block=FINE_BLOCKS[1])
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g_fine = state(*backend.patch.fine_shape, 2)
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backend.initialize(state(NY, NX, 1), g_fine)
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c, f = backend._levels
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coarse, cur, spare = c.current, f.current, f.spare
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flags, kind = backend._fine_flags, backend._kind
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backend.step(1)
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backend.close()
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order = [(name, a[0]) for name, a in calls]
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self.assertEqual(order, [("BlockExplode", coarse),
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("BlockHaloCopy", cur), ("BlockCurvedBoundary", cur), ("BlockStep", cur),
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("BlockHaloCopy", spare), ("BlockCurvedBoundary", spare),
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("BlockStep", spare), ("PatchCoarseStep", coarse)])
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steps = [a for name, a in calls if name == "BlockStep"]
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self.assertEqual([a[1] for a in steps], [spare, cur])
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for a in steps:
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self.assertIs(a[3], flags) # blocked flags, not null
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self.assertIs(a[4], kind)
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def test_tables_map_back_to_dense_slots_in_order(self):
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patch = FixedPatch2D.from_config(config("periodic"), BOUNDS)
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fy, fx = patch.fine_shape
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cells = fy * fx
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order = np.argsort(patch.coalesce_coarse)
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for by, bx in FINE_BLOCKS:
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layout = BlockLayout2D.build(fy, fx, by, bx, periodic=(False, False))
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core = set(layout.owner.ravel().tolist())
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targets, children, kind = blocked_fine_tables(patch, layout, order)
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# Explosion targets: owner cores of exactly the non-ACTIVE fine cells.
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np.testing.assert_array_equal(targets, layout.owner.ravel()[patch.ghost_fine])
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self.assertEqual(sorted(targets.tolist()),
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sorted(layout.owner[patch.fine_kind != ACTIVE].tolist()))
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# Children: same rows/child order, same (q, dense cell), owner cores.
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L = int(np.prod(layout.local_shape))
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b, rest = np.divmod(children, np.uint64(9 * L))
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q, c = np.divmod(rest, np.uint64(L))
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dense = patch.coalesce_fine[order]
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np.testing.assert_array_equal(q, dense // cells)
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np.testing.assert_array_equal(b * np.uint64(L) + c,
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layout.owner.ravel()[(dense % cells).astype(np.intp)])
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self.assertTrue(set((b * np.uint64(L) + c).ravel().tolist()) <= core)
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# Kinds follow owner cores; unused halo entries are OUTER.
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np.testing.assert_array_equal(kind[layout.owner.ravel().astype(np.intp)],
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patch.fine_kind.ravel())
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self.assertEqual(set(np.unique(kind).tolist()) - {ACTIVE, INNER_GHOST, OUTER_GHOST}, set())
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class BlockedFineGPU(unittest.TestCase):
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def test_dense_and_blocked_fine_storage_are_bit_identical(self):
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from CelerisLab.cuda.context import CudaContext
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for mode in ("periodic", "slot_reservoir"):
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cfg = config(mode)
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patch = FixedPatch2D.from_config(cfg, BOUNDS)
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g_coarse, g_fine = state(NY, NX, 1), state(*patch.fine_shape, 2)
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results = []
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with CudaContext(0) as context:
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for fine_block in (None, *FINE_BLOCKS):
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with FixedPatchStepper(cfg, BOUNDS, context=context, coarse_boundary=mode,
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fine_block=fine_block) as backend:
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backend.initialize(g_coarse, g_fine)
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backend.step(STEPS)
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self.assertEqual(backend.step_count, STEPS)
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results.append((backend.download(), raw(backend)))
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(dense_public, dense_raw), blocked = results[0], results[1:]
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kind = patch.fine_kind
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self.assertTrue(np.isnan(dense_raw[1][:, kind == OUTER_GHOST]).all(), mode)
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# INNER populations may be NaN after hop 2 (pulled from poisoned OUTER);
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# their exact pattern is pinned by the dense-vs-blocked comparison below.
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self.assertTrue(np.isfinite(dense_raw[1][:, kind == ACTIVE]).all(), mode)
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for fine_block, (public, raw_arrays) in zip(FINE_BLOCKS, blocked):
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msg = f"{mode} fine_block={fine_block}"
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for a, b in zip(public + tuple(raw_arrays), dense_public + tuple(dense_raw)):
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np.testing.assert_array_equal(a, b, err_msg=msg) # values and NaN masks
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print(f"{mode}: dense vs {len(blocked)} fine-block layouts bit-identical "
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f"after {STEPS} coarse steps", flush=True)
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def test_passive_circle_dense_and_blocked_fine_storage_are_bit_identical(self):
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from CelerisLab.cuda.context import CudaContext
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for mode in ("periodic", "slot_reservoir"):
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cfg = config(mode)
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patch, boundary = fine_circle(mode)
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solid = (boundary.flags.reshape(patch.fine_shape) & SOLID) != 0
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g_coarse, g_fine = state(NY, NX, 1), state(*patch.fine_shape, 2)
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results = []
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with CudaContext(0) as context:
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for fine_block in (None, *FINE_BLOCKS):
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with FixedPatchStepper(cfg, BOUNDS, context=context, coarse_boundary=mode,
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fine_objects=[circle()], fine_block=fine_block) as backend:
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backend.initialize(g_coarse, g_fine)
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backend.step(STEPS)
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results.append((backend.download(), raw(backend)))
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(dense_public, dense_raw), blocked = results[0], results[1:]
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fine = dense_raw[1]
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self.assertTrue(np.isfinite(fine[:, (patch.fine_kind == ACTIVE) & ~solid]).all(), mode)
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self.assertTrue(np.isnan(fine[:, solid]).all(), mode)
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self.assertTrue(np.isnan(fine[:, patch.fine_kind == OUTER_GHOST]).all(), mode)
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for fine_block, (public, raw_arrays) in zip(FINE_BLOCKS, blocked):
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msg = f"circle {mode} fine_block={fine_block}"
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for a, b in zip(public + tuple(raw_arrays), dense_public + tuple(dense_raw)):
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np.testing.assert_array_equal(a, b, err_msg=msg) # values and NaN masks
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print(f"circle {mode}: dense vs {len(blocked)} fine-block layouts bit-identical "
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f"after {STEPS} coarse steps", flush=True)
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if __name__ == "__main__":
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unittest.main()
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