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evaluate.py
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evaluate.py
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import output_funcs as of
import cost_model_funcs as cmf
import bsg_ite
import classes as cls
import sys
import os
import numpy as np
from copy import deepcopy
from bsgutils import utilization_rate_optimizer, check_mem_ut_after_CM
import msg
import pickle
import copy
import bsg_exh
import time
from multiprocessing import Pool, Process, cpu_count
from datetime import datetime
from pathlib import Path
from itertools import repeat
from classes.layer_rounding import mem_access_count_correct
from im2col_funcs import pw_layer_col2im
from output_funcs import CommonSetting, print_xml, print_yaml
import loma
def tl_worker(tl_list, input_settings, mem_scheme, layer, spatial_loop, spatial_loop_fractional, spatial_loop_comb,
ii_su, active_mac_cost, idle_mac_cost, occupied_area, im2col_need_correct):
[layer_origin, layer_rounded] = layer
pickle_enable = input_settings.tm_search_result_saving
energy_collect = None
utilization_collect = None
latency_collect = None
if pickle_enable:
group_count = layer_origin.G
energy_collect = []
utilization_collect = []
latency_collect = []
min_energy = float('inf')
min_energy_utilization = 0
max_utilization = 0
max_utilization_energy = float('inf')
best_output_energy = None
best_output_utilization = None
try:
greedy_mapping_flag = mem_scheme.greedy_mapping_flag[ii_su]
footer_info = mem_scheme.footer_info[ii_su]
except:
greedy_mapping_flag = False
footer_info = None
for idx, tl in enumerate(tl_list):
temporal_loop = cls.TemporalLoop.extract_loop_info(layer_rounded, tl, spatial_loop)
loop = cls.Loop.extract_loop_info(layer_rounded, temporal_loop, spatial_loop, input_settings.precision,
input_settings.fixed_temporal_mapping)
# TODO
# if im2col_need_correct is True:
# tl_col2im = tl_col2im_transfer(tl, layer_origin_7D)
if input_settings.spatial_unrolling_mode in [4, 5]:
############# Advanced User Configuration #############
mem_energy_saving_when_BW_under_utilized = False
#######################################################
temporal_loop_fractional = cls.TemporalLoop.extract_loop_info(layer_origin, tl, spatial_loop_fractional)
loop_fractional = cls.Loop.extract_loop_info(layer_origin, temporal_loop_fractional, spatial_loop_fractional,
input_settings.precision,
input_settings.fixed_temporal_mapping)
if mem_energy_saving_when_BW_under_utilized is False:
loop_fractional = mem_access_count_correct(loop_fractional, loop)
else:
loop_fractional = loop
msc = copy.deepcopy(mem_scheme)
ii = 0
utilization = cls.Utilization.get_utilization(layer_rounded, temporal_loop, spatial_loop_comb, loop,
input_settings.mac_array_info, msc.mem_size,
msc.mem_share, msc.mem_type,
input_settings.mac_array_stall,
input_settings.precision, msc.mem_bw)
total_cost_layer = 0
# loop.array_wire_distance = {'W': [], 'I': [], 'O': []}
operand_cost = {'W': [], 'I': [], 'O': []}
schedule_info = {
'temporal': tl,
'spatial': mem_scheme.spatial_unrolling[ii_su],
'flooring': mem_scheme.flooring[ii_su]
}
for operand in ['W', 'I', 'O']:
for level in range(0, len(tl[operand])):
operand_cost[operand].append(
cmf.get_operand_level_energy_cost(operand, level, msc.mem_cost,
input_settings.mac_array_info,
schedule_info, loop_fractional,
msc.mem_fifo, msc, input_settings.precision,
utilization, ii))
# TODO
# loop.array_wire_distance[operand].append(
# cmf.get_operand_level_wire_distance(operand, level,
# schedule_info,
# input_settings.mac_array_info, loop,
# msc.mem_fifo))
total_cost_layer += np.sum(operand_cost[operand])
total_cost_layer += active_mac_cost + idle_mac_cost
''' for pickle file (collecting all temporal mappings' energy and array utilization)'''
if pickle_enable:
print("Index: %d Energy: %d Utilization: %.3f" % (
idx, int(group_count * total_cost_layer), utilization.mac_utilize_no_load))
energy_collect.append(int(group_count * total_cost_layer))
utilization_collect.append(utilization.mac_utilize_no_load)
latency_collect.append(utilization.latency_no_load)
if (total_cost_layer < min_energy) or (
total_cost_layer == min_energy and utilization.mac_utilize_no_load > min_energy_utilization):
min_energy_utilization = utilization.mac_utilize_no_load
min_energy = total_cost_layer
output_result = of.CostModelOutput(total_cost_layer, deepcopy(operand_cost),
(active_mac_cost, idle_mac_cost),
deepcopy(temporal_loop.temporal_loop),
deepcopy(mem_scheme.spatial_unrolling[ii_su]),
deepcopy(mem_scheme.flooring[ii_su]),
deepcopy(loop_fractional), deepcopy(spatial_loop),
greedy_mapping_flag, footer_info,
deepcopy(temporal_loop), occupied_area,
utilization, ii)
best_output_energy = output_result
if (utilization.mac_utilize_no_load > max_utilization) or (
utilization.mac_utilize_no_load == max_utilization and total_cost_layer < max_utilization_energy):
max_utilization = utilization.mac_utilize_no_load
max_utilization_energy = total_cost_layer
output_result = of.CostModelOutput(total_cost_layer, deepcopy(operand_cost),
(active_mac_cost, idle_mac_cost),
deepcopy(temporal_loop.temporal_loop),
deepcopy(mem_scheme.spatial_unrolling[ii_su]),
deepcopy(mem_scheme.flooring[ii_su]),
deepcopy(loop_fractional), deepcopy(spatial_loop),
greedy_mapping_flag, footer_info,
deepcopy(temporal_loop), occupied_area,
utilization, ii)
best_output_utilization = output_result
return (min_energy, min_energy_utilization, best_output_energy,
max_utilization_energy, max_utilization, best_output_utilization,
energy_collect, utilization_collect, latency_collect)
def mem_scheme_su_evaluate(input_settings, layer_, im2col_layer, layer_index, layer_info, mem_scheme, mem_scheme_index,
ii_su, spatial_unrolling, spatial_unrolling_count, im2col_need_correct, multi_manager):
mem_scheme_count = multi_manager.mem_scheme_count
list_min_energy = multi_manager.list_min_energy
list_min_en_output = multi_manager.list_min_en_output
list_max_utilization = multi_manager.list_max_utilization
list_max_ut_output = multi_manager.list_max_ut_output
list_tm_count_en = multi_manager.list_tm_count_en
list_tm_count_ut = multi_manager.list_tm_count_ut
list_sim_time = multi_manager.list_sim_time
'''
Distinguish different layer parameters
layer_7D_origin -- multi_manager.layer_spec.layer_info[layer_index], layer
layer_3D_origin -- multi_manager.layer_info_im2col[layer_index], im2col_layer
layer_3D/7D_rounded -- layer_info[layer_index][ii_su] (w/wo im2col)
'''
t1 = time.time()
# for pickle file
pickle_enable = input_settings.tm_search_result_saving
# make sure the # of level in spatial unrolling and in memory scheme match.
if input_settings.fixed_spatial_unrolling is True and input_settings.mem_hierarchy_single_simulation is False:
mem_scheme = cmf.su_correction(mem_scheme)
if input_settings.spatial_unrolling_mode in [4, 5]:
layer_post = layer_info[layer_index][ii_su]
spatial_loop = cls.SpatialLoop.extract_loop_info(mem_scheme.spatial_unrolling[ii_su], layer_post)
spatial_loop_fractional = cls.SpatialLoop.extract_loop_info(mem_scheme.fraction_spatial_unrolling[ii_su],
layer_post)
spatial_loop_comb = [spatial_loop, spatial_loop_fractional]
else:
layer_post = layer_info[layer_index]
spatial_loop = cls.SpatialLoop.extract_loop_info(mem_scheme.spatial_unrolling[ii_su], layer_post)
spatial_loop_fractional = None
spatial_loop_comb = [spatial_loop, spatial_loop]
# TODO MAC area (multiplier and adder area) is not included.
# occupied_area format: [total_area, active_area]
occupied_area = msg.get_mem_scheme_area(mem_scheme, ii_su)
active_mac_cost = cmf.get_active_mac_cost(layer_, input_settings.mac_array_info['single_mac_energy'])
layer_rounded = cls.Layer.extract_layer_info(layer_post)
idle_mac_cost = cmf.get_idle_mac_cost(layer_, layer_rounded, input_settings.mac_array_info['array_size'],
input_settings.mac_array_info['idle_mac_energy'],
mem_scheme.spatial_unrolling)
# print('user-defined mem ut', mem_scheme.mem_utilization_rate)
mem_scheme.mem_utilization_rate, good_scheme = utilization_rate_optimizer(mem_scheme.mem_size,
mem_scheme.spatial_unrolling[ii_su],
layer_post,
input_settings.precision,
mem_scheme.mem_utilization_rate,
spatial_loop.unit_unique)
# redo_flag check if generated memory utilization Th is respected.
redo_flag = True
iterate_time = 0
mem_ut_iter_max = 1 # 5
previous_TM_found = 0
previous_best_en = [None, None]
previous_best_ut = [None, None]
loma_search_engine = input_settings.tmg_search_method == 2
while (redo_flag and iterate_time < mem_ut_iter_max and not loma_search_engine) or (redo_flag and input_settings.fixed_temporal_mapping):
# print('generated mem ut', mem_scheme.mem_utilization_rate)
if not input_settings.utilization_optimizer_pruning:
good_scheme = True
tl_list = []
if not good_scheme:
print('Utilization pruning active. Mem scheme sub-optimal')
discard_mem_scheme = True
if good_scheme:
# print('SU', ii_su + 1, '/', len(mem_scheme.spatial_unrolling), mem_scheme.spatial_unrolling[ii_su],
# mem_scheme.su_utilization[ii_su])
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
print(current_time, str(input_settings.layer_filename.split('/')[-1]), 'L', layer_index, ', M',
mem_scheme_index + 1, '/', mem_scheme_count, ', SU', ii_su + 1,
'/', len(mem_scheme.spatial_unrolling), ' TMG started')
if not input_settings.fixed_temporal_mapping:
if input_settings.tmg_search_method == 0:
tl_list, tl_combinations = bsg_ite.bsg(mem_scheme.mem_size, mem_scheme.mem_share,
input_settings.precision,
mem_scheme.mem_utilization_rate,
layer_post,
mem_scheme.spatial_unrolling[ii_su], layer_, mem_scheme,
input_settings)
if input_settings.tmg_search_method == 1:
tl_list = bsg_exh.bsg(mem_scheme.mem_size, mem_scheme.mem_share, input_settings.precision,
mem_scheme.mem_utilization_rate, layer_post,
layer_index,
mem_scheme.spatial_unrolling[ii_su], input_settings.drc_enabled,
input_settings.stationary_optimization_enable)
tl_combinations = len(tl_list)
####################### Advanced User Configuration #######################
# fixed_order = [5, 6, 7]
# tl_list = bsg_exh.bsg_fixed_order(fixed_order,
# mem_scheme.mem_size,
# mem_scheme.mem_share,
# input_settings.precision,
# mem_scheme.mem_utilization_rate,
# layer_post, layer_index,
# mem_scheme.spatial_unrolling[ii_su])
# tl_combinations = len(tl_list)
###########################################################################
else:
tl_list.append(input_settings.temporal_mapping_single)
tl_combinations = 1
t2 = time.time()
t_tmg = int(t2 - t1)
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
if tl_list:
if (not input_settings.fixed_temporal_mapping) and (input_settings.tmg_search_method == 0):
print(current_time, str(input_settings.layer_filename.split('/')[-1]), 'L', layer_index, ', M',
mem_scheme_index + 1, '/', mem_scheme_count, ', SU', ii_su + 1,
'/', len(mem_scheme.spatial_unrolling), ' TMG finished', '| Elapsed time:', t_tmg, 'sec',
'| Valid TMs found: ( partial:', tl_combinations, ', final:', len(tl_list), ')')
else:
print(current_time, str(input_settings.layer_filename.split('/')[-1]), 'L', layer_index, ', M',
mem_scheme_index + 1, '/', mem_scheme_count, ', SU', ii_su + 1,
'/', len(mem_scheme.spatial_unrolling), ' TMG Finished', '| Elapsed time:', t_tmg, 'sec',
'| Valid TMs found:', len(tl_list))
else:
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
print(current_time, str(input_settings.layer_filename.split('/')[-1]), 'L', layer_index, ', M',
mem_scheme_index + 1, '/', mem_scheme_count, ', SU', ii_su + 1,
'/', len(mem_scheme.spatial_unrolling), ' No TM found')
return
current_TM_found = len(tl_list)
# if previous_TM_found is equal to current_TM_found, no need to activate CM.
# if previous_TM_found > current_TM_found:
# raise ValueError('memory utilization updating process went wrong.')
if (previous_TM_found < current_TM_found or iterate_time == mem_ut_iter_max-1) or (input_settings.fixed_temporal_mapping):
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
print(current_time, str(input_settings.layer_filename.split('/')[-1]), 'L', layer_index, ', M',
mem_scheme_index + 1, '/', mem_scheme_count, ', SU', ii_su + 1,
'/', len(mem_scheme.spatial_unrolling), ' CM started')
# 'layer' is the original 7D layer, now it's got to be overwritten.
# 'im2col_layer' is the original 3D/7D layer, depending on im2col_enable
# 'layer_rounded' is the rounded 3D/7D layer, depending on im2col_enable
layer = [im2col_layer, layer_rounded]
if input_settings.temporal_mapping_multiprocessing > 1:
# Convert tl_list to chunked list to pass to parallel cores
n_processes = min(cpu_count(), input_settings.temporal_mapping_multiprocessing)
chunk_size = int(tl_combinations / n_processes) + (tl_combinations % n_processes > 0) # avoids import math
tl_count = len(tl_list)
tl_list = [tl_list[i:i + chunk_size] for i in range(0, tl_combinations, chunk_size)]
# Create list of repeated arguments passed to parallel tl_worker functions
fixed_args = [input_settings, mem_scheme, layer, spatial_loop, spatial_loop_fractional, spatial_loop_comb,
ii_su, active_mac_cost, idle_mac_cost[ii_su], occupied_area, im2col_need_correct]
# Call the worker function for each chunk
pool = Pool(processes=n_processes)
results = pool.starmap(tl_worker, [[tl_chunk] + fixed_args for tl_chunk in tl_list])
else:
n_processes = 1
chunk_size = tl_combinations
tl_count = len(tl_list)
results = [tl_worker(tl_list, input_settings, mem_scheme, layer, spatial_loop,
spatial_loop_fractional, spatial_loop_comb, ii_su, active_mac_cost,
idle_mac_cost[ii_su], occupied_area, im2col_need_correct)]
best_output_energy = None
best_output_utilization = None
best_energy = float('inf')
best_energy_utilization = 0
best_utilization = 0
best_utilization_energy = float('inf')
# Create pickle file to append to if pickle_enable
if pickle_enable:
parent_folder = "%s/all_tm_results/" % (input_settings.results_path)
rf = "%s/%s_L_%d_SU_%d" % (parent_folder, input_settings.results_filename, layer_index, ii_su + 1)
rf_en = rf + '_energy.pickle'
rf_ut = rf + '_utilization.pickle'
rf_lat = rf + '_latency.pickle'
rf_en_ut = rf + '_combined.pickle'
# Create parent folder if it does not exist
Path(parent_folder).mkdir(parents=True, exist_ok=True)
# Loop through the best energy/ut found by the parallel processes to find the overall best one
for (min_en, min_en_ut, min_en_output, max_ut_en, max_ut, max_ut_output, en_collect, ut_collect, lat_collect) in results:
if (min_en < best_energy or (min_en == best_energy and min_en_ut > best_energy_utilization)):
best_energy = min_en
best_energy_utilization = min_en_ut
best_output_energy = min_en_output
if (max_ut > best_utilization or (max_ut == best_utilization and max_ut_en < best_utilization_energy)):
best_utilization_energy = max_ut_en
best_utilization = max_ut
best_output_utilization = max_ut_output
# Save the collected (energy,ut) from every temporal mapping if required
if pickle_enable:
# Save energy
with open(rf_en, 'ab') as f:
pickle.dump(en_collect, f)
f.close()
# Save utilization
# with open(rf_ut, 'ab') as f:
# pickle.dump(ut_collect, f)
# f.close()
# Save latency
with open(rf_lat, 'ab') as f:
pickle.dump(lat_collect, f)
f.close()
# Save combined (en,ut) tuples
# combined = zip(en_collect, ut_collect)
# with open(rf_en_ut, 'ab') as f:
# for elem in combined:
# pickle.dump(elem, f)
# f.close()
# TODO this is a fast fixing, not the final solution
# in order to remove the memory utilization TH from user-defined parameter
# while making sure find the optimum design point.
group_count = layer[0].G
current_best_en = [int(round(group_count*best_energy)), best_energy_utilization]
current_best_ut = [int(round(group_count*best_utilization_energy)), best_utilization]
# Check whether the internal generated memory utilization threshold is respected.
# If not, redo the whole bsg with a reduced memory utilization TH.
redo_flag, mem_scheme.mem_utilization_rate = \
check_mem_ut_after_CM(best_output_energy.utilization.mem_utilize_shared,
best_output_utilization.utilization.mem_utilize_shared,
mem_scheme.mem_utilization_rate, current_best_en, current_best_ut,
previous_best_en, previous_best_ut)
# print('previous_best_en', previous_best_en)
# print('current_best_en', current_best_en)
# print('previous_best_ut', previous_best_ut)
# print('current_best_ut', current_best_ut)
iterate_time += 1
if redo_flag and iterate_time < mem_ut_iter_max:
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
print(current_time, str(input_settings.layer_filename.split('/')[-1]), 'L', layer_index, ', M',
mem_scheme_index + 1, '/', mem_scheme_count, ', SU', ii_su + 1, '/',
spatial_unrolling_count, ' Internal memory utilization threshold adjusting ... (%d)' % iterate_time)
previous_TM_found = current_TM_found
previous_best_en = current_best_en
previous_best_ut = current_best_ut
if loma_search_engine and not input_settings.fixed_temporal_mapping:
lpf_limit = input_settings.max_nb_lpf_layer
tl_list, nonmerged_count_dict, loop_type_order, tl_combinations = loma.og(layer_post, spatial_unrolling, lpf_limit)
t2 = time.time()
t_tmg = int(t2 - t1)
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
if tl_list:
str_format = "{0} {1} L {2} , M {3} / {4} , SU {5} / {6} TMG Finished | Elapsed time: {7} sec | Valid TMs found {8:,}"
print(str_format.format(current_time, str(input_settings.layer_filename.split('/')[-1]), layer_index,
mem_scheme_index + 1, mem_scheme_count, ii_su + 1, len(mem_scheme.spatial_unrolling), t_tmg, tl_combinations))
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
print(current_time, str(input_settings.layer_filename.split('/')[-1]), 'L', layer_index, ', M',
mem_scheme_index + 1, '/', mem_scheme_count, ', SU', ii_su + 1,
'/', len(mem_scheme.spatial_unrolling), ' CM started ', end="")
# 'layer' is the original 7D layer, now it's got to be overwritten.
# 'im2col_layer' is the original 3D/7D layer, depending on im2col_enable
# 'layer_rounded' is the rounded 3D/7D layer, depending on im2col_enable
layer = [im2col_layer, layer_rounded]
################################# SPLITTING INTO CHUNKS ##################################
tl_count = tl_combinations
first_loop_type = loop_type_order[0]
tl_count_first = nonmerged_count_dict[first_loop_type]
n_processes = min(tl_count_first, cpu_count(), input_settings.temporal_mapping_multiprocessing)
print("| Launching {0} threads, each consisting of {1:,} orderings".format(n_processes, int(tl_combinations/n_processes)))
tl_list_split = [{} for _ in range(n_processes)]
for loop_type in tl_list.keys():
if loop_type == first_loop_type:
# Split the orderings for first loop type into n_processes chunks of (roughly) equal size
k, m = divmod(tl_count_first, n_processes)
for i in range(n_processes):
tl_list_split[i][loop_type] = tl_list[loop_type][i*k + min(i,m):(i+1)*k + min(i+1,m)]
else:
for i in range(n_processes):
tl_list_split[i][loop_type] = tl_list[loop_type]
precision = input_settings.precision
layer_comb = [layer_, layer_rounded]
active_mac_cost = cmf.get_active_mac_cost(layer_, input_settings.mac_array_info['single_mac_energy'])
idle_mac_cost = cmf.get_idle_mac_cost(layer_, layer_rounded, input_settings.mac_array_info['array_size'],
input_settings.mac_array_info['idle_mac_energy'],
mem_scheme.spatial_unrolling)[ii_su]
mac_costs = [active_mac_cost, idle_mac_cost]
fixed_args = [nonmerged_count_dict, loop_type_order, tl_combinations, input_settings, spatial_loop_comb, mem_scheme, precision, layer_comb, mac_costs, ii_su]
################################# CALL PARALLEL PROCESSES ##################################
pool = Pool(processes=n_processes)
results = pool.starmap(loma.tl_worker_new, [[tl_chunk] + fixed_args for tl_chunk in tl_list_split])
pool.close()
################################# POST PROCESSING ##################################
best_output_energy = None
best_output_utilization = None
best_energy = float('inf')
best_energy_utilization = 0
best_utilization = 0
best_utilization_energy = float('inf')
# Create pickle file to append to if pickle_enable
if pickle_enable:
parent_folder = "%s/all_tm_results/" % (input_settings.results_path)
rf = "%s/%s_L_%d_SU_%d" % (parent_folder, input_settings.results_filename, layer_index, ii_su + 1)
rf_en = rf + '_energy.pickle'
rf_ut = rf + '_utilization.pickle'
rf_lat = rf + '_latency.pickle'
rf_en_ut = rf + '_combined.pickle'
# Create parent folder if it does not exist
Path(parent_folder).mkdir(parents=True, exist_ok=True)
# Loop through the best energy/ut found by the parallel processes to find the overall best one
for result in results:
if isinstance(result, Exception):
continue
min_en, min_en_ut, min_en_output, max_ut_en, max_ut, max_ut_output, en_collect, ut_collect, lat_collect = result
if (min_en < best_energy or (min_en == best_energy and min_en_ut > best_energy_utilization)):
best_energy = min_en
best_energy_utilization = min_en_ut
best_output_energy = min_en_output
if (max_ut > best_utilization or (max_ut == best_utilization and max_ut_en < best_utilization_energy)):
best_utilization_energy = max_ut_en
best_utilization = max_ut
best_output_utilization = max_ut_output
# Save the collected (energy,ut) from every temporal mapping if required
if pickle_enable:
# Save energy
with open(rf_en, 'ab') as f:
pickle.dump(en_collect, f)
f.close()
# Save utilization
# with open(rf_ut, 'ab') as f:
# pickle.dump(ut_collect, f)
# f.close()
# Save latency
with open(rf_lat, 'ab') as f:
pickle.dump(lat_collect, f)
f.close()
# Save combined (en,ut) tuples
# combined = zip(en_collect, ut_collect)
# with open(rf_en_ut, 'ab') as f:
# for elem in combined:
# pickle.dump(elem, f)
# f.close()
# Convert output, which is just best allocated order at this point, to a CostModelOutput object
if best_output_utilization is None or best_output_energy is None:
return
best_output_energy = loma.get_cost_model_output(best_output_energy, input_settings, mem_scheme, layer_comb, spatial_loop_comb, ii_su)
best_output_utilization = loma.get_cost_model_output(best_output_utilization, input_settings, mem_scheme, layer_comb, spatial_loop_comb, ii_su)
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
print(current_time, str(input_settings.layer_filename.split('/')[-1]), 'L', layer_index, ', M',
mem_scheme_index + 1, '/', mem_scheme_count, ', SU', ii_su + 1, '/',
spatial_unrolling_count, ' CM finished', end='')
discard_mem_scheme = False
if tl_list and not discard_mem_scheme:
t3 = time.time()
t_cm = int(t3 - t2)
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
group_count = layer[0].G
if input_settings.fixed_temporal_mapping:
print(
' | Elapsed time: {0:d} sec | (energy, mac_utilization, area): ({1:.3E}, {2:.3f}, {3:.3E})'.format(
int(t_cm), int(round(group_count*best_energy)), best_energy_utilization, int(round(best_output_energy.area[0]))))
else:
print(
' | Elapsed time: {0:d} sec | [min en: ({1:.3E}, {2:.3f}, {3:.3E}) max ut: ({4:.3E}, {5:.3f}, {6:.3E})] in all TMs'.format(
int(t_cm), int(round(group_count*best_energy)), best_energy_utilization, int(round(best_output_energy.area[0])),
int(round(group_count*best_utilization_energy)), best_utilization, int(round(best_output_utilization.area[0]))))
if input_settings.fixed_temporal_mapping or input_settings.tmg_search_method != 0:
tm_count = tl_count
else:
tm_count = {'partial': tl_combinations, 'final': tl_count}
else:
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
print()
print(current_time, ' L', layer_index, ', M ', mem_scheme_index + 1, ': no tl list for layer', layer_index)
mem_scheme_str = 'M_%d' % (mem_scheme_index + 1)
layer_str = 'L_%d' % (layer_index)
mem_scheme_su_str = 'M_%d_SU_%d_%d' % (mem_scheme_index + 1, spatial_unrolling_count, ii_su + 1)
list_min_energy[mem_scheme_str][layer_str]['best_tm_each_su'].update({mem_scheme_su_str: (best_energy, best_energy_utilization)})
list_min_en_output[mem_scheme_str][layer_str]['best_tm_each_su'].update({mem_scheme_su_str: best_output_energy})
list_max_utilization[mem_scheme_str][layer_str]['best_tm_each_su'].update({mem_scheme_su_str: (best_utilization_energy, best_utilization)})
list_max_ut_output[mem_scheme_str][layer_str]['best_tm_each_su'].update({mem_scheme_su_str: best_output_utilization})
list_tm_count_en[mem_scheme_str][layer_str]['best_tm_each_su'].update({mem_scheme_su_str: tm_count})
list_tm_count_ut[mem_scheme_str][layer_str]['best_tm_each_su'].update({mem_scheme_su_str: tm_count})
list_sim_time[mem_scheme_str][layer_str]['best_tm_each_su'].update({mem_scheme_su_str: (t_tmg + t_cm)})
# Save best results for this MEM + LAYER + SU
if input_settings.save_results_on_the_fly:
sim_time = t_tmg + t_cm
mem_scheme_count_str = '%d/%d' % (mem_scheme_index + 1, multi_manager.mem_scheme_count)
spatial_unrolling_count_str = '%d/%d' % (ii_su + 1, spatial_unrolling_count)
common_settings = CommonSetting(input_settings, layer_index, mem_scheme_count_str, spatial_unrolling_count_str, mem_scheme)
mem_scheme_su_save_str = '_M%d_SU%d' % (mem_scheme_index + 1, ii_su + 1)
sub_path = '/all_su_best_tm_otf/'
rf_base = input_settings.results_path + '%s' + input_settings.results_filename
rf_ending_en = '_min_en'
rf_ending_ut = '_max_ut'
rf_en = (rf_base % sub_path) + '_L' + str(layer_index) + mem_scheme_su_save_str + rf_ending_en
rf_ut = (rf_base % sub_path) + '_L' + str(layer_index) + mem_scheme_su_save_str + rf_ending_ut
j = input_settings.layer_number.index(layer_index)
if input_settings.im2col_enable_pw and (input_settings.spatial_unrolling_mode not in [4, 5]):
if multi_manager.pw_im2col_flag[j]:
best_output_energy.spatial_scheme, \
best_output_energy.flooring, \
best_output_energy.temporal_scheme = \
pw_layer_col2im(best_output_energy.spatial_scheme,
best_output_energy.flooring,
best_output_energy.temporal_scheme, layer[0].size_list)
best_output_utilization.spatial_scheme, \
best_output_utilization.flooring, \
best_output_utilization.temporal_scheme = \
pw_layer_col2im(best_output_utilization.spatial_scheme,
best_output_utilization.flooring,
best_output_utilization.temporal_scheme, layer[0].size_list)
tm_count_en = tl_count
tm_count_ut = tl_count
print_type = input_settings.result_print_type
print_mode = input_settings.result_print_mode
if print_type == "xml":
print_xml(rf_en, layer[0], mem_scheme, best_output_energy, common_settings, tm_count_en, sim_time, print_mode)
print_xml(rf_ut, layer[0], mem_scheme, best_output_utilization, common_settings, tm_count_ut, sim_time, print_mode)
else:
print_yaml(rf_en, layer[0], mem_scheme, best_output_energy, common_settings, tm_count_en, sim_time, print_mode)
print_yaml(rf_ut, layer[0], mem_scheme, best_output_utilization, common_settings, tm_count_ut, sim_time, print_mode)
def mem_scheme_evaluate(input_settings, layer_index, layer, im2col_layer, mem_scheme, mem_scheme_index, multi_manager):
mem_scheme_count = multi_manager.mem_scheme_count
# Check if this is a duplicate layer
if layer.is_duplicate:
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
# print(current_time, str(input_settings.layer_filename.split('/')[-1]), 'L', layer_index,
# 'is a duplicate of L', layer.parent, '. Skipping exploration.')
return
# Check if xml for this network + layer + mem scheme + su combination already exists. If so, return (skip)
# ONLY works with 1 spatial unrolling !!
# path = input_settings.results_path + '/all_su_best_tm/' + input_settings.results_filename + \
# '_L%d_M%d_SU1_max_ut.xml' % (layer_index, mem_scheme_index + 1)
# if os.path.isfile(path):
# print("Returning because xml already exists for L%d_M%d" % (layer_index, mem_scheme_index + 1))
# return
if input_settings.im2col_enable_all:
layer_info = deepcopy(multi_manager.layer_info_im2col)
current_layer = multi_manager.layer_spec.layer_info[layer_index]
if (current_layer['FX'] == 1 and current_layer['FY'] == 1) or \
(current_layer['OX'] == 1 and current_layer['OY'] == 1) or \
len(mem_scheme.mem_unroll['I']) <= input_settings.im2col_top_mem_level + 1:
im2col_need_correct = False
else:
im2col_need_correct = True
else:
layer_info = deepcopy(multi_manager.layer_spec.layer_info)
im2col_need_correct = False
# print('Layer', layer_index, layer_info[layer_index])
t1 = time.time()
min_energy_utilization = 0
discard_mem_scheme = False
if discard_mem_scheme:
return
if input_settings.fixed_spatial_unrolling:
mem_scheme.spatial_unrolling = [input_settings.spatial_unrolling_single]
mem_scheme.flooring = [input_settings.flooring_single]
spatial_unrolling = [input_settings.spatial_unrolling_single]
if not input_settings.fixed_spatial_unrolling:
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
print(current_time, str(input_settings.layer_filename.split('/')[-1]), 'L', layer_index, ', M',
mem_scheme_index + 1, '/', mem_scheme_count, ' SUG started')
# greedy mapping without hint
if input_settings.spatial_unrolling_mode == 5:
layer_rounded = cls.layer_rounding.LayerRound2(layer_info[layer_index],
input_settings.mac_array_info['array_size'],
input_settings.spatial_utilization_threshold)
layer_info[layer_index] = layer_rounded.round_layer_info
aux_layer_to_su_hint_table = layer_rounded.aux_layer_to_su_hint_table
fraction_su = layer_rounded.fraction_su
ideal_su = layer_rounded.ideal_su
unrolling_scheme_list = layer_rounded.unrolling_scheme_list
mem_scheme.greedy_mapping_flag = layer_rounded.greedy_mapping_flag
mem_scheme.footer_info = layer_rounded.footer_info
mem_scheme.su_utilization = layer_rounded.spatial_utilization_list
spatial_unrolling = []
flooring = []
fraction_spatial_unrolling = []
mem_unroll_active = []
mem_unroll_total = []
for idd, aux_layer_idx in enumerate(range(len(layer_info[layer_index]))):
su_hint_idx = aux_layer_to_su_hint_table[aux_layer_idx]
spatial_unrolling_, flooring_, mem_scheme, not_good = msg.spatial_unrolling_generator_with_hint(
mem_scheme, input_settings.mac_array_info['array_size'],
layer_info[layer_index][aux_layer_idx], [unrolling_scheme_list[su_hint_idx]],
input_settings.memory_unroll_fully_flexible)
if not spatial_unrolling_:
continue
spatial_unrolling_, fraction_spatial_unrolling_ = \
msg.su_reformat(spatial_unrolling_, ideal_su[aux_layer_idx], fraction_su[aux_layer_idx])
spatial_unrolling += spatial_unrolling_
flooring += flooring_
fraction_spatial_unrolling += fraction_spatial_unrolling_
mem_unroll_active_, mem_unroll_total_ = cmf.get_mem_complete_unrolling_count(
spatial_unrolling_[0], flooring_[0], input_settings.mac_array_info['array_size'])
mem_unroll_active.append(mem_unroll_active_)
mem_unroll_total.append(mem_unroll_total_)
mem_scheme.fraction_spatial_unrolling = fraction_spatial_unrolling
# greedy mapping with hint
elif input_settings.spatial_unrolling_mode == 4:
layer_rounded = cls.layer_rounding.LayerRound(layer_info[layer_index],
input_settings.mac_array_info['array_size'],
input_settings.unrolling_scheme_list,
input_settings.unrolling_size_list,
input_settings.spatial_utilization_threshold)
layer_info[layer_index] = layer_rounded.round_layer_info
aux_layer_to_su_hint_table = layer_rounded.aux_layer_to_su_hint_table
fraction_su = layer_rounded.fraction_su
ideal_su = layer_rounded.ideal_su
mem_scheme.greedy_mapping_flag = layer_rounded.greedy_mapping_flag
mem_scheme.footer_info = layer_rounded.footer_info
mem_scheme.su_utilization = layer_rounded.spatial_utilization_list
spatial_unrolling = []
flooring = []
fraction_spatial_unrolling = []
mem_unroll_active = []
mem_unroll_total = []
for idd, aux_layer_idx in enumerate(range(len(layer_info[layer_index]))):
su_hint_idx = aux_layer_to_su_hint_table[aux_layer_idx]
spatial_unrolling_, flooring_, mem_scheme, not_good = msg.spatial_unrolling_generator_with_hint(
mem_scheme, input_settings.mac_array_info['array_size'],
layer_info[layer_index][aux_layer_idx],
[input_settings.unrolling_scheme_list[su_hint_idx]],
input_settings.memory_unroll_fully_flexible)
spatial_unrolling_, fraction_spatial_unrolling_ = \
msg.su_reformat(spatial_unrolling_, ideal_su[aux_layer_idx], fraction_su[aux_layer_idx])
spatial_unrolling += spatial_unrolling_
flooring += flooring_
fraction_spatial_unrolling += fraction_spatial_unrolling_
mem_unroll_active_, mem_unroll_total_ = cmf.get_mem_complete_unrolling_count(
spatial_unrolling_[0], flooring_[0], input_settings.mac_array_info['array_size'])
mem_unroll_active.append(mem_unroll_active_)
mem_unroll_total.append(mem_unroll_total_)
mem_scheme.fraction_spatial_unrolling = fraction_spatial_unrolling
# hint_driven (prime factor factorization based)
elif input_settings.spatial_unrolling_mode == 3:
spatial_unrolling, flooring, mem_scheme, not_good = msg.spatial_unrolling_generator_with_hint(
mem_scheme, input_settings.mac_array_info['array_size'], layer_info[layer_index],
input_settings.unrolling_scheme_list, input_settings.memory_unroll_fully_flexible)
mem_scheme.fraction_spatial_unrolling = spatial_unrolling
mem_scheme.greedy_mapping_flag = [False] * len(spatial_unrolling)
mem_scheme.footer_info = [0] * len(spatial_unrolling)
mem_unroll_active = []
mem_unroll_total = []
for su_id, _ in enumerate(spatial_unrolling):
mem_unroll_active_, mem_unroll_total_ = cmf.get_mem_complete_unrolling_count(
spatial_unrolling[su_id], flooring[su_id], input_settings.mac_array_info['array_size'])
mem_unroll_active.append(mem_unroll_active_)
mem_unroll_total.append(mem_unroll_total_)
# spatial unrolling full search based on user-defined spatial_utilization_threshold
else:
even = (mem_scheme.mem_unroll['W'][0] == mem_scheme.mem_unroll['I'][0] == mem_scheme.mem_unroll['O'][0])
if even and not input_settings.memory_unroll_fully_flexible:
spatial_unrolling, flooring, mem_scheme, not_good = msg.spatial_unrolling_generator_even(
mem_scheme, input_settings.mac_array_info['array_size'], layer_info[layer_index],
input_settings.precision, input_settings.spatial_utilization_threshold,
input_settings.spatial_unrolling_mode)
else:
spatial_unrolling, flooring, mem_scheme, not_good = msg.spatial_unrolling_generator_uneven(
mem_scheme, input_settings.mac_array_info['array_size'], layer_info[layer_index],
input_settings.precision, input_settings.spatial_utilization_threshold,
input_settings.spatial_unrolling_mode, input_settings.memory_unroll_fully_flexible)
mem_scheme.fraction_spatial_unrolling = spatial_unrolling
mem_scheme.greedy_mapping_flag = [False] * len(spatial_unrolling)
mem_scheme.footer_info = [0] * len(spatial_unrolling)
mem_unroll_active = []
mem_unroll_total = []
for su_id, _ in enumerate(spatial_unrolling):
mem_unroll_active_, mem_unroll_total_ = cmf.get_mem_complete_unrolling_count(
spatial_unrolling[su_id], flooring[su_id], input_settings.mac_array_info['array_size'])
mem_unroll_active.append(mem_unroll_active_)
mem_unroll_total.append(mem_unroll_total_)
mem_scheme.mem_unroll_complete = {'mem_unroll_active': mem_unroll_active, 'mem_unroll_total': mem_unroll_total}
mem_scheme.spatial_unrolling = spatial_unrolling
mem_scheme.flooring = flooring
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
print(current_time, str(input_settings.layer_filename.split('/')[-1]), 'L', layer_index, ', M',
mem_scheme_index + 1, '/', mem_scheme_count, ' SUG finished',
'| Valid SU found:', len(spatial_unrolling))
if not mem_scheme.spatial_unrolling:
discard_mem_scheme = True
print('Layer', layer_index, ': no valid spatial unrolling found')
# Set the spatial unrolling count to 0 for this mem scheme + layer combination
mem_scheme_str = 'M_%d' % (mem_scheme_index + 1)
layer_str = 'L_%d' % (layer_index)
multi_manager.list_su_count[mem_scheme_str][layer_str] = len(spatial_unrolling)
return
for su_idx, su_ in enumerate(spatial_unrolling):
print('-SU', su_idx + 1, '/', len(mem_scheme.spatial_unrolling), mem_scheme.spatial_unrolling[su_idx])
''' input_settings.su_parallel_processing SU parallel '''
TIMEOUT = 36000
start = time.time()
su_count = len(spatial_unrolling)
su_number = list(range(0, su_count, 1))
su_chunk_list = [su_number[i:i + input_settings.su_parallel_processing] for i in
range(0, su_count, input_settings.su_parallel_processing)]
for su_chunk in su_chunk_list:
procs = []
su_zipped = [(su_idx, spatial_unrolling[su_idx]) for su_idx in su_chunk]
for ii_su, su in su_zipped:
p = Process(target=mem_scheme_su_evaluate,
args=(input_settings, layer, im2col_layer, layer_index, layer_info, mem_scheme,
mem_scheme_index, ii_su, su, su_count, im2col_need_correct, multi_manager))
procs.append(p)
for p in procs: p.start()
while time.time() - start <= TIMEOUT: # and all([p.is_alive() for p in procs]):
if any(p.is_alive() for p in procs):
break
time.sleep(1)
else:
print('MEM SCHEME EVALUATE : TIMED OUT - KILLING ALL PROCESSES')
for p in procs:
p.terminate()
p.join()
for p in procs: p.join()
# Get the results from mem_scheme_su_evaluate
list_min_energy = multi_manager.list_min_energy
list_min_en_output = multi_manager.list_min_en_output
list_max_utilization = multi_manager.list_max_utilization
list_max_ut_output = multi_manager.list_max_ut_output
list_tm_count_en = multi_manager.list_tm_count_en
list_tm_count_ut = multi_manager.list_tm_count_ut
list_sim_time = multi_manager.list_sim_time
list_su_count = multi_manager.list_su_count
mem_scheme_str = 'M_%d' % (mem_scheme_index + 1)
layer_str = 'L_%d' % (layer_index)
if len(list_min_energy[mem_scheme_str][layer_str]['best_tm_each_su']) == 0:
list_su_count[mem_scheme_str][layer_str] = 0
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
print(current_time, str(input_settings.layer_filename.split('/')[-1]), 'L', layer_index, ', M',
mem_scheme_index + 1, '/', mem_scheme_count, ' No TM found')
return
else:
t2 = time.time() - t1
best_en = sys.float_info.max
best_en_ut = 0
for mem_su_str, (en, ut) in list_min_energy[mem_scheme_str][layer_str]['best_tm_each_su'].items():
if (en < best_en) or (en == best_en and ut > best_en_ut):
best_en = en
best_en_ut = ut
best_en_mem_su_str = mem_su_str
best_en_output = list_min_en_output[mem_scheme_str][layer_str]['best_tm_each_su'].get(best_en_mem_su_str)
tm_count_en = list_tm_count_en[mem_scheme_str][layer_str]['best_tm_each_su'].get(best_en_mem_su_str)
list_tm_count_en[mem_scheme_str][layer_str]['best_su_each_mem'].update({best_en_mem_su_str: tm_count_en})
list_min_energy[mem_scheme_str][layer_str]['best_su_each_mem'].update(
{best_en_mem_su_str: (best_en, best_en_ut)})
list_min_en_output[mem_scheme_str][layer_str]['best_su_each_mem'].update({best_en_mem_su_str: best_en_output})
list_min_en_output[mem_scheme_str][layer_str]['su_count'] = len(spatial_unrolling)
list_sim_time[mem_scheme_str][layer_str]['best_su_each_mem'].update({best_en_mem_su_str: t2})
list_su_count[mem_scheme_str][layer_str] = len(spatial_unrolling)
best_ut = 0
best_ut_en = sys.float_info.max
for mem_su_str, (en, ut) in list_max_utilization[mem_scheme_str][layer_str]['best_tm_each_su'].items():
if ut > best_ut or (ut == best_ut and en < best_ut_en):
best_ut = ut
best_ut_en = en
best_ut_mem_su_str = mem_su_str
best_ut_output = list_max_ut_output[mem_scheme_str][layer_str]['best_tm_each_su'].get(best_ut_mem_su_str)
tm_count_ut = list_tm_count_ut[mem_scheme_str][layer_str]['best_tm_each_su'].get(best_ut_mem_su_str)
list_tm_count_ut[mem_scheme_str][layer_str]['best_su_each_mem'].update({best_ut_mem_su_str: tm_count_ut})
list_max_utilization[mem_scheme_str][layer_str]['best_su_each_mem'].update({best_ut_mem_su_str: (best_ut_en, best_ut)})
list_max_ut_output[mem_scheme_str][layer_str]['best_su_each_mem'].update({best_ut_mem_su_str: best_ut_output})
list_sim_time[mem_scheme_str][layer_str]['best_su_each_mem'].update({best_ut_mem_su_str: t2})
# Delete all results in 'best_tm_each_su' if save_all_spatial_unrolling_result is false,
# As this information is not required anymore
if not input_settings.su_search_result_saving:
del list_tm_count_en[mem_scheme_str][layer_str]['best_tm_each_su']
del list_min_en_output[mem_scheme_str][layer_str]['best_tm_each_su']
del list_min_energy[mem_scheme_str][layer_str]['best_tm_each_su']
del list_tm_count_ut[mem_scheme_str][layer_str]['best_tm_each_su']
del list_max_ut_output[mem_scheme_str][layer_str]['best_tm_each_su']
del list_max_utilization[mem_scheme_str][layer_str]['best_tm_each_su']
del list_sim_time[mem_scheme_str][layer_str]['best_tm_each_su']
if not input_settings.fixed_spatial_unrolling:
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
group_count = layer.G
print(
'{0:s} {1:s} L {2:d}, M {3:d}, SU {4:s} Min En: ({5:.3E}, {6:.3f}, {7:.3E}) in all SUs and TMs'.format(
current_time, str(input_settings.layer_filename.split('/')[-1]), layer_index, mem_scheme_index + 1,
best_en_mem_su_str.split('_')[-1], int(group_count*best_en), best_en_ut, int(round(best_en_output.area[0]))))
print(
'{0:s} {1:s} L {2:d}, M {3:d}, SU {4:s} Max Ut: ({5:.3E}, {6:.3f}, {7:.3E}) in all SUs and TMs'.format(
current_time, str(input_settings.layer_filename.split('/')[-1]), layer_index, mem_scheme_index + 1,
best_ut_mem_su_str.split('_')[-1], int(group_count*best_ut_en), best_ut, int(round(best_ut_output.area[0]))))
def mem_scheme_list_evaluate(input_settings, mem_scheme, mem_scheme_index, layers, multi_manager):
mem_scheme_count = multi_manager.mem_scheme_count
print('MEM HIERARCHY ', mem_scheme_index + 1, '/', mem_scheme_count)
print('memory size:', mem_scheme.mem_size)
if input_settings.memory_unroll_fully_flexible:
print('memory unroll: unfixed')
else:
print('memory unroll:', mem_scheme.mem_unroll)
print('memory share:', mem_scheme.mem_share)
layer_chunk_list = [layers[i:i + input_settings.layer_parallel_processing] for i in
range(0, len(layers), input_settings.layer_parallel_processing)]
if input_settings.im2col_enable_all:
im2col_layer_chunk_list = [multi_manager.layers_im2col[i:i + input_settings.layer_parallel_processing] for i in
range(0, len(multi_manager.layers_im2col), input_settings.layer_parallel_processing)]
else:
im2col_layer_chunk_list = layer_chunk_list
for ii_layer_chunk, layer_chunk in enumerate(layer_chunk_list):
procs = []
for ii_layer_index, layer in enumerate(layer_chunk):
layer_idx = ii_layer_chunk * input_settings.layer_parallel_processing + ii_layer_index
layer_number = input_settings.layer_number[layer_idx]
im2col_layer = im2col_layer_chunk_list[ii_layer_chunk][ii_layer_index]
procs.append(Process(target=mem_scheme_evaluate,
args=(input_settings, layer_number, layer, im2col_layer,
mem_scheme, mem_scheme_index, multi_manager)))
for p in procs: p.start()
for p in procs: p.join()
def optimal_su_evaluate(input_settings, layers, multi_manager):
""" Collect the optimum spatial unrolling results for all memory schemes """
# Get the results from multi_manager
mem_scheme_sim = multi_manager.mem_scheme_sim
list_tm_count_en = multi_manager.list_tm_count_en
list_min_energy = multi_manager.list_min_energy
list_min_en_output = multi_manager.list_min_en_output
list_tm_count_ut = multi_manager.list_tm_count_ut
list_max_utilization = multi_manager.list_max_utilization
list_max_ut_output = multi_manager.list_max_ut_output
list_sim_time = multi_manager.list_sim_time
list_sim_time_en = multi_manager.list_sim_time_en
list_sim_time_ut = multi_manager.list_sim_time_ut
# Iterate through the processed layers
# Saving for each layer the best mem + su + tm combination
for layer_index in input_settings.layer_number:
layer_str = 'L_%d' % layer_index
en_best_su_each_mem = []
en_output_best_su_each_mem = []
ut_best_su_each_mem = []
ut_output_best_su_each_mem = []
# Aggregate results for every memory scheme to a list
for mem_str in ['M_%d' % (i + 1) for i in range(len(mem_scheme_sim))]:
en_best_su_each_mem.append(list_min_energy[mem_str][layer_str]['best_su_each_mem'])
en_output_best_su_each_mem.append(list_min_en_output[mem_str][layer_str]['best_su_each_mem'])
ut_best_su_each_mem.append(list_max_utilization[mem_str][layer_str]['best_su_each_mem'])
ut_output_best_su_each_mem.append(list_max_ut_output[mem_str][layer_str]['best_su_each_mem'])
if not en_best_su_each_mem:
raise ValueError('No valid design point found. Please consider changing search method in the setting file.')
best_en = sys.float_info.max
best_en_ut = 0
for idd, su_dict in enumerate(en_best_su_each_mem):
try:
m_su_idx, (en, ut) = list(su_dict.items())[0]
except:
# No TM was found
continue
if (en < best_en) or (en == best_en and ut > best_en_ut):
best_en = en