def build_legalization(self, params, placedb, data_collections, device):
"""
@brief legalization
@param params parameters
@param placedb placement database
@param data_collections a collection of all data and variables required for constructing the ops
@param device cpu or cuda
"""
# for movable macro legalization
# the number of bins control the search granularity
ml = macro_legalize.MacroLegalize(
node_size_x=data_collections.node_size_x,
node_size_y=data_collections.node_size_y,
node_weights=data_collections.num_pins_in_nodes,
flat_region_boxes=data_collections.flat_region_boxes,
flat_region_boxes_start=data_collections.flat_region_boxes_start,
node2fence_region_map=data_collections.node2fence_region_map,
xl=placedb.xl,
yl=placedb.yl,
xh=placedb.xh,
yh=placedb.yh,
site_width=placedb.site_width,
row_height=placedb.row_height,
num_bins_x=placedb.num_bins_x,
num_bins_y=placedb.num_bins_y,
num_movable_nodes=placedb.num_movable_nodes,
num_terminal_NIs=placedb.num_terminal_NIs,
num_filler_nodes=placedb.num_filler_nodes)
# for standard cell legalization
gl = greedy_legalize.GreedyLegalize(
node_size_x=data_collections.node_size_x,
node_size_y=data_collections.node_size_y,
node_weights=data_collections.num_pins_in_nodes,
flat_region_boxes=data_collections.flat_region_boxes,
flat_region_boxes_start=data_collections.flat_region_boxes_start,
node2fence_region_map=data_collections.node2fence_region_map,
xl=placedb.xl,
yl=placedb.yl,
xh=placedb.xh,
yh=placedb.yh,
site_width=placedb.site_width,
row_height=placedb.row_height,
num_bins_x=1,
num_bins_y=64,
#num_bins_x=64, num_bins_y=64,
num_movable_nodes=placedb.num_movable_nodes,
num_terminal_NIs=placedb.num_terminal_NIs,
num_filler_nodes=placedb.num_filler_nodes)
# for standard cell legalization
al = abacus_legalize.AbacusLegalize(
node_size_x=data_collections.node_size_x,
node_size_y=data_collections.node_size_y,
node_weights=data_collections.num_pins_in_nodes,
flat_region_boxes=data_collections.flat_region_boxes,
flat_region_boxes_start=data_collections.flat_region_boxes_start,
node2fence_region_map=data_collections.node2fence_region_map,
xl=placedb.xl,
yl=placedb.yl,
xh=placedb.xh,
yh=placedb.yh,
site_width=placedb.site_width,
row_height=placedb.row_height,
num_bins_x=1,
num_bins_y=64,
#num_bins_x=64, num_bins_y=64,
num_movable_nodes=placedb.num_movable_nodes,
num_terminal_NIs=placedb.num_terminal_NIs,
num_filler_nodes=placedb.num_filler_nodes)
def build_legalization_op(pos):
logging.info("Start legalization")
pos1 = ml(pos, pos)
pos2 = gl(pos1, pos1)
legal = self.op_collections.legality_check_op(pos2)
if not legal:
logging.error("legality check failed in greedy legalization")
return pos2
return al(pos1, pos2)
return build_legalization_op
def test_greedyLegalizeRandom(self):
dtype = np.float64
xl = 1.0
yl = 1.0
xh = 51.0
yh = 51.0
xx = np.random.uniform(xl, xh, size=6).astype(dtype)
yy = np.random.uniform(yl, yh, size=6).astype(dtype)
node_size_x = np.array([10, 15, 5, 4, 2, 4]).astype(dtype)
node_size_y = np.array([10, 20, 30, 2, 4, 6]).astype(dtype)
num_nodes = len(xx)
num_terminals = 0
num_terminal_NIs = 0
num_filler_nodes = 0
num_movable_nodes = len(
xx) - num_terminals - num_terminal_NIs - num_filler_nodes
site_width = 1
row_height = 2
num_bins_x = 2
num_bins_y = 2
print("xx = %s" % (xx))
print("yy = %s" % (yy))
plot(
"initial.png", xx, yy, node_size_x, node_size_y, xl, yl, xh, yh,
num_bins_x, num_bins_y, num_movable_nodes + num_terminals +
num_terminal_NIs + num_filler_nodes, num_movable_nodes,
num_movable_nodes + num_terminals + num_terminal_NIs,
num_filler_nodes)
# test cpu
custom = macro_legalize.MacroLegalize(
torch.from_numpy(node_size_x),
torch.from_numpy(node_size_y),
xl=xl,
yl=yl,
xh=xh,
yh=yh,
site_width=site_width,
row_height=row_height,
num_bins_x=num_bins_x,
num_bins_y=num_bins_y,
num_movable_nodes=num_movable_nodes,
num_terminal_NIs=num_terminal_NIs,
num_filler_nodes=num_filler_nodes)
pos = Variable(torch.from_numpy(np.concatenate([xx, yy])))
result = custom(pos, pos)
print("custom_result = ", result)
print("average displacement = %g" %
(np.sum(np.absolute(result.numpy() - np.concatenate([xx, yy]))) /
num_movable_nodes))
plot(
"final.png",
result.numpy()[0:len(xx)],
result.numpy()[len(xx):], node_size_x, node_size_y, xl, yl, xh, yh,
num_bins_x, num_bins_y, num_movable_nodes + num_terminals +
num_terminal_NIs + num_filler_nodes, num_movable_nodes,
num_movable_nodes + num_terminals + num_terminal_NIs,
num_filler_nodes)
# test cuda
if torch.cuda.device_count():
custom_cuda = macro_legalize.MacroLegalize(
torch.from_numpy(node_size_x).cuda(),
torch.from_numpy(node_size_y).cuda(),
xl=xl,
yl=yl,
xh=xh,
yh=yh,
site_width=site_width,
row_height=row_height,
num_bins_x=num_bins_x,
num_bins_y=num_bins_y,
num_movable_nodes=num_movable_nodes,
num_terminal_NIs=num_terminal_NIs,
num_filler_nodes=num_filler_nodes)
pos = Variable(torch.from_numpy(np.concatenate([xx, yy]))).cuda()
result_cuda = custom_cuda(pos, pos)
print("custom_result = ", result_cuda.data.cpu())