update drawer
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liulixinkerry
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@ -6,7 +6,7 @@ We are happy to announce that [Xplace 2.0](https://ieeexplore.ieee.org/abstract/
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- Support deterministic mode with only 5~25% extra GP runtime overhead.
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- Implement an extremely fast GPU-accelerated detailed-routability-driven placement algorithm Xplace-Route.
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- Integrate with a GPU-accelerated detailed placer and a GPU-accelerated global router [GGR](cpp_to_py/gpugr/README.md).
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- Support a superfast **GPU-accelerated place and global route flow**! Input your LEF/DEF, the flow will output the **placement DEF** and the **global routing guide**!
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- Support a superfast **GPU-accelerated place and global route flow**! Input your LEF/DEF, the flow will output the **placement DEF** and the **global routing guide**! [xplace_route_flow.png](img/xplace_route_overview.png)
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- Provide benchmark download and preprocess scripts, and three routability evaluation scripts.
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- Code refactoring.
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@ -649,33 +649,34 @@ def external_detail_placement(input_file, data: PlaceData, args, logger, eval_mo
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def default_detail_placement(node_pos, gpdb, rawdb, ps, data: PlaceData, args, logger):
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dp_start_time = None
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dp_end_time = None
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dp_hpwl = -1
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torch.cuda.synchronize(node_pos.device)
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dp_start_time = time.time()
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lg_start_time = time.time()
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if args.legalization:
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node_pos = run_lg(node_pos, data, args, logger)
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torch.cuda.synchronize(node_pos.device)
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lg_end_time = time.time()
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if args.draw_placement:
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info = ("%d_lg" % ps.iter, None, data.design_name)
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draw_fig_with_cairo_cpp(node_pos, data.node_size, data, info, args)
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torch.cuda.synchronize(node_pos.device)
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dp_start_time = time.time()
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if args.detail_placement:
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node_pos = run_dp(node_pos, data, args, logger)
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torch.cuda.synchronize(node_pos.device)
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node_pos = run_dp_route_opt(node_pos, gpdb, rawdb, ps, data, args, logger)
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dp_end_time = time.time()
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logger.info("Finish detailed placement. LG Time: %.4f DP Time: %.4f LG+DP Time: %.4f" % (
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lg_end_time - dp_start_time, dp_end_time - lg_end_time, dp_end_time - dp_start_time
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lg_end_time - lg_start_time, dp_end_time - dp_start_time, dp_end_time - lg_start_time
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))
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# Evaluate
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dp_hpwl = get_obj_hpwl(node_pos, data, args).item()
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info = (ps.iter + 1, dp_hpwl, data.design_name)
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info = ("%d_dp" % (ps.iter + 1), dp_hpwl, data.design_name)
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if args.draw_placement:
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draw_fig_with_cairo_cpp(node_pos, data.node_size, data, info, args)
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logger.info("After DP, HPWL: %.4E" % dp_hpwl)
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lg_time = lg_end_time - dp_start_time
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dp_time = dp_end_time - lg_end_time
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lg_time = lg_end_time - lg_start_time
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dp_time = dp_end_time - dp_start_time
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return node_pos, dp_hpwl, lg_time, dp_time
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@ -318,7 +318,7 @@ def run_placement_main_nesterov(args, logger):
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node_pos, density_map_layer, init_density_map, data, args
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)
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hpwl, overflow = hpwl.item(), overflow.item()
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info = (iteration + 1, hpwl, data.design_name)
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info = ("%d_gp" % (iteration + 1), hpwl, data.design_name)
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if args.draw_placement:
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draw_fig_with_cairo_cpp(node_pos, data.node_size, data, info, args)
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logger.info("After GP, best solution eval, exact HPWL: %.4E exact Overflow: %.4f" % (hpwl, overflow))
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@ -10,54 +10,6 @@ matplotlib_logger = logging.getLogger("matplotlib")
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matplotlib_logger.setLevel(logging.INFO)
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def scatter_drawer(pos: torch.Tensor, fix_mask: torch.Tensor, filename, title, args):
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res_root = os.path.join(args.result_dir, args.exp_id)
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png_path = os.path.join(res_root, args.eval_dir, filename)
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if not os.path.exists(os.path.dirname(png_path)):
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os.makedirs(os.path.dirname(png_path))
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# pos = pos.cpu().numpy()
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# pos = pos.T
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# plt.scatter(pos[0], pos[1])
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mov_pos = pos[fix_mask.squeeze(1) < 0.5].T.cpu().numpy()
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fix_pos = pos[fix_mask.squeeze(1) > 0.5].T.cpu().numpy()
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plt.scatter(mov_pos[0], mov_pos[1], label="mov")
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plt.scatter(fix_pos[0], fix_pos[1], label="fix")
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plt.legend()
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plt.title(title)
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plt.savefig(png_path)
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plt.close()
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def draw_fig(batch, pos, fix_mask, info, args):
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epoch, idx, iteration, hpwl = info
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filename = "epoch%d_id%d_iter%d.png" % (epoch, idx, iteration)
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title = "hpwl %.4f" % hpwl
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num_items = batch.num_of_graph_nodes[0]
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scatter_drawer(pos[:num_items], fix_mask[:num_items], filename, title, args)
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def scatter_drawer_new(pos: torch.Tensor, filename, title, args):
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res_root = os.path.join(args.result_dir, args.exp_id)
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png_path = os.path.join(res_root, args.eval_dir, filename)
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if not os.path.exists(os.path.dirname(png_path)):
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os.makedirs(os.path.dirname(png_path))
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pos = pos.cpu().numpy()
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pos = pos.T
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plt.scatter(pos[0], pos[1])
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plt.title(title)
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plt.savefig(png_path)
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plt.close()
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def draw_fig_new(pos, info, args):
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iteration, hpwl, design_name = info
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filename = "%s_iter%d.png" % (design_name, iteration)
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title = "hpwl %.4f" % hpwl
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scatter_drawer_new(pos, filename, title, args)
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def draw_fig_with_cairo(
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mov_node_pos,
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mov_node_size,
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@ -73,7 +25,7 @@ def draw_fig_with_cairo(
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import cairocffi as cairo
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iteration, hpwl, design_name = info
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filename = "%s_iter%d.png" % (design_name, iteration)
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filename = "%s_iter%s.png" % (design_name, iteration)
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res_root = os.path.join(args.result_dir, args.exp_id)
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png_path = os.path.join(res_root, args.eval_dir, filename)
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if not os.path.exists(os.path.dirname(png_path)):
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@ -159,7 +111,7 @@ def draw_fig_with_cairo_cpp(node_pos, node_size, data, info, args, base_size=204
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node_name: List[str] = ["%d" % i for i in range(node_pos.shape[0])]
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iteration, hpwl, design_name = info
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filename = "%s_iter%d.png" % (design_name, iteration)
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filename = "%s_iter%s.png" % (design_name, iteration)
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res_root = os.path.join(args.result_dir, args.exp_id)
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png_path: str = os.path.join(res_root, args.eval_dir, filename)
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if not os.path.exists(os.path.dirname(png_path)):
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@ -216,7 +168,7 @@ def visualize_electronic_variables(density_map, potential_map, force_map, info,
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return png_path
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# 1) Visualize density_map
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filename = "%s_iter%d_density.png" % (design_name, iteration)
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filename = "%s_iter%s_density.png" % (design_name, iteration)
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png_path = get_png_path(filename)
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fig, ax = plt.subplots(figsize=(12, 10))
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im = ax.imshow(density_map.cpu().numpy(), cmap="YlGnBu")
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@ -226,7 +178,7 @@ def visualize_electronic_variables(density_map, potential_map, force_map, info,
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plt.close()
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# 2) Visualize potential_map
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filename = "%s_iter%d_potential.png" % (design_name, iteration)
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filename = "%s_iter%s_potential.png" % (design_name, iteration)
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png_path = get_png_path(filename)
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fig, ax = plt.subplots(figsize=(12, 10))
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im = ax.imshow(potential_map.cpu().numpy(), cmap="YlGnBu")
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@ -236,7 +188,7 @@ def visualize_electronic_variables(density_map, potential_map, force_map, info,
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plt.close()
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# 3) Visualize force_map
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filename = "%s_iter%d_force.png" % (design_name, iteration)
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filename = "%s_iter%s_force.png" % (design_name, iteration)
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png_path = get_png_path(filename)
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# 3.1) Init background image
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GRID_SIZE = 100
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@ -279,7 +231,7 @@ def draw_grad_abs_mean(
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wl_grads, density_grads, iterations, info, args,
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):
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iteration, design_name = info
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filename = "%s_iter%d_grad_magnitude_mean.png" % (design_name, iteration)
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filename = "%s_iter%s_grad_magnitude_mean.png" % (design_name, iteration)
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res_root = os.path.join(args.result_dir, args.exp_id)
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png_path = os.path.join(res_root, args.eval_dir, filename)
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if not os.path.exists(os.path.dirname(png_path)):
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