remove .pyc file and update readme

README.md

@@ -2,6 +2,69 @@
 
 A new chip placement method based on visual representation learning. 
 
+###  Publication
+Lai, Yao, Yao Mu, and Ping Luo. "Maskplace: Fast chip placement via reinforced visual representation learning." Advances in Neural Information Processing Systems 35 (2022): 24019-24030. (NeurIPS 2022, **spotlight**)
+
+[paper](https://arxiv.org/pdf/2211.13382.pdf)
+
+
+### Usage
+
+You can start easily by using the following script.
+
+```
+cd maskplace
+python PPO2.py
+```
+
+### Parameter
+
+- **gamma** Decay factor.
+- **seed** Random seed.
+- **disable_tqdm** Whether to disable the progress bar.
+- **lr** Learning rate.
+- **log-interval** Interval between training status logs.
+- **pnm** Number of place modules for each placement trajectory. 
+- **benchmark** Circuit benchmark.
+- **soft_coefficient** Whether to constriant the actions based on the wiremask.
+- **batch_size** Batch size.
+- **is_test** Testing mode based on the trained agent.
+- **save_fig** Whether to save placement figures.
+
+
+### Benchmark
+The repo has provided the benchmark *adaptec1* and *ariane*. For other benchmarks, you can download them by the following the link:
+
+http://www.cerc.utexas.edu/~zixuan/ispd2005dp.tar.xz
+
+
+### Dependency
+- [Python](https://www.python.org/) >= 3.9
+
+- [Pytorch](https://pytorch.org/) >= 1.10
+
+  - Other versions may also work, but not tested
+
+- [gym](https://www.gymlibrary.dev/index.html) >= 0.21.0
+- [matplotlib](https://matplotlib.org/) >= 3.7.1
+- [tqdm](https://tqdm.github.io/)
+- [protobuf](https://pypi.org/project/protobuf/) (for benchmark *ariane*)
+
+### Citation
+If you find our paper/code useful in your research, please cite
+
+```
+@article{lai2022maskplace,
+  title={Maskplace: Fast chip placement via reinforced visual representation learning},
+  author={Lai, Yao and Mu, Yao and Luo, Ping},
+  journal={Advances in Neural Information Processing Systems},
+  volume={35},
+  pages={24019--24030},
+  year={2022}
+}
+```
+
+ 
 ### The placement process animation
 
 Benchmark: Bigblue3
@@ -14,6 +77,14 @@ Benchmark: Bigblue3
 |<img src="imgs/view_img.gif" width=250>|<img src="imgs/pos_img_next.gif" width=250> | <img src="imgs/net_img_next.gif" width=250>|
 
 
+### Standard Cell Placement
+
+Fix macros and use DREAMPlace (classic optimization-based method) to place standard cells.
+
+|<center>adaptec2</center>| <center>adaptec4 </center>| <center> bigblue3 </center>|
+|---|---|---|
+|<img src="imgs/stdcell_a2.gif" width="250">|<img src="imgs/stdcell_a4.gif" width="250">|<img src="imgs/stdcell_b3.gif" width="250">|
+
 ### Full Benchmark demonstration
 
 
@@ -22,21 +93,31 @@ Benchmark: Bigblue3
 |adaptec1|<img src="imgs/dreamplace/adaptec1.png" width="160">|<img src="imgs/graph/adaptec1.png" width="160">|<img src="imgs/deeppr/adaptec1.png" width="160">|<img src="imgs/maskplace/adaptec1.png" width="160">|
 |HPWL (10<sup>5</sup>)|17.94|26.05|21.36|<strong>6.57</strong>|
 |Wirel (10<sup>5</sup>)|19.24|28.54|25.64|<strong>7.36</strong>|
+|Overlap|0.34%|1.89%|32.03%|<strong>0</strong>|
 |adaptec2|<img src="imgs/dreamplace/adaptec2.png" width="160">|<img src="imgs/graph/adaptec2.png" width="160"> | <img src="imgs/deeppr/adaptec2.png" width="160">|<img src="imgs/maskplace/adaptec2.png" width="160">|
 |HPWL (10<sup>5</sup>)|135.32|359.35|197.13|<strong>79.98</strong>|
 |Wirel (10<sup>5</sup>)|140.91|381.64|205.78|<strong>83.59</strong>|
+|Overlap|0.16%|1.54%|49.10%|<strong>0</strong>|
 |adaptec3|<img src="imgs/dreamplace/adaptec3.png" width="160">|<img src="imgs/graph/adaptec3.png" width="160"> | <img src="imgs/deeppr/adaptec3.png" width="160">|<img src="imgs/maskplace/adaptec3.png" width="160">|
 |HPWL (10<sup>5</sup>)|112.28|392.66|340.29|<strong>79.33</strong>|
 |Wirel (10<sup>5</sup>)|119.23|409.37|372.02|<strong>85.28</strong>|
+|Overlap|<strong>0</strong>|1.26%|29.10%|<strong>0</strong>|
 |adaptec4|<img src="imgs/dreamplace/adaptec4.png" width="160">|<img src="imgs/graph/adaptec4.png" width="160"> | <img src="imgs/deeppr/adaptec4.png" width="160">|<img src="imgs/maskplace/adaptec4.png" width="160">|
 |HPWL (10<sup>5</sup>)|<strong>37.77</strong>|152.89|243.12|75.75|
 |Wirel (10<sup>5</sup>)|<strong>47.90</strong>|179.43|290.14|88.87|
+|Overlap|<strong>0</strong>|7.43%|19.29%|<strong>0</strong>|
 |bigblue1|<img src="imgs/dreamplace/bigblue1.png" width="160">|<img src="imgs/graph/bigblue1.png" width="160"> | <img src="imgs/deeppr/bigblue1.png" width="160">|<img src="imgs/maskplace/bigblue1.png" width="160">|
 |HPWL (10<sup>5</sup>)|2.50|8.32|20.49|<strong>2.42</strong>|
 |Wirel (10<sup>5</sup>)|3.41|10.00|25.68|<strong>3.14</strong>|
+|Overlap|<strong>0</strong>|2.48%|9.33%|<strong>0</strong>|
 |bigblue3|<img src="imgs/dreamplace/bigblue3.png" width="160">|<img src="imgs/graph/bigblue3.png" width="160"> | <img src="imgs/deeppr/bigblue3.png" width="160">|<img src="imgs/maskplace/bigblue3.png" width="160">|
 |HPWL (10<sup>5</sup>)|104.05|345.49|439.09|<strong>82.61</strong>|
 |Wirel (10<sup>5</sup>)|107.58|373.33|517.86|<strong>88.51</strong>|
+|Overlap|8.06%|0.80%|85.23%|<strong>0</strong>|
 |ariane|<img src="imgs/dreamplace/ariane.png" width="160">|<img src="imgs/graph/ariane.png" width="160"> | <img src="imgs/deeppr/ariane.png" width="160">|<img src="imgs/maskplace/ariane.png" width="160">|
 |HPWL (10<sup>5</sup>)|20.30|16.83|51.43|<strong>14.86</strong>|
 |Wirel (10<sup>5</sup>)|21.72|18.48|55.85|<strong>15.80</strong>|
+|Overlap|<strong>0.78%</strong>|3.72%|38.91%|1.94%|
+
+
+

maskplace/PPO2.py

@@ -0,0 +1,412 @@
+import argparse
+import pickle
+from collections import namedtuple
+
+import os
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+import numpy as np
+import matplotlib.pyplot as plt
+
+import gym
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torch.distributions import Normal
+from torch.distributions import Categorical
+from torch.utils.data.sampler import BatchSampler, SubsetRandomSampler
+
+import place_env
+import torchvision
+from place_db import PlaceDB
+import time
+from tqdm import tqdm
+import random
+from comp_res import comp_res
+from torch.utils.tensorboard import SummaryWriter   
+
+# set device to cpu or cuda
+device = torch.device('cuda')
+
+if(torch.cuda.is_available()): 
+    device = torch.device('cuda:0') 
+    torch.cuda.empty_cache()
+    print("Device set to : " + str(torch.cuda.get_device_name(device)))
+else:
+    print("Device set to : cpu")
+
+# Parameters
+parser = argparse.ArgumentParser(description='Solve the Pendulum-v0 with PPO')
+parser.add_argument(
+    '--gamma', type=float, default=0.95, metavar='G', help='discount factor (default: 0.9)')
+parser.add_argument('--seed', type=int, default=42, metavar='N', help='random seed (default: 0)')
+parser.add_argument('--disable_tqdm', type=int, default=1)
+parser.add_argument('--lr', type=float, default=2.5e-3)
+parser.add_argument(
+    '--log-interval',
+    type=int,
+    default=10,
+    metavar='N',
+    help='interval between training status logs (default: 10)')
+parser.add_argument('--pnm', type=int, default=128)
+parser.add_argument('--benchmark', type=str, default='adaptec1')
+parser.add_argument('--soft_coefficient', type=float, default = 1)
+parser.add_argument('--batch_size', type=int, default=64)
+parser.add_argument('--is_test', action='store_true', default=False)
+parser.add_argument('--save_fig', action='store_true', default=False)
+args = parser.parse_args()
+writer = SummaryWriter('./tb_log')
+
+benchmark = args.benchmark
+placedb = PlaceDB(benchmark)
+grid = 224
+placed_num_macro = args.pnm
+if args.pnm > placedb.node_cnt:
+    placed_num_macro = placedb.node_cnt
+    args.pnm = placed_num_macro
+env = gym.make('place_env-v0', placedb = placedb, placed_num_macro = placed_num_macro, grid = grid).unwrapped
+
+num_emb_state = 64 + 2 + 1
+num_state = 1 + grid*grid*5 + 2
+
+def seed_torch(seed=0):
+    random.seed(seed)
+    np.random.seed(seed)
+    os.environ['PYTHONHASHSEED'] = str(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.manual_seed(seed)
+    env.seed(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+num_action = env.action_space.shape
+seed_torch(args.seed)
+
+Transition = namedtuple('Transition',['state', 'action', 'reward', 'a_log_prob', 'next_state', 'reward_intrinsic'])
+TrainingRecord = namedtuple('TrainRecord',['episode', 'reward'])
+print("seed = {}".format(args.seed))
+print("lr = {}".format(args.lr))
+print("placed_num_macro = {}".format(args.pnm))
+
+
+class MyCNN(nn.Module):
+    def __init__(self):
+        super(MyCNN, self).__init__()
+        self.cnn = nn.Sequential(
+            nn.Conv2d(4, 8, 1),
+            nn.ReLU(),
+            nn.Conv2d(8, 8, 1),
+            nn.ReLU(),
+            nn.Conv2d(8, 1, 1),
+        )
+    def forward(self, x):
+        return self.cnn(x)
+
+
+class MyCNNCoarse(nn.Module):
+    def __init__(self, res_net):
+        super(MyCNNCoarse, self).__init__()
+        self.cnn = res_net.to(device)
+        self.cnn.fc = torch.nn.Linear(512, 16*7*7)
+        self.deconv = nn.Sequential(
+            nn.ConvTranspose2d(16, 8, 3, stride=2, padding=1, output_padding = 1), #14
+            nn.ReLU(),
+            nn.ConvTranspose2d(8, 4, 3, stride=2, padding=1, output_padding = 1), #28
+            nn.ReLU(),
+            nn.ConvTranspose2d(4, 2, 3, stride=2, padding=1, output_padding = 1), #56
+            nn.ReLU(),
+            nn.ConvTranspose2d(2, 1, 3, stride=2, padding=1, output_padding = 1), #112
+            nn.ReLU(),
+            nn.ConvTranspose2d(1, 1, 3, stride=2, padding=1, output_padding = 1), #224
+        )
+    def forward(self, x):
+        x = self.cnn(x).reshape(-1, 16, 7, 7)
+        return self.deconv(x)
+
+
+class Actor(nn.Module):
+    def __init__(self, cnn, gcn, cnn_coarse):
+        super(Actor, self).__init__()
+        self.fc1 = nn.Linear(num_emb_state, 512)
+        self.fc2 = nn.Linear(512, 64)
+        self.fc3 = nn.Linear(64, grid * grid)
+        self.cnn = cnn
+        self.cnn_coarse = cnn_coarse
+        self.gcn = None
+        self.softmax = nn.Softmax(dim=-1)
+        self.merge = nn.Conv2d(2, 1, 1)
+
+    def forward(self, x, graph = None, cnn_res = None, gcn_res = None, graph_node = None):
+        if not cnn_res:
+            cnn_input = x[:, 1+grid*grid*1: 1+grid*grid*5].reshape(-1, 4, grid, grid)
+            mask = x[:, 1+grid*grid*2: 1+grid*grid*3].reshape(-1, grid, grid)
+            mask = mask.flatten(start_dim=1, end_dim=2)
+            cnn_res = self.cnn(cnn_input)
+            coarse_input = torch.cat((x[:, 1: 1+grid*grid*2].reshape(-1, 2, grid, grid),
+                                        x[:, 1+grid*grid*3: 1+grid*grid*4].reshape(-1, 1, grid, grid)
+                                        ),dim= 1).reshape(-1, 3, grid, grid)
+            cnn_coarse_res = self.cnn_coarse(coarse_input)
+            cnn_res = self.merge(torch.cat((cnn_res, cnn_coarse_res), dim=1))
+        net_img = x[:, 1+grid*grid: 1+grid*grid*2]
+        net_img = net_img + x[:, 1+grid*grid*2: 1+grid*grid*3] * 10
+        net_img_min = net_img.min() + args.soft_coefficient
+        mask2 = net_img.le(net_img_min).logical_not().float()
+
+        x = cnn_res
+        x = x.reshape(-1, grid * grid)
+        x = torch.where(mask + mask2 >=1.0, -1.0e10, x.double())
+        x = self.softmax(x)
+
+        return x, cnn_res, gcn_res
+
+
+class Critic(nn.Module):
+    def __init__(self, cnn, gcn, cnn_coarse, res_net):
+        super(Critic, self).__init__()
+        self.fc1 = nn.Linear(64, 64)
+        self.fc2 = nn.Linear(64, 64)
+        self.state_value = nn.Linear(64, 1)
+        self.pos_emb = nn.Embedding(1400, 64)
+        self.cnn = cnn
+        self.gcn = gcn
+    def forward(self, x, graph = None, cnn_res = None, gcn_res = None, graph_node = None):
+        x1 = F.relu(self.fc1(self.pos_emb(x[:, 0].long())))
+        x2 = F.relu(self.fc2(x1))
+        value = self.state_value(x2)
+        return value
+
+
+class PPO():
+    clip_param = 0.2
+    max_grad_norm = 0.5
+    ppo_epoch = 10
+    if placed_num_macro:
+        buffer_capacity = 10 * (placed_num_macro)
+    else:
+        buffer_capacity = 5120
+    batch_size = args.batch_size
+    print("batch_size = {}".format(batch_size))
+
+    def __init__(self):
+        super(PPO, self).__init__()
+        self.gcn = None
+        self.resnet = torchvision.models.resnet18(pretrained=True)
+        self.cnn = MyCNN().to(device)
+        self.cnn_coarse = MyCNNCoarse(self.resnet).to(device)
+        self.actor_net = Actor(cnn = self.cnn, gcn = self.gcn, cnn_coarse = self.cnn_coarse).float().to(device)
+        self.critic_net = Critic(cnn = self.cnn, gcn = self.gcn,  cnn_coarse = None, res_net = self.resnet).float().to(device)
+        self.buffer = []
+        self.counter = 0
+        self.training_step = 0
+        self.actor_optimizer = optim.Adam(self.actor_net.parameters(), args.lr)
+        self.critic_net_optimizer = optim.Adam(self.critic_net.parameters(), args.lr)
+
+    def load_param(self, path):
+        checkpoint = torch.load(path, map_location=torch.device(device))
+        self.actor_net.load_state_dict(checkpoint['actor_net_dict'])
+        self.critic_net.load_state_dict(checkpoint['critic_net_dict'])
+    
+    def select_action(self, state):
+        state = torch.from_numpy(state).float().to(device).unsqueeze(0)
+        with torch.no_grad():
+            action_probs, _, _ = self.actor_net(state)
+        dist = Categorical(action_probs)
+        action = dist.sample()
+        action_log_prob = dist.log_prob(action)
+        return action.item(), action_log_prob.item()
+
+    def get_value(self, state):
+        state = torch.from_numpy(state)
+        with torch.no_grad():
+            value = self.critic_net(state)
+        return value.item()
+
+    def save_param(self, running_reward):
+        strftime = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
+        if not os.path.exists("save_models"):
+            os.mkdir("save_models")
+        torch.save({"actor_net_dict": self.actor_net.state_dict(),
+                    "critic_net_dict": self.critic_net.state_dict()},
+                    "./save_models/net_dict-{}-{}-".format(benchmark, placed_num_macro)+strftime+"{}".format(int(running_reward))+".pkl")
+
+    def store_transition(self, transition):
+        self.buffer.append(transition)
+        self.counter+=1
+        return self.counter % self.buffer_capacity == 0
+
+    def update(self):
+        state = torch.tensor(np.array([t.state for t in self.buffer]), dtype=torch.float)
+        action = torch.tensor(np.array([t.action for t in self.buffer]), dtype=torch.float).view(-1, 1).to(device)
+        reward = torch.tensor(np.array([t.reward for t in self.buffer]), dtype=torch.float).view(-1, 1).to(device)
+        old_action_log_prob = torch.tensor(np.array([t.a_log_prob for t in self.buffer]), dtype=torch.float).view(-1, 1).to(device)
+        del self.buffer[:]
+        target_list = []
+        target = 0
+        for i in range(reward.shape[0]-1, -1, -1):
+            if state[i, 0] >= placed_num_macro - 1:
+                target = 0
+            r = reward[i, 0].item()
+            target = r + args.gamma * target
+            target_list.append(target)
+        target_list.reverse()
+        target_v_all = torch.tensor(np.array([t for t in target_list]), dtype=torch.float).view(-1, 1).to(device)
+       
+        for _ in range(self.ppo_epoch): # iteration ppo_epoch 
+            for index in tqdm(BatchSampler(SubsetRandomSampler(range(self.buffer_capacity)), self.batch_size, True),
+                disable = args.disable_tqdm):
+                self.training_step +=1
+                
+                action_probs, _, _ = self.actor_net(state[index].to(device))
+                dist = Categorical(action_probs)
+                action_log_prob = dist.log_prob(action[index].squeeze())
+                ratio = torch.exp(action_log_prob - old_action_log_prob[index].squeeze())
+                target_v = target_v_all[index]                
+                critic_net_output = self.critic_net(state[index].to(device))
+                advantage = (target_v - critic_net_output).detach()
+
+                L1 = ratio * advantage.squeeze() 
+                L2 = torch.clamp(ratio, 1-self.clip_param, 1+self.clip_param) * advantage.squeeze() 
+                action_loss = -torch.min(L1, L2).mean() # MAX->MIN desent
+
+                self.actor_optimizer.zero_grad()
+                action_loss.backward()
+                nn.utils.clip_grad_norm_(self.actor_net.parameters(), self.max_grad_norm)
+                self.actor_optimizer.step()
+
+                value_loss = F.smooth_l1_loss(self.critic_net(state[index].to(device)), target_v)
+                self.critic_net_optimizer.zero_grad()
+                value_loss.backward()
+                nn.utils.clip_grad_norm_(self.critic_net.parameters(), self.max_grad_norm)
+                self.critic_net_optimizer.step()
+
+                writer.add_scalar('action_loss', action_loss, self.training_step)
+                writer.add_scalar('value_loss', value_loss, self.training_step)
+
+
+def save_placement(file_path, node_pos, ratio):
+    fwrite = open(file_path, 'w')
+    node_place = {}
+    for node_name in node_pos:
+
+        x, y,_ , _ = node_pos[node_name]
+        x = round(x * ratio + ratio) 
+        y = round(y * ratio + ratio)
+        node_place[node_name] = (x, y)
+    print("len node_place", len(node_place))
+    for node_name in placedb.node_info:
+        if node_name not in node_place:
+            continue
+        x, y = node_place[node_name]
+        fwrite.write('{}\t{}\t{}\t:\tN /FIXED\n'.format(node_name, x, y))
+    print(".pl has been saved to {}.".format(file_path))
+
+
+def main():
+
+    agent = PPO()
+    strftime = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) 
+    
+    training_records = []
+    running_reward = -1000000
+    
+
+    log_file_name = "logs/log_"+ benchmark + "_" + strftime + "_seed_"+ str(args.seed) + "_pnm_" + str(args.pnm) + ".csv"
+    if not os.path.exists("logs"):
+        os.mkdir("logs")
+    fwrite = open(log_file_name, "w")
+    load_model_path = None
+   
+    if load_model_path:
+       agent.load_param(load_model_path)
+    
+    best_reward = running_reward
+    if args.is_test:
+        torch.inference_mode()
+
+    for i_epoch in range(100000):
+        score = 0
+        raw_score = 0
+        start = time.time()
+        state = env.reset()
+
+        done = False
+        while done is False:
+            state_tmp = state.copy()
+            action, action_log_prob = agent.select_action(state)
+        
+            next_state, reward, done, info = env.step(action)
+            assert next_state.shape == (num_state, )
+            reward_intrinsic = 0
+            if not args.is_test:
+                trans = Transition(state_tmp, action, reward / 200.0, action_log_prob, next_state, reward_intrinsic)
+            if not args.is_test and agent.store_transition(trans):                
+                assert done == True
+                agent.update()
+            score += reward
+            raw_score += info["raw_reward"]
+            state = next_state
+        end = time.time()
+
+        if i_epoch == 0:
+            running_reward = score
+        running_reward = running_reward * 0.9 + score * 0.1
+        print("score = {}, raw_score = {}".format(score, raw_score))
+
+        if running_reward > best_reward * 0.975:
+            best_reward = running_reward
+            if i_epoch >= 10:
+                agent.save_param(running_reward)
+                if args.save_fig:
+                    strftime_now = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
+                    if not os.path.exists("figures"):
+                        os.mkdir("figures")
+                    env.save_fig("./figures/{}{}.png".format(strftime_now,int(raw_score)))
+                    print("save_figure: figures/{}{}.png".format(strftime_now,int(raw_score)))
+                try:
+                    print("start try")
+                    # cost is the routing estimation based on the MST algorithm
+                    hpwl, cost = comp_res(placedb, env.node_pos, env.ratio)
+                    print("hpwl = {:.2f}\tcost = {:.2f}".format(hpwl, cost))
+                except:
+                    assert False
+        
+        if args.is_test:
+            print("save node_pos")
+            hpwl, cost = comp_res(placedb, env.node_pos, env.ratio)
+            print("hpwl = {:.2f}\tcost = {:.2f}".format(hpwl, cost))
+            print("time = {}s".format(end-start))
+            pl_file_path = "{}-{}-{}.pl".format(benchmark, int(hpwl), time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) ) 
+            save_placement(pl_file_path, env.node_pos, env.ratio)
+            strftime_now = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
+            pl_path = 'gg_place_new/{}-{}-{}-{}.pl'.format(benchmark, strftime_now, int(hpwl), int(cost))
+            fwrite_pl = open(pl_path, 'w')
+            for node_name in env.node_pos:
+                if node_name == "V":
+                    continue
+                x, y, size_x, size_y = env.node_pos[node_name]
+                x = x * env.ratio + placedb.node_info[node_name]['x'] /2.0
+                y = y * env.ratio + placedb.node_info[node_name]['y'] /2.0
+                fwrite_pl.write("{}\t{:.4f}\t{:.4f}\n".format(node_name, x, y))
+            fwrite_pl.close()
+            strftime_now = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
+            env.save_fig("./figures/{}-{}-{}-{}.png".format(benchmark, strftime_now, int(hpwl), int(cost)))
+        
+        training_records.append(TrainingRecord(i_epoch, running_reward))
+        if i_epoch % 1 ==0:
+            print("Epoch {}, Moving average score is: {:.2f} ".format(i_epoch, running_reward))
+            fwrite.write("{},{},{:.2f},{}\n".format(i_epoch, score, running_reward, agent.training_step))
+            fwrite.flush()
+        writer.add_scalar('reward', running_reward, i_epoch)
+        if running_reward > -100:
+            print("Solved! Moving average score is now {}!".format(running_reward))
+            env.close()
+            agent.save_param()
+            break
+        if i_epoch % 100 == 0:
+            if placed_num_macro is None:
+                env.write_gl_file("./gl/{}{}.gl".format(strftime, int(score)))
+
+        
+if __name__ == '__main__':
+    main()

maskplace/adaptec1/adaptec1.aux

@@ -0,0 +1 @@
+RowBasedPlacement :  adaptec1.nodes  adaptec1.nets  adaptec1.wts  adaptec1.pl  adaptec1.scl

maskplace/adaptec1/adaptec1.wts

@@ -0,0 +1,4 @@
+UCLA wts 1.0
+# Created	:	Jan  6 2005
+# User   	:	Gi-Joon Nam & Mehmet Yildiz at IBM Austin Research({gnam, mcan}@us.ibm.com)
+

maskplace/ariane/graph.proto

@@ -0,0 +1,56 @@
+syntax = "proto3";
+
+package tensorflow;
+
+// import "tensorflow/core/framework/function.proto";
+// import "tensorflow/core/framework/node_def.proto";
+// import "tensorflow/core/framework/versions.proto";
+
+option cc_enable_arenas = true;
+option java_outer_classname = "GraphProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/graph_go_proto";
+
+// Represents the graph of operations
+message GraphDef {
+  repeated NodeDef node = 1;
+
+  // Compatibility versions of the graph.  See core/public/version.h for version
+  // history.  The GraphDef version is distinct from the TensorFlow version, and
+  // each release of TensorFlow will support a range of GraphDef versions.
+  VersionDef versions = 4;
+
+  // Deprecated single version field; use versions above instead.  Since all
+  // GraphDef changes before "versions" was introduced were forward
+  // compatible, this field is entirely ignored.
+  int32 version = 3 [deprecated = true];
+
+  // "library" provides user-defined functions.
+  //
+  // Naming:
+  //   * library.function.name are in a flat namespace.
+  //     NOTE: We may need to change it to be hierarchical to support
+  //     different orgs. E.g.,
+  //     { "/google/nn", { ... }},
+  //     { "/google/vision", { ... }}
+  //     { "/org_foo/module_bar", { ... }}
+  //     map<string, FunctionDefLib> named_lib;
+  //   * If node[i].op is the name of one function in "library",
+  //     node[i] is deemed as a function call. Otherwise, node[i].op
+  //     must be a primitive operation supported by the runtime.
+  //
+  //
+  // Function call semantics:
+  //
+  //   * The callee may start execution as soon as some of its inputs
+  //     are ready. The caller may want to use Tuple() mechanism to
+  //     ensure all inputs are ready in the same time.
+  //
+  //   * The consumer of return values may start executing as soon as
+  //     the return values the consumer depends on are ready.  The
+  //     consumer may want to use Tuple() mechanism to ensure the
+  //     consumer does not start until all return values of the callee
+  //     function are ready.
+  FunctionDefLibrary library = 2;
+}

maskplace/ariane/laiyao.proto

@@ -0,0 +1,57 @@
+syntax = "proto3";
+
+message AttrValue {
+    // LINT.IfChange
+    message ListValue {
+      repeated bytes s = 2;                        // "list(string)"
+      repeated int64 i = 3 [packed = true];        // "list(int)"
+      repeated float f = 4 [packed = true];        // "list(float)"
+      repeated bool b = 5 [packed = true];         // "list(bool)"
+    //   repeated DataType type = 6 [packed = true];  // "list(type)"
+    //   repeated TensorShapeProto shape = 7;         // "list(shape)"
+    //   repeated TensorProto tensor = 8;             // "list(tensor)"
+      repeated NameAttrList func = 9;              // "list(attr)"
+    }
+    // LINT.ThenChange(https://www.tensorflow.org/code/tensorflow/c/c_api.cc)
+  
+    oneof value {
+      bytes s = 2;                 // "string"
+      int64 i = 3;                 // "int"
+      float f = 4;                 // "float"
+      bool b = 5;                  // "bool"
+      DataType type = 6;           // "type"
+    //   TensorShapeProto shape = 7;  // "shape"
+    //   TensorProto tensor = 8;      // "tensor"
+    //   ListValue list = 1;          // any "list(...)"
+  
+      // "func" represents a function. func.name is a function's name or
+      // a primitive op's name. func.attr.first is the name of an attr
+      // defined for that function. func.attr.second is the value for
+      // that attr in the instantiation.
+      NameAttrList func = 10;
+  
+      // This is a placeholder only used in nodes defined inside a
+      // function.  It indicates the attr value will be supplied when
+      // the function is instantiated.  For example, let us suppose a
+      // node "N" in function "FN". "N" has an attr "A" with value
+      // placeholder = "foo". When FN is instantiated with attr "foo"
+      // set to "bar", the instantiated node N's attr A will have been
+      // given the value "bar".
+      string placeholder = 9;
+    }
+  }
+  
+  // A list of attr names and their values. The whole list is attached
+  // with a string name.  E.g., MatMul[T=float].
+message NameAttrList {
+    string name = 1;
+    map<string, AttrValue> attr = 2;
+}
+message NodeDef {
+    string name = 1;
+    repeated string input = 2;
+    map<string, AttrValue> attr = 5;
+}
+message GraphDef {
+    repeated NodeDef node = 1;
+}

maskplace/ariane/laiyao_pb2.py

@@ -0,0 +1,434 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: laiyao.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='laiyao.proto',
+  package='',
+  syntax='proto3',
+  serialized_options=None,
+  serialized_pb=_b('\n\x0claiyao.proto\"\xe0\x01\n\tAttrValue\x12\x0b\n\x01s\x18\x02 \x01(\x0cH\x00\x12\x0b\n\x01i\x18\x03 \x01(\x03H\x00\x12\x0b\n\x01\x66\x18\x04 \x01(\x02H\x00\x12\x0b\n\x01\x62\x18\x05 \x01(\x08H\x00\x12\x1d\n\x04\x66unc\x18\n \x01(\x0b\x32\r.NameAttrListH\x00\x12\x15\n\x0bplaceholder\x18\t \x01(\tH\x00\x1a`\n\tListValue\x12\t\n\x01s\x18\x02 \x03(\x0c\x12\r\n\x01i\x18\x03 \x03(\x03\x42\x02\x10\x01\x12\r\n\x01\x66\x18\x04 \x03(\x02\x42\x02\x10\x01\x12\r\n\x01\x62\x18\x05 \x03(\x08\x42\x02\x10\x01\x12\x1b\n\x04\x66unc\x18\t \x03(\x0b\x32\r.NameAttrListB\x07\n\x05value\"|\n\x0cNameAttrList\x12\x0c\n\x04name\x18\x01 \x01(\t\x12%\n\x04\x61ttr\x18\x02 \x03(\x0b\x32\x17.NameAttrList.AttrEntry\x1a\x37\n\tAttrEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\x19\n\x05value\x18\x02 \x01(\x0b\x32\n.AttrValue:\x02\x38\x01\"\x81\x01\n\x07NodeDef\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\r\n\x05input\x18\x02 \x03(\t\x12 \n\x04\x61ttr\x18\x05 \x03(\x0b\x32\x12.NodeDef.AttrEntry\x1a\x37\n\tAttrEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\x19\n\x05value\x18\x02 \x01(\x0b\x32\n.AttrValue:\x02\x38\x01\"\"\n\x08GraphDef\x12\x16\n\x04node\x18\x01 \x03(\x0b\x32\x08.NodeDefb\x06proto3')
+)
+
+
+
+
+_ATTRVALUE_LISTVALUE = _descriptor.Descriptor(
+  name='ListValue',
+  full_name='AttrValue.ListValue',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='s', full_name='AttrValue.ListValue.s', index=0,
+      number=2, type=12, cpp_type=9, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='i', full_name='AttrValue.ListValue.i', index=1,
+      number=3, type=3, cpp_type=2, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=_b('\020\001'), file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='f', full_name='AttrValue.ListValue.f', index=2,
+      number=4, type=2, cpp_type=6, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=_b('\020\001'), file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='b', full_name='AttrValue.ListValue.b', index=3,
+      number=5, type=8, cpp_type=7, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=_b('\020\001'), file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='func', full_name='AttrValue.ListValue.func', index=4,
+      number=9, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=136,
+  serialized_end=232,
+)
+
+_ATTRVALUE = _descriptor.Descriptor(
+  name='AttrValue',
+  full_name='AttrValue',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='s', full_name='AttrValue.s', index=0,
+      number=2, type=12, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b(""),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='i', full_name='AttrValue.i', index=1,
+      number=3, type=3, cpp_type=2, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='f', full_name='AttrValue.f', index=2,
+      number=4, type=2, cpp_type=6, label=1,
+      has_default_value=False, default_value=float(0),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='b', full_name='AttrValue.b', index=3,
+      number=5, type=8, cpp_type=7, label=1,
+      has_default_value=False, default_value=False,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='func', full_name='AttrValue.func', index=4,
+      number=10, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='placeholder', full_name='AttrValue.placeholder', index=5,
+      number=9, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[_ATTRVALUE_LISTVALUE, ],
+  enum_types=[
+  ],
+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+    _descriptor.OneofDescriptor(
+      name='value', full_name='AttrValue.value',
+      index=0, containing_type=None, fields=[]),
+  ],
+  serialized_start=17,
+  serialized_end=241,
+)
+
+
+_NAMEATTRLIST_ATTRENTRY = _descriptor.Descriptor(
+  name='AttrEntry',
+  full_name='NameAttrList.AttrEntry',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='key', full_name='NameAttrList.AttrEntry.key', index=0,
+      number=1, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='value', full_name='NameAttrList.AttrEntry.value', index=1,
+      number=2, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  serialized_options=_b('8\001'),
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=312,
+  serialized_end=367,
+)
+
+_NAMEATTRLIST = _descriptor.Descriptor(
+  name='NameAttrList',
+  full_name='NameAttrList',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='name', full_name='NameAttrList.name', index=0,
+      number=1, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='attr', full_name='NameAttrList.attr', index=1,
+      number=2, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[_NAMEATTRLIST_ATTRENTRY, ],
+  enum_types=[
+  ],
+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=243,
+  serialized_end=367,
+)
+
+
+_NODEDEF_ATTRENTRY = _descriptor.Descriptor(
+  name='AttrEntry',
+  full_name='NodeDef.AttrEntry',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='key', full_name='NodeDef.AttrEntry.key', index=0,
+      number=1, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='value', full_name='NodeDef.AttrEntry.value', index=1,
+      number=2, type=11, cpp_type=10, label=1,
+      has_default_value=False, default_value=None,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  serialized_options=_b('8\001'),
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=312,
+  serialized_end=367,
+)
+
+_NODEDEF = _descriptor.Descriptor(
+  name='NodeDef',
+  full_name='NodeDef',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='name', full_name='NodeDef.name', index=0,
+      number=1, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='input', full_name='NodeDef.input', index=1,
+      number=2, type=9, cpp_type=9, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='attr', full_name='NodeDef.attr', index=2,
+      number=5, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[_NODEDEF_ATTRENTRY, ],
+  enum_types=[
+  ],
+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=370,
+  serialized_end=499,
+)
+
+
+_GRAPHDEF = _descriptor.Descriptor(
+  name='GraphDef',
+  full_name='GraphDef',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='node', full_name='GraphDef.node', index=0,
+      number=1, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  serialized_options=None,
+  is_extendable=False,
+  syntax='proto3',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=501,
+  serialized_end=535,
+)
+
+_ATTRVALUE_LISTVALUE.fields_by_name['func'].message_type = _NAMEATTRLIST
+_ATTRVALUE_LISTVALUE.containing_type = _ATTRVALUE
+_ATTRVALUE.fields_by_name['func'].message_type = _NAMEATTRLIST
+_ATTRVALUE.oneofs_by_name['value'].fields.append(
+  _ATTRVALUE.fields_by_name['s'])
+_ATTRVALUE.fields_by_name['s'].containing_oneof = _ATTRVALUE.oneofs_by_name['value']
+_ATTRVALUE.oneofs_by_name['value'].fields.append(
+  _ATTRVALUE.fields_by_name['i'])
+_ATTRVALUE.fields_by_name['i'].containing_oneof = _ATTRVALUE.oneofs_by_name['value']
+_ATTRVALUE.oneofs_by_name['value'].fields.append(
+  _ATTRVALUE.fields_by_name['f'])
+_ATTRVALUE.fields_by_name['f'].containing_oneof = _ATTRVALUE.oneofs_by_name['value']
+_ATTRVALUE.oneofs_by_name['value'].fields.append(
+  _ATTRVALUE.fields_by_name['b'])
+_ATTRVALUE.fields_by_name['b'].containing_oneof = _ATTRVALUE.oneofs_by_name['value']
+_ATTRVALUE.oneofs_by_name['value'].fields.append(
+  _ATTRVALUE.fields_by_name['func'])
+_ATTRVALUE.fields_by_name['func'].containing_oneof = _ATTRVALUE.oneofs_by_name['value']
+_ATTRVALUE.oneofs_by_name['value'].fields.append(
+  _ATTRVALUE.fields_by_name['placeholder'])
+_ATTRVALUE.fields_by_name['placeholder'].containing_oneof = _ATTRVALUE.oneofs_by_name['value']
+_NAMEATTRLIST_ATTRENTRY.fields_by_name['value'].message_type = _ATTRVALUE
+_NAMEATTRLIST_ATTRENTRY.containing_type = _NAMEATTRLIST
+_NAMEATTRLIST.fields_by_name['attr'].message_type = _NAMEATTRLIST_ATTRENTRY
+_NODEDEF_ATTRENTRY.fields_by_name['value'].message_type = _ATTRVALUE
+_NODEDEF_ATTRENTRY.containing_type = _NODEDEF
+_NODEDEF.fields_by_name['attr'].message_type = _NODEDEF_ATTRENTRY
+_GRAPHDEF.fields_by_name['node'].message_type = _NODEDEF
+DESCRIPTOR.message_types_by_name['AttrValue'] = _ATTRVALUE
+DESCRIPTOR.message_types_by_name['NameAttrList'] = _NAMEATTRLIST
+DESCRIPTOR.message_types_by_name['NodeDef'] = _NODEDEF
+DESCRIPTOR.message_types_by_name['GraphDef'] = _GRAPHDEF
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+AttrValue = _reflection.GeneratedProtocolMessageType('AttrValue', (_message.Message,), dict(
+
+  ListValue = _reflection.GeneratedProtocolMessageType('ListValue', (_message.Message,), dict(
+    DESCRIPTOR = _ATTRVALUE_LISTVALUE,
+    __module__ = 'laiyao_pb2'
+    # @@protoc_insertion_point(class_scope:AttrValue.ListValue)
+    ))
+  ,
+  DESCRIPTOR = _ATTRVALUE,
+  __module__ = 'laiyao_pb2'
+  # @@protoc_insertion_point(class_scope:AttrValue)
+  ))
+_sym_db.RegisterMessage(AttrValue)
+_sym_db.RegisterMessage(AttrValue.ListValue)
+
+NameAttrList = _reflection.GeneratedProtocolMessageType('NameAttrList', (_message.Message,), dict(
+
+  AttrEntry = _reflection.GeneratedProtocolMessageType('AttrEntry', (_message.Message,), dict(
+    DESCRIPTOR = _NAMEATTRLIST_ATTRENTRY,
+    __module__ = 'laiyao_pb2'
+    # @@protoc_insertion_point(class_scope:NameAttrList.AttrEntry)
+    ))
+  ,
+  DESCRIPTOR = _NAMEATTRLIST,
+  __module__ = 'laiyao_pb2'
+  # @@protoc_insertion_point(class_scope:NameAttrList)
+  ))
+_sym_db.RegisterMessage(NameAttrList)
+_sym_db.RegisterMessage(NameAttrList.AttrEntry)
+
+NodeDef = _reflection.GeneratedProtocolMessageType('NodeDef', (_message.Message,), dict(
+
+  AttrEntry = _reflection.GeneratedProtocolMessageType('AttrEntry', (_message.Message,), dict(
+    DESCRIPTOR = _NODEDEF_ATTRENTRY,
+    __module__ = 'laiyao_pb2'
+    # @@protoc_insertion_point(class_scope:NodeDef.AttrEntry)
+    ))
+  ,
+  DESCRIPTOR = _NODEDEF,
+  __module__ = 'laiyao_pb2'
+  # @@protoc_insertion_point(class_scope:NodeDef)
+  ))
+_sym_db.RegisterMessage(NodeDef)
+_sym_db.RegisterMessage(NodeDef.AttrEntry)
+
+GraphDef = _reflection.GeneratedProtocolMessageType('GraphDef', (_message.Message,), dict(
+  DESCRIPTOR = _GRAPHDEF,
+  __module__ = 'laiyao_pb2'
+  # @@protoc_insertion_point(class_scope:GraphDef)
+  ))
+_sym_db.RegisterMessage(GraphDef)
+
+
+_ATTRVALUE_LISTVALUE.fields_by_name['i']._options = None
+_ATTRVALUE_LISTVALUE.fields_by_name['f']._options = None
+_ATTRVALUE_LISTVALUE.fields_by_name['b']._options = None
+_NAMEATTRLIST_ATTRENTRY._options = None
+_NODEDEF_ATTRENTRY._options = None
+# @@protoc_insertion_point(module_scope)

maskplace/ariane/read_info.py

@@ -0,0 +1,62 @@
+# import tensorflow as tf
+
+from google.protobuf import text_format
+
+import laiyao_pb2
+
+
+def load_pbtxt_file(path):
+    """Read .pbtxt file.
+    
+    Args: 
+        path: Path to StringIntLabelMap proto text file (.pbtxt file).
+        
+    Returns:
+        A StringIntLabelMapProto.
+        
+    Raises:
+        ValueError: If path is not exist.
+    """
+    # if not tf.gfile.Exists(path):
+    #     raise ValueError('`path` is not exist.')
+        
+    # with tf.gfile.GFile(path, 'r') as fid:
+    #     pbtxt_string = fid.read()
+    #     pbtxt = laiyao_pb2.StudentInfo()
+    #     try:
+    #         text_format.Merge(pbtxt_string, pbtxt)
+    #     except text_format.ParseError:
+    #         pbtxt.ParseFromString(pbtxt_string)
+    fid = open(path, 'r')
+    pbtxt_string = fid.read()
+    pbtxt = laiyao_pb2.GraphDef()
+    try:
+        text_format.Merge(pbtxt_string, pbtxt)
+    except text_format.ParseError:
+        pbtxt.ParseFromString(pbtxt_string)
+    return pbtxt
+
+
+def get_netlist_info_dict(path):
+    """Reads a .pbtxt file and returns a dictionary.
+    
+    Args:
+        path: Path to StringIntLabelMap proto text file.
+        
+    Returns:
+        A dictionary mapping class names to indices.
+    """
+    pbtxt = load_pbtxt_file(path)
+    
+    # result_dict = {}
+    # for node in pbtxt.node:
+    #     print("node_name: {}".format(node.name))
+    return pbtxt
+
+
+def main():
+    get_netlist_info_dict('netlist.pb.txt')
+
+
+if __name__ == "__main__":
+    main()

maskplace/comp_res.py

@@ -0,0 +1,51 @@
+from place_db import PlaceDB
+from prim import prim_real
+import pickle
+
+def comp_res(placedb, node_pos, ratio):
+
+    hpwl = 0.0
+    cost = 0.0
+    for net_name in placedb.net_info:
+        max_x = 0.0
+        min_x = placedb.max_height * 1.1
+        max_y = 0.0
+        min_y = placedb.max_height * 1.1
+        for node_name in placedb.net_info[net_name]["nodes"]:
+            if node_name not in node_pos:
+                continue
+            h = placedb.node_info[node_name]['x']
+            w = placedb.node_info[node_name]['y']
+            pin_x = node_pos[node_name][0] * ratio + h / 2.0 + placedb.net_info[net_name]["nodes"][node_name]["x_offset"]
+            pin_y = node_pos[node_name][1] * ratio + w / 2.0 + placedb.net_info[net_name]["nodes"][node_name]["y_offset"]
+            max_x = max(pin_x, max_x)
+            min_x = min(pin_x, min_x)
+            max_y = max(pin_y, max_y)
+            min_y = min(pin_y, min_y)
+        for port_name in placedb.net_info[net_name]["ports"]:
+            h = placedb.port_info[port_name]['x']
+            w = placedb.port_info[port_name]['y']
+            pin_x = h
+            pin_y = w
+            max_x = max(pin_x, max_x)
+            min_x = min(pin_x, min_x)
+            max_y = max(pin_y, max_y)
+            min_y = min(pin_y, min_y)
+        if min_x <= placedb.max_height:
+            hpwl_tmp = (max_x - min_x) + (max_y - min_y)
+        else:
+            hpwl_tmp = 0
+        if "weight" in placedb.net_info[net_name]:
+            hpwl_tmp *= placedb.net_info[net_name]["weight"]
+        hpwl += hpwl_tmp
+        net_node_set = set.union(set(placedb.net_info[net_name]["nodes"]),
+                            set(placedb.net_info[net_name]["ports"]))
+        for net_node in list(net_node_set):
+            if net_node not in node_pos and net_node not in placedb.port_info:
+                net_node_set.discard(net_node)
+        prim_cost = prim_real(net_node_set, node_pos, placedb.net_info[net_name]["nodes"], ratio, placedb.node_info, placedb.port_info)
+        if "weight" in placedb.net_info[net_name]:
+            prim_cost *= placedb.net_info[net_name]["weight"]
+        assert hpwl_tmp <= prim_cost +1e-5
+        cost += prim_cost
+    return hpwl, cost

maskplace/place_db.py

@@ -0,0 +1,257 @@
+import numpy as np
+import os
+import random
+from operator import itemgetter
+from itertools import combinations
+from place_db_proto import get_node_info
+from place_db_proto import get_net_info
+import sys
+import pickle
+sys.path.append('ariane')
+from ariane.read_info import get_netlist_info_dict
+# Macro dict (macro id -> name, x, y)
+
+def read_node_file(fopen, benchmark):
+    node_info = {}
+    node_info_raw_id_name ={}
+    node_cnt = 0
+    for line in fopen.readlines():
+        if not line.startswith("\t"):
+            continue
+        line = line.strip().split()
+        if line[-1] != "terminal":
+            continue
+        node_name = line[0]
+        x = int(line[1])
+        y = int(line[2])
+        node_info[node_name] = {"id": node_cnt, "x": x , "y": y }
+        node_info_raw_id_name[node_cnt] = node_name
+        node_cnt += 1
+    print("len node_info", len(node_info))
+    return node_info, node_info_raw_id_name
+
+
+def read_net_file(fopen, node_info):
+    net_info = {}
+    net_name = None
+    net_cnt = 0
+    for line in fopen.readlines():
+        if not line.startswith("\t") and not line.startswith("NetDegree"):
+            continue
+        line = line.strip().split()
+        if line[0] == "NetDegree":
+            net_name = line[-1]
+        else:
+            node_name = line[0]
+            if node_name in node_info:
+                if not net_name in net_info:
+                    net_info[net_name] = {}
+                    net_info[net_name]["nodes"] = {}
+                    net_info[net_name]["ports"] = {}
+                if not node_name in net_info[net_name]["nodes"]:
+                    x_offset = float(line[-2])
+                    y_offset = float(line[-1])
+                    net_info[net_name]["nodes"][node_name] = {}
+                    net_info[net_name]["nodes"][node_name] = {"x_offset": x_offset, "y_offset": y_offset}
+    for net_name in list(net_info.keys()):
+        if len(net_info[net_name]["nodes"]) <= 1:
+            net_info.pop(net_name)
+    for net_name in net_info:
+        net_info[net_name]['id'] = net_cnt
+        net_cnt += 1
+    print("adjust net size = {}".format(len(net_info)))
+    return net_info
+
+
+def get_comp_hpwl_dict(node_info, net_info):
+    comp_hpwl_dict = {}
+    for net_name in net_info:
+        max_idx = 0
+        for node_name in net_info[net_name]["nodes"]:
+            max_idx = max(max_idx, node_info[node_name]["id"])
+        if not max_idx in comp_hpwl_dict:
+            comp_hpwl_dict[max_idx] = []
+        comp_hpwl_dict[max_idx].append(net_name)
+    return comp_hpwl_dict
+
+
+def get_node_to_net_dict(node_info, net_info):
+    node_to_net_dict = {}
+    for node_name in node_info:
+        node_to_net_dict[node_name] = set()
+    for net_name in net_info:
+        for node_name in net_info[net_name]["nodes"]:
+            node_to_net_dict[node_name].add(net_name)
+    return node_to_net_dict
+
+
+def get_port_to_net_dict(port_info, net_info):
+    port_to_net_dict = {}
+    for port_name in port_info:
+        port_to_net_dict[port_name] = set()
+    for net_name in net_info:
+        for port_name in net_info[net_name]["ports"]:
+            port_to_net_dict[port_name].add(net_name)
+    return port_to_net_dict
+
+def read_pl_file(fopen, node_info):
+    max_height = 0
+    max_width = 0
+    for line in fopen.readlines():
+        if not line.startswith('o'):
+            continue
+        line = line.strip().split()
+        node_name = line[0]
+        if not node_name in node_info:
+            continue
+        place_x = int(line[1])
+        place_y = int(line[2])
+        max_height = max(max_height, node_info[node_name]["x"] + place_x)
+        max_width = max(max_width, node_info[node_name]["y"] + place_y)
+        node_info[node_name]["raw_x"] = place_x
+        node_info[node_name]["raw_y"] = place_y
+    return max(max_height, max_width), max(max_height, max_width)
+
+
+def get_node_id_to_name(node_info, node_to_net_dict):
+    node_name_and_num = []
+    for node_name in node_info:
+        node_name_and_num.append((node_name, len(node_to_net_dict[node_name])))
+    node_name_and_num = sorted(node_name_and_num, key=itemgetter(1), reverse = True)
+    print("node_name_and_num", node_name_and_num)
+    node_id_to_name = [node_name for node_name, _ in node_name_and_num]
+    for i, node_name in enumerate(node_id_to_name):
+        node_info[node_name]["id"] = i
+    return node_id_to_name
+
+
+def get_node_id_to_name_topology(node_info, node_to_net_dict, net_info, benchmark):
+    node_id_to_name = []
+    adjacency = {}
+    for net_name in net_info:
+        for node_name_1, node_name_2 in list(combinations(net_info[net_name]['nodes'],2)):
+            if node_name_1 not in adjacency:
+                adjacency[node_name_1] = set()
+            if node_name_2 not in adjacency:
+                adjacency[node_name_2] = set()
+            adjacency[node_name_1].add(node_name_2)
+            adjacency[node_name_2].add(node_name_1)
+
+    visited_node = set()
+
+    node_net_num = {}
+    for node_name in node_info:
+        node_net_num[node_name] = len(node_to_net_dict[node_name])
+    
+    node_net_num_fea= {}
+    node_net_num_max = max(node_net_num.values())
+    print("node_net_num_max", node_net_num_max)
+    for node_name in node_info:
+        node_net_num_fea[node_name] = node_net_num[node_name]/node_net_num_max
+    
+    node_area_fea = {}
+    node_area_max_node = max(node_info, key = lambda x : node_info[x]['x'] * node_info[x]['y'])
+    node_area_max = node_info[node_area_max_node]['x'] * node_info[node_area_max_node]['y']
+    print("node_area_max = {}".format(node_area_max))
+    for node_name in node_info:
+        node_area_fea[node_name] = node_info[node_name]['x'] * node_info[node_name]['y'] / node_area_max
+    
+    if "V" in node_info:
+        add_node = "V"
+        visited_node.add(add_node)
+        node_id_to_name.append((add_node, node_net_num[add_node]))
+        node_net_num.pop(add_node)
+    
+    add_node = max(node_net_num, key = lambda v: node_net_num[v])
+    visited_node.add(add_node)
+    node_id_to_name.append((add_node, node_net_num[add_node]))
+    node_net_num.pop(add_node)
+
+    while len(node_id_to_name) < len(node_info):
+        candidates = {}
+        for node_name in visited_node:
+            if node_name not in adjacency:
+                continue
+            for node_name_2 in adjacency[node_name]:
+                if node_name_2 in visited_node:
+                    continue
+                if node_name_2 not in candidates:
+                    candidates[node_name_2] = 0
+                candidates[node_name_2] += 1
+        for node_name in node_info:
+            if node_name not in candidates and node_name not in visited_node:
+                candidates[node_name] = 0
+        if len(candidates) > 0:
+            if benchmark != 'ariane':
+                if benchmark == "bigblue3":
+                    add_node = max(candidates, key = lambda v: candidates[v]*1 + node_net_num[v]*100000 +\
+                        node_info[v]['x']*node_info[v]['y'] * 1 +int(hash(v)%10000)*1e-6)
+                else:
+                    add_node = max(candidates, key = lambda v: candidates[v]*1 + node_net_num[v]*1000 +\
+                        node_info[v]['x']*node_info[v]['y'] * 1 +int(hash(v)%10000)*1e-6)
+            else:
+                add_node = max(candidates, key = lambda v: candidates[v]*30000 + node_net_num[v]*1000 +\
+                    node_info[v]['x']*node_info[v]['y']*1 +int(hash(v)%10000)*1e-6)
+        else:
+            if benchmark != 'ariane':
+                if benchmark == "bigblue3":
+                    add_node = max(node_net_num, key = lambda v: node_net_num[v]*100000 + node_info[v]['x']*node_info[v]['y']*1)
+                else:
+                    add_node = max(node_net_num, key = lambda v: node_net_num[v]*1000 + node_info[v]['x']*node_info[v]['y']*1)
+            else:
+                add_node = max(node_net_num, key = lambda v: node_net_num[v]*1000 + node_info[v]['x']*node_info[v]['y']*1)
+
+        visited_node.add(add_node)
+        node_id_to_name.append((add_node, node_net_num[add_node])) 
+        node_net_num.pop(add_node)
+    for i, (node_name, _) in enumerate(node_id_to_name):
+        node_info[node_name]["id"] = i
+    # print("node_id_to_name")
+    # print(node_id_to_name)
+    node_id_to_name_res = [x for x, _ in node_id_to_name]
+    return node_id_to_name_res
+
+
+class PlaceDB():
+
+    def __init__(self, benchmark = "adaptec1"):
+        self.benchmark = benchmark
+        if benchmark == "ariane" or benchmark == "sample_clustered":
+            path = benchmark + '/netlist.pb.txt'
+            pbtxt = get_netlist_info_dict(path)
+            self.node_info, self.node_info_raw_id_name = get_node_info(pbtxt)
+            self.node_cnt = len(self.node_info)
+            self.net_info, self.port_info = get_net_info(pbtxt)
+            self.net_cnt = len(self.net_info)
+            self.max_height, self.max_width = 357, 357
+            self.port_to_net_dict = get_port_to_net_dict(self.port_info, self.net_info)
+        else:
+            assert os.path.exists(benchmark)
+            node_file = open(os.path.join(benchmark, benchmark+".nodes"), "r")
+            self.node_info, self.node_info_raw_id_name = read_node_file(node_file, benchmark)
+            pl_file = open(os.path.join(benchmark, benchmark+".pl"), "r")
+            self.port_info = {}
+            self.node_cnt = len(self.node_info)
+            node_file.close()
+            net_file = open(os.path.join(benchmark, benchmark+".nets"), "r")
+            self.net_info = read_net_file(net_file, self.node_info)
+            self.net_cnt = len(self.net_info)
+            net_file.close()
+            pl_file = open(os.path.join(benchmark, benchmark+".pl"), "r")
+            self.max_height, self.max_width = read_pl_file(pl_file, self.node_info)
+            pl_file.close()
+            self.port_to_net_dict = {}
+        self.node_to_net_dict = get_node_to_net_dict(self.node_info, self.net_info)
+        self.node_id_to_name = get_node_id_to_name_topology(self.node_info, self.node_to_net_dict, self.net_info, self.benchmark)
+
+    def debug_str(self):
+        print("node_cnt = {}".format(len(self.node_info)))
+        print("net_cnt = {}".format(len(self.net_info)))
+        print("max_height = {}".format(self.max_height))
+        print("max_width = {}".format(self.max_width))
+
+
+if __name__ == "__main__":
+    placedb = PlaceDB("ariane")
+    placedb.debug_str()
+

maskplace/place_db_proto.py

@@ -0,0 +1,117 @@
+import sys
+sys.path.append('ariane')
+from ariane.read_info import get_netlist_info_dict
+from tqdm import tqdm
+
+def get_node_info(pbtxt):
+    node_info = {}
+    node_info_raw_id_name = {}
+    node_cnt = 0
+    area_sum = 0.0
+
+    for node in pbtxt.node:
+        if node.attr['type'].placeholder.upper() != "MACRO":
+            continue
+        node_name = node.name
+        x = float(node.attr['width'].f)
+        y = float(node.attr['height'].f)
+        node_info[node_name] = {"id": node_cnt, "x": x, "y": y}
+        area_sum += x * y
+        if node.attr['type'].placeholder == "MACRO":
+            node_info[node_name]["is_hard"] = 1
+        else:
+            node_info[node_name]["is_hard"] = 0
+        node_info_raw_id_name[node_cnt] = node_name
+        node_cnt += 1
+    print("area_sum = {}".format(area_sum))
+    return node_info, node_info_raw_id_name
+
+
+def get_net_info(pbtxt):
+    net_info = {}
+    net_name = None
+    net_cnt = 0
+    pin_cnt = 0
+    pin_info = {}
+    port_info = {}
+    for node in pbtxt.node:
+        if node.attr['type'].placeholder.upper() == "MACRO":
+            continue
+        pin_name = node.name
+        if node.attr['type'].placeholder.upper() == "PORT":
+            x = float(node.attr['x'].f)
+            y = float(node.attr['y'].f)
+            port_info[pin_name] = {"x": x, "y": y}
+        elif node.attr['type'].placeholder.upper() == "MACRO_PIN":
+            macro_name = node.attr['macro_name'].placeholder
+            x_offset = float(node.attr['x_offset'].f)
+            y_offset = float(node.attr['y_offset'].f)
+            pin_info[pin_name] = {"node_name": macro_name, "x_offset": x_offset, "y_offset": y_offset}
+            pin_cnt += 1
+    print("pin_cnt = {}".format(pin_cnt))
+    for node in pbtxt.node:
+        net_name = node.name
+        if node.attr['type'].placeholder.upper() == "MACRO":
+            continue
+        net_info[net_name] = {}
+        net_info[net_name]["nodes"] = {}
+        net_info[net_name]["ports"] = {}
+        if 'weight' in node.attr:
+            net_info[net_name]["weight"] = float(node.attr['weight'].f)
+        else:
+            net_info[net_name]["weight"] = 1.0
+        for pin_name in node.input:
+            if pin_name in port_info:
+                assert pin_name not in net_info[net_name]["ports"]
+                net_info[net_name]["ports"][pin_name] = {}
+                net_info[net_name]["ports"][pin_name]["x"] = port_info[pin_name]["x"]
+                net_info[net_name]["ports"][pin_name]["y"] = port_info[pin_name]["y"]
+            elif pin_name in pin_info:
+                node_name = pin_info[pin_name]["node_name"]
+                if node_name in net_info[net_name]["nodes"]:
+                    if "x_offsets" not in net_info[net_name]["nodes"][node_name]:
+                        net_info[net_name]["nodes"][node_name]["x_offsets"] = [net_info[net_name]["nodes"][node_name]["x_offset"]]
+                        net_info[net_name]["nodes"][node_name]["y_offsets"] = [net_info[net_name]["nodes"][node_name]["y_offset"]]
+                    net_info[net_name]["nodes"][node_name]["x_offsets"].append(pin_info[pin_name]["x_offset"])
+                    net_info[net_name]["nodes"][node_name]["y_offsets"].append(pin_info[pin_name]["y_offset"])
+                net_info[net_name]["nodes"][node_name] = {}
+                net_info[net_name]["nodes"][node_name]["x_offset"] = pin_info[pin_name]["x_offset"]
+                net_info[net_name]["nodes"][node_name]["y_offset"] = pin_info[pin_name]["y_offset"]
+            else:
+                assert False
+        out_pin_name = net_name
+        if out_pin_name in port_info:
+            assert out_pin_name not in net_info[net_name]["ports"]
+            net_info[net_name]["ports"][out_pin_name] = {}
+            net_info[net_name]["ports"][out_pin_name]["x"] = port_info[out_pin_name]["x"]
+            net_info[net_name]["ports"][out_pin_name]["y"] = port_info[out_pin_name]["y"]
+        elif out_pin_name in pin_info:
+            node_name = pin_info[out_pin_name]["node_name"]
+            assert node_name not in net_info[net_name]["nodes"]
+            net_info[net_name]["nodes"][node_name] = {}
+            net_info[net_name]["nodes"][node_name]["x_offset"] = pin_info[out_pin_name]["x_offset"]
+            net_info[net_name]["nodes"][node_name]["y_offset"] = pin_info[out_pin_name]["y_offset"]
+        else:
+            print("out_pin_name = {}".format(out_pin_name))
+            assert False
+
+    for net_name in list(net_info.keys()):
+        if len(net_info[net_name]["nodes"]) + \
+            len(net_info[net_name]["ports"]) <= 1:
+            net_info.pop(net_name)
+    for net_name in net_info:
+        net_info[net_name]['id'] = net_cnt
+        net_cnt += 1
+    print("adjust net size = {}".format(len(net_info)))
+    return net_info, port_info
+
+                
+def main():
+    path = 'ariane/netlist.pb.txt'
+    pbtxt = get_netlist_info_dict(path)
+    node_info = get_node_info(pbtxt)
+    net_info, port_info = get_net_info(pbtxt)
+
+
+if __name__ == "__main__":
+    main()

maskplace/place_env/__init__.py

@@ -0,0 +1,6 @@
+from gym.envs.registration import register
+
+register(
+	id = 'place_env-v0',
+	entry_point = 'place_env.place_env:PlaceEnv'
+)

maskplace/place_env/place_env.py

@@ -0,0 +1,301 @@
+import math
+import gym
+from gym import spaces
+import numpy as np
+import sys
+sys.path.append("..")
+from place_db import PlaceDB
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+import time
+
+class PlaceEnv(gym.Env):
+
+    def __init__(self, placedb, placed_num_macro = None, grid = 224):
+        
+        # need to get GCN vector and CNN
+        print("grid * grid", grid * grid)
+        print("placedb.node_cnt", placedb.node_cnt)
+        print("placedb.net_cnt", placedb.net_cnt)
+        assert grid * grid >= placedb.node_cnt 
+        self.grid = grid
+        self.max_height = placedb.max_height
+        self.max_width = placedb.max_width
+        self.placedb = placedb
+        self.num_macro = placedb.node_cnt
+        self.placed_num_macro = placed_num_macro
+        self.num_net = placedb.net_cnt
+        self.node_name_list = placedb.node_id_to_name
+        self.action_space = spaces.Discrete(self.grid * self.grid)
+        self.state = None
+        self.net_min_max_ord = {}
+        self.node_pos = {}
+        self.net_placed_set = {}
+        self.last_reward = 0
+        self.num_macro_placed = 0
+        self.node_x_max = 0
+        self.node_x_min = self.grid
+        self.node_y_max = 0
+        self.node_y_min = self.grid
+        self.ratio = self.placedb.max_height / self.grid
+        print("self.ratio = {:.2f}".format(self.ratio))
+    
+    def reset(self):
+        self.num_macro_placed = 0
+        num_macro = self.num_macro
+        canvas = np.zeros((self.grid, self.grid))
+        self.node_pos = {}
+        self.net_min_max_ord = {}
+        self.net_fea = np.zeros((self.num_net, 4))
+        self.net_fea[:, 0] = 0
+        self.net_fea[:, 1] = 1.0
+        self.net_fea[:, 2] = 0
+        self.net_fea[:, 3] = 1.0
+        self.rudy = np.zeros((self.grid, self.grid))
+        for port_name in self.placedb.port_to_net_dict:
+            for net_name in self.placedb.port_to_net_dict[port_name]:
+                pin_x = round(self.placedb.port_info[port_name]['x'] / self.ratio)
+                pin_y = round(self.placedb.port_info[port_name]['y'] / self.ratio)
+                if net_name in self.net_min_max_ord:
+                    if pin_x > self.net_min_max_ord[net_name]['max_x']:
+                        self.net_min_max_ord[net_name]['max_x'] = pin_x
+                        self.net_fea[self.placedb.net_info[net_name]['id']][1] = pin_x / self.grid
+                    elif pin_x < self.net_min_max_ord[net_name]['min_x']:
+                        self.net_min_max_ord[net_name]['max_y'] = pin_y
+                        self.net_fea[self.placedb.net_info[net_name]['id']][0] = pin_x / self.grid
+                    if pin_y > self.net_min_max_ord[net_name]['max_y']:
+                        self.net_min_max_ord[net_name]['max_y'] = pin_y
+                        self.net_fea[self.placedb.net_info[net_name]['id']][3] = pin_y / self.grid
+                    elif pin_y < self.net_min_max_ord[net_name]['min_y']:
+                        self.net_min_max_ord[net_name]['min_y'] = pin_y
+                        self.net_fea[self.placedb.net_info[net_name]['id']][2] = pin_y / self.grid
+                else:
+                    self.net_min_max_ord[net_name] = {}
+                    self.net_min_max_ord[net_name]['max_x'] = pin_x
+                    self.net_min_max_ord[net_name]['min_x'] = pin_x
+                    self.net_min_max_ord[net_name]['max_y'] = pin_y
+                    self.net_min_max_ord[net_name]['min_y'] = pin_y
+                    self.net_fea[self.placedb.net_info[net_name]['id']][1] = pin_x / self.grid
+                    self.net_fea[self.placedb.net_info[net_name]['id']][0] = pin_x / self.grid
+                    self.net_fea[self.placedb.net_info[net_name]['id']][3] = pin_y / self.grid
+                    self.net_fea[self.placedb.net_info[net_name]['id']][2] = pin_y / self.grid
+        self.net_placed_set = {}
+        self.num_macro_placed = 0
+        
+        net_img = np.zeros((self.grid, self.grid))
+        net_img_2 = np.zeros((self.grid, self.grid))
+
+        next_x = math.ceil(max(1, self.placedb.node_info[self.node_name_list[self.num_macro_placed]]['x'] / self.ratio))
+        next_y = math.ceil(max(1, self.placedb.node_info[self.node_name_list[self.num_macro_placed]]['y'] / self.ratio))
+        mask = self.get_mask(canvas, next_x, next_y)
+        next_x_2 = math.ceil(max(1, self.placedb.node_info[self.node_name_list[self.num_macro_placed+1]]['x'] / self.ratio))
+        next_y_2 = math.ceil(max(1, self.placedb.node_info[self.node_name_list[self.num_macro_placed+1]]['y'] / self.ratio))
+        mask_2 = self.get_mask(canvas, next_x_2, next_y_2)
+        for net_name in self.placedb.net_info:
+            self.net_placed_set[net_name] = set()
+        self.state = np.concatenate((np.array([self.num_macro_placed]), canvas.flatten(), 
+            net_img.flatten(), mask.flatten(), net_img_2.flatten(), mask_2.flatten(), 
+            np.array([next_x/self.grid, next_y/self.grid])), axis = 0)
+        self.node_x_max = 0
+        self.node_x_min = self.grid
+        self.node_y_max = 0
+        self.node_y_min = self.grid
+
+        return self.state
+
+    def save_fig(self, file_path):
+        fig1 = plt.figure()
+        ax1 = fig1.add_subplot(111, aspect='equal')
+        ax1.axes.xaxis.set_visible(False)
+        ax1.axes.yaxis.set_visible(False)
+        for node_name in self.node_pos:
+            x, y, size_x, size_y = self.node_pos[node_name]
+            ax1.add_patch(
+                patches.Rectangle(
+                    (x/self.grid, y/self.grid),   # (x,y)
+                    size_x/self.grid,          # width
+                    size_y/self.grid, linewidth=1, edgecolor='k',
+                )
+            )
+        fig1.savefig(file_path, dpi=90, bbox_inches='tight')
+        plt.close()
+    # WireMask
+    def get_net_img(self, is_next_next = False):
+        net_img = np.zeros((self.grid, self.grid))
+        if not is_next_next:
+            next_node_name = self.placedb.node_id_to_name[self.num_macro_placed]
+        elif self.num_macro_placed + 1 < len(self.placedb.node_id_to_name):
+            next_node_name = self.placedb.node_id_to_name[self.num_macro_placed + 1]
+        else:
+            return net_img
+
+        for net_name in self.placedb.node_to_net_dict[next_node_name]:
+            if net_name in self.net_min_max_ord:
+                delta_pin_x = round((self.placedb.node_info[next_node_name]['x']/2 + \
+                    self.placedb.net_info[net_name]["nodes"][next_node_name]["x_offset"])/self.ratio)
+                delta_pin_y = round((self.placedb.node_info[next_node_name]['y']/2 + \
+                    self.placedb.net_info[net_name]["nodes"][next_node_name]["y_offset"])/self.ratio)
+                start_x = self.net_min_max_ord[net_name]['min_x'] - delta_pin_x
+                end_x = self.net_min_max_ord[net_name]['max_x'] - delta_pin_x
+                start_y = self.net_min_max_ord[net_name]['min_y'] - delta_pin_y
+                end_y = self.net_min_max_ord[net_name]['max_y'] - delta_pin_y
+                start_x = min(start_x, self.grid)
+                start_y = min(start_y, self.grid)
+                if not 'weight' in self.placedb.net_info[net_name]:
+                    weight = 1.0
+                else:
+                    weight = self.placedb.net_info[net_name]['weight']
+                for i in range(0, start_x):
+                    net_img[i, :] += (start_x - i) * weight
+                for i in range(end_x+1, self.grid):
+                    net_img[i, :] +=  (i- end_x) * weight
+                for j in range(0, start_y):
+                    net_img[:, j] += (start_y - j) * weight
+                for j in range(end_y+1, self.grid):
+                    net_img[:, j] += (j - start_y) * weight
+        return net_img
+
+    def step(self, action):
+        err_msg = f"{action!r} ({type(action)}) invalid"
+        assert self.action_space.contains(action), err_msg
+
+        canvas = self.state[1: 1+self.grid*self.grid].reshape(self.grid, self.grid)
+        mask = self.state[1+self.grid*self.grid*2: 1+self.grid*self.grid*3].reshape(self.grid, self.grid)
+        reward = 0
+        x = round(action // self.grid)
+        y = round(action % self.grid)
+    
+        if mask[x][y] == 1:
+            reward += -200000
+                
+        node_name = self.placedb.node_id_to_name[self.num_macro_placed]
+        size_x = math.ceil(max(1, self.placedb.node_info[node_name]['x']/self.ratio))
+        size_y = math.ceil(max(1, self.placedb.node_info[node_name]['y']/self.ratio))
+
+        assert abs(size_x - self.state[-2]*self.grid) < 1e-5
+        assert abs(size_y - self.state[-1]*self.grid) < 1e-5
+
+        canvas[x : x+size_x, y : y+size_y] = 1.0
+        canvas[x : x + size_x, y] = 0.5
+        if y + size_y -1 < self.grid:
+            canvas[x : x + size_x, max(0, y + size_y -1)] = 0.5
+        canvas[x, y: y + size_y] = 0.5
+        if x + size_x - 1 < self.grid:
+            canvas[max(0, x+size_x-1), y: y + size_y] = 0.5
+        self.node_pos[self.node_name_list[self.num_macro_placed]] = (x, y, size_x, size_y)
+
+        for net_name in self.placedb.node_to_net_dict[node_name]:
+            self.net_placed_set[net_name].add(node_name)
+            pin_x = round((x * self.ratio + self.placedb.node_info[node_name]['x']/2 + \
+                    self.placedb.net_info[net_name]["nodes"][node_name]["x_offset"])/self.ratio)
+            pin_y = round((y * self.ratio + self.placedb.node_info[node_name]['y']/2 + \
+                self.placedb.net_info[net_name]["nodes"][node_name]["y_offset"])/self.ratio)
+            if net_name in self.net_min_max_ord:
+                start_x = self.net_min_max_ord[net_name]['min_x']
+                end_x = self.net_min_max_ord[net_name]['max_x']
+                start_y = self.net_min_max_ord[net_name]['min_y']
+                end_y = self.net_min_max_ord[net_name]['max_y']
+                delta_x = end_x - start_x
+                delta_y = end_y - start_y
+                if delta_x > 0 or delta_y > 0:
+                    self.rudy[start_x : end_x +1, start_y: end_y +1] -= 1/(delta_x+1) + 1/(delta_y+1) 
+                weight = 1.0
+                if 'weight' in self.placedb.net_info[net_name]:
+                    weight = self.placedb.net_info[net_name]['weight']
+ 
+                if pin_x > self.net_min_max_ord[net_name]['max_x']:
+                    reward += weight * (self.net_min_max_ord[net_name]['max_x'] - pin_x)
+                    self.net_min_max_ord[net_name]['max_x'] = pin_x
+                    self.net_fea[self.placedb.net_info[net_name]['id']][1] = pin_x / self.grid
+                elif pin_x < self.net_min_max_ord[net_name]['min_x']:
+                    reward += weight * (pin_x - self.net_min_max_ord[net_name]['min_x'])
+                    self.net_min_max_ord[net_name]['min_x'] = pin_x
+                    self.net_fea[self.placedb.net_info[net_name]['id']][0] = pin_x / self.grid
+                if pin_y > self.net_min_max_ord[net_name]['max_y']:
+                    reward += weight * (self.net_min_max_ord[net_name]['max_y'] - pin_y)
+                    self.net_min_max_ord[net_name]['max_y'] = pin_y
+                    self.net_fea[self.placedb.net_info[net_name]['id']][3] = pin_y / self.grid
+                elif pin_y < self.net_min_max_ord[net_name]['min_y']:
+                    reward += weight * (pin_y - self.net_min_max_ord[net_name]['min_y'])
+                    self.net_min_max_ord[net_name]['min_y'] = pin_y
+                    self.net_fea[self.placedb.net_info[net_name]['id']][2] = pin_y / self.grid
+                start_x = self.net_min_max_ord[net_name]['min_x']
+                end_x = self.net_min_max_ord[net_name]['max_x']
+                start_y = self.net_min_max_ord[net_name]['min_y']
+                end_y = self.net_min_max_ord[net_name]['max_y']
+                delta_x = end_x - start_x
+                delta_y = end_y - start_y
+                self.rudy[start_x : end_x +1, start_y: end_y +1] += 1/(delta_x+1) + 1/(delta_y+1) 
+            else:
+                self.net_min_max_ord[net_name] = {}
+                self.net_min_max_ord[net_name]['max_x'] = pin_x
+                self.net_min_max_ord[net_name]['min_x'] = pin_x
+                self.net_min_max_ord[net_name]['max_y'] = pin_y
+                self.net_min_max_ord[net_name]['min_y'] = pin_y
+                start_x = self.net_min_max_ord[net_name]['min_x']
+                end_x = self.net_min_max_ord[net_name]['max_x']
+                start_y = self.net_min_max_ord[net_name]['min_y']
+                end_y = self.net_min_max_ord[net_name]['max_y']
+                self.net_fea[self.placedb.net_info[net_name]['id']][1] = pin_x / self.grid
+                self.net_fea[self.placedb.net_info[net_name]['id']][0] = pin_x / self.grid
+                self.net_fea[self.placedb.net_info[net_name]['id']][3] = pin_y / self.grid
+                self.net_fea[self.placedb.net_info[net_name]['id']][2] = pin_y / self.grid
+                reward += 0
+
+        self.num_macro_placed += 1
+        net_img = np.zeros((self.grid, self.grid))
+        net_img_2 = np.zeros((self.grid, self.grid))
+
+        if self.num_macro_placed < self.placed_num_macro:
+            net_img = self.get_net_img()
+            net_img_2 = self.get_net_img(is_next_next= True)
+            if net_img.max() >0 or net_img_2.max()>0:
+                net_img /= max(net_img.max(), net_img_2.max())
+                net_img_2 /= max(net_img.max(), net_img_2.max())
+
+        if self.node_x_max < x:
+            self.node_x_max = x
+        if self.node_x_min > x:
+            self.node_x_min = x
+        if self.node_y_max < y:
+            self.node_y_max = y
+        if self.node_y_min > y:
+            self.node_y_min = y
+
+        if self.num_macro_placed == self.num_macro or \
+            (self.placed_num_macro is not None and self.num_macro_placed == self.placed_num_macro): 
+            done = True
+        else:
+            done = False
+        mask = np.ones((self.grid, self.grid))
+        mask_2 = np.ones((self.grid, self.grid))
+        if not done: # get next macro size and pre-mask the solution
+            next_x = math.ceil(max(1, self.placedb.node_info[self.placedb.node_id_to_name[self.num_macro_placed]]['x']/self.ratio))
+            next_y = math.ceil(max(1, self.placedb.node_info[self.placedb.node_id_to_name[self.num_macro_placed]]['y']/self.ratio))
+            mask = self.get_mask(canvas, next_x, next_y)
+            if self.num_macro_placed + 1 < self.placed_num_macro:
+                next_x_2 = math.ceil(max(1, self.placedb.node_info[self.placedb.node_id_to_name[self.num_macro_placed+1]]['x']/self.ratio))
+                next_y_2 = math.ceil(max(1, self.placedb.node_info[self.placedb.node_id_to_name[self.num_macro_placed+1]]['y']/self.ratio))
+                mask_2 = self.get_mask(canvas, next_x_2, next_y_2)
+        else:
+            next_x = 0
+            next_y = 0
+        self.state = np.concatenate((np.array([self.num_macro_placed]), canvas.flatten(), 
+            net_img.flatten(), mask.flatten(), net_img_2.flatten(), mask_2.flatten(),
+            np.array([next_x/self.grid, next_y/self.grid])), axis = 0)
+        return self.state, reward, done, {"raw_reward": reward, "net_img": net_img, "mask": mask}
+    
+    # PositionMask
+    def get_mask(self, canvas, next_x, next_y):
+        mask = np.zeros((self.grid, self.grid))
+        for node_name in self.node_pos:
+            startx = max(0, self.node_pos[node_name][0] - next_x + 1)
+            starty = max(0, self.node_pos[node_name][1] - next_y + 1)
+            endx = min(self.node_pos[node_name][0] + self.node_pos[node_name][2] - 1, self.grid - 1)
+            endy = min(self.node_pos[node_name][1] + self.node_pos[node_name][3] - 1, self.grid - 1)
+            mask[startx: endx + 1, starty : endy + 1] = 1
+        mask[self.grid - next_x + 1:,:] = 1
+        mask[:, self.grid - next_y + 1:] = 1
+        return mask
+
+    

maskplace/prim.py

@@ -0,0 +1,47 @@
+from itertools import combinations
+from heapq import *
+
+def prim_real(vertexs_tmp, node_pos, net_info, ratio, node_info, port_info):# vertexs, edges,start='D'):
+    vertexs = list(vertexs_tmp)
+    if len(vertexs)<=1:
+        return 0
+    adjacent_dict = {}
+    for node in vertexs:
+        adjacent_dict[node] = []
+    for node1, node2 in list(combinations(vertexs, 2)):
+        if node1 in node_pos:
+            pin_x_1 = node_pos[node1][0] * ratio + node_info[node1]["x"] / 2 + net_info[node1]["x_offset"] # )//ratio
+            pin_y_1 = node_pos[node1][1] * ratio + node_info[node1]["y"] / 2 + net_info[node1]["y_offset"] # )//ratio
+        else:
+            pin_x_1 = port_info[node1]['x']
+            pin_y_1 = port_info[node1]['y']
+        if node2 in node_pos:
+            pin_x_2 = node_pos[node2][0] * ratio + node_info[node2]["x"] / 2 + net_info[node2]["x_offset"] # )//ratio
+            pin_y_2 = node_pos[node2][1] * ratio + node_info[node2]["y"] / 2 + net_info[node2]["y_offset"] # )//ratio
+        else:
+            pin_x_2 = port_info[node2]['x']
+            pin_y_2 = port_info[node2]['y']
+        weight = abs(pin_x_1-pin_x_2) + \
+                abs(pin_y_1-pin_y_2)
+        adjacent_dict[node1].append((weight, node1, node2))
+        adjacent_dict[node2].append((weight, node2, node1))
+
+    start = vertexs[0]
+    minu_tree = []
+    visited = set()
+    visited.add(start)
+    adjacent_vertexs_edges = adjacent_dict[start]
+    heapify(adjacent_vertexs_edges)
+    cost = 0
+    cnt = 0
+    while cnt < len(vertexs)-1:
+        weight, v1, v2 = heappop(adjacent_vertexs_edges)
+        if v2 not in visited:
+            visited.add(v2)
+            minu_tree.append((weight, v1, v2))
+            cost += weight
+            cnt += 1
+            for next_edge in adjacent_dict[v2]:
+                if next_edge[2] not in visited:
+                    heappush(adjacent_vertexs_edges, next_edge)
+    return cost