Placement Viewer

Utilities/placement_viewer/README.md

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+# Utility function to visualize placement in image format
+
+This utlity script uses the plc_client binary provided with the Google Circuit Training to plot the placement of macros and standard cells in an image format. The input to the placement viewer is the placement files in prototxt and .plc format
+
+### Install Google Circuit Training plc_wrapper_main library
+```
+# Install TF-Agents with nightly versions of Reverb and TensorFlow 2.x
+$  pip install tf-agents-nightly[reverb]
+# Copy the placement cost binary to /usr/local/bin and makes it executable.
+$  sudo curl https://storage.googleapis.com/rl-infra-public/circuit-training/placement_cost/plc_wrapper_main \
+     -o  /usr/local/bin/plc_wrapper_main
+$ sudo chmod 555 /usr/local/bin/plc_wrapper_main
+$ Clone the circuit-training repo.
+$  git clone https://github.com/google-research/circuit-training.git
+```
+### Place the placement_viewer in circuit_training/circuit_training/
+
+## Testing: 
+```
+$ python3 -m circuit_training.placement_viewer.placement_viewer_test 
+```
+
+## Execution: 
+```
+$ python3 -m circuit_training.placement_viewer.placement_viewer  
+  --init_file: Path to the init file. 
+  --netlist_file: Path to the input netlist file. 
+  --img_name: Path to/Prefix of the name of output image file.
+```  
+
+#### Example plot: Ariane RISC-V placement done by Circuit Training after training from scratch i.e.  [Full Scale Ariane example](https://github.com/google-research/circuit_training/blob/main/docs/ARIANE.md "Full Scale Ariane example") 
+
+![picture alt](https://github.com/Maria-UET/MacroPlacement/blob/bc90e6deaceca15ff0ce846f7c441eddc3f44034/Utilities/placement_viewer/test_data/ariane/ariane_final_placement.png "Ariane RISC-V placement done by Circuit Training after training from scratch")

Utilities/placement_viewer/placement_viewer.py

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+import matplotlib. pyplot as plt
+from matplotlib.patches import Rectangle
+from absl import app
+from absl import flags
+from typing import Sequence, Tuple
+
+from circuit_training.environment import plc_client
+
+flags.DEFINE_string("netlist_file", None, "Path to the input netlist file.")
+flags.DEFINE_string("init_file", None, "Path to the init file.")
+flags.DEFINE_string("img_name", None, "Path to/Prefix of the name of output image file.")
+
+flags.mark_flags_as_required([
+    "netlist_file",
+    "init_file",
+    "img_name",
+])
+
+FLAGS = flags.FLAGS
+
+
+def _draw_placement(plc: plc_client.PlacementCost, img_name: str) -> None:
+    plt.figure()
+    fig, ax = plt.subplots()
+    cm=['blue','red']
+    plt.xlim(0, plc.get_canvas_width_height()[0])
+    plt.ylim(0, plc.get_canvas_width_height()[1])
+
+    for i in plc.get_macro_indices():
+        x = plc.get_node_location(i)[0]-(plc.get_node_width_height(i)[0]/2)
+        y = plc.get_node_location(i)[1]-(plc.get_node_width_height(i)[1]/2)
+        w = plc.get_node_width_height(i)[0]
+        h = plc.get_node_width_height(i)[1]
+        orient = plc.get_macro_orientation(i)
+        if orient in [ 'E', 'FE', 'W', 'FW']:
+            h = plc.get_node_width_height(i)[0]
+            w = plc.get_node_width_height(i)[1]
+
+        ax.add_patch(Rectangle((x, y),
+                            w,h ,
+                            facecolor = cm[plc.is_node_soft_macro(i)],
+                            edgecolor ='black',
+                            linewidth = 1,
+                            linestyle="solid")) 
+    plt.savefig(img_name)
+
+
+def _get_final_canvas_dims(plc: plc_client.PlacementCost) -> Tuple[float, float, int, int]:
+        with open(FLAGS.init_file) as f:
+            lines = f.readlines()
+
+        cols= int(lines[4].split(':')[1].split(' ')[1])
+        rows = int(lines[4].split(':')[2].split(' ')[1])
+        width = float(lines[5].split(':')[1].split(' ')[1])
+        height = float(lines[5].split(':')[2].split(' ')[1])
+        return (width, height, cols, rows)
+
+
+def viewer(argv: Sequence[str]) -> None:
+    if len(argv) > 3:
+        raise app.UsageError("Too many command-line arguments.")
+
+    plc = plc_client.PlacementCost(netlist_file=FLAGS.netlist_file)
+
+    # draw the initial placement based on the netlist protobuf file
+    _draw_placement(plc, img_name=FLAGS.img_name+'_initial_placement')
+    print('Initial dimensions of the canvas:', plc.get_canvas_width_height())
+
+    # draw the final placement based on the init file
+    width, height, cols, rows = _get_final_canvas_dims(plc)
+    plc.set_canvas_size(width, height) # set canvas size from the init file
+    plc.set_placement_grid(cols, rows) # set grid from the init file
+    print('Init status: ', plc.restore_placement(FLAGS.init_file)) # restore placement from the init file
+    print('Final dimensions of the canvas:', plc.get_canvas_width_height())
+    _draw_placement(plc, img_name=FLAGS.img_name+'_final_placement')
+
+
+if __name__ == "__main__":
+    app.run(viewer)

Utilities/placement_viewer/placement_viewer_test.py

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+"""Tests for placement_viewer."""
+import os
+import unittest
+import matplotlib.pyplot as plt
+from absl import app, flags
+from typing import Sequence
+
+from circuit_training.environment import plc_client
+from placement_viewer import placement_viewer
+
+
+class TestPlacementViewer(unittest.TestCase):
+    def test_plc_viewer(self):
+
+        test_dir = "circuit_training/placement_viewer/test_data/ariane/"
+        netlist_file = os.path.join(test_dir, "netlist.pb.txt")
+        init_file = os.path.join(test_dir, "rl_opt_placement.plc")
+        img_name = os.path.join(test_dir, "ariane")
+        os.system( "python3 -m circuit_training.placement_viewer.placement_viewer \
+            --netlist_file "+  netlist_file+
+                " --init_file " + init_file+
+                " --img_name " + img_name )
+
+        self.assertTrue((plt.imread(os.path.join(test_dir, "ariane_initial_placement.png")) ==
+         plt.imread(os.path.join(test_dir, "test_initial_placement.png"))).all())
+
+        self.assertTrue((plt.imread(os.path.join(test_dir, "ariane_final_placement.png")) ==
+        plt.imread(os.path.join(test_dir, "test_final_placement.png"))).all())
+
+
+if __name__ == '__main__':
+  TestPlacementViewer().test_plc_viewer()