Upload plc_client_os

Plc_client/README.md

+# PlacementCost Class:
+*Circuit Training Open Source* is an effort to open-source the entire framework for generating chip floor plans
+with distributed deep reinforcement learning. This framework is originally reproduces the
+methodology published in the Nature 2021 paper:
+*[A graph placement methodology for fast chip design.](https://www.nature.com/articles/s41586-021-03544-w)
+Azalia Mirhoseini, Anna Goldie, Mustafa Yazgan, Joe Wenjie Jiang, Ebrahim
+Songhori, Shen Wang, Young-Joon Lee, Eric Johnson, Omkar Pathak, Azade Nazi,
+Jiwoo Pak, Andy Tong, Kavya Srinivasa, William Hang, Emre Tuncer, Quoc V. Le,
+James Laudon, Richard Ho, Roger Carpenter & Jeff Dean, 2021. Nature, 594(7862),
+pp.207-212.
+[[PDF]](https://www.nature.com/articles/s41586-021-03544-w.epdf?sharing_token=tYaxh2mR5EozfsSL0WHZLdRgN0jAjWel9jnR3ZoTv0PW0K0NmVrRsFPaMa9Y5We9O4Hqf_liatg-lvhiVcYpHL_YQpqkurA31sxqtmA-E1yNUWVMMVSBxWSp7ZFFIWawYQYnEXoBE4esRDSWqubhDFWUPyI5wK_5B_YIO-D_kS8%3D)*
+
+## Quick Start
+Under `MACROPLACEMENT` main directory, run the following command:
+```
+python -m Plc_client.plc_client_os_test
+
+```
+
+## What do we open-source here?
+The current Circuit Training framework requires user to download an executable binary <em>plc_wrapper_main</em> in order to run. The <em>plc_wrapper_main</em> is not open-sourced and not documented anywhere publically. [plc_client.py](https://github.com/google-research/circuit_training/blob/main/circuit_training/environment/plc_client.py) then talks to this binary excutable for crtical information to build a training environment, extract a state observation and even call a force-directed placer. This is an attempt to open-source this "last piece" to the puzzle and hopefully reproduce a fully transparent framework to the public.
+
+## How do I open-source?
+
+All current progress can be reviewed [here](https://github.com/Dinple/circuit_training_os/blob/main/circuit_training/environment/plc_client_os.py). I have generated numerous testcases, varying from a few macros to hundreds of different modules. The purpose of these testcases is to study the behavior of <em>plc_wrapper_main</em> in a scalable way. I have also set up testbench to compare my result to the result from [plc_client.py](https://github.com/Dinple/circuit_training_os/blob/main/circuit_training/environment/plc_client.py).
+
+## What is the end-goal?
+
+The first step and the current step of this open-source effor is to reproduce similar results to Google's <em>plc_wrapper_main</em> in the testbench. The final step will be plugging [plc_client_os.py](https://github.com/Dinple/circuit_training_os/blob/main/circuit_training/environment/plc_client_os.py) into the Circuit Training Framework and reproduce a quality training.
+
+## Reference
+
+```
+@article{mirhoseini2021graph,
+  title={A graph placement methodology for fast chip design},
+  author={Mirhoseini, Azalia and Goldie, Anna and Yazgan, Mustafa and Jiang, Joe
+  Wenjie and Songhori, Ebrahim and Wang, Shen and Lee, Young-Joon and Johnson,
+  Eric and Pathak, Omkar and Nazi, Azade and Pak, Jiwoo and Tong, Andy and
+  Srinivasa, Kavya and Hang, William and Tuncer, Emre and V. Le, Quoc and
+  Laudon, James and Ho, Richard and Carpenter, Roger and Dean, Jeff},
+  journal={Nature},
+  volume={594},
+  number={7862},
+  pages={207--212},
+  year={2021},
+  publisher={Nature Publishing Group}
+}
+```
+
+```
+@misc{CircuitTraining2021,
+  title = {{Circuit Training}: An open-source framework for generating chip
+  floor plans with distributed deep reinforcement learning.},
+  author = {Guadarrama, Sergio and Yue, Summer and Boyd, Toby and Jiang, Joe
+  Wenjie and Songhori, Ebrahim and Tam, Terence and Mirhoseini, Azalia},
+  howpublished = {\url{https://github.com/google_research/circuit_training}},
+  url = "https://github.com/google_research/circuit_training",
+  year = 2021,
+  note = "[Online; accessed 21-December-2021]"
+}
+```
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Plc_client/plc_client_os_test.py

+import unittest
+import os
+from Plc_client import plc_client_os as plc_client
+
+class CircuitDataBaseTest(unittest.TestCase):
+    NETLIST_PATH = "./Plc_client/test/ariane/netlist.pb.txt"
+
+    def test_HPWL(self):
+        self.plc = plc_client.PlacementCost(netlist_file=self.NETLIST_PATH,
+                                                macro_macro_x_spacing = 50,
+                                                macro_macro_y_spacing = 50)
+        assert(int(self.plc.get_wirelength()) == 834660)
+
+if __name__ == '__main__':
+    unittest.main()
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