ClaudeandClaude Opus 5.5
442979b4db
- _exchange_spacing/_exchange_centers (two more per-level lists, only read for levels that have a boundary) become _BlockedBoundary.spacing/centers. - _level_blocks dropped: remap_finest reads each layout's own .block. - _device_boundaries -> _boundaries: the record holds host geometry, tables, Jets and device handles, so 'device' was misleading (the old host-only _boundaries list no longer exists). Tests migrated mechanically. Remaining per-level lists (_layouts, _level_cfgs, _leaf, _state, _boundaries) are the immutable construction plan, built before the memory preflight and kept after close; _levels holds device handles only and is cleared on close. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
248 lines
13 KiB
Python
248 lines
13 KiB
Python
"""GPU gates for raw exchange. Run explicitly; requires PyCUDA and CUDA.
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Independent census reads actual staged incoming/outgoing populations, including
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non-body ACTIVE-boundary flux, then checks postcollision fluid momentum/mass.
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This is a discrete implementation gate, NOT a physical nozzle-load validation.
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"""
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from dataclasses import replace
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import unittest
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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
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from CelerisLab.body.jets import Jet, Tube, Parallel
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from CelerisLab.config import LBMConfig
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from CelerisLab.cuda.context import CudaContext
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from CelerisLab.lbm.refinement_stepper import FixedPatchStepper
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from CelerisLab.lbm.descriptors import D2Q9_EX, D2Q9_EY, SOLID
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CX, CY = np.asarray(D2Q9_EX), np.asarray(D2Q9_EY)
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OPP = np.array([np.flatnonzero((CX == -x) & (CY == -y))[0] for x, y in zip(CX, CY)])
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W = np.array([4/9] + [1/9]*4 + [1/36]*4, np.float32)
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CFG = LBMConfig(nx=32, ny=24, viscosity=.1)
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BOUNDS = (8, 6, 24, 18)
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def circle():
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return SimObject(17, geometry=CircleGeometry(16., 12., 2.15), center=(16., 12.), radius=2.15)
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def jet():
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# Asymmetric aperture avoids hiding exchange errors by paired-jet cancellation.
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return Jet(Tube((16., 12.), 0., 3., .7), Parallel(0.), amplitude=0.)
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def state(shape, ux=.013):
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cu = CX[:, None, None]*ux
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values = W[:, None, None]*(1+3*cu+4.5*cu*cu-1.5*ux*ux)
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return np.broadcast_to(values, shape).astype(np.float32).copy()
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def download(pointer, shape, dtype=np.float32):
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result = np.empty(shape, dtype)
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cuda.memcpy_dtoh(result, pointer)
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return result
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def totals(g, leaf):
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values = g[:, leaf].astype(np.float64)
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return np.array([(values*CX[:, None]).sum(), (values*CY[:, None]).sum(), values.sum()])
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def census(run, index, buffer):
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"""Independent incoming/outgoing face census on ACTIVE fluid cells.
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Reads the receiver's actual block window, not a freshly gathered owner image
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(that would erase BFL/restriction staging). Classifies external flux solely
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from geometry, without consulting raw observation arrays.
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"""
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level = run._levels[index]
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layout, b = level.layout, level.boundary
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g = download(buffer, layout.blocked_shape)
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dense = layout.to_dense(g)
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leaf = run.leaf[index]
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ny, nx = leaf.shape
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solid = (b.host.flags.reshape(ny, nx) & SOLID) != 0
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y, x = np.nonzero(leaf)
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owner = layout.owner[y, x].astype(np.intp)
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cells = int(np.prod(layout.local_shape))
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block, cell = np.divmod(owner, cells)
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flat = g.reshape(layout.nblocks, 9, cells)
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external = np.zeros(3, np.float64)
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for q, (cx, cy) in enumerate(zip(CX, CY)):
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sy, sx = y-cy, x-cx
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incoming = flat[block, q, cell-cy*layout.local_shape[1]-cx].astype(np.float64)
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entering = ~leaf[sy, sx] & ~solid[sy, sx]
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external += np.array([cx, cy, 1])*incoming[entering].sum()
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dy, dx = y+cy, x+cx
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leaving = ~leaf[dy, dx] & ~solid[dy, dx]
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outgoing = flat[block, q, cell].astype(np.float64)
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external -= np.array([cx, cy, 1])*outgoing[leaving].sum()
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receiver, direction, _, _, rx, ry, _, _ = b.host.curved_host
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owner = layout.owner.ravel()[receiver].astype(np.intp)
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block, cell = np.divmod(owner, cells)
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a = flat[block, direction, cell].astype(np.float64)
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source = cell+CY[direction]*layout.local_shape[1]+CX[direction]
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incoming = flat[block, OPP[direction], source].astype(np.float64)
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jx, jy = CX[direction]*(a+incoming), CY[direction]*(a+incoming)
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independent = np.column_stack((jx, jy, rx*jy-ry*jx, incoming-a))
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return totals(dense, leaf), external, independent
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def fine(block):
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"""Two-level circle+jet construction on BOUNDS, blocked with a (12, 8) root tile."""
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return dict(refinements=[BOUNDS], level_blocks=[(12, 8), block],
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level_objects=[(), (circle(),)], level_jets=[(), (jet(),)])
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class RawExchangeGPU(unittest.TestCase):
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def test_row_ledger_interval_jet_switch_and_no_population_effect(self):
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with CudaContext(0) as ctx:
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for block in ((32, 40), (16, 20), (8, 10)):
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with FixedPatchStepper(CFG, context=ctx, **fine(block), observe_raw_exchange=True) as measured, \
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FixedPatchStepper(CFG, context=ctx, **fine(block)) as plain:
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arrays = [state(l.shape) for l in measured._levels]
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measured.initialize(*arrays)
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plain.initialize(*arrays)
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b = measured._boundaries[1]
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count = len(b.tables['cell'])
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kernel = measured._levels[1].kernel
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samples = []
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def audited_step(src, dst, *args, **kw):
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measured._stream.synchronize()
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before, external, independent = census(measured, 1, src)
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raw = download(b.last_raw, (count, 4))
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np.testing.assert_allclose(raw, independent, rtol=2e-6, atol=2e-7)
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kernel(src, dst, *args, **kw)
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measured._stream.synchronize()
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after = measured._levels[1].layout.to_dense(
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download(dst, measured._levels[1].layout.blocked_shape))
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change = totals(after, measured.leaf[1])-before
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expected = external + np.array([-raw[:, 0].astype(float).sum(),
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-raw[:, 1].astype(float).sum(),
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raw[:, 3].astype(float).sum()])
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np.testing.assert_allclose(change, expected, rtol=2e-4, atol=8e-5)
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samples.append(raw.copy())
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measured._levels[1].kernel = audited_step
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for speed in (0., .002, -.001, 0.):
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measured.set_jet_amplitude(0, speed, level=1)
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plain.set_jet_amplitude(0, speed, level=1)
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samples.clear()
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measured.step()
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plain.step()
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for a, e in zip(measured.download(), plain.download()):
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np.testing.assert_array_equal(a, e)
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integral = download(b.interval_integral, (count, 4), np.float64)
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expected = sum(s.astype(float)*[.25, .25, .125, .25] for s in samples)
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np.testing.assert_array_equal(integral, expected)
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last = download(b.last_raw, (count, 4))
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np.testing.assert_array_equal(last, samples[-1])
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obs = measured.read_raw_exchange(0, level=1)
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assert obs['sample_count'] == 2
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assert b.tables['aperture'].any()
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measured.step(0)
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np.testing.assert_array_equal(measured.read_raw_exchange(0, level=1)['total']['impulse'],
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obs['total']['impulse'])
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def test_depth_four_interval_storage_and_sampling(self):
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cfg = LBMConfig(nx=48, ny=48, viscosity=.02)
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obj = SimObject(0, geometry=CircleGeometry(16., 16., .5), center=(16., 16.), radius=.5)
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drive = Jet(Tube((16., 16.), 0., .75, .2), Parallel(0.), amplitude=0.)
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settings = dict(refinements=[[(6, 6, 26, 26)], [(20, 20, 44, 44)], [(56, 56, 72, 72)]],
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level_blocks=[(4, 4)]*4, level_objects=[(), (), (), (obj,)],
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level_jets=[(), (), (), (drive,)])
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with CudaContext(0) as ctx:
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with FixedPatchStepper(cfg, context=ctx, observe_raw_exchange=True, **settings) as run, \
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FixedPatchStepper(cfg, context=ctx, **settings) as plain:
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arrays = [state(l.shape, ux=0.) for l in run._levels]
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run.initialize(*arrays); plain.initialize(*arrays)
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b = run._boundaries[3]
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count = len(b.tables['cell'])
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kernel = run._levels[3].kernel
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samples, independent_samples = [], []
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def audited_step(src, dst, *args, **kw):
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run._stream.synchronize()
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before, external, independent = census(run, 3, src)
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raw = download(b.last_raw, (count, 4))
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np.testing.assert_allclose(raw, independent, rtol=2e-6, atol=2e-7)
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kernel(src, dst, *args, **kw)
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run._stream.synchronize()
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after = run._levels[3].layout.to_dense(
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download(dst, run._levels[3].layout.blocked_shape))
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change = totals(after, run.leaf[3])-before
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expected = external + np.array([-independent[:, 0].sum(),
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-independent[:, 1].sum(), independent[:, 3].sum()])
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np.testing.assert_allclose(change, expected, rtol=2e-4, atol=8e-5)
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samples.append(raw)
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independent_samples.append(independent)
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run._levels[3].kernel = audited_step
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for speed in (0., .002, 0.):
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run.set_jet_amplitude(0, speed, level=3)
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plain.set_jet_amplitude(0, speed, level=3)
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samples.clear(); independent_samples.clear()
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run.step(); plain.step()
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self.assertEqual(len(samples), 8)
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weights = np.array([1/64, 1/64, 1/512, 1/64])
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integral = download(b.interval_integral, (count, 4), np.float64)
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np.testing.assert_array_equal(integral, sum(v.astype(float)*weights for v in samples))
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np.testing.assert_allclose(integral, sum(v*weights for v in independent_samples),
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rtol=2e-6, atol=2e-7)
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np.testing.assert_array_equal(download(b.last_raw, (count, 4)), samples[-1])
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for actual, expected in zip(run.download(), plain.download()):
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np.testing.assert_array_equal(actual, expected)
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result = run.read_raw_exchange(0, level=3)
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self.assertEqual(result['sample_count'], 8)
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self.assertEqual(result['time_start'], run.step_count-1)
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if run.step_count == 1:
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np.testing.assert_allclose(result['total']['force'], 0, atol=2e-7)
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self.assertAlmostEqual(result['total']['torque'], 0, delta=2e-7)
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def test_uniform_whole_domain_fine_short_time_reference(self):
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"""Same root geometry, nu and Jet speed on a whole-domain fine mesh.
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Eight local hops are a short-time equivalence check, not convergence.
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The test adapter only enables the standalone kernel's optional inputs.
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"""
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fine_cfg = replace(CFG, nx=64, ny=48, viscosity=.2)
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obj = SimObject(0, geometry=CircleGeometry(31.5, 23.5, 4.3),
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center=(31.5, 23.5), radius=4.3)
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with CudaContext(0) as ctx:
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with FixedPatchStepper(fine_cfg, context=ctx, refinements=[], level_blocks=[(8, 8)],
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level_objects=[(obj,)]) as uniform, \
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FixedPatchStepper(CFG, context=ctx, **fine((16, 20)), observe_raw_exchange=True) as amr:
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amr.initialize(*(state(l.shape) for l in amr._levels))
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uniform.initialize(state((9, 48, 64)))
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b = amr._boundaries[1]
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count = len(b.tables['cell'])
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links = obj.geometry.build_cut_links(64, 48)
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np.testing.assert_array_equal([v.dir for v in links], b.tables['dir'])
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np.testing.assert_allclose([v.q for v in links], b.tables['q'], atol=2e-7, rtol=0)
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# Same fine-grid force arms after translation to the same center: the
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# single level's static BFL gets the AMR Jet column and force arms; the raw
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# output pointer is substituted at launch (no stepper-side accumulation).
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root = uniform._boundaries[0]
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velocity = uniform._upload(b.tables['surface_velocity'])
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root.device.update(surface_velocity=velocity, rx=uniform._upload(b.tables['rx']),
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ry=uniform._upload(b.tables['ry']))
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raw = uniform._allocate(count*16)
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original = root.kernel
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samples = []
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def measured_boundary(*args, **kw):
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original(*args[:10], raw, **kw) # BlockCurvedBoundary raw-exchange output
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uniform._stream.synchronize()
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samples.append(download(raw, (count, 4)))
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root.kernel = measured_boundary
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for speed in (0., .002, -.001, 0.):
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amr.set_jet_amplitude(0, speed, level=1)
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cuda.memcpy_htod(velocity, b.tables['surface_velocity'])
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samples.clear()
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amr.step(); uniform.step(2)
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expected = sum(v.astype(float)*[.25, .25, .125, .25] for v in samples)
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actual = download(b.interval_integral, (count, 4), np.float64)
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np.testing.assert_allclose(actual, expected, rtol=1e-5, atol=2e-6)
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if __name__ == '__main__':
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unittest.main()
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