def multiAcc_dse():
# define the network parameter containers
conv_N = []
conv_M = []
conv_r = []
conv_R = []
conv_K = []
conv_S = []
flag = []
cut_flag = []
pool_N = []
sub_conv_N = []
sub_conv_M = []
sub_conv_r = []
sub_conv_R = []
sub_conv_K = []
sub_conv_S = []
sub_flag = []
pair_1 = []
pair_2 = []
pair_3 = []
lat_1 = 0
lat_2 = 0
lat_3 = 0
sub_lat_list = []
lat_list = []
util_1 = 0
util_2 = 0
util_3 = 0
sub_util_list = []
util_list = []
OPs = 0
sub_pair_list = []
item_list = []
pair_list = []
overall_lat = 60551400
layer_list = []
gop_list = []
"""
step 1: extract model from txt file with parameter no_include_fc / include_fc
"""
conv_N, conv_M, conv_r, conv_R, conv_K, conv_S, conv_G, flag, cut_flag, pool_N = model_extract(
'no_include_fc')
# print("Extracted cut flag: ", cut_flag)
# print("Extracted pool flag:", flag)
OPs = gop_calculate(conv_N, conv_M, conv_R, conv_K)
max_layerout = max_layer_dataout(conv_N, conv_M, conv_R, conv_K)
print_line("Model extract phase")
print("1: ", "Model extracted")
print("1: ", "Overall convolution operation required: ", OPs)
print("1: ", "Max layer output data: ", max_layerout)
# print_line("Model split finish")
"""
step 2: randomly cluster, param k=4, layer label results are in item
"""
print_line("Model partition phase")
for i in range(0, len(conv_N)):
layer_list.append(i)
# kmeans=clusters_layers_kmeans(conv_N, conv_M, conv_r, conv_R, conv_K, conv_S, 2)
# print kmeans
partition_location, diff_ratio = model_partition_by_gop(
conv_N, conv_M, conv_r, conv_R, conv_K, conv_S, conv_G, flag, cut_flag)
print("2: layers extracted", conv_N)
print("2: layers cutable ", cut_flag)
print("2: partition location", partition_location)
print("2: diff_ratio: ", diff_ratio)
sub_conv_N, sub_conv_M, sub_conv_r, sub_conv_R, sub_conv_K, sub_conv_S, sub_flag \
=model_partition_ordered(conv_N, conv_M, conv_r, conv_R, conv_K, conv_S, conv_G, flag, partition_location[0]+1, partition_location[1]+1)
# print "2: Best partition output: ", partition_location, diff_ratio
print("2:", sub_conv_N)
sub_gop_list = []
for i in range(0, len(sub_conv_N)):
sub_gop_list.append(
gop_calculate(sub_conv_N[i], sub_conv_M[i], sub_conv_R[i],
sub_conv_K[i]))
print("2: gop of sub_nets", sub_gop_list)
print("2: length of sub_conv_N", len(sub_conv_N[0]), len(sub_conv_N[1]),
len(sub_conv_N[2]))
print("2", sub_flag)
print("2: length of sub_flag", len(sub_flag[0]), len(sub_flag[1]),
len(sub_flag[2]))
sub_pair_list = []
sub_lat_list = []
sub_util_list = []
print_line("Best Configuration Search")
overall_start = time.time()
# acc_cluster_num = 3
# pair_list, item_list, gop_list, util_list = global_search(layer_list, acc_cluster_num, conv_N, conv_M, conv_r, conv_R, conv_K, conv_S, flag, overall_lat)
# pair_list, gop_list, util_list = per_die_config_dse_multiAcc(sub_conv_N, sub_conv_M, sub_conv_r, sub_conv_R, sub_conv_K,
# sub_conv_S, sub_flag)
pair_list = per_die_config_dse_multiAcc_flex(sub_conv_N, sub_conv_M,
sub_conv_r, sub_conv_R,
sub_conv_K, sub_conv_S,
sub_flag)
overall_end = time.time()
print_line("DSE Output")
print("Best Configuration Search Results for layer accelerators: ")
for i in range(0, len(pair_list)):
print(pair_list[i])
acc_task_list, total_acc_num = acc_task_analysis(pair_list, sub_conv_N, sub_conv_M, sub_conv_r, \
sub_conv_R, sub_conv_K, sub_conv_S, sub_flag)
print("Accelerator task list: ")
for acc_num in range(0, len(acc_task_list)):
print("acc core", acc_num, " task list: ", acc_task_list[acc_num])
print_line("Subnet Task Out")
subnet_task_list = subnet_task_analysis(pair_list, acc_task_list, sub_conv_N, sub_conv_M, sub_conv_r, \
sub_conv_R, sub_conv_K, sub_conv_S, sub_flag)
print("sub net interface list:")
for i in range(0, len(subnet_task_list)):
print(subnet_task_list[i])
print_line("Write out configurations")
print(len(pair_list), "sub-nets are generated")
print(total_acc_num, "accelerators are written into the cofig file")
generate_param_file(pair_list, pool_N, acc_task_list, subnet_task_list,
"acc_ins_params.txt")
print_line("netGen run time system info")
print("Overall time cost:", overall_end - overall_start, "s")
print_line("line")
print_line("test")
print(
conv_net_perf(sub_conv_N[2], sub_conv_M[2], sub_conv_R[2],
sub_conv_S[2], sub_conv_K[2], sub_flag[2], 8, 274, 37, 4,
4))
def multiAcc_dse():
# define the network parameter containers
conv_N = []
conv_M = []
conv_r = []
conv_R = []
conv_K = []
conv_S = []
flag = []
cut_flag = []
sub_conv_N = []
sub_conv_M = []
sub_conv_r = []
sub_conv_R = []
sub_conv_K = []
sub_conv_S = []
sub_flag = []
pair_1 = []
pair_2 = []
pair_3 = []
lat_1 = 0
lat_2 = 0
lat_3 = 0
sub_lat_list = []
lat_list = []
util_1 = 0
util_2 = 0
util_3 = 0
sub_util_list = []
util_list = []
OPs = 0
sub_pair_list = []
item_list = []
pair_list = []
overall_lat = 60551400
layer_list = []
gop_list = []
"""
step 1: extract model from txt file with parameter no_include_fc / include_fc
"""
conv_N, conv_M, conv_r, conv_R, conv_K, conv_S, conv_G, flag, cut_flag = model_extract(
'no_include_fc')
print("Extracted cut flag: ", cut_flag)
OPs = gop_calculate(conv_N, conv_M, conv_R, conv_K)
max_layerout = max_layer_dataout(conv_N, conv_M, conv_R, conv_K)
print_line("Model extract phase")
print "1: ", "Model extracted"
print "1: ", "Overall convolution operation required: ", OPs
print "1: ", "Max layer output data: ", max_layerout
# print_line("Model split finish")
"""
step 2: randomly cluster, param k=4, layer label results are in item
"""
print_line("Model partition phase")
for i in range(0, len(conv_N)):
layer_list.append(i)
# kmeans=clusters_layers_kmeans(conv_N, conv_M, conv_r, conv_R, conv_K, conv_S, 2)
# print kmeans
partition_location, diff_ratio = model_partition_by_gop(
conv_N, conv_M, conv_r, conv_R, conv_K, conv_S, conv_G, flag, cut_flag)
print "2: layers extracted", conv_N
print "2: layers cutable ", cut_flag
print "2: partition location", partition_location
print "2: diff_ratio: ", diff_ratio
sub_conv_N, sub_conv_M, sub_conv_r, sub_conv_R, sub_conv_K, sub_conv_S, sub_flag \
=model_partition_ordered(conv_N, conv_M, conv_r, conv_R, conv_K, conv_S, conv_G, flag, partition_location[0]+1, partition_location[1]+1)
# print "2: Best partition output: ", partition_location, diff_ratio
print "2:", sub_conv_N
sub_gop_list = []
for i in range(0, len(sub_conv_N)):
sub_gop_list.append(
gop_calculate(sub_conv_N[i], sub_conv_M[i], sub_conv_r[i],
sub_conv_K[i]))
print "2: gop of sub_nets", sub_gop_list
print "2: length of sub_conv_N", len(sub_conv_N[0]), len(
sub_conv_N[1]), len(sub_conv_N[2])
print "2", sub_flag
print "2: length of sub_flag", len(sub_flag[0]), len(sub_flag[1]), len(
sub_flag[2])
sub_pair_list = []
sub_lat_list = []
sub_util_list = []
print_line("Best Configuration Search")
overall_start = time.time()
# acc_cluster_num = 3
# pair_list, item_list, gop_list, util_list = global_search(layer_list, acc_cluster_num, conv_N, conv_M, conv_r, conv_R, conv_K, conv_S, flag, overall_lat)
# pair_list, gop_list, util_list = per_die_config_dse_multiAcc(sub_conv_N, sub_conv_M, sub_conv_r, sub_conv_R, sub_conv_K,
# sub_conv_S, sub_flag)
pair_list = per_die_config_dse_multiAcc_flex(sub_conv_N, sub_conv_M,
sub_conv_r, sub_conv_R,
sub_conv_K, sub_conv_S,
sub_flag)
overall_end = time.time()
print_line("DSEoutpout")
print "Best Configuration Search Results: "
for i in range(0, len(pair_list)):
print pair_list[i]
# print item_list
#print "gop_list: ", gop_list
#print "pair_list: ", pair_list
#print "util_list: ", util_list
# for i in range(0, len(util_list)):
# print util_list[i], sum(util_list[i])
print "------------------------Final optimal configuration-------------------------------"
# print "Network clustered results =", item_list[util_list.index(min(util_list))]
# print "<Tm, Tn> = ", pair_list[util_list.index(min(util_list))]
# print "Estimated overall latency = ", min(util_list)
print "Overall time cost:", overall_end - overall_start, "s"
print "----------------------------------------------------------------------------------"
# item = return_partition(layer_list, 4, False)
#
# '''step 3: split the layers based on label clustering results'''
# print("layer number is: ", int(len(conv_N)))
# sub_conv_N, sub_conv_M, sub_conv_r, sub_conv_R, sub_conv_K, sub_conv_S, sub_flag \
# = model_split_by_list(conv_N, conv_M, conv_r, conv_R, conv_K, conv_S, flag, item)
# print sub_conv_N
# print "model clustering test done!!!"
#
# '''step 4: do local search for all sub-models and find optimial <Tm, Tn> pair, lat, and util'''
# sub_pair_list, sub_lat_list, sub_util_list = \
# local_search(sub_conv_N, sub_conv_M, sub_conv_r, sub_conv_R, sub_conv_K, sub_conv_S, sub_flag)
# print sub_pair_list, sub_lat_list, sub_util_list
#
# if max(sub_lat_list) < overall_lat:
# overall_lat = max(sub_lat_list)
# if len(pair_list) < 10:
# pair_list.append(sub_pair_list)
# pair_list.append([overall_lat])
# else:
# max_among_mins = pair_list.index(max(overall_lat))
# pair_list.remove(pair_list[max_among_mins])
# pair_list.append(sub_pair_list)
# pair_list.append([overall_lat])
# print(pair_1, "%.2f" % util_1, pair_2, "%.2f" % util_2, pair_3, "%.2f" % util_3, lat_1, lat_2, lat_3)
# for i in range(1, int(len(conv_N)-1)):
# for j in range(int(i+1), int(len(conv_N))):
# for i in range(1, 10):
# for j in range(1, 10):
# sub_conv_N, sub_conv_M, sub_conv_r, sub_conv_R, sub_conv_K, sub_conv_S, sub_flag = model_split_ordered(conv_N, conv_M, conv_r, conv_R, conv_K, conv_S, flag, i, j)
# sub_conv_N, sub_conv_M, sub_conv_r, sub_conv_R, sub_conv_K, sub_conv_S, sub_flag = model_split_unordered(conv_N, conv_M, conv_r, conv_R, conv_K, conv_S, flag)
# sub_conv_N, sub_conv_M, sub_conv_r, sub_conv_R, sub_conv_K, sub_conv_S, sub_flag = model_split_by_label(conv_N, conv_M, conv_r, conv_R, conv_K, conv_S, flag, kmeans.labels_)
# print(sub_conv_N)
# pair_1, lat_1, pair_2, lat_2, pair_3, lat_3, util_1, util_2, util_3 = local_search(sub_conv_N, sub_conv_M, sub_conv_r, sub_conv_R, sub_conv_K, sub_conv_S, sub_flag)
#
# print(i, j, pair_1, "%.2f" % util_1, pair_2, "%.2f" % util_2, pair_3, "%.2f" % util_3, lat_1, lat_2, lat_3)
#
# if max(lat_1, lat_2, lat_3) < overall_lat:
# overall_lat = max(lat_1, lat_2, lat_3)
# # if len(pair_list) < 50:
# pair_list.append([i, j])
# pair_list.append(pair_1)
# pair_list.append(pair_2)
# pair_list.append(pair_3)
# pair_list.append([overall_lat])
# # else:
# # max_among_mins = pair_list.index(max(overall_lat))
# # pair_list.remove(pair_list[max_among_mins])
# # pair_list.append(pair_1)
# # pair_list.append(pair_2)
# # pair_list.append(pair_3)
# # pair_list.append(overall_lat)
# print(pair_list)
# #step 3:
# find_min_in_pairs()
# min_among_mins = pair_list.index(min(overall_lat))
# print(pair_list[min_among_mins])
print "---------------------------- test part -------------------------------------------"
print conv_net_perf(sub_conv_N[2], sub_conv_M[2], sub_conv_R[2],
sub_conv_S[2], sub_conv_K[2], sub_flag[2], 8, 274, 37,
4, 4)
def model_partition_by_gop(conv_N, conv_M, conv_r, conv_R, conv_K, conv_S,
conv_G, flag, cut_flag):
sub_conv_N = []
sub_conv_M = []
sub_conv_r = []
sub_conv_R = []
sub_conv_K = []
sub_conv_S = []
sub_flag = []
balance_ratio = 0
min_ration = 0.5
min_pair = [0, 0]
sub_gops = [[], [], []]
model_len = int(len(conv_N))
for i in range(0, model_len - 2):
for j in range(i + 1, model_len - 1):
sub_conv_N, sub_conv_M, sub_conv_r, sub_conv_R, sub_conv_K, sub_conv_S, sub_flag = model_partition_ordered(
conv_N, conv_M, conv_r, conv_R, conv_K, conv_S, conv_G, flag,
i + 1, j + 1)
# print sub_conv_N
for k in range(0, 3):
sub_gops[k] = gop_calculate(sub_conv_N[k], sub_conv_M[k],
sub_conv_R[k], sub_conv_K[k])
# sub_gops[k] = conv_net_perf_theo(sub_conv_N[k], sub_conv_M[k], sub_conv_R[k], sub_conv_K[k])
balance_ratio = (max(sub_gops) - min(sub_gops)) / float(
min(sub_gops))
# print sub_gops
# print "2: ", i, j, sub_gops, balance_ratio, sub_conv_N, sub_conv_M
print("Verigy cut status: ", i, j, cut_flag[i], cut_flag[j])
if i == 0 and j == 1:
min_ration = balance_ratio
print("initial balance ratio: ", balance_ratio)
min_pair = [i, j]
else:
if cut_flag[i] == 1 & cut_flag[j] == 1:
if balance_ratio < min_ration:
min_ration = balance_ratio
min_pair = [i, j]
print "min_ratio: ", min_ration, min_pair
return min_pair, min_ration