From 8b7d27cbd0a34164783c6515f2939f4fbc2e7cde Mon Sep 17 00:00:00 2001
From: sgauthamr2001 <sgauthamr2001@gmail.com>
Date: Sat, 4 Jan 2025 22:21:40 -0800
Subject: [PATCH] Fix formatting tools/
---
tools/run_optimizer.py | 175 +++++++++++++++++++++++++++--------------
1 file changed, 116 insertions(+), 59 deletions(-)
diff --git a/tools/run_optimizer.py b/tools/run_optimizer.py
index b8b5573..da614b0 100644
--- a/tools/run_optimizer.py
+++ b/tools/run_optimizer.py
@@ -7,19 +7,24 @@ import interstellar as cm
import datetime
import json
-def basic_optimizer(arch_info, network_info, schedule_info=None, basic=False, verbose=False):
- resource = cm.Resource.arch(arch_info)
+def basic_optimizer(
+ arch_info, network_info, schedule_info=None, basic=False, verbose=False
+):
+
+ resource = cm.Resource.arch(arch_info)
layer = cm.Layer.layer(network_info)
schedule = cm.Schedule.schedule(schedule_info) if schedule_info != None else None
- smallest_cost, best_mapping_point, perf = cm.optimizer.opt_optimizer(resource, layer, schedule, verbose)
-
+ smallest_cost, best_mapping_point, perf = cm.optimizer.opt_optimizer(
+ resource, layer, schedule, verbose
+ )
+
level_costs = cm.cost_model.get_cost(resource, best_mapping_point, layer, verbose)
- #if verbose or basic:
- if not verbose:
+ # if verbose or basic:
+ if not verbose:
print("Optimal_Energy_(pJ): ", smallest_cost)
-
- #print("Cost for Each Level (pJ): ", level_costs) #TODO
+
+ # print("Cost for Each Level (pJ): ", level_costs) #TODO
print("Best_Schedule_(innermost_loop_to_outermost_loop): ")
cm.utils.print_best_schedule(best_mapping_point)
@@ -27,50 +32,61 @@ def basic_optimizer(arch_info, network_info, schedule_info=None, basic=False, ve
def mem_explore_optimizer(arch_info, network_info, schedule_info, verbose=False):
-
- assert "explore_points" in arch_info, "missing explore_points in arch file"
- assert "capacity_scale" in arch_info, "missing capacity_scale in arch file"
- assert "access_cost_scale" in arch_info, "missing access_cost_scale in arch file"
+
+ assert "explore_points" in arch_info, "missing explore_points in arch file"
+ assert "capacity_scale" in arch_info, "missing capacity_scale in arch file"
+ assert "access_cost_scale" in arch_info, "missing access_cost_scale in arch file"
cwd = os.getcwd()
-# output_filename = os.path.join(cwd, "dataset", network_info['layer_name'] + '_128.csv')
+ # output_filename = os.path.join(cwd, "dataset", network_info['layer_name'] + '_128.csv')
explore_points = arch_info["explore_points"]
energy_list = np.zeros(tuple(explore_points))
summary_array = np.zeros([np.product(explore_points), 12])
- #TODO support more than two levels of explorations
+ # TODO support more than two levels of explorations
capacity0 = arch_info["capacity"][0]
capacity1 = arch_info["capacity"][1]
cost0 = arch_info["access_cost"][0]
cost1 = arch_info["access_cost"][1]
i = 0
for x in range(explore_points[0]):
- arch_info["capacity"][0] = capacity0 * (arch_info["capacity_scale"][0]**x)
- arch_info["access_cost"][0] = cost0 * (arch_info["access_cost_scale"][0]**x)
+ arch_info["capacity"][0] = capacity0 * (arch_info["capacity_scale"][0] ** x)
+ arch_info["access_cost"][0] = cost0 * (arch_info["access_cost_scale"][0] ** x)
for y in range(explore_points[1]):
- #if x == 0 and y < 1:
+ # if x == 0 and y < 1:
# continue
- arch_info["capacity"][1] = capacity1 * (arch_info["capacity_scale"][1]**y)
- arch_info["access_cost"][1] = cost1 * (arch_info["access_cost_scale"][1]**y)
+ arch_info["capacity"][1] = capacity1 * (arch_info["capacity_scale"][1] ** y)
+ arch_info["access_cost"][1] = cost1 * (
+ arch_info["access_cost_scale"][1] ** y
+ )
print(arch_info)
- energy, perf = basic_optimizer(arch_info, network_info, schedule_info, False, verbose)
+ energy, perf = basic_optimizer(
+ arch_info, network_info, schedule_info, False, verbose
+ )
energy_list[x][y] = energy
- cur_point = list(network_info["layer_info"])[:9] + arch_info["capacity"][:-1] + [energy]
+ cur_point = (
+ list(network_info["layer_info"])[:9]
+ + arch_info["capacity"][:-1]
+ + [energy]
+ )
summary_array[i] = cur_point
-# np.savetxt(output_filename, summary_array, delimiter=",")
+ # np.savetxt(output_filename, summary_array, delimiter=",")
i += 1
print(list(energy_list))
print("optimal energy for all memory systems: ", np.min(np.array(energy_list)))
+
def mac_explore_optimizer(arch_info, network_info, schedule_info, verbose=False):
-
+
dataflow_res = []
- #TODO check the case when parallel count larger than layer dimension size
+ # TODO check the case when parallel count larger than layer dimension size
dataflow_generator = dataflow_generator_function(arch_info)
for dataflow in dataflow_generator:
- energy, perf = basic_optimizer(arch_info, network_info, schedule_info, False, verbose)
+ energy, perf = basic_optimizer(
+ arch_info, network_info, schedule_info, False, verbose
+ )
dataflow_res.append[energy]
-
+
if verbose:
print("optimal energy for all dataflows: ", dataflow_res)
@@ -79,22 +95,28 @@ def mac_explore_optimizer(arch_info, network_info, schedule_info, verbose=False)
def dataflow_explore_optimizer(arch_info, network_info, file_name, verbose=False):
- assert arch_info["parallel_count"][0] > 1, \
- "parallel count has to be more than 1 for dataflow exploration"
+ assert (
+ arch_info["parallel_count"][0] > 1
+ ), "parallel count has to be more than 1 for dataflow exploration"
- resource = cm.Resource.arch(arch_info)
+ resource = cm.Resource.arch(arch_info)
layer = cm.Layer.layer(network_info)
- dataflow_tb = cm.mapping_point_generator.dataflow_exploration(resource, layer, file_name, verbose)
-
+ dataflow_tb = cm.mapping_point_generator.dataflow_exploration(
+ resource, layer, file_name, verbose
+ )
+
if verbose:
print("dataflow table done ")
-def mem_explore_optimizer_4_level(arch_info, network_info, schedule_info, verbose=False):
- assert "explore_points" in arch_info, "missing explore_points in arch file"
- assert "capacity_scale" in arch_info, "missing capacity_scale in arch file"
- assert "access_cost_scale" in arch_info, "missing access_cost_scale in arch file"
+
+def mem_explore_optimizer_4_level(
+ arch_info, network_info, schedule_info, verbose=False
+):
+ assert "explore_points" in arch_info, "missing explore_points in arch file"
+ assert "capacity_scale" in arch_info, "missing capacity_scale in arch file"
+ assert "access_cost_scale" in arch_info, "missing access_cost_scale in arch file"
cwd = os.getcwd()
-# output_filename = os.path.join(cwd, "dataset", network_info['layer_name'] + '_128.csv')
+ # output_filename = os.path.join(cwd, "dataset", network_info['layer_name'] + '_128.csv')
explore_points = arch_info["explore_points"]
energy_list = np.zeros(tuple(explore_points))
summary_array = np.zeros([np.product(explore_points), 13])
@@ -106,32 +128,51 @@ def mem_explore_optimizer_4_level(arch_info, network_info, schedule_info, verbos
cost2 = arch_info["access_cost"][2]
i = 0
for x in range(explore_points[0]):
- arch_info["capacity"][0] = capacity0 * (arch_info["capacity_scale"][0]**x)
- arch_info["access_cost"][0] = cost0 * (arch_info["access_cost_scale"][0]**x)
+ arch_info["capacity"][0] = capacity0 * (arch_info["capacity_scale"][0] ** x)
+ arch_info["access_cost"][0] = cost0 * (arch_info["access_cost_scale"][0] ** x)
for y in range(explore_points[1]):
- arch_info["capacity"][1] = capacity1 * (arch_info["capacity_scale"][1]**y)
- arch_info["access_cost"][1] = cost1 * (arch_info["access_cost_scale"][1]**y)
+ arch_info["capacity"][1] = capacity1 * (arch_info["capacity_scale"][1] ** y)
+ arch_info["access_cost"][1] = cost1 * (
+ arch_info["access_cost_scale"][1] ** y
+ )
for z in range(explore_points[2]):
- arch_info["capacity"][2] = capacity2 * (arch_info["capacity_scale"][2]**z)
- arch_info["access_cost"][2] = cost2 * (arch_info["access_cost_scale"][2]**z)
+ arch_info["capacity"][2] = capacity2 * (
+ arch_info["capacity_scale"][2] ** z
+ )
+ arch_info["access_cost"][2] = cost2 * (
+ arch_info["access_cost_scale"][2] ** z
+ )
print(arch_info)
-
+
# TODO: Calculate the area of the design
# total_area = sum(area(MAC)) + sum(2 * area(register file)) + 2 * area(buffer)
- area = (2 * (arch_info["capacity"][0] + arch_info["capacity"][1] + arch_info["capacity"][2]) \
- + 2 * arch_info["capacity"][3]) / 1e9
-
+ area = (
+ 2
+ * (
+ arch_info["capacity"][0]
+ + arch_info["capacity"][1]
+ + arch_info["capacity"][2]
+ )
+ + 2 * arch_info["capacity"][3]
+ ) / 1e9
+
print("Area: ", area)
if area > 2:
energy_list[x][y][z] = np.nan
print("INFO: Architecture does not fulfill area constraint.")
continue
try:
- energy, perf = basic_optimizer(arch_info, network_info, schedule_info, False, verbose)
+ energy, perf = basic_optimizer(
+ arch_info, network_info, schedule_info, False, verbose
+ )
energy_list[x][y][z] = energy
- cur_point = list(network_info["layer_info"])[:9] + arch_info["capacity"][:-1] + [energy]
+ cur_point = (
+ list(network_info["layer_info"])[:9]
+ + arch_info["capacity"][:-1]
+ + [energy]
+ )
summary_array[i] = cur_point
- # np.savetxt(output_filename, summary_array, delimiter=",")
+ # np.savetxt(output_filename, summary_array, delimiter=",")
i += 1
except Exception as e:
energy_list[x][y][z] = np.nan
@@ -144,24 +185,39 @@ def mem_explore_optimizer_4_level(arch_info, network_info, schedule_info, verbos
if __name__ == "__main__":
parser = argparse.ArgumentParser()
- parser.add_argument("type", choices=["basic", "mem_explore", "dataflow_explore", "mem_explore_4"], help="optimizer type")
+ parser.add_argument(
+ "type",
+ choices=["basic", "mem_explore", "dataflow_explore", "mem_explore_4"],
+ help="optimizer type",
+ )
parser.add_argument("arch", help="architecture specification")
parser.add_argument("network", help="network specification")
parser.add_argument("-s", "--schedule", help="restriction of the schedule space")
- parser.add_argument("-n", "--name", default="dataflow_table", help="name for the dumped pickle file")
- parser.add_argument("-v", "--verbose", action='count', help="vebosity")
- parser.add_argument("-j", "--json_name", default="result.json", help="result json file name for basic search")
+ parser.add_argument(
+ "-n", "--name", default="dataflow_table", help="name for the dumped pickle file"
+ )
+ parser.add_argument("-v", "--verbose", action="count", help="vebosity")
+ parser.add_argument(
+ "-j",
+ "--json_name",
+ default="result.json",
+ help="result json file name for basic search",
+ )
args = parser.parse_args()
if args.json_name == "result.json":
- json_name = "result_" + datetime.datetime.now().strftime("%m-%d_%H_%M_%S") + ".json"
+ json_name = (
+ "result_" + datetime.datetime.now().strftime("%m-%d_%H_%M_%S") + ".json"
+ )
else:
json_name = args.json_name
start = time.time()
- arch_info, network_info, schedule_info = cm.extract_input.extract_info(args)
+ arch_info, network_info, schedule_info = cm.extract_input.extract_info(args)
if args.type == "basic":
- energy, perf = basic_optimizer(arch_info, network_info, schedule_info, True, args.verbose)
+ energy, perf = basic_optimizer(
+ arch_info, network_info, schedule_info, True, args.verbose
+ )
json_data = {}
json_data["runtime"] = perf
json_data["energy"] = energy
@@ -174,7 +230,8 @@ if __name__ == "__main__":
elif args.type == "dataflow_explore":
dataflow_explore_optimizer(arch_info, network_info, args.name, args.verbose)
elif args.type == "mem_explore_4":
- mem_explore_optimizer_4_level(arch_info, network_info, schedule_info, args.verbose)
+ mem_explore_optimizer_4_level(
+ arch_info, network_info, schedule_info, args.verbose
+ )
end = time.time()
- print("Elapsed_Time_(s): ", round((end-start), 2))
-
+ print("Elapsed_Time_(s): ", round((end - start), 2))
--
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