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Merge branch 'main' of github.com:TILOS-AI-Institute/MacroPlacement into main

parents 286dd9bd 06a420cc
Showing with 5576 additions and 63 deletions
.gitignore
......@@ -4,11 +4,34 @@ Flows/job
Flows/util/__pycache__
CodeElements/*/*/__pycache__
CodeElements/Plc_client/test/
CodeElements/Plc_client/test/*/*
CodeElements/Plc_client/plc_client_os.py
CodeElements/Plc_client/test/*
CodeElements/Plc_client/__pycache__/*
CodeElements/Plc_client/proto_reader.py
CodeElements/Plc_client/plc_client.py
CodeElements/failed_proxy_plc/*
CodeElements/EvalCT/test/g657_ub5_nruns10_c5_r3_v3_rc1
CodeElements/EvalCT/snapshot*
CodeElements/EvalCT/circuit_training
CodeElements/EvalCT/__pycache__/
CodeElements/EvalCT/eval_run*.plc
CodeElements/EvalCT/eval_run*.plc
CodeElements/EvalCT/saved_policy/run_00/111/train/*
CodeElements/EvalCT/saved_policy/run_00/111/snapshot*.plc
CodeElements/EvalCT/saved_policy/run_00/111/rl*.plc
CodeElements/EvalCT/saved_policy/run_os_64128_g657_ub5_nruns10_c5_r3_v3_rc1
CodeElements/epoch_*.plc
CodeElements/FDPlacement/test/*
CodeElements/Plc_client/placement_util*.py
CodeElements/StatTest/test/ariane/*_vs_*
CodeElements/StatTest/test/flow2_68_1.3_ct/*
CodeElements/StatTest/test/ariane/*.plc
CodeElements/*/__pycache__/*
CodeElements/*.png
CodeElements/StatTest/*.csv
CodeElements/os.txt
CodeElements/EvalCT/test/ariane/*.plc
CodeElements/FDPlacement/fd.txt
CodeElements/StatTest/test/ariane/*.pb.txt
CodeElements/Plc_client/failed_proxy_plc/*
__pycache__/
__pycache__
......
CodeElements/EvalCT/README.md 0 → 100644
# Circuit Training Model Evaulation
## Quick Start
First, make sure you are under `CodeElements/EvalCT/`. To run `eval_ct.py`, we need to download `circuit_training` code under the same directory. Run the following command to get the stable version that we have tested.
```
# Compatible with Revision #90. Latest Version might cause errors
svn export -r 90 --force https://github.com/google-research/circuit_training.git/trunk/circuit_training
```
Next, we need to prepare your trained policy and the testcase you want to evaluate on. Assume you have trained models (which usually can be found under `./logs`), copy the run folder into `saved_model` folder. Make sure your testcase is under `./test`. The `ckptID` is the policy checkpoint ID saved after each iteration.
Finally, run the following command with path to netlist file, initial placement file and model run directory path.
```
$ python3 -m eval_ct --netlist ./test/ariane/netlist.pb.txt\
--plc ./test/ariane/initial.plc\
--rundir run_00\
--ckptID policy_checkpoint_0000103984
```
The placement will be stored under `CodeElements/EvalCT/` and named as `eval_[RUN_DIR]_to_[TESTCASE].plc`.
## Trained Policy
We are providing one of the run we trained from scratch using Google's Ariane testcase. **This should not be taken as representing the potential of Circuit Training**. We are only providing these trained weights here for the sake of testing. Please feel free to load any of your own trained weights. You may find similar file structure under `./logs` after training.
## View Your Result
You can view the result by supplying this placement file into the open-sourced Plc_client testbench and use the `display_canvas` function.
CodeElements/EvalCT/eval_ct.py 0 → 100755
import collections
import functools
import os
import time
from typing import Text
import statistics
import re
from absl import app
from absl import flags
from absl.flags import argparse_flags
from circuit_training.environment import environment
from circuit_training.environment import placement_util
from tf_agents.experimental.distributed import reverb_variable_container
from tf_agents.metrics import py_metric
from tf_agents.metrics import py_metrics
from tf_agents.policies import greedy_policy # pylint: disable=unused-import
from tf_agents.policies import py_tf_eager_policy
from tf_agents.policies import policy_loader
from tf_agents.train import actor
from tf_agents.train import learner
from tf_agents.train.utils import train_utils
from tf_agents.trajectories import trajectory
from tf_agents.utils import common
from tf_agents.policies import greedy_policy # pylint: disable=unused-import
from tf_agents.system import system_multiprocessing as multiprocessing
"""
Example
At ./MacroPlacement/CodeElement/EvalCT, run the following command:
$ cd EvalCT && python3 -m eval_ct --netlist ./test/ariane/netlist.pb.txt\
--plc ./test/ariane/initial.plc\
--rundir run_00\
--ckptID policy_checkpoint_0000103984\
&& cd -
"""
# InfoMetric_wirelength = 0.09238692254177283
# InfoMetric_congestion = 0.9468230846211636
# InfoMetric_density = 0.5462616496124097
class InfoMetric(py_metric.PyStepMetric):
"""Observer for graphing the environment info metrics."""
def __init__(
self,
env,
info_metric_key: Text,
buffer_size: int = 1,
name: Text = 'InfoMetric',
):
"""Observer reporting TensorBoard metrics at the end of each episode.
Args:
env: environment.
info_metric_key: a string key from the environment info to report,
e.g. wirelength, density, congestion.
buffer_size: size of the buffer for calculating the aggregated metrics.
name: name of the observer object.
"""
super(InfoMetric, self).__init__(name + '_' + info_metric_key)
self._env = env
self._info_metric_key = info_metric_key
self._buffer = collections.deque(maxlen=buffer_size)
def call(self, traj: trajectory.Trajectory):
"""Report the requested metrics at the end of each episode."""
# We collect the metrics from the info from the environment instead.
# The traj argument is kept to be compatible with the actor/learner API
# for metrics.
del traj
if self._env.done:
# placement_util.save_placement(self._env._plc, './reload_weight.plc', '')
metric_value = self._env.get_info()[self._info_metric_key]
self._buffer.append(metric_value)
def result(self):
return statistics.mean(self._buffer)
def reset(self):
self._buffer.clear()
def evaulate(model_dir, ckpt_id, create_env_fn):
# Create the path for the serialized greedy policy.
policy_saved_model_path = os.path.join(model_dir,
learner.POLICY_SAVED_MODEL_DIR,
learner.GREEDY_POLICY_SAVED_MODEL_DIR)
try:
assert os.path.isdir(policy_saved_model_path)
print("#[POLICY SAVED MODEL PATH] " + policy_saved_model_path)
except AssertionError:
print("[ERROR POLICY SAVED MODEL PATH NOT FOUND] " + policy_saved_model_path)
exit(0)
policy_saved_chkpt_path = os.path.join(model_dir,
learner.POLICY_SAVED_MODEL_DIR,
"checkpoints", ckpt_id)
try:
assert os.path.isdir(policy_saved_chkpt_path)
print("#[POLICY SAVED CHECKPOINT PATH] " + policy_saved_chkpt_path)
except AssertionError:
print("[ERROR POLICY SAVED CHECKPOINT PATH NOT FOUND] " + policy_saved_chkpt_path)
exit(0)
saved_model_pb_path = os.path.join(policy_saved_model_path, 'saved_model.pb')
try:
assert os.path.isfile(saved_model_pb_path)
print("#[SAVED MODEL PB PATH] " + saved_model_pb_path)
except AssertionError:
print("[ERROR SAVE MODEL PB PATH NOT FOUND] " + saved_model_pb_path)
exit(0)
policy = policy_loader.load(policy_saved_model_path, policy_saved_chkpt_path)
policy.get_initial_state()
print(policy.variables()[0].numpy())
train_step = train_utils.create_train_step()
# Create the environment.
env = create_env_fn()
# Create the evaluator actor.
info_metrics = [
InfoMetric(env, 'wirelength'),
InfoMetric(env, 'congestion'),
InfoMetric(env, 'density'),
]
eval_actor = actor.Actor(
env,
policy,
train_step,
episodes_per_run=1,
summary_dir=os.path.join(model_dir, learner.TRAIN_DIR, 'eval'),
metrics=[
py_metrics.NumberOfEpisodes(),
py_metrics.EnvironmentSteps(),
py_metrics.AverageReturnMetric(
name='eval_episode_return', buffer_size=1),
py_metrics.AverageEpisodeLengthMetric(buffer_size=1),
] + info_metrics,
name='performance')
eval_actor.run_and_log()
def main(args):
NETLIST_FILE = args.netlist
INIT_PLACEMENT = args.plc
POLICY_CHECKPOINT_ID = args.ckptID
GLOBAL_SEED = 111
CD_RUNTIME = False
RUN_NAME = args.rundir
# extract eval testcase name
EVAL_TESTCASE = re.search("/test/(.+?)/netlist.pb.txt", NETLIST_FILE).group(1)
create_env_fn = functools.partial(
environment.create_circuit_environment,
netlist_file=NETLIST_FILE,
init_placement=INIT_PLACEMENT,
is_eval=True,
save_best_cost=True,
output_plc_file=str('./eval_' + RUN_NAME + '_to_' + EVAL_TESTCASE + '.plc'),
global_seed=GLOBAL_SEED,
cd_finetune=CD_RUNTIME
)
evaulate(model_dir=os.path.join("./saved_policy", RUN_NAME, str(GLOBAL_SEED)),
ckpt_id=POLICY_CHECKPOINT_ID, create_env_fn=create_env_fn)
def parse_flags(argv):
parser = argparse_flags.ArgumentParser(
description='An argparse + app.run example')
parser.add_argument("--netlist", required=True,
help="Path to netlist in pb.txt")
parser.add_argument("--plc", required=True,
help="Path to plc in .plc")
parser.add_argument("--rundir", required=True,
help="Path to run directory that contains saved policies")
parser.add_argument("--ckptID", required=True,
help="Policy checkpoint ID")
return parser.parse_args(argv[1:])
if __name__ == '__main__':
app.run(main, flags_parser=parse_flags)
\ No newline at end of file
CodeElements/EvalCT/saved_policy/run_00/111/policies/collect_policy/policy_specs.pbtxt 0 → 100644
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CodeElements/EvalCT/saved_policy/run_00/111/policies/greedy_policy/policy_specs.pbtxt 0 → 100644
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CodeElements/EvalCT/saved_policy/run_00/111/policies/policy/policy_specs.pbtxt 0 → 100644
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CodeElements/EvalCT/test/ariane/initial.plc 0 → 100644
# Placement file for Circuit Training
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# Date : 2022-03-13 09:30:00
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837 45.068 0.1975 - 1
838 44.764 0.1975 - 1
839 44.536 0.1975 - 1
840 44.232 0.1975 - 1
841 77.14 0.1975 - 1
842 78.584 0.1975 - 1
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846 101.052 0.177 - 1
847 101.304 0.177 - 1
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857 120.232 0.1975 - 1
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864 114.38 0.1975 - 1
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866 70.376 0.1975 - 1
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876 100.092 0.1975 - 1
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888 112.252 0.1975 - 1
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891 107.478 0.177 - 1
892 100.776 0.1975 - 1
893 114.836 0.1975 - 1
894 111.132 0.177 - 1
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896 102.98 0.1975 - 1
897 103.284 0.1975 - 1
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900 95 0.1975 - 1
901 103.512 0.1975 - 1
902 105.032 0.1975 - 1
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906 111.948 0.1975 - 1
907 87.704 0.1975 - 1
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911 66.728 0.1975 - 1
912 84.968 0.1975 - 1
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914 97.888 0.1975 - 1
915 90.592 0.1975 - 1
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920 106.628 0.1975 - 1
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924 110.124 0.1975 - 1
925 96.976 0.1975 - 1
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930 89.224 0.1975 - 1
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937 92.948 0.1975 - 1
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940 91.098 0.177 - 1
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950 105.564 0.1975 - 1
951 65.512 0.1975 - 1
952 103.068 0.177 - 1
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954 110.124 0.177 - 1
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959 95.228 0.1975 - 1
960 97.28 0.1975 - 1
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963 96.216 0.1975 - 1
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967 123.88 0.1975 - 1
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969 96.748 0.1975 - 1
970 97.812 0.1975 - 1
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980 120.384 0.1975 - 1
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988 117.952 0.1975 - 1
989 118.256 0.1975 - 1
990 118.104 0.1975 - 1
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992 70.224 0.1975 - 1
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1000 113.924 0.1975 - 1
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1002 95.988 0.1975 - 1
1003 95.256 0.177 - 1
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1005 109.872 0.177 - 1
1006 103.664 0.1975 - 1
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1016 83.904 0.1975 - 1
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1019 64.068 0.1975 - 1
1020 63.384 0.1975 - 1
1021 92.644 0.1975 - 1
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1024 66.88 0.1975 - 1
1025 63.004 0.1975 - 1
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1081 61.992 0.177 - 1
1082 61.488 0.177 - 1
1083 61.256 0.1975 - 1
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1095 62.396 0.1975 - 1
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1098 65.142 0.177 - 1
1099 65.74 0.1975 - 1
1100 65.588 0.1975 - 1
1101 65.436 0.1975 - 1
1102 64.676 0.1975 - 1
1103 64.144 0.1975 - 1
1104 62.7 0.1975 - 1
1105 65.056 0.1975 - 1
1106 62.852 0.1975 - 1
1107 63.46 0.1975 - 1
1108 63.308 0.1975 - 1
1109 63.156 0.1975 - 1
1110 63.764 0.1975 - 1
1111 67.184 0.1975 - 1
1112 74.936 0.1975 - 1
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1114 74.024 0.1975 - 1
1115 70.832 0.1975 - 1
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1119 125.324 0.1975 - 1
1120 125.172 0.1975 - 1
1121 125.02 0.1975 - 1
1122 124.868 0.1975 - 1
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1125 122.816 0.1975 - 1
1126 122.664 0.1975 - 1
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1128 101.764 0.1975 - 1
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1130 65.816 0.1975 - 1
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1153 80.636 0.1975 - 1
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1177 71.972 0.1975 - 1
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1187 72.124 0.1975 - 1
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1190 70.984 0.1975 - 1
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1192 72.276 0.1975 - 1
1193 73.112 0.1975 - 1
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1199 43.092 0.1975 - 1
1200 42.788 0.1975 - 1
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1202 42.18 0.1975 - 1
1203 41.876 0.1975 - 1
1204 41.572 0.1975 - 1
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1215 38.988 0.1975 - 1
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1224 63.992 0.1975 - 1
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1229 175.408 0.1975 - 1
1230 176.396 0.1975 - 1
13201 312.56 86.67 N 0
13202 311.427 48.15 N 0
13203 312.446 28.89 N 0
13204 316.315 279.27 N 0
13205 339.102 202.23 N 0
13206 275.824 242.801 N 0
13207 283.204 164.783 N 0
13208 334.681 260.01 N 0
13209 341.914 182.97 N 0
13210 246.176 261.084 N 0
13211 280.392 184.043 N 0
13212 313.296 67.41 N 0
13213 341.914 96.3945 N 0
13214 341.914 25.804 N 0
13215 305.18 260.01 N 0
13216 306.276 145.523 N 0
13217 275.53 262.061 N 0
13218 276.002 89.6095 N 0
13219 304.886 223.541 N 0
13220 338.293 163.71 N 0
13221 245.947 241.824 N 0
13222 255.343 126.263 N 0
13223 340.845 115.655 N 0
13224 341.801 45.064 N 0
13225 312.387 9.63 N 0
13226 339.102 221.49 N 0
13227 308.938 125.19 N 0
13228 280.392 203.303 N 0
13229 311.121 105.93 N 0
13230 334.435 240.75 N 0
13231 336.435 144.45 N 0
13232 251.037 203.303 N 0
13233 263.221 145.523 N 0
13234 341.94 80.429 N 0
13235 342.005 61.334 N 0
13236 341.75 8.8835 N 0
13237 310.058 221.211 N 0
13238 310.892 163.978 N 0
13239 275.16 222.564 N 0
13240 251.037 184.043 N 0
13241 309.746 202.23 N 0
13242 312.56 182.97 N 0
13243 245.796 222.564 N 0
13244 253.85 164.783 N 0
13245 74.907 260.01 N 0
13246 133.617 199.473 N 0
13247 132.098 59.588 N 0
13248 102.743 131.443 N 0
13249 141.417 263.497 N 0
13250 132.965 177.525 N 0
13251 179.206 12.269 N 0
13252 102.743 112.183 N 0
13253 162.972 218.125 N 0
13254 179.669 100.485 N 0
13255 162.972 198.865 N 0
13256 16.1975 240.75 N 0
13257 16.1975 221.49 N 0
13258 32.4305 9.63 N 0
13259 14.6775 144.449 N 0
13260 112.061 279.27 N 0
13261 107.075 150.704 N 0
13262 73.3875 48.15 N 0
13263 44.0325 86.67 N 0
13264 141.417 244.237 N 0
13265 161.452 139.005 N 0
13266 162.972 179.605 N 0
13267 53.3515 298.53 N 0
13268 74.907 221.49 N 0
13269 91.1405 9.63 N 0
13270 73.3875 105.93 N 0
13271 112.061 235.806 N 0
13272 136.43 158.265 N 0
13273 122.926 35.1435 N 0
13274 103.454 92.9235 N 0
13275 184.513 256.645 N 0
13276 190.807 121.825 N 0
13277 192.327 179.605 N 0
13278 45.552 240.75 N 0
13279 77.7195 150.704 N 0
13280 149.85 15.8835 N 0
13281 102.743 73.664 N 0
13282 104.262 216.546 N 0
13283 132.098 131.443 N 0
13284 120.495 9.63 N 0
13285 102.743 54.4035 N 0
13286 200.126 218.125 N 0
13287 183.952 81.225 N 0
13288 195.139 160.344 N 0
13289 45.552 260.009 N 0
13290 14.6775 163.709 N 0
13291 14.6775 28.89 N 0
13292 44.0325 105.93 N 0
13293 74.907 240.75 N 0
13294 74.907 202.23 N 0
13295 44.0325 48.15 N 0
13296 73.3875 86.67 N 0
13297 221.682 198.865 N 0
13298 224.495 141.084 N 0
13299 221.682 179.605 N 0
13300 23.996 279.27 N 0
13301 16.1975 202.23 N 0
13302 14.6775 68.771 N 0
13303 44.0325 144.449 N 0
13304 141.417 282.758 N 0
13305 44.9005 182.969 N 0
13306 44.0325 67.41 N 0
13307 14.6775 125.19 N 0
13308 214.042 256.645 N 0
13309 246.646 98.4 N 0
13310 192.327 198.865 N 0
13311 53.3515 317.79 N 0
13312 15.5455 182.969 N 0
13313 14.6775 49.5115 N 0
13314 73.3875 125.19 N 0
13315 82.706 279.27 N 0
13316 45.552 202.23 N 0
13317 61.7855 28.89 N 0
13318 14.6775 105.93 N 0
13319 170.771 237.385 N 0
13320 161.452 119.746 N 0
13321 165.785 160.344 N 0
13322 45.552 221.49 N 0
13323 104.262 196.786 N 0
13324 61.7855 9.63 N 0
13325 48.3645 163.709 N 0
13326 103.61 169.964 N 0
13327 74.2555 182.969 N 0
13328 91.1405 28.89 N 0
13329 73.3875 67.41 N 0
13330 202.511 237.385 N 0
13331 195.139 141.084 N 0
13332 224.495 160.344 N 0
13333 133.758 100.485 N 0
13334 314.774 37.7577 N 0
13354 321.319 150.392 N 0
13357 261.626 236.421 N 0
13376 296.244 176.092 N 0
13386 283.374 138.768 N 0
13402 326.521 53.7131 N 0
13425 326.608 41.6708 N 0
13434 321.074 137.556 N 0
13472 290.107 130.575 N 0
13476 326.595 49.2981 N 0
13492 326.276 54.0355 N 0
13495 321.519 140.008 N 0
13499 284.779 224.782 N 0
13510 293.144 142.187 N 0
13517 326.511 51.92 N 0
13521 322 153.348 N 0
13530 269.47 232.643 N 0
13540 278.592 154.63 N 0
13543 123.101 127.011 N 0
13558 117.969 71.3248 N 0
13563 88.962 87.4022 N 0
13592 88.4983 86.1273 N 0
13613 86.4914 138.08 N 0
13619 120.142 162.016 N 0
13624 82.4604 151.047 N 0
13642 87.6245 140.205 N 0
13645 80.9853 135.647 N 0
13653 95.5688 206.634 N 0
13656 96.8993 41.5312 N 0
13666 88.4369 100.588 N 0
13679 150.993 168.362 N 0
13682 175.763 140.293 N 0
13696 177.169 146.562 N 0
13705 87.3942 161.363 N 0
13709 92.0591 205.221 N 0
13715 174.78 147.58 N 0
13721 91.0729 206.703 N 0
13727 73.7316 148.143 N 0
13734 79.0765 104.493 N 0
13746 179.82 139.149 N 0
13755 29.0271 78.8238 N 0
13764 68.6123 130.151 N 0
13771 83.272 151.49 N 0
13787 88.7318 175.028 N 0
13798 77.3018 149.05 N 0
13815 88.5304 39.3821 N 0
13829 179.826 140.706 N 0
13835 94.6554 41.9804 N 0
13866 100.606 31.389 N 0
13933 108.264 22.5057 N 0
14088 106.277 34.2939 N 0
14093 118.846 57.1702 N 0
14098 124.433 41.798 N 0
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14144 311.024 46.0592 N 0
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14179 200.462 22.8211 N 0
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14235 263.511 27.3878 N 0
14247 278.378 8.02347 N 0
14263 218.066 117.565 N 0
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14280 277.564 9.57611 N 0
14306 119.573 111.528 N 0
14341 118.803 96.819 N 0
14371 123.97 142.269 N 0
14402 124.989 143.425 N 0
14407 280.091 22.6121 N 0
14421 273.786 33.6657 N 0
14426 175.256 141.003 N 0
14433 201.572 7.41756 N 0
14450 119.908 21.8108 N 0
14454 288.025 144.486 N 0
14484 142.959 109.375 N 0
14496 206.947 91.8626 N 0
14514 212.974 45.9913 N 0
14561 263.428 45.2239 N 0
14584 215.057 14.4005 N 0
14591 282.982 13.0023 N 0
14608 151.44 78.4743 N 0
14628 183.885 38.587 N 0
14637 278.034 109.528 N 0
14670 250.361 62.409 N 0
14678 219.423 16.9586 N 0
14684 271.133 114.807 N 0
14720 203.603 3.43601 N 0
14731 276.635 74.5329 N 0
14746 82.7451 138.678 N 0
14798 276.261 64.1613 N 0
14814 264.593 39.4665 N 0
14837 160.057 105.841 N 0
14851 293.505 80.0543 N 0
14858 189.5 55.1511 N 0
14868 289.422 120.227 N 0
14897 207.974 22.3375 N 0
14928 123.263 44.6681 N 0
14943 292.286 68.3698 N 0
14954 298.106 134.262 N 0
14977 248.093 49.4194 N 0
14986 257.367 66.8619 N 0
15021 290.554 28.637 N 0
15035 269.327 107.408 N 0
15070 256.779 71.6205 N 0
15109 69.1817 135.071 N 0
15161 173.702 36.7422 N 0
15185 292.748 62.7555 N 0
15200 120.407 82.3172 N 0
15241 294.299 61.1636 N 0
15247 248.357 18.5064 N 0
15264 228.846 38.0744 N 0
15292 203.368 35.4342 N 0
15321 90.0041 41.9847 N 0
15335 221.061 129.12 N 0
15348 196.496 63.8841 N 0
15364 134.505 72.2374 N 0
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15380 284.392 71.3934 N 0
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15467 152.034 1.88292 N 0
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15477 160.705 71.1757 N 0
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15523 174.076 49.8379 N 0
15539 284.454 114.128 N 0
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15586 220.818 85.8166 N 0
15601 250.079 33.7099 N 0
15622 195.765 10.8115 N 0
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15735 64.9814 132.086 N 0
15770 221.057 62.7469 N 0
15790 88.2288 98.3159 N 0
15826 195.164 24.4106 N 0
15838 265.999 35.3773 N 0
15848 242.373 15.7005 N 0
15859 279.794 119.871 N 0
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15943 156.708 96.9843 N 0
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17536 218.2 100.872 N 0
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18950 293.904 63.691 N 0
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19069 214.596 23.541 N 0
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19224 213.852 62.8326 N 0
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19433 170.717 71.6308 N 0
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19497 185.415 49.24 N 0
19500 209.693 112.391 N 0
19521 291.07 62.2123 N 0
19528 261.42 35.3658 N 0
19544 197.405 13.9894 N 0
19552 295.046 83.0625 N 0
19564 171.172 35.7241 N 0
19576 234.528 69.285 N 0
19590 274.4 18.9775 N 0
19600 192.118 68.5477 N 0
19614 274.42 30.6377 N 0
19634 289.719 10.0873 N 0
19643 180.148 28.3612 N 0
19668 165.06 76.525 N 0
19695 276.26 61.3041 N 0
19703 149.413 111.78 N 0
19737 284.55 107.056 N 0
19757 144.641 65.798 N 0
19766 123.752 93.6217 N 0
19779 167.273 17.7213 N 0
19786 264.869 108.614 N 0
19793 286.237 144.826 N 0
19803 239.346 112.228 N 0
19830 276.684 47.9957 N 0
19838 152.203 101.775 N 0
19872 189.16 43.8103 N 0
19877 137.679 117.257 N 0
19891 239.314 63.4282 N 0
19905 204.956 20.4283 N 0
19914 132.293 110.267 N 0
19946 154.169 58.0537 N 0
19954 178.924 58.488 N 0
19964 248.891 114.323 N 0
19983 195.247 0.921335 N 0
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20008 163.471 48.0851 N 0
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20020 192.094 23.8369 N 0
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20051 243.601 150.414 N 0
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20102 74.6075 136.047 N 0
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21026 56.2014 119.979 N 0
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21987 196.951 100.349 N 0
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22000 96.6128 41.5601 N 0
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22056 196.921 78.4681 N 0
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22930 71.0571 129.881 N 0
22939 176.158 174.48 N 0
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23023 175.713 147.981 N 0
23038 90.251 227.317 N 0
23044 92.6258 206.565 N 0
23048 86.9235 136.916 N 0
23051 114.656 25.6419 N 0
23056 88.179 40.2285 N 0
23059 105.932 19.38 N 0
23062 180.68 209.969 N 0
23067 87.6945 136.193 N 0
23070 89.7034 234.545 N 0
23076 89.6985 241.474 N 0
23081 63.156 162.488 N 0
23084 29.1855 80.3765 N 0
23087 88.179 90.583 N 0
23090 63.2851 270.317 N 0
23096 63.156 163 N 0
23099 29.241 84.7997 N 0
23104 105.769 267.069 N 0
23110 96.057 180.454 N 0
23114 134.503 142.837 N 0
23117 67.9077 273.144 N 0
23122 67.9055 162.7 N 0
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23141 66.1393 173.219 N 0
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23247 46.88 38.4 N 0
23250 88.4722 94.4615 N 0
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23764 270.379 55.56 N 0
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23817 236.034 210.162 N 0
23835 296.485 191.697 N 0
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23899 128.618 19.605 N 0
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23912 129.732 78.12 N 0
23915 160.955 107.364 N 0
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23924 163.561 89.64 N 0
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23938 146.176 142.8 N 0
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23944 121.436 44.8935 N 0
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23960 119.563 96.7765 N 0
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23975 224.951 42.48 N 0
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24000 90.5423 42.9421 N 0
24009 102.264 44.6535 N 0
24012 267.733 127.926 N 0
24016 217.632 74.581 N 0
24019 279.595 154.596 N 0
24022 314.636 157.267 N 0
24026 151.546 153.734 N 0
24032 148.038 185.598 N 0
24036 146.495 143.961 N 0
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24085 187.081 66.8735 N 0
24088 241.002 58.2995 N 0
24093 88.2659 95.9705 N 0
24107 118.448 104.946 N 0
24111 119.301 77.52 N 0
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24121 180.554 210.827 N 0
24133 202.191 96.3633 N 0
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24218 265.801 101.157 N 0
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24254 132.363 14.831 N 0
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24282 239.364 59.1843 N 0
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24311 54.183 115.767 N 0
24314 194.461 90.975 N 0
24317 298.595 106.92 N 0
24320 28.6425 25.32 N 0
24323 117.58 70.3568 N 0
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24332 266.473 99.428 N 0
24335 28.6425 24.36 N 0
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24381 82.8657 139.832 N 0
24393 204.932 46.434 N 0
24396 234.142 41.7002 N 0
24400 192.374 67.2547 N 0
24411 235.237 54.4498 N 0
24431 198.781 92.9365 N 0
24438 146.889 68.0145 N 0
24441 178.804 132.572 N 0
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24452 263.348 114.398 N 0
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24468 273.918 20.0129 N 0
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24477 154.46 67.35 N 0
24480 240.975 55.5985 N 0
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24488 210.002 55.069 N 0
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24519 290.096 154.297 N 0
24525 175.389 71.16 N 0
24528 245.691 53.6037 N 0
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24540 226.692 92.1438 N 0
24548 164.298 82.9279 N 0
24564 193.214 97.2629 N 0
24570 167.796 92.344 N 0
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24577 169.161 90.7355 N 0
24580 135.038 112.483 N 0
24583 119.4 59.4692 N 0
24587 153.013 140.673 N 0
24594 227.59 20.256 N 0
24597 182.889 110.235 N 0
24600 142.731 141.451 N 0
24603 245.293 84.3375 N 0
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24616 85.9173 172.848 N 0
24628 238.435 171.101 N 0
24631 279.173 151.615 N 0
24634 110.566 22.6615 N 0
24691 59.4225 78.6 N 0
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24711 157.572 109.996 N 0
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24720 205.386 53.3227 N 0
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24748 117.506 49.838 N 0
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24783 208.389 94.0126 N 0
24797 88.2616 84.9088 N 0
24813 58.482 127.08 N 0
24816 122.515 70.4995 N 0
24819 28.6425 35.88 N 0
24822 228.863 96.815 N 0
24828 218.499 120.752 N 0
24831 234.505 48.4731 N 0
24867 90.2053 39.7381 N 0
24886 291.436 89.726 N 0
24889 149.954 168.728 N 0
24892 146.775 121.693 N 0
24895 241.516 77.26 N 0
24898 63.954 58.32 N 0
24901 164.878 109.596 N 0
24904 252.453 61.32 N 0
24907 115.189 66.7431 N 0
24913 189.163 60.713 N 0
24916 216.915 92.4641 N 0
24927 227.94 110.435 N 0
24934 232.116 57.5212 N 0
24962 217.052 131.334 N 0
CodeElements/EvalCT/test/ariane/netlist.pb.txt 0 → 100644
This source diff could not be displayed because it is too large. You can view the blob instead.
CodeElements/Plc_client/plc_client_os.py
......@@ -10,6 +10,7 @@ import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import numpy as np
import traceback, sys
import random
"""plc_client_os docstrings.
......@@ -144,6 +145,7 @@ class PlacementCost(object):
"""
private function: Protobuf Netlist Parser
"""
print("#[INFO] Reading from " + self.netlist_file)
with open(self.netlist_file) as fp:
line = fp.readline()
node_cnt = 0
......@@ -245,6 +247,7 @@ class PlacementCost(object):
if node_name == "__metadata__":
# skipping metadata header
logging.info('[INFO NETLIST PARSER] skipping invalid net input')
elif attr_dict['type'][1] == 'macro':
# soft macro
# check if all required information is obtained
......@@ -387,7 +390,8 @@ class PlacementCost(object):
# store current node indx
self.port_indices.append(node_cnt-1)
# mapping connection degree to each macros
# 1. mapping connection degree to each macros
# 2. update offset based on Hard macro orientation
self.__update_connection()
# all hard macros are placed on canvas initially
......@@ -439,6 +443,9 @@ class PlacementCost(object):
# Width and Height should be defined on the same one-line
_width = float(line_item[1])
_height = float(line_item[3])
elif all(it in line_item for it in ['Area', 'stdcell', 'macros']):
# Total core area of modules
_area = float(line_item[3])
elif "Area" in line_item:
# Total core area of modules
_area = float(line_item[1])
......@@ -482,7 +489,7 @@ class PlacementCost(object):
elif all(it in line_item for it in ['MACROs'])\
and len(line_item) == 2:
_macros_cnt = int(line_item[1])
elif all(re.match(r'[0-9NEWS\.\-]+', it) for it in line_item)\
elif all(re.match(r'[0-9FNEWS\.\-]+', it) for it in line_item)\
and len(line_item) == 5:
# [node_index] [x] [y] [orientation] [fixed]
_node_plc[int(line_item[0])] = line_item[1:]
......@@ -556,9 +563,10 @@ class PlacementCost(object):
# restore placement for each module
try:
# print(sorted(list(info_dict['node_plc'].keys())))
assert sorted(self.port_indices +\
self.hard_macro_indices +\
self.soft_macro_indices) == list(info_dict['node_plc'].keys())
self.soft_macro_indices) == sorted(list(info_dict['node_plc'].keys()))
except AssertionError:
print('[ERROR PLC INDICES MISMATCH]', len(sorted(self.port_indices +\
self.hard_macro_indices +\
......@@ -572,6 +580,7 @@ class PlacementCost(object):
mod_y = float(info_dict['node_plc'][mod_idx][1])
mod_orient = info_dict['node_plc'][mod_idx][2]
mod_ifFixed = int(info_dict['node_plc'][mod_idx][3])
except Exception as e:
print('[ERROR PLC PARSER] %s' % str(e))
......@@ -589,6 +598,7 @@ class PlacementCost(object):
# set meta information
if ifReadComment:
print("[INFO] Retrieving Meta information from .plc comments")
self.set_canvas_size(info_dict['width'], info_dict['height'])
self.set_placement_grid(info_dict['columns'], info_dict['rows'])
self.set_block_name(info_dict['block'])
......@@ -619,6 +629,10 @@ class PlacementCost(object):
print("[ERROR UPDATE CONNECTION] MACRO pins not found")
continue
# also update pin offset based on macro orientation
orientation = macro.get_orientation()
self.update_macro_orientation(macro_idx, orientation)
# Soft macro
elif self.is_node_soft_macro(macro_idx):
if macro_name in self.soft_macros_to_inpins.keys():
......@@ -709,8 +723,9 @@ class PlacementCost(object):
# Retrieve current pin node position
pin_node = self.modules_w_pins[pin_idx]
pin_node_x_offset, pin_node_y_offset = pin_node.get_offset()
# Google's Plc client DOES NOT compute (node_position + pin_offset) when reading input
return (ref_node_x + pin_node_x_offset, ref_node_y + pin_node_y_offset)
# return pin_node.get_pos()
def get_wirelength(self) -> float:
"""
......@@ -726,6 +741,9 @@ class PlacementCost(object):
x_coord = []
y_coord = []
# default value of weight
weight_fact = 1.0
# NOTE: connection only defined on PORT, soft/hard macro pins
if curr_type == "PORT" and mod.get_sink():
# add source position
......@@ -739,39 +757,47 @@ class PlacementCost(object):
# retrieve sink object
sink = self.modules_w_pins[sink_idx]
# only consider placed sink
ref_sink = self.modules_w_pins[self.get_ref_node_id(sink_idx)]
if not ref_sink.get_placed_flag():
continue
# retrieve location
# ref_sink = self.modules_w_pins[self.get_ref_node_id(sink_idx)]
# if not placed, skip this edge
# if not ref_sink.get_placed_flag():
# x_coord.append(0)
# y_coord.append(0)
# else:# retrieve location
x_coord.append(self.__get_pin_position(sink_idx)[0])
y_coord.append(self.__get_pin_position(sink_idx)[1])
elif curr_type == "MACRO_PIN":
ref_mod = self.modules_w_pins[self.get_ref_node_id(mod_idx)]
if not ref_mod.get_placed_flag():
continue
# # if not placed, skip this edge
# if not ref_mod.get_placed_flag():
# continue
# get pin weight
weight_fact = mod.get_weight()
# add source position
x_coord.append(self.__get_pin_position(mod_idx)[0])
y_coord.append(self.__get_pin_position(mod_idx)[1])
if mod.get_sink():
if mod.get_weight() != 0:
norm_fact = mod.get_weight()
for input_list in mod.get_sink().values():
for sink_name in input_list:
# retrieve indx in modules_w_pins
input_idx = self.mod_name_to_indices[sink_name]
# sink_ref_mod = self.modules_w_pins[self.get_ref_node_id(mod_idx)]
# if not placed, skip this edge
# if not sink_ref_mod.get_placed_flag():
# x_coord.append(0)
# y_coord.append(0)
# else:
# retrieve location
x_coord.append(self.__get_pin_position(input_idx)[0])
y_coord.append(self.__get_pin_position(input_idx)[1])
if x_coord:
if norm_fact != 1.0:
total_hpwl += norm_fact * \
total_hpwl += weight_fact * \
(abs(max(x_coord) - min(x_coord)) + \
abs(max(y_coord) - min(y_coord)))
else:
total_hpwl += (abs(max(x_coord) - min(x_coord))\
+ abs(max(y_coord) - min(y_coord)))
return total_hpwl
def abu(self, xx, n = 0.1):
......@@ -988,8 +1014,8 @@ class PlacementCost(object):
module = self.modules_w_pins[module_idx]
# skipping unplaced module
if not module.get_placed_flag():
continue
# if not module.get_placed_flag():
# continue
module_h = module.get_height()
module_w = module.get_width()
......@@ -1720,7 +1746,6 @@ class PlacementCost(object):
[IGNORE] THIS DOES NOT AFFECT DENSITY. SHOULD WE IMPLEMENT THIS AT ALL?
make soft macros as squares
"""
return
for mod_idx in self.soft_macro_indices:
mod = self.modules_w_pins[mod_idx]
mod_area = mod.get_width() * mod.get_height()
......@@ -1945,6 +1970,47 @@ class PlacementCost(object):
mod.set_orientation(orientation)
macro = self.modules_w_pins[node_idx]
macro_name = macro.get_name()
hard_macro_pins = self.hard_macros_to_inpins[macro_name]
orientation = macro.get_orientation()
# update all pin offset
for pin_name in hard_macro_pins:
pin = self.modules_w_pins[self.mod_name_to_indices[pin_name]]
x_offset, y_offset = pin.get_offset()
x_offset_org = x_offset
if orientation == "N":
pass
elif orientation == "FN":
x_offset = -x_offset
pin.set_offset(x_offset, y_offset)
elif orientation == "S":
x_offset = -x_offset
y_offset = -y_offset
pin.set_offset(x_offset, y_offset)
elif orientation == "FS":
y_offset = -y_offset
pin.set_offset(x_offset, y_offset)
elif orientation == "E":
x_offset = y_offset
y_offset = -x_offset_org
pin.set_offset(x_offset, y_offset)
elif orientation == "FE":
x_offset = -y_offset
y_offset = -x_offset_org
pin.set_offset(x_offset, y_offset)
elif orientation == "W":
x_offset = -y_offset
y_offset = x_offset_org
pin.set_offset(x_offset, y_offset)
elif orientation == "FW":
x_offset = y_offset
y_offset = x_offset_org
pin.set_offset(x_offset, y_offset)
def update_port_sides(self):
"""
Define Port "Side" by its location on canvas
......@@ -2317,6 +2383,356 @@ class PlacementCost(object):
plt.show()
plt.close('all')
'''
FD Placement below shares the same functionality as the FDPlacement/fd_placement.py
'''
def __ifOverlap(self, u_i, v_i, ux=0, uy=0, vx=0, vy=0):
'''
Detect if the two modules are overlapping or not (w/o using block structure)
'''
# extract first macro
u_side = self.modules_w_pins[u_i].get_height()
u_x1 = self.modules_w_pins[u_i].get_pos()[0] + ux - u_side/2 # left
u_x2 = self.modules_w_pins[u_i].get_pos()[0] + ux + u_side/2 # right
u_y1 = self.modules_w_pins[u_i].get_pos()[1] + uy + u_side/2 # top
u_y2 = self.modules_w_pins[u_i].get_pos()[1] + uy - u_side/2 # bottom
# extract second macro
v_side = self.modules_w_pins[v_i].get_height()
v_x1 = self.modules_w_pins[v_i].get_pos()[0] + vx - v_side/2 # left
v_x2 = self.modules_w_pins[v_i].get_pos()[0] + vx + v_side/2 # right
v_y1 = self.modules_w_pins[v_i].get_pos()[1] + vy + v_side/2 # top
v_y2 = self.modules_w_pins[v_i].get_pos()[1] + vy - v_side/2 # bottom
return u_x1 < v_x2 and u_x2 > v_x1 and u_y1 > v_y2 and u_y2 < v_y1
def __repulsive_force(self, repel_factor, node_i, node_j):
'''
Calculate repulsive force between two nodes node_i, node_j
'''
if repel_factor == 0.0:
return 0.0, 0.0
# retrieve module instance
mod_i = self.modules_w_pins[node_i]
mod_j = self.modules_w_pins[node_j]
# retrieve module position
x_i, y_i = mod_i.get_pos()
x_j, y_j = mod_j.get_pos()
# get dist between x and y
x_dist = x_i - x_j
y_dist = y_i - y_j
# get dist of hypotenuse
hypo_dist = math.sqrt(x_dist**2 + y_dist**2)
# compute force in x and y direction
if hypo_dist <= 1e-10:
return math.sqrt(repel_factor), math.sqrt(repel_factor)
else:
f_x = repel_factor * x_dist / hypo_dist
f_y = repel_factor * y_dist / hypo_dist
return f_x, f_y
def __repulsive_force_hard_macro(self, repel_factor, h_node_i, s_node_j):
'''
Calculate repulsive force between hard macro and soft macro
'''
if repel_factor == 0.0:
return 0.0, 0.0
# retrieve module instance
h_mod_i = self.modules_w_pins[h_node_i]
s_mod_j = self.modules_w_pins[s_node_j]
# retrieve module position
x_i, y_i = h_mod_i.get_pos()
x_j, y_j = s_mod_j.get_pos()
# get dist between x and y
x_dist = x_i - x_j
y_dist = y_i - y_j
# get dist of hypotenuse
hypo_dist = math.sqrt(x_dist**2 + y_dist**2)
# compute force in x and y direction
if hypo_dist <= 1e-10 or self.__ifOverlap(h_node_i, s_node_j):
return x_dist/hypo_dist * (h_mod_i.get_height()/2 + s_mod_j.get_height()/2),\
y_dist/hypo_dist * (h_mod_i.get_height()/2 + s_mod_j.get_height()/2)
else:
return 0.0, 0.0
def __attractive_force(self, io_factor, attract_factor, node_i, node_j, io_flag = True, attract_exponent = 1):
'''
Calculate repulsive force between two nodes node_i, node_j
'''
# retrieve module instance
mod_i = self.modules_w_pins[node_i]
mod_j = self.modules_w_pins[node_j]
# retrieve module position
x_i, y_i = mod_i.get_pos()
x_j, y_j = mod_j.get_pos()
# get dist between x and y
x_dist = x_i - x_j - mod_i.get_height()/2 - mod_j.get_height()/2
y_dist = y_i - y_j - mod_i.get_height()/2 - mod_j.get_height()/2
# get dist of hypotenuse
hypo_dist = math.sqrt(x_dist**2 + y_dist**2)
# compute force in x and y direction
if hypo_dist <= 0.0 or self.__ifOverlap(u_i=node_i, v_i=node_j):
return 0.0, 0.0
else:
if io_flag:
temp_f = io_factor * (hypo_dist ** attract_exponent)
else:
temp_f = attract_factor * (hypo_dist ** attract_exponent)
f_x = x_dist / hypo_dist * temp_f
f_y = y_dist / hypo_dist * temp_f
return f_x, f_y
def __centralize(self, mod_id):
'''
Pull the modules to the nearest center of the gridcell
'''
mod = self.modules_w_pins[mod_id]
mod_x, mod_y = mod.get_pos()
# compute grid cell col
# why / 2.0?
col = round((mod_x - self.grid_width / 2.0) / self.grid_width)
if (col < 0):
col = 0
elif col > self.grid_col - 1:
col = self.grid_col - 1
row = round((mod_y - self.grid_height / 2.0) / self.grid_height)
if (row < 0):
row = 0
elif row > self.grid_row - 1:
row = self.grid_row - 1
mod.set_pos((col + 0.5) * self.grid_width, (row + 0.5) * self.grid_height)
def __centeralize_circle(self, mod_id):
'''
Pull the modules to a randomized unit circle in the center of the canvas
'''
r = 1 * math.sqrt(random.random())
theta = random.random() * 2 * math.pi
centerX = self.width / 2
centerY = self.height / 2
self.modules_w_pins[mod_id].set_pos(centerX + r * math.cos(theta), centerY + r * math.sin(theta))
def __boundary_check(self, mod_id):
'''
Make sure all the clusters are placed within the canvas
'''
mod = self.modules_w_pins[mod_id]
mod_x, mod_y = mod.get_pos()
if mod_x < 0.0:
mod_x = 0.0
if mod_x > self.width:
mod_x = self.width
if mod_y < 0.0:
mod_y = 0.0
if mod_y > self.height:
mod_y = self.height
mod.set_pos(mod_x, mod_y)
def __fd_placement(self, io_factor, max_displacement, attract_factor, repel_factor):
'''
Force-directed Placement for standard-cell clusters
'''
# store x/y displacement for all soft macro disp
soft_macro_disp = {}
for mod_idx in self.soft_macro_indices:
soft_macro_disp[mod_idx] = [0.0, 0.0]
def add_displace(mod_id, x_disp, y_disp):
'''
Add the displacement
'''
soft_macro_disp[mod_id][0] += x_disp
soft_macro_disp[mod_id][1] += y_disp
def update_location(mod_id, x_disp, y_disp):
'''
Update the displacement to the coordiante
'''
x_pos, y_pos = self.modules_w_pins[mod_id].get_pos()
# logging.info("{} {} {} {}".format(x_pos, y_pos, x_disp, y_disp))
self.modules_w_pins[mod_id].set_pos(x_pos + x_disp, y_pos + y_disp)
def piecewise_sigmoid(x, shift = 50):
if x >= 0:
return 1/(math.exp(-x+shift) + 1)
else:
return -1/(math.exp(x+shift) + 1)
##SOFT_SOFT REPEL###############################################################################################
# calculate the repulsive forces
# repulsive forces between stdcell clusters
if repel_factor != 0.0:
# temp storing the soft macro count
xr_collection = [0] * len(self.modules_w_pins)
yr_collection = [0] * len(self.modules_w_pins)
# repulsive forces between stdcell clusters and stdcell clusters
for mod_i in self.soft_macro_indices:
for mod_j in self.soft_macro_indices:
if (mod_i <= mod_j):
continue
repul_x, repul_y = self.__repulsive_force(repel_factor=repel_factor,
node_i=mod_i, node_j=mod_j)
xr_collection[mod_i] += 1.0 * repul_x
yr_collection[mod_i] += 1.0 * repul_y
xr_collection[mod_j] += -1.0 * repul_x
yr_collection[mod_j] += -1.0 * repul_y
# finding max x y displacement
max_x_disp, max_y_disp = (0.0, 0.0)
for xr, yr in zip(xr_collection, yr_collection):
if xr != 0.0:
max_x_disp = max(max_x_disp, abs(xr))
if yr != 0.0:
max_y_disp = max(max_y_disp, abs(yr))
# prevent zero division
if max_x_disp == 0.0:
max_x_disp = 1.0
if max_y_disp == 0.0:
max_y_disp = 1.0
scaling = 2.0
for mod_idx in self.soft_macro_indices:
add_displace(mod_idx, scaling * xr_collection[mod_idx] / max_x_disp, scaling * yr_collection[mod_idx] / max_y_disp)
##SOFT_HARD REPEL###############################################################################################
if repel_factor != 0.0:
# temp storing the soft macro count
xr_collection = [0] * len(self.modules_w_pins)
yr_collection = [0] * len(self.modules_w_pins)
# repulsive forces between stdcell clusters and macros
for mod_i in self.soft_macro_indices:
for mod_j in self.hard_macro_indices:
repul_x, repul_y = self.__repulsive_force_hard_macro(repel_factor=repel_factor,
h_node_i=mod_i, s_node_j=mod_j)
xr_collection[mod_i] += 1.0 * repul_x
yr_collection[mod_i] += 1.0 * repul_y
# finding max x y displacement
max_x_disp, max_y_disp = (0.0, 0.0)
for xr, yr in zip(xr_collection, yr_collection):
if xr != 0.0:
max_x_disp = max(max_x_disp, abs(xr))
if yr != 0.0:
max_y_disp = max(max_y_disp, abs(yr))
# prevent zero division
if max_x_disp == 0.0:
max_x_disp = 1.0
if max_y_disp == 0.0:
max_y_disp = 1.0
scaling = 4.0
for mod_idx in self.soft_macro_indices:
add_displace(mod_idx, scaling * xr_collection[mod_idx] / max_x_disp, scaling * yr_collection[mod_idx] / max_y_disp)
##NET ATTRACT###################################################################################################
if attract_factor != 0.0:
# temp storing the soft macro count
xr_collection = [0] * len(self.modules_w_pins)
yr_collection = [0] * len(self.modules_w_pins)
# calculate the attractive force
# traverse each edge
# the adj_matrix is a symmetric matrix
for driver_pin_idx, driver_pin in enumerate(self.modules_w_pins):
# only for soft macro
if driver_pin_idx in self.soft_macro_pin_indices and driver_pin.get_sink():
driver_mod_idx = self.get_ref_node_id(driver_pin_idx)
for sink_pin_name in driver_pin.get_sink().keys():
sink_mod_idx = self.mod_name_to_indices[sink_pin_name]
# if overlapped, dont attract further
if self.__ifOverlap(driver_mod_idx, sink_mod_idx):
continue
attrac_x, attrac_y = self.__attractive_force(io_factor=io_factor,
attract_factor=attract_factor,
node_i=driver_mod_idx,
node_j=sink_mod_idx
)
# if overlapped, dont attract further
if self.__ifOverlap(driver_mod_idx, sink_mod_idx, attrac_x, attrac_y):
continue
xr_collection[driver_mod_idx] += piecewise_sigmoid(-1.0 * attrac_x)
yr_collection[driver_mod_idx] += piecewise_sigmoid(-1.0 * attrac_y)
# finding max x y displacement
max_x_disp, max_y_disp = (0.0, 0.0)
for xr, yr in zip(xr_collection, yr_collection):
if xr != 0.0:
max_x_disp = max(max_x_disp, abs(xr))
if yr != 0.0:
max_y_disp = max(max_y_disp, abs(yr))
# prevent zero division
if max_x_disp == 0.0:
max_x_disp = 1.0
if max_y_disp == 0.0:
max_y_disp = 1.0
# not too much attract
scaling = 0.1
for mod_idx in self.soft_macro_indices:
add_displace(mod_idx, scaling * xr_collection[mod_idx] / max_x_disp, scaling * yr_collection[mod_idx] / max_y_disp)
for mod_idx in soft_macro_disp.keys():
# push all the macros to the nearest center of gridcell
update_location(mod_idx, *soft_macro_disp[mod_idx])
# Moved to here to save a for loop
# Based on our understanding, the stdcell clusters can be placed
# at any place in the canvas instead of the center of gridcells
self.__boundary_check(mod_idx)
def optimize_stdcells(self, use_current_loc, move_stdcells, move_macros,
log_scale_conns, use_sizes, io_factor, num_steps,
max_move_distance, attract_factor, repel_factor):
# initialize the position for all the macros and stdcell clusters
# YW: here I will ignore centering Macros since CT placement does that
for mod_idx in self.soft_macro_indices:
self.__centeralize_circle(mod_id = mod_idx)
for epoch_id, iterations in enumerate(num_steps):
logging.info("#[OPTIMIZING STDCELs] at num_step {}:".format(str(epoch_id)))
print("[INFO] max_displaccment = ", max_move_distance[epoch_id])
print("[INFO] attractive_factor = ", attract_factor[epoch_id])
print("[INFO] repulsive_factor = ", repel_factor[epoch_id])
print("[INFO] io_factor = ", io_factor)
print("[INFO] number of iteration = ", iterations)
for iter in range(iterations):
logging.info("# iteration {}:".format(str(iter)))
self.__fd_placement(io_factor=io_factor,
max_displacement=max_move_distance[epoch_id],
attract_factor=attract_factor[epoch_id],
repel_factor=repel_factor[epoch_id])
self.save_placement('epoch_{}.plc'.format(str(epoch_id)))
# Board Entity Definition
class Port:
def __init__(self, name, x = 0.0, y = 0.0, side = "BOTTOM"):
......@@ -2337,6 +2753,12 @@ class PlacementCost(object):
def get_orientation(self):
return self.orientation
def get_height(self):
return 0
def get_width(self):
return 0
def add_connection(self, module_name):
# NOTE: assume PORT names does not contain slash
ifPORT = False
......@@ -2700,6 +3122,10 @@ class PlacementCost(object):
def get_offset(self):
return self.x_offset, self.y_offset
def set_offset(self, x_offset, y_offset):
self.x_offset = x_offset
self.y_offset = y_offset
def get_name(self):
return self.name
......@@ -2734,19 +3160,6 @@ class PlacementCost(object):
def get_type(self):
return "MACRO_PIN"
# TODO finish this
# class StandardCell:
# def __init__( self, name,
# x = 0.0, y = 0.0, weight = 1.0):
# self.name = name
# self.x = float(x)
# self.y = float(y)
# self.x_offset = 0.0 # not used
# self.y_offset = 0.0 # not used
# self.macro_name = macro_name
# self.weight = weight
# self.sink = {}
def main():
test_netlist_dir = './Plc_client/test/'+\
'ariane_68_1.3'
......
CodeElements/Plc_client/plc_client_os_test.py
......@@ -4,7 +4,6 @@ import pandas as pd
import sys
import os
import traceback
import argparse
import math
import re
from random import randrange
......@@ -57,6 +56,18 @@ Example:
--marv 8.339\
--smooth 2
$ python3 -m Plc_client.plc_client_os_test --netlist ./Plc_client/test/g657_ub5_nruns10_c5_r3_v3_rc1/netlist.pb.txt\
--plc ./Plc_client/test/g657_ub5_nruns10_c5_r3_v3_rc1/legalized.plc\
--width 1357.360\
--height 1356.880\
--col 22\
--row 30\
--rpmh 11.285\
--rpmv 12.605\
--marh 7.143\
--marv 8.339\
--smooth 0
$ python3 -m Plc_client.plc_client_os_test --netlist ./Plc_client/test/0P2M0m/netlist.pb.txt\
--width 500\
--height 500\
......@@ -68,6 +79,18 @@ Example:
--marv 5\
--smooth 2
$ python3 -m Plc_client.plc_client_os_test --netlist ./Plc_client/test/ariane_fd/ariane.pb.txt\
--plc ./Plc_client/test/ariane_fd/ariane.plc\
--width 1599.99\
--height 1598.8\
--col 27\
--row 23\
--rpmh 70.330\
--rpmv 74.510\
--marh 51.790\
--marv 51.790\
--smooth 2
Todo:
* Clean up code
* Extract argument from command line
......@@ -214,7 +237,7 @@ class PlacementCostTest():
print(" +++ TEST METADATA: PASS +++")
print(" +++++++++++++++++++++++++++")
def view_canvas(self, ifInital, ifReadComment):
def view_canvas(self, ifInital=False, ifReadComment=False):
print("############################ VIEW CANVAS ############################")
self.plc_os = plc_client_os.PlacementCost(netlist_file=self.NETLIST_PATH,
macro_macro_x_spacing=50,
......@@ -229,9 +252,17 @@ class PlacementCostTest():
ifInital=ifInital,
ifValidate=False,
ifReadComment=ifReadComment)
self.plc_os.set_routes_per_micron(self.RPMH, self.RPMV)
self.plc_os.set_macro_routing_allocation(self.MARH, self.MARV)
self.plc_os.set_congestion_smooth_range(self.SMOOTH)
self.plc_os.set_canvas_size(self.CANVAS_WIDTH, self.CANVAS_HEIGHT)
self.plc_os.set_placement_grid(self.GRID_COL, self.GRID_ROW)
# show canvas
self.plc_os.make_soft_macros_square()
self.plc_os.display_canvas(annotate=False, amplify=False)
def test_proxy_cost(self):
print("############################ TEST PROXY COST ############################")
# Google's Binary Executable
......@@ -245,6 +276,8 @@ class PlacementCostTest():
self.plc.get_overlap_threshold()
print("overlap_threshold default", self.plc.get_overlap_threshold())
# self.plc.make_soft_macros_square()
if self.PLC_PATH:
print("#[PLC FILE FOUND] Loading info from .plc file")
self.plc_os.set_canvas_boundary_check(False)
......@@ -257,22 +290,33 @@ class PlacementCostTest():
else:
print("#[PLC FILE MISSING] Using only netlist info")
# self.plc.make_soft_macros_square()
self.plc.set_routes_per_micron(self.RPMH, self.RPMV)
self.plc_os.set_routes_per_micron(self.RPMH, self.RPMV)
# self.plc.make_soft_macros_square()
self.plc.set_macro_routing_allocation(self.MARH, self.MARV)
self.plc_os.set_macro_routing_allocation(self.MARH, self.MARV)
# self.plc.make_soft_macros_square()
self.plc.set_congestion_smooth_range(self.SMOOTH)
self.plc_os.set_congestion_smooth_range(self.SMOOTH)
self.plc.set_canvas_size(self.CANVAS_WIDTH, self.CANVAS_HEIGHT)
# self.plc.make_soft_macros_square()
self.plc.set_placement_grid(self.GRID_COL, self.GRID_ROW)
# self.plc.make_soft_macros_square() # in effect
self.plc.set_canvas_size(self.CANVAS_WIDTH, self.CANVAS_HEIGHT)
self.plc_os.set_canvas_size(self.CANVAS_WIDTH, self.CANVAS_HEIGHT)
self.plc_os.set_placement_grid(self.GRID_COL, self.GRID_ROW)
self.plc.make_soft_macros_square()
self.plc_os.make_soft_macros_square()
# self.plc.make_soft_macros_square()
# self.plc_os.make_soft_macros_square()
# [IGNORE] create_blockage must be defined BEFORE set_canvas_size
# and set_placement_grid in order to be considered on the canvas
......@@ -297,7 +341,7 @@ class PlacementCostTest():
str(self.plc.get_cost()), self.plc_os.get_cost()))
print("GL WIRELENGTH: ", self.plc.get_wirelength())
print("OS WIRELENGTH: ", self.plc_os.get_wirelength())
exit(1)
# exit(1)
# Density
try:
......@@ -421,19 +465,11 @@ class PlacementCostTest():
except Exception as e:
print("[ERROR WIRELENGTH] Discrepancies found when computing wirelength -- GL {}, OS {}".format(
str(self.plc.get_cost()), self.plc_os.get_cost()))
# if remove all soft macros
# soft_macro_indices = [
# m for m in self.plc.get_macro_indices() if self.plc.is_node_soft_macro(m)
# ]
# for mod_idx in soft_macro_indices:
# self.plc_os.unplace_node(mod_idx)
# self.plc.unplace_node(mod_idx)
print("GL WIRELENGTH: ", self.plc.get_wirelength())
print("OS WIRELENGTH: ", self.plc_os.get_wirelength())
def test_proxy_density(self):
print("############################ TEST PROXY DENSITY ############################")
# Google's Binary Executable
......@@ -718,18 +754,6 @@ class PlacementCostTest():
init_placement=self.PLC_PATH
)
if self.PLC_PATH:
print("#[PLC FILE FOUND] Loading info from .plc file")
self.plc_util_os.set_canvas_boundary_check(False)
self.plc_util_os.restore_placement(self.PLC_PATH,
ifInital=True,
ifValidate=True,
ifReadComment=False)
self.plc_util.set_canvas_boundary_check(False)
self.plc_util.restore_placement(self.PLC_PATH)
else:
print("#[PLC FILE MISSING] Using only netlist info")
self.extractor = observation_extractor.ObservationExtractor(
plc=self.plc_util, observation_config=self._observation_config
)
......@@ -843,6 +867,42 @@ class PlacementCostTest():
self.plc_util_os.display_canvas(annotate=False)
def test_fd(self):
print("############################ TEST GOOGLE's FD Placer ############################")
self.plc_util = placement_util.create_placement_cost(
plc_client=plc_client,
netlist_file=self.NETLIST_PATH,
init_placement=self.PLC_PATH
)
self.plc_util_os = placement_util.create_placement_cost(
plc_client=plc_client_os,
netlist_file=self.NETLIST_PATH,
init_placement=self.PLC_PATH
)
self.plc_util.set_routes_per_micron(self.RPMH, self.RPMV)
self.plc_util_os.set_routes_per_micron(self.RPMH, self.RPMV)
self.plc_util.set_macro_routing_allocation(self.MARH, self.MARV)
self.plc_util_os.set_macro_routing_allocation(self.MARH, self.MARV)
self.plc_util.set_congestion_smooth_range(self.SMOOTH)
self.plc_util_os.set_congestion_smooth_range(self.SMOOTH)
self.plc_util.set_canvas_size(self.CANVAS_WIDTH, self.CANVAS_HEIGHT)
self.plc_util.set_placement_grid(self.GRID_COL, self.GRID_ROW)
self.plc_util_os.set_canvas_size(self.CANVAS_WIDTH, self.CANVAS_HEIGHT)
self.plc_util_os.set_placement_grid(self.GRID_COL, self.GRID_ROW)
placement_util.fd_placement_schedule(self.plc_util)
for node_index in placement_util.nodes_of_types(self.plc_util, ['MACRO']):
x_pos, y_pos = self.plc_util.get_node_location(node_index)
self.plc_util_os.set_soft_macro_position(node_index, x_pos, y_pos)
self.plc_util_os.display_canvas(annotate=False, amplify=False)
def test_environment(self):
print("############################ TEST ENVIRONMENT ############################")
env = environment.CircuitEnv(
......@@ -912,6 +972,26 @@ class PlacementCostTest():
print(" +++ TEST ENVIRONMENT: PASS +++")
print(" ++++++++++++++++++++++++++++++")
def test_fd_placement(self):
print("############################ TEST FDPLACEMENT ############################")
self.plc_util_os = placement_util.create_placement_cost(
plc_client=plc_client_os,
netlist_file=self.NETLIST_PATH,
init_placement=self.PLC_PATH
)
# placement util is incapable of setting routing resources
self.plc_util_os.set_routes_per_micron(self.RPMH, self.RPMV)
self.plc_util_os.set_macro_routing_allocation(self.MARH, self.MARV)
self.plc_util_os.set_congestion_smooth_range(self.SMOOTH)
self.plc_util_os.set_canvas_size(self.CANVAS_WIDTH, self.CANVAS_HEIGHT)
self.plc_util_os.set_placement_grid(self.GRID_COL, self.GRID_ROW)
placement_util.fd_placement_schedule(self.plc_util_os)
self.plc_util_os.display_canvas(annotate=False, amplify=False)
def parse_flags(argv):
parser = argparse_flags.ArgumentParser(
......@@ -970,7 +1050,8 @@ def main(args):
Uncomment any available tests
"""
# PCT.test_metadata()
PCT.test_proxy_cost()
# PCT.test_proxy_cost()
# PCT.test_proxy_hpwl()
# PCT.test_proxy_density()
# PCT.test_proxy_congestion()
# PCT.test_placement_util(keep_save_file=False)
......@@ -978,7 +1059,9 @@ def main(args):
# PCT.test_miscellaneous()
# PCT.test_observation_extractor()
# PCT.view_canvas()
# PCT.test_fd()
# PCT.test_environment()
PCT.test_fd_placement()
if __name__ == '__main__':
......
CodeElements/Plc_client/stat_test.py 0 → 100644
import re
import os, sys
import math
import numpy as np
import logging
import matplotlib.pyplot as plt
import pandas as pd
from torch import argmax
# disable scientific notation
np.set_printoptions(suppress=True)
# print full array
np.set_printoptions(threshold=sys.maxsize)
'''
META INFO
'''
# Directory that stores all plc file (must come from the same netlist)
PLC_DIR = "Plc_client/test/ariane"
assert os.path.isdir(PLC_DIR)
# Top X% of largest movement range
TOP_X = 1
# List to store every plc coordinate
PLC_COORD = []
# scan through every .plc file
for __, __, files in os.walk(PLC_DIR):
for plc_file in files:
if plc_file.endswith((".plc")):
plc_pth = os.path.join(PLC_DIR, plc_file)
print("[INFO] Reading plc file {}".format(plc_pth))
# store in numpy array for ease of computation
temp_coord = np.empty((1,2), float)
for cnt, line in enumerate(open(plc_pth, 'r')):
line_item = re.findall(r'[0-9A-Za-z\.\-]+', line)
# skip empty lines
if len(line_item) == 0:
continue
if all(re.match(r'[0-9FNEWS\.\-]+', it) for it in line_item)\
and len(line_item) == 5:
# extract pos
temp_coord = np.append(temp_coord, np.array([[float(line_item[1]),float(line_item[2])]]), axis=0)
# remove header row
temp_coord = temp_coord[1:, :]
# make sure every plc is aligned
if PLC_COORD:
assert PLC_COORD[-1].shape == temp_coord.shape
PLC_COORD.append(temp_coord)
print(temp_coord)
del temp_coord
# store all pair-wise distance
abs_dist_plc = np.empty((PLC_COORD[-1].shape[0],1), float)
# pair-wise distance of all plc files
for i in range(len(PLC_COORD)):
for j in range(len(PLC_COORD)):
if i == j:
continue
# find x/y position diff
diff_coord = PLC_COORD[i] - PLC_COORD[j]
# x_diff^2, y_diff^2
diff_coord = np.power(diff_coord, 2)
# sqrt(x_diff^2 + y_diff^2)
abs_dist_coord = np.sqrt(diff_coord[:, 0] + diff_coord[:, 1])
abs_dist_plc = np.append(abs_dist_plc, abs_dist_coord.reshape((-1, 1)), axis=1)
# remove header col
abs_dist_plc = abs_dist_plc[:, 1:]
TOP_N = int(math.floor(abs_dist_plc.shape[0] * (TOP_X/100.0)))
print(TOP_N)
'''
MACRO placement maximum distance + visual
'''
# across all the plc diff, the max distance [row wise]
max_dist = np.amax(abs_dist_plc, axis=1)
# top-n max distance
topn_max_dist_idx = np.argpartition(max_dist, -TOP_N)[-TOP_N:]
topn_max_dist_val = np.take(max_dist, topn_max_dist_idx)
x = range(topn_max_dist_val.shape[0])
y = topn_max_dist_val
n = topn_max_dist_idx
fig, ax = plt.subplots()
ax.set_title("Top {}% Maximum Placement Range".format(TOP_X))
ax.scatter(x, y, c = 'b')
ax.set_xlabel("module index")
ax.set_ylabel("distance")
for i, txt in enumerate(n):
ax.annotate(txt, (x[i], y[i]))
plt.show()
'''
MACRO placement box plot visual
'''
abs_dist_plc_df = pd.DataFrame(data=abs_dist_plc)
topn_max_dist_df = abs_dist_plc_df.iloc[topn_max_dist_idx, :]
topn_max_dist_df.T.boxplot()
plt.title("Top {}% Placement Range".format(TOP_X))
plt.xlabel("module index")
plt.ylabel("distance")
plt.show()
'''
MACRO placement variane test
'''
'''
MACRO placement std dev test
'''
CodeElements/StatTest/proxy_corr_test.py 0 → 100755
import re
import os, sys
import math
import numpy as np
import logging
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
SHEET_ID = '1dtG4uHzdw-Lfe_Vcm5uBRNjjxXNA4gmVarTr86hjYVo'
SHEET_NAME = 'Proxy_Cost_Comparison'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
proxy_df = pd.read_csv(url)
proxy_df = proxy_df.loc[:, ~proxy_df.isnull().all()]
print(proxy_df.columns)
proxy_df['postCTS_Congestion (V)'] = proxy_df['postCTS_Congestion (V)'].str.rstrip('%').astype('float') / 100.0
# compute correlation between postRouteOpt_std_cell_area and density_cost
print("postRoute_std_cell_area VS. Density_Cost",
proxy_df["postRoute_std_cell_area (um^2)"].corr(proxy_df["Density_Cost"]))
print("postRouteOpt_std_cell_area VS. Density_Cost",
proxy_df["postRouteOpt_std_cell_area (um^2)"].corr(proxy_df["Density_Cost"]))
# compute correlation between postRouteOpt_wirelength (um) and wirelength_cost
print("postRoute_wirelength VS. Wirelength_Cost",
proxy_df["postRoute_wirelength (um)"].corr(proxy_df["Wirelength_Cost"]))
print("postRouteOpt_wirelength VS. Wirelength_Cost",
proxy_df["postRouteOpt_wirelength (um)"].corr(proxy_df["Wirelength_Cost"]))
# compute correlation between postCTS_Congestion and congestion_cost
print("postCTS_Congestion VS. Congestion_Cost",
proxy_df["postCTS_Congestion (V)"].corr(proxy_df["Congestion_Cost"]))
\ No newline at end of file
CodeElements/StatTest/stat_test.py 0 → 100755
import re
import os, sys
import math
import numpy as np
import logging
import matplotlib.pyplot as plt
import pandas as pd
from StatTest import util
# disable scientific notation
np.set_printoptions(suppress=True)
# print full array
np.set_printoptions(threshold=sys.maxsize)
"""Statistical Test docstrings.
* Robustness
* Use EvalCT for fixed policy rollout on the same training
set but different initialization.
* Stability
* Macro Movement Range
* Additional Note
* Loading weight back for training
"""
######################## META INFO ########################
# Directory that stores all plc file (must come from the same netlist)
PLC_DIR = "StatTest/test/flow2_68_1.3_ct"
PLC_PATH_COLLECTION = []
assert os.path.isdir(PLC_DIR)
# Top X% of largest movement range
TOP_X = 5
# List to store every plc coordinate
PLC_COORD = []
# hold hard macro, soft macro, port count
def init_method(plc_dir):
"""
Scan through every .plc file
"""
for __, __, files in os.walk(plc_dir):
for plc_file in files:
if plc_file.endswith((".plc")):
plc_pth = os.path.join(plc_dir, plc_file)
# plc path
PLC_PATH_COLLECTION.append(plc_pth)
print("#[INFO] Reading plc file {}".format(plc_pth))
# store in numpy array for ease of computation
temp_coord = np.empty((1,2), float)
for cnt, line in enumerate(open(plc_pth, 'r')):
line_item = re.findall(r'[0-9A-Za-z\.\-]+', line)
# skip empty lines
if len(line_item) == 0:
continue
if all(re.match(r'[0-9FNEWS\.\-]+', it) for it in line_item)\
and len(line_item) == 5:
# extract pos
temp_coord = np.append(temp_coord, np.array([[float(line_item[1]),float(line_item[2])]]), axis=0)
elif all(it in line_item for it in ['HARD', 'MACROs'])\
and len(line_item) == 3:
hard_macros_cnt = int(line_item[2])
elif all(it in line_item for it in ['PORTs'])\
and len(line_item) == 2:
ports_cnt = int(line_item[1])
elif all(it in line_item for it in ['SOFT', 'MACROs'])\
and len(line_item) == 3:
soft_macros_cnt = int(line_item[2])
# remove header row
temp_coord = temp_coord[1:, :]
# make sure every plc is aligned
if PLC_COORD:
assert PLC_COORD[-1].shape == temp_coord.shape
assert temp_coord.shape[0] == hard_macros_cnt + soft_macros_cnt + ports_cnt
PLC_COORD.append(temp_coord)
# print(temp_coord)
del temp_coord
return ports_cnt, hard_macros_cnt, soft_macros_cnt
def get_abs_dist():
# store all pair-wise distance
abs_dist_plc = np.empty((PLC_COORD[-1].shape[0],1), float)
# pair-wise distance of all plc files
for i in range(len(PLC_COORD)):
for j in range(len(PLC_COORD)):
if i == j:
continue
# find x/y position diff
diff_coord = PLC_COORD[i] - PLC_COORD[j]
# x_diff^2, y_diff^2
diff_coord = np.power(diff_coord, 2)
# sqrt(x_diff^2 + y_diff^2)
abs_dist_coord = np.sqrt(diff_coord[:, 0] + diff_coord[:, 1])
abs_dist_plc = np.append(abs_dist_plc, abs_dist_coord.reshape((-1, 1)), axis=1)
# remove header col
return abs_dist_plc[:, 1:]
def main():
ports_cnt, hard_macros_cnt, soft_macros_cnt = init_method(PLC_DIR)
abs_dist_plc = get_abs_dist()
print(hard_macros_cnt, ports_cnt, soft_macros_cnt)
hard_abs_dist_plc = abs_dist_plc[ports_cnt:(hard_macros_cnt+ports_cnt), :]
soft_abs_dist_plc = abs_dist_plc[ports_cnt+hard_macros_cnt:hard_macros_cnt+ports_cnt+soft_macros_cnt, :]
# PORT_IDX = list(range(0, ports_cnt, 1))
# HARD_MACRO_IDX = list(range(ports_cnt, hard_macros_cnt+ports_cnt, 1))
# SOFT_MACRO_IDX = list(range(hard_macros_cnt+ports_cnt, hard_macros_cnt+soft_macros_cnt+ports_cnt, 1))
# top n hard macro
HM_TOP_N = int(math.floor(hard_abs_dist_plc.shape[0] * (TOP_X/100.0)))
# top n soft macro
SM_TOP_N = int(math.floor(soft_abs_dist_plc.shape[0] * (TOP_X/100.0)))
print("[INFO] Using TOP {}% Largest Hard Macro Movement --- {} Macros in total.".format(TOP_X, HM_TOP_N))
print("[INFO] Using TOP {}% Largest Soft Macro Movement --- {} Macros in total.".format(TOP_X, SM_TOP_N))
############ HARD MACRO placement range maximum distance + visual ###############
# across all the plc diff, the max distance [row wise]
hm_max_dist = np.amax(hard_abs_dist_plc, axis=1)
# top-n max distance
hm_topn_max_dist_idx = np.argpartition(hm_max_dist, -HM_TOP_N)[-HM_TOP_N:]
hm_topn_max_dist_val = np.take(hm_max_dist, hm_topn_max_dist_idx)
x = range(hm_topn_max_dist_val.shape[0])
y = hm_topn_max_dist_val
n = hm_topn_max_dist_idx
fig, ax = plt.subplots()
ax.set_title("Top {}% Hard Macro Maximum Placement Range".format(TOP_X))
ax.scatter(x, y, c = 'b')
ax.set_xlabel("module index")
ax.set_ylabel("distance")
for i, txt in enumerate(n):
ax.annotate(txt, (x[i], y[i]))
plt.show()
############ SOFT MACRO placement range maximum distance + visual ###############
# across all the plc diff, the max distance [row wise]
sm_max_dist = np.amax(soft_abs_dist_plc, axis=1)
# top-n max distance
sm_topn_max_dist_idx = np.argpartition(sm_max_dist, -SM_TOP_N)[-SM_TOP_N:]
sm_topn_max_dist_val = np.take(sm_max_dist, sm_topn_max_dist_idx)
x = range(sm_topn_max_dist_val.shape[0])
y = sm_topn_max_dist_val
n = sm_topn_max_dist_idx
fig, ax = plt.subplots()
ax.set_title("Top {}% Soft Macro Maximum Placement Range".format(TOP_X))
ax.scatter(x, y, c = 'b')
ax.set_xlabel("module index")
ax.set_ylabel("distance")
for i, txt in enumerate(n):
ax.annotate(txt, (x[i], y[i]))
plt.show()
######################## HARD MACRO placement range box plot visual #############
hard_abs_dist_plc_df = pd.DataFrame(data=hard_abs_dist_plc)
hm_topn_max_dist_df = hard_abs_dist_plc_df.iloc[hm_topn_max_dist_idx, :]
hm_topn_max_dist_df.T.boxplot()
plt.title("Top {}% Hard Macro Placement Range".format(TOP_X))
plt.xlabel("module index")
plt.ylabel("distance")
plt.show()
######################## SOFT MACRO placement range box plot visual #############
soft_abs_dist_plc_df = pd.DataFrame(data=soft_abs_dist_plc)
sm_topn_max_dist_df = soft_abs_dist_plc_df.iloc[sm_topn_max_dist_idx, :]
sm_topn_max_dist_df.T.boxplot()
plt.title("Top {}% Soft Macro Placement Range".format(TOP_X))
plt.xlabel("module index")
plt.ylabel("distance")
plt.show()
######################## Density Heatmap ########################
util.extract_density_map(
os.path.join(PLC_DIR, "netlist.pb.txt"),
PLC_PATH_COLLECTION[0],
ifshow=True
)
######################## Congestion Heatmap #####################
util.extract_congestion_map(
os.path.join(PLC_DIR, "netlist.pb.txt"),
PLC_PATH_COLLECTION[0],
marh=7.143,
marv=8.339,
rpmh=11.285,
rpmv=12.605,
congestion_smooth_range=2,
ifshow=True
)
######################## L1 Norm & SSIM #####################
# pair-wise distance of all plc files
DENS_SMI = []
DENS_L1 = []
VCONG_SMI = []
VCONG_L1 = []
HCONG_SMI = []
HCONG_L1 = []
for i in range(len(PLC_PATH_COLLECTION)):
for j in range(len(PLC_PATH_COLLECTION)):
if i == j:
continue
print("####### Heat Map Comparison between {} and {} #######".format(os.path.basename(PLC_PATH_COLLECTION[i])
,os.path.basename(PLC_PATH_COLLECTION[j])))
dens_i = util.extract_density_map(os.path.join(PLC_DIR, "netlist.pb.txt"),
PLC_PATH_COLLECTION[i],
ifshow=False)
dens_j = util.extract_density_map(os.path.join(PLC_DIR, "netlist.pb.txt"),
PLC_PATH_COLLECTION[j],
ifshow=False)
print("#[INFO] Density map SMI: {}".format(util.SSIM(dens_i, dens_j)))
print("#[INFO] Density map L1 Dist: {}".format(util.l1_norm(dens_i, dens_j)))
DENS_SMI.append(util.SSIM(dens_i, dens_j))
DENS_L1.append(util.l1_norm(dens_i, dens_j))
vcong_i, hcong_i = util.extract_congestion_map(
os.path.join(PLC_DIR, "netlist.pb.txt"),
PLC_PATH_COLLECTION[i],
marh=7.143,
marv=8.339,
rpmh=11.285,
rpmv=12.605,
congestion_smooth_range=2,
ifshow=False
)
vcong_j, hcong_j = util.extract_congestion_map(
os.path.join(PLC_DIR, "netlist.pb.txt"),
PLC_PATH_COLLECTION[j],
marh=7.143,
marv=8.339,
rpmh=11.285,
rpmv=12.605,
congestion_smooth_range=2,
ifshow=False
)
print("#[INFO] V Congestion map SMI: {}".format(util.SSIM(vcong_i, vcong_j)))
print("#[INFO] V Congestion map L1 Dist: {}".format(util.l1_norm(vcong_i, vcong_j)))
print("#[INFO] H Congestion map SMI: {}".format(util.SSIM(hcong_i, hcong_j)))
print("#[INFO] H Congestion map L1 Dist: {}".format(util.l1_norm(hcong_i, hcong_j)))
VCONG_SMI.append(util.SSIM(vcong_i, vcong_j))
VCONG_L1.append(util.l1_norm(vcong_i, vcong_j))
HCONG_SMI.append(util.SSIM(hcong_i,hcong_j))
HCONG_L1.append(util.l1_norm(hcong_i, hcong_j))
print("DENS_SMI Range ({} ~ {})".format(min(DENS_SMI), max(DENS_SMI)))
print("DENS_L1 Range ({} ~ {})".format(min(DENS_L1), max(DENS_L1)))
print("VCONG_SMI Range ({} ~ {})".format(min(VCONG_SMI), max(VCONG_SMI)))
print("VCONG_L1 Range ({} ~ {})".format(min(VCONG_L1), max(VCONG_L1)))
print("HCONG_SMI Range ({} ~ {})".format(min(HCONG_SMI), max(HCONG_SMI)))
print("HCONG_L1 Range ({} ~ {})".format(min(HCONG_L1), max(HCONG_L1)))
if __name__ == "__main__":
main()
\ No newline at end of file
CodeElements/StatTest/util.py 0 → 100644
import os, sys
import pandas as pd
import numpy as np
import seaborn as sns
from Plc_client import plc_client_os
from Plc_client import placement_util_os as placement_util
import matplotlib.pyplot as plt
from skimage.metrics import structural_similarity
def heatmap(arr, title, show=True):
"""
util function for generating heat map from numpy arrays
"""
sns.set()
ax = sns.heatmap(arr, vmin=0, vmax=1, cmap="YlGnBu")
ax.set_title(title)
ax.set_xlabel("Columns")
ax.set_ylabel("Rows")
if show:
plt.show()
else:
plt.clf()
def extract_density_map(netlist_path, plc_path, ifshow=True):
"""
wrapper function for extracting density map
"""
plc_util_os = placement_util.create_placement_cost(
plc_client=plc_client_os,
netlist_file=netlist_path,
init_placement=plc_path
)
grid_cols, grid_rows = plc_util_os.get_grid_num_columns_rows()
dens_map = np.array(plc_util_os.get_grid_cells_density()).reshape(grid_rows, grid_cols)
heatmap(dens_map, "Placement Density Heatmap", ifshow)
return dens_map
def extract_congestion_map(netlist_path, plc_path, rpmh, rpmv, marh, marv, congestion_smooth_range, ifshow=True):
"""
wrapper function for extracting congestion map
"""
plc_util_os = placement_util.create_placement_cost(
plc_client=plc_client_os,
netlist_file=netlist_path,
init_placement=plc_path
)
plc_util_os.set_routes_per_micron(rpmh, rpmv)
plc_util_os.set_macro_routing_allocation(marh, marv)
plc_util_os.set_congestion_smooth_range(congestion_smooth_range)
grid_cols, grid_rows = plc_util_os.get_grid_num_columns_rows()
# vertical routing congestion map
vcong_map = np.array(plc_util_os.get_vertical_routing_congestion()).reshape(grid_rows, grid_cols)
heatmap(vcong_map, "Placement Vertical Congestion Heatmap", ifshow)
# horizontal routing congestion map
hcong_map = np.array(plc_util_os.get_horizontal_routing_congestion()).reshape(grid_rows, grid_cols)
heatmap(hcong_map, "Placement Horizontal Congestion Heatmap", ifshow)
return vcong_map, hcong_map
def SSIM(a, b):
return structural_similarity(a, b)
def l1_norm(a, b):
return np.linalg.norm(normalize(a)-normalize(b), ord=1)
def normalize(a):
return (a - np.min(a)) / (np.max(a) - np.min(a))
\ No newline at end of file
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