Move host-only blocked table helpers into refinement_tables
Pure move of blocked_fine_tables, blocked_kind, blocked_base_tables, _BlockedBoundary and _prepare_blocked_boundary out of the pycuda-importing refinement_stepper into a numpy-only module, so CPU tests can import them directly. refinement_stepper re-exports the public names; no behaviour change. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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Claude
co-authored by
Claude Opus 5.5
Claude Opus 5.5
parent
60ad6b8b0d
commit
77c034a29a
@@ -15,37 +15,14 @@ import numpy as np
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import pycuda.driver as cuda
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from ..cuda import compiler_v2 as compiler
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from .blocks import BlockLayout2D, blocked_link_tables
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from .curved_links import surface_link_velocity
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from ..body.jets import jet_link_shapes, jet_link_velocity
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from .blocks import BlockLayout2D
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from ..body.jets import jet_link_velocity
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from .block_stepper import MAX_GRID_DIM
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from .refinement import (ACTIVE, HALO, OUTER_GHOST, FixedPatch2D, FixedPatchSet2D,
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from .refinement import (ACTIVE, HALO, FixedPatch2D, FixedPatchSet2D,
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nest_patch, nest_patch_set, prepare_fine_boundary)
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from .descriptors import SOLID, D2Q9_EX, D2Q9_EY
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def blocked_fine_tables(patch, layout, order):
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"""Remap fixed-patch fine tables to owner-core blocked storage (host only).
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Returns (explosion target block cells, coalescence child slots in the same
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rows/child order as ``patch.coalesce_fine[order]``, per-block-cell kind with
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unused halo entries OUTER). Coarse donors/targets are unchanged.
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"""
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owner = layout.owner.ravel().astype(np.intp)
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cells = patch.fine_shape[0] * patch.fine_shape[1]
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q, cell = np.divmod(patch.coalesce_fine[order], np.uint64(cells))
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kind = blocked_kind(layout, patch.fine_kind)
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return (owner[patch.ghost_fine.astype(np.intp)].astype(np.uint64),
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layout.slot(owner[cell.astype(np.intp)], q), kind)
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def blocked_kind(layout, dense_kind):
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"""Per-block-cell kind; unused halo entries and unstored holes stay OUTER."""
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owner = layout.owner.ravel().astype(np.intp)
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owned = layout.owned.ravel() # sparse multipatch levels leave holes unstored
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kind = np.full(layout.nblocks * int(np.prod(layout.local_shape)), OUTER_GHOST, np.uint8)
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kind[owner[owned]] = np.asarray(dense_kind).ravel()[owned]
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return kind
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from .refinement_tables import (blocked_base_tables, blocked_fine_tables, # noqa: F401 (re-export)
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blocked_kind, _prepare_blocked_boundary)
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from .descriptors import SOLID
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@dataclass
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@@ -62,36 +39,6 @@ class _Pair:
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parent_cells: int # parent block window cells (explode donor stride)
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def blocked_base_tables(patch, layout, order, wall_aware=False, *, composed_kind=None):
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"""Parent donor cells and receiver-local arrival slots, in transfer row order.
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Stage arrivals only after child reads and parent halo gather, immediately
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before parent pull. Source *owner* slots are wrong at block seams.
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Nested pairs must pass composed_kind to retain inherited INNER/OUTER and
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sparse holes; donors and receivers must be owned, uncovered ACTIVE cells.
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"""
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owner = layout.owner.ravel()
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q, receiver = np.divmod(patch.coalesce_coarse[order], np.uint64(owner.size))
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if composed_kind is None:
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dense_kind = np.where(patch.covered, OUTER_GHOST, ACTIVE).astype(np.uint8)
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if wall_aware:
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dense_kind[[0, -1]] = OUTER_GHOST
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else:
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dense_kind = np.asarray(composed_kind)
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if dense_kind.shape != patch.coarse_shape:
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raise ValueError("composed parent kind has the wrong shape")
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donors = patch.ghost_coarse.astype(np.intp)
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receivers = receiver.astype(np.intp)
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legal = layout.owned.ravel() & (dense_kind.ravel() == ACTIVE) & ~patch.covered.ravel()
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if not legal[donors].all() or not legal[receivers].all():
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raise ValueError("child donors/receivers need owned uncovered parent ACTIVE cells")
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offset = (np.asarray(D2Q9_EY)[q.astype(np.intp)] * layout.local_shape[1]
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+ np.asarray(D2Q9_EX)[q.astype(np.intp)])
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source = owner[receivers].astype(np.int64) - offset
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return (owner[donors].astype(np.uint64), layout.slot(source, q),
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blocked_kind(layout, dense_kind))
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@dataclass
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class _Level:
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shape: tuple
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@@ -108,35 +55,6 @@ class _Level:
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boundary: object = None
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@dataclass
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class _BlockedBoundary:
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"""One level's stationary BFL data and mutable Jet velocity column."""
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host: object
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tables: dict
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jets: tuple
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actuated: bool
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jet_cache: object = None
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flags: object = None
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device: object = None
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kernel: object = None
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def _prepare_blocked_boundary(boundary, layout, cfg, jets):
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if boundary is None or layout is None:
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return None
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fluid_idx, dirs, q, body_id, rx, ry, _, fallback = boundary.curved_host
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tables, _ = blocked_link_tables(layout, boundary.flags, fluid_idx, dirs, q, fallback)
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actuated = bool(jets) or any(o.surface_actuation is not None for o in boundary.objects)
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if actuated:
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cfg = replace(cfg)
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cfg.validate()
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tables["surface_velocity"], _ = surface_link_velocity(
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boundary.objects, cfg, boundary.flags, fluid_idx, dirs, body_id, rx, ry,
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jets=jets, hits=boundary.curved_hits)
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return _BlockedBoundary(boundary, tables, jets, actuated,
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jet_link_shapes(jets, boundary.curved_hits) if jets else None)
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class FixedPatchStepper:
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"""D2Q9/SRT/FP32, one unforced internal 2:1 patch in an x-periodic box.
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@@ -0,0 +1,95 @@
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# CelerisLab/lbm/refinement_tables.py
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"""Host-only (numpy) mapping of fixed-patch transfer and boundary tables to block storage.
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No CUDA import: CPU tests import these directly. refinement_stepper re-exports the names.
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"""
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from dataclasses import dataclass, replace
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import numpy as np
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from .blocks import blocked_link_tables
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from ..body.jets import jet_link_shapes, surface_link_velocity
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from .descriptors import D2Q9_EX, D2Q9_EY
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from .refinement import ACTIVE, OUTER_GHOST
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def blocked_fine_tables(patch, layout, order):
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"""Remap fixed-patch fine tables to owner-core blocked storage (host only).
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Returns (explosion target block cells, coalescence child slots in the same
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rows/child order as ``patch.coalesce_fine[order]``, per-block-cell kind with
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unused halo entries OUTER). Coarse donors/targets are unchanged.
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"""
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owner = layout.owner.ravel().astype(np.intp)
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cells = patch.fine_shape[0] * patch.fine_shape[1]
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q, cell = np.divmod(patch.coalesce_fine[order], np.uint64(cells))
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kind = blocked_kind(layout, patch.fine_kind)
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return (owner[patch.ghost_fine.astype(np.intp)].astype(np.uint64),
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layout.slot(owner[cell.astype(np.intp)], q), kind)
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def blocked_kind(layout, dense_kind):
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"""Per-block-cell kind; unused halo entries and unstored holes stay OUTER."""
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owner = layout.owner.ravel().astype(np.intp)
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owned = layout.owned.ravel() # sparse multipatch levels leave holes unstored
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kind = np.full(layout.nblocks * int(np.prod(layout.local_shape)), OUTER_GHOST, np.uint8)
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kind[owner[owned]] = np.asarray(dense_kind).ravel()[owned]
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return kind
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def blocked_base_tables(patch, layout, order, wall_aware=False, *, composed_kind=None):
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"""Parent donor cells and receiver-local arrival slots, in transfer row order.
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Stage arrivals only after child reads and parent halo gather, immediately
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before parent pull. Source *owner* slots are wrong at block seams.
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Nested pairs must pass composed_kind to retain inherited INNER/OUTER and
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sparse holes; donors and receivers must be owned, uncovered ACTIVE cells.
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"""
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owner = layout.owner.ravel()
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q, receiver = np.divmod(patch.coalesce_coarse[order], np.uint64(owner.size))
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if composed_kind is None:
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dense_kind = np.where(patch.covered, OUTER_GHOST, ACTIVE).astype(np.uint8)
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if wall_aware:
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dense_kind[[0, -1]] = OUTER_GHOST
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else:
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dense_kind = np.asarray(composed_kind)
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if dense_kind.shape != patch.coarse_shape:
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raise ValueError("composed parent kind has the wrong shape")
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donors = patch.ghost_coarse.astype(np.intp)
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receivers = receiver.astype(np.intp)
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legal = layout.owned.ravel() & (dense_kind.ravel() == ACTIVE) & ~patch.covered.ravel()
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if not legal[donors].all() or not legal[receivers].all():
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raise ValueError("child donors/receivers need owned uncovered parent ACTIVE cells")
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offset = (np.asarray(D2Q9_EY)[q.astype(np.intp)] * layout.local_shape[1]
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+ np.asarray(D2Q9_EX)[q.astype(np.intp)])
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source = owner[receivers].astype(np.int64) - offset
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return (owner[donors].astype(np.uint64), layout.slot(source, q),
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blocked_kind(layout, dense_kind))
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@dataclass
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class _BlockedBoundary:
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"""One level's stationary BFL data and mutable Jet velocity column."""
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host: object
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tables: dict
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jets: tuple
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actuated: bool
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jet_cache: object = None
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flags: object = None
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device: object = None
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kernel: object = None
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def _prepare_blocked_boundary(boundary, layout, cfg, jets):
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if boundary is None or layout is None:
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return None
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fluid_idx, dirs, q, body_id, rx, ry, _, fallback = boundary.curved_host
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tables, _ = blocked_link_tables(layout, boundary.flags, fluid_idx, dirs, q, fallback)
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actuated = bool(jets) or any(o.surface_actuation is not None for o in boundary.objects)
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if actuated:
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cfg = replace(cfg)
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cfg.validate()
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tables["surface_velocity"], _ = surface_link_velocity(
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boundary.objects, cfg, boundary.flags, fluid_idx, dirs, body_id, rx, ry,
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jets=jets, hits=boundary.curved_hits)
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return _BlockedBoundary(boundary, tables, jets, actuated,
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jet_link_shapes(jets, boundary.curved_hits) if jets else None)
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