55 lines
2.1 KiB
Python
55 lines
2.1 KiB
Python
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import torch
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from .database import PlaceData
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from .core import NodePosToPinPosFunction, get_hpwl, masked_scale_hpwl
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def get_obj_value(pin_pos, density_map, data, args):
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with torch.no_grad():
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hpwl = torch.sum(get_hpwl(data, pin_pos.detach()))
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overflow_sum = ((density_map - args.target_density) * data.bin_area).clamp_(min=0.0).sum()
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overflow = overflow_sum / data.total_mov_area_without_filler
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return hpwl, overflow
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def evaluate_placement(node_pos, density_map_layer, init_density_map, data: PlaceData, args):
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# NOTE: since some nets are masked in WAWirelengthLossAndHPWL, hpwl
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# from WAWirelengthLossAndHPWL may underestimate, this function return the
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# exact value of hpwl
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# Original overflow calculation uses the clamp node size (expand ratio),
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# this function uses the exact node size to evaluate the overflow
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mov_lhs, mov_rhs = data.movable_index
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fix_lhs, fix_rhs = data.fixed_connected_index
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conn_node_pos = torch.cat([
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node_pos[mov_lhs:mov_rhs], node_pos[fix_lhs:fix_rhs]
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], dim=0)
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pin_pos = NodePosToPinPosFunction.apply(
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conn_node_pos, data.pin_id2node_id, data.pin_rel_cpos
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)
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density_map = density_map_layer.get_density_map_naive(
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node_pos[mov_lhs:mov_rhs], data.node_size[mov_lhs:mov_rhs], init_density_map
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)
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hpwl, overflow = get_obj_value(pin_pos, density_map, data, args)
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return hpwl, overflow
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def fast_evaluator(
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mov_node_pos,
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constraint_fn=None,
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mov_node_size=None,
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init_density_map=None,
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density_map_layer=None,
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conn_fix_node_pos=None,
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ps=None,
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data=None,
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args=None,
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):
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mov_lhs, mov_rhs = data.movable_index
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mov_node_pos = constraint_fn(mov_node_pos)
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conn_node_pos = mov_node_pos[mov_lhs:mov_rhs, ...]
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conn_node_pos = torch.cat([conn_node_pos, conn_fix_node_pos], dim=0)
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masked_hpwl = masked_scale_hpwl(
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conn_node_pos, data.pin_id2node_id, data.pin_rel_cpos,
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data.hyperedge_list, data.hyperedge_list_end, data.net_mask, data.hpwl_scale
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)
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overflow = density_map_layer.direct_calc_overflow(
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mov_node_pos, mov_node_size, init_density_map
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)
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return masked_hpwl, overflow
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