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Move to incremental approach to improve memory usage on large layouts

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performance_testing
jan 2 years ago
parent 36d6663dbe
commit 8ff8a47784
4 changed files with 169 additions and 97 deletions
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6
examples/check.py
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@ -10,11 +10,11 @@ import snarled
import snarled.interfaces.masque
connectivity = {
connectivity = [
((1, 0), (1, 2), (2, 0)), #M1 to M2 (via V12)
((1, 0), (1, 3), (3, 0)), #M1 to M3 (via V13)
((2, 0), (2, 3), (3, 0)), #M2 to M3 (via V23)
}
]
#cells, props = gdsii.readfile('connectivity.gds')
@ -22,7 +22,7 @@ cells, props = oasis.readfile('connectivity.oas')
topcell = cells['top']
polys, labels = snarled.interfaces.masque.read_cell(topcell, connectivity)
nets_info = snarled.trace_connectivity(polys, labels, connectivity)
nets_info = snarled.trace_connectivity_preloaded(polys, labels, connectivity)
print('\nFinal nets:')
print([kk for kk in sorted(nets_info.nets.keys()) if isinstance(kk.name, str)])

2
snarled/__init__.py
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@ -13,7 +13,7 @@ The main functionality is in `trace_connectivity`.
Useful classes, namely `NetsInfo` and `NetName`, are in `snarled.tracker`.
`snarled.interfaces` contains helper code for interfacing with other packages.
"""
from .main import trace_connectivity
from .main import trace_connectivity, trace_connectivity_preloaded
from .tracker import NetsInfo, NetName
from . import interfaces

246
snarled/main.py
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@ -1,7 +1,7 @@
"""
Main connectivity-checking functionality for `snarled`
"""
from typing import Tuple, List, Dict, Set, Optional, Union, Sequence, Mapping
from typing import Tuple, List, Dict, Set, Optional, Union, Sequence, Mapping, Callable
from collections import defaultdict
from pprint import pformat
import logging
@ -20,16 +20,14 @@ from .utils import connectivity2layers
logger = logging.getLogger(__name__)
def trace_connectivity(
def trace_connectivity_preloaded(
polys: Mapping[layer_t, Sequence[ArrayLike]],
labels: Mapping[layer_t, Sequence[Tuple[float, float, str]]],
connectivity: Sequence[Tuple[layer_t, Optional[layer_t], layer_t]],
clipper_scale_factor: int = int(2 ** 24),
) -> NetsInfo:
"""
Analyze the electrical connectivity of the layout.
This is the primary purpose of `snarled`.
Analyze the electrical connectivity of the provided layout.
The resulting `NetsInfo` will contain only disjoint `nets`, and its `net_aliases` can be used to
understand which nets are shorted (and therefore known by more than one name).
@ -55,70 +53,106 @@ def trace_connectivity(
Returns:
`NetsInfo` object describing the various nets and their connectivities.
"""
#
# Figure out which layers are metals vs vias, and run initial union on each layer
#
metal_layers, via_layers = connectivity2layers(connectivity)
def get_layer(layer: layer_t) -> Tuple[Sequence[ArrayLike], Sequence[Tuple[float, float, str]]]:
return polys[layer], labels[layer]
metal_polys = {layer: union_input_polys(scale_to_clipper(polys[layer], clipper_scale_factor))
for layer in metal_layers}
via_polys = {layer: union_input_polys(scale_to_clipper(polys[layer], clipper_scale_factor))
for layer in via_layers}
return trace_connectivity(get_layer, connectivity, clipper_scale_factor)
#
# Check each polygon for labels, and assign it to a net (possibly anonymous).
#
def trace_connectivity(
get_layer: Callable[[layer_t], Tuple[Sequence[ArrayLike], Sequence[Tuple[float, float, str]]]],
connectivity: Sequence[Tuple[layer_t, Optional[layer_t], layer_t]],
clipper_scale_factor: int = int(2 ** 24),
) -> NetsInfo:
"""
Analyze the electrical connectivity of a layout.
The resulting `NetsInfo` will contain only disjoint `nets`, and its `net_aliases` can be used to
understand which nets are shorted (and therefore known by more than one name).
This function attempts to reduce memory usage by lazy-loading layout data (layer-by-layer) and
pruning away layers for which all interactions have already been computed.
TODO: In the future, this will be extended to cover partial loading of spatial extents in
addition to layers.
Args:
get_layer: When called, `polys, labels = get_layer(layer)` should return the geometry and labels
on that layer. Returns
polys, A list of polygons (Nx2 arrays of vertices) on the layer. The structure looks like
`[poly0, poly1, ..., [(x0, y0), (x1, y1), ...]]`
labels, A list of "named points" which are used to assign names to the nets they touch.
A list of (x, y, name) tuples targetting this layer.
`[(x0, y0, name0), (x1, y1, name1), ...]`
connectivity: A sequence of 3-tuples specifying the electrical connectivity between layers.
Each 3-tuple looks like `(top_layer, via_layer, bottom_layer)` and indicates that
`top_layer` and `bottom_layer` are electrically connected at any location where
shapes are present on all three (top, via, and bottom) layers.
`via_layer` may be `None`, in which case any overlap between shapes on `top_layer`
and `bottom_layer` is automatically considered a short (with no third shape necessary).
NOTE that the order in which connectivity is specified (i.e. top-level ordering of the
tuples) directly sets the order in which the layers are loaded and merged, and thus
has a significant impact on memory usage by determining when layers can be pruned away.
Try to group entries by the layers they affect!
clipper_scale_factor: `pyclipper` uses 64-bit integer math, while we accept either floats or ints.
The coordinates from `polys` are scaled by this factor to put them roughly in the middle of
the range `pyclipper` wants; you may need to adjust this if you are already using coordinates
with large integer values.
Returns:
`NetsInfo` object describing the various nets and their connectivities.
"""
loaded_layers = set()
nets_info = NetsInfo()
merge_groups: List[List[NetName]] = []
for layer in metal_layers:
point_xys = []
point_names = []
for x, y, point_name in labels[layer]:
point_xys.append((x, y))
point_names.append(point_name)
for ii, (top_layer, via_layer, bot_layer) in enumerate(connectivity):
for metal_layer in (top_layer, bot_layer):
if metal_layer in loaded_layers:
continue
# Load and run initial union on each layer
raw_polys, labels = get_layer(metal_layer)
polys = union_input_polys(scale_to_clipper(raw_polys, clipper_scale_factor))
for poly in metal_polys[layer]:
found_nets = label_poly(poly, point_xys, point_names, clipper_scale_factor)
# Check each polygon for labels, and assign it to a net (possibly anonymous).
nets_on_layer, merge_groups = label_polys(polys, labels, clipper_scale_factor)
for name, net_polys in nets_on_layer.items():
nets_info.nets[name][metal_layer] += hier2oriented(net_polys)
if found_nets:
name = NetName(found_nets[0])
else:
name = NetName() # Anonymous net
# Merge any nets that were shorted by having their labels on the same polygon
for group in merge_groups:
logger.warning(f'Nets {group} are shorted on layer {metal_layer}')
first_net, *defunct_nets = group
for defunct_net in defunct_nets:
nets_info.merge(first_net, defunct_net)
nets_info.nets[name][layer].append(poly)
loaded_layers.add(metal_layer)
if len(found_nets) > 1:
# Found a short
poly = pformat(scale_from_clipper(poly.Contour, clipper_scale_factor))
logger.warning(f'Nets {found_nets} are shorted on layer {layer} in poly:\n {poly}')
merge_groups.append([name] + [NetName(nn) for nn in found_nets[1:]])
# Load and union vias
via_raw_polys, _labels = get_layer(via_layer)
via_polys = hier2oriented(union_input_polys(
scale_to_clipper(via_raw_polys, clipper_scale_factor)
))
#
# Merge any nets that were shorted by having their labels on the same polygon
#
for group in merge_groups:
first_net, *defunct_nets = group
for defunct_net in defunct_nets:
nets_info.merge(first_net, defunct_net)
# Figure out which nets are shorted by vias, then merge them
merge_pairs = find_merge_pairs(nets_info.nets, top_layer, bot_layer, via_polys)
for net_a, net_b in merge_pairs:
nets_info.merge(net_a, net_b)
#
# Convert to non-hierarchical polygon representation
#
for net in nets_info.nets.values():
for layer in net:
#net[layer] = union_evenodd(hier2oriented(net[layer]))
net[layer] = hier2oriented(net[layer])
for layer in via_polys:
via_polys[layer] = hier2oriented(via_polys[layer])
remaining_layers = set()
for layer_a, _, layer_b in connectivity[ii + 1:]:
remaining_layers.add(layer_a)
remaining_layers.add(layer_b)
#
# Figure out which nets are shorted by vias, then merge them
#
merge_pairs = find_merge_pairs(connectivity, nets_info.nets, via_polys)
for net_a, net_b in merge_pairs:
nets_info.merge(net_a, net_b)
finished_layers = loaded_layers - remaining_layers
for layer in finished_layers:
nets_info.prune(layer)
return nets_info
@ -166,12 +200,44 @@ def union_input_polys(polys: Sequence[ArrayLike]) -> List[PyPolyNode]:
return outer_nodes
def label_polys(
polys: Sequence[PyPolyNode],
labels: Sequence[Tuple[float, float, str]],
clipper_scale_factor: int,
) -> Tuple[
defaultdict[NetName, List[PyPolyNode]],
List[List[NetName]]
]:
merge_groups = []
point_xys = []
point_names = []
nets = defaultdict(list)
for x, y, point_name in labels:
point_xys.append((x, y))
point_names.append(point_name)
for poly in polys:
found_nets = label_poly(poly, point_xys, point_names, clipper_scale_factor)
if found_nets:
name = NetName(found_nets[0])
else:
name = NetName() # Anonymous net
nets[name].append(poly)
if len(found_nets) > 1:
# Found a short
poly = pformat(scale_from_clipper(poly.Contour, clipper_scale_factor))
merge_groups.append([name] + [NetName(nn) for nn in found_nets[1:]])
return nets, merge_groups
def label_poly(
poly: PyPolyNode,
point_xys: ArrayLike,
point_names: Sequence[str],
clipper_scale_factor: int = int(2 ** 24),
clipper_scale_factor: int,
) -> List[str]:
"""
Given a `PyPolyNode` (a polygon, possibly with holes) and a sequence of named points,
@ -209,60 +275,54 @@ def label_poly(
def find_merge_pairs(
connectivity: connectivity_t,
nets: Mapping[NetName, Mapping[layer_t, Sequence[contour_t]]],
via_polys: Mapping[layer_t, Sequence[contour_t]],
top_layer: layer_t,
bot_layer: layer_t,
via_polys: Optional[Sequence[contour_t]],
) -> Set[Tuple[NetName, NetName]]:
"""
Given a collection of (possibly anonymous) nets, figure out which pairs of
nets are shorted through a via (and thus should be merged).
Args:
connectivity: A sequence of 3-tuples specifying the electrical connectivity between layers.
Each 3-tuple looks like `(top_layer, via_layer, bottom_layer)` and indicates that
`top_layer` and `bottom_layer` are electrically connected at any location where
shapes are present on all three (top, via, and bottom) layers.
`via_layer` may be `None`, in which case any overlap between shapes on `top_layer`
and `bottom_layer` is automatically considered a short (with no third shape necessary).
nets: A collection of all nets (seqences of polygons in mappings indexed by `NetName`
and layer). See `NetsInfo.nets`.
via_polys: A collection of all vias (in a mapping indexed by layer).
top_layer: Layer name of first layer
bot_layer: Layer name of second layer
via_polys: Sequence of via contours. `None` denotes to vias necessary (overlap is sufficent).
Returns:
A set containing pairs of `NetName`s for each pair of nets which are shorted.
"""
merge_pairs = set()
for top_layer, via_layer, bot_layer in connectivity:
if via_layer is not None:
vias = via_polys[via_layer]
if not vias:
logger.warning(f'No vias on layer {via_layer}')
if via_polys is not None and not via_polys:
logger.warning(f'No vias between layers {top_layer}, {bot_layer}')
return merge_pairs
for top_name in nets.keys():
top_polys = nets[top_name][top_layer]
if not top_polys:
continue
for bot_name in nets.keys():
if bot_name == top_name:
continue
for top_name in nets.keys():
top_polys = nets[top_name][top_layer]
if not top_polys:
name_pair: Tuple[NetName, NetName] = tuple(sorted((top_name, bot_name))) #type: ignore
if name_pair in merge_pairs:
continue
for bot_name in nets.keys():
if bot_name == top_name:
continue
name_pair: Tuple[NetName, NetName] = tuple(sorted((top_name, bot_name))) #type: ignore
if name_pair in merge_pairs:
continue
bot_polys = nets[bot_name][bot_layer]
if not bot_polys:
continue
bot_polys = nets[bot_name][bot_layer]
if not bot_polys:
continue
if via_layer is not None:
via_top = intersection_evenodd(top_polys, vias)
overlap = intersection_evenodd(via_top, bot_polys)
else:
overlap = intersection_evenodd(top_polys, bot_polys) # TODO verify there aren't any suspicious corner cases for this
if not overlap:
continue
if via_polys is not None:
via_top = intersection_evenodd(top_polys, via_polys)
overlap = intersection_evenodd(via_top, bot_polys)
else:
overlap = intersection_evenodd(top_polys, bot_polys) # TODO verify there aren't any suspicious corner cases for this
if overlap:
merge_pairs.add(name_pair)
return merge_pairs

12
snarled/tracker.py
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@ -99,6 +99,8 @@ class NetsInfo:
"""
net_a = self.resolve_name(net_a)
net_b = self.resolve_name(net_b)
if net_a is net_b:
return
# Always keep named nets if the other is anonymous
keep_net, old_net = sorted((net_a, net_b))
@ -110,6 +112,16 @@ class NetsInfo:
self.nets[keep_net][layer] += self.nets[old_net][layer]
del self.nets[old_net]
def prune(self, layer: layer_t) -> None:
"""
Delete all geometry for the given layer.
Args:
layer: The layer to "forget"
"""
for net in self.nets.values():
if layer in net:
del net[layer]
def get_shorted_nets(self) -> List[Set[NetName]]:
"""

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