test update

.gitignore

@@ -5,9 +5,13 @@ Flows/util/__pycache__
 CodeElements/*/*/__pycache__
 CodeElements/Plc_client/test/
 CodeElements/Plc_client/test/*/*
-CodeElements/Plc_client/plc_client_os.py
 CodeElements/Plc_client/__pycache__/*
 CodeElements/Plc_client/proto_reader.py
 CodeElements/Plc_client/plc_client.py
 CodeElements/failed_proxy_plc/*
-CodeElements/CTModel/*
+CodeElements/EvalCT/saved_policy/*
\ No newline at end of file
+CodeElements/EvalCT/test/*
+CodeElements/EvalCT/snapshot*
+CodeElements/EvalCT/circuit_training
+CodeElements/EvalCT/__pycache__/
+CodeElements/EvalCT/eval_run*.plc

CodeElements/EvalCT/eval_ct.py

+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:
+
+        $ python3 -m eval_ct --netlist ./test/ariane/netlist.pb.txt\
+            --plc ./test/ariane/initial.plc\
+            --rundir run_os_64128_g657_ub5_nruns10_c5_r3_v3_rc1
+
+
+"""
+
+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, 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/policy_checkpoint_0000107200")
+    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)
+
+    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
+    GLOBAL_SEED = 111
+    CD_RUNTIME = False
+    RUN_NAME = args.rundir
+    EVAL_TESTCASE = re.search("/test/(.+?)/netlist.pb.txt", NETLIST_FILE).group(1)
+    print(EVAL_TESTCASE)
+
+    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)), 
+                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")
+    return parser.parse_args(argv[1:])
+
+if __name__ == '__main__':
+    app.run(main, flags_parser=parse_flags)
\ No newline at end of file

CodeElements/Plc_client/plc_client_os.py

@@ -1742,7 +1742,6 @@ 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()

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\
@@ -252,6 +263,8 @@ 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)
@@ -264,22 +277,33 @@ 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
@@ -428,15 +452,6 @@ 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())
 
@@ -978,7 +993,7 @@ 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()