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Pipeline_Exhaustive.py
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import csv
import numpy as np
import pandas as pd
import re
import time
alex_layers = 8
gg_layers = 13
res_layers = 18
mobi_layers = 15
squ_layers = 10
file="pred"
pd_latency = pd.read_csv('schedule/'+file+'/'+'res_latency1.csv')
model_layers = mobi_layers
pipeline = [
["gpu", "1b", "1b", "1b", "1b", "1s", "1s", "1s", "1s"],
["gpu", "1b", "1b", "1b", "1b", "2s", "1s", "1s"],
["gpu", "1b", "1b", "1b", "1b", "1s", "2s", "1s"],
["gpu", "1b", "1b", "1b", "1b", "1s", "1s", "2s"],
["gpu", "1b", "1b", "1b", "1b", "2s", "2s"],
["gpu", "1b", "1b", "1b", "1b", "3s", "1s"],
["gpu", "1b", "1b", "1b", "1b", "1s", "3s"],
["gpu", "1b", "1b", "1b", "1b", "4s"],
["gpu", "2b", "1b", "1b", "1s", "1s", "1s", "1s"],
["gpu", "2b", "1b", "1b", "2s", "1s", "1s"],
["gpu", "2b", "1b", "1b", "1s", "2s", "1s"],
["gpu", "2b", "1b", "1b", "1s", "1s", "2s"],
["gpu", "2b", "1b", "1b", "2s", "2s"],
["gpu", "2b", "1b", "1b", "3s", "1s"],
["gpu", "2b", "1b", "1b", "1s", "3s"],
["gpu", "2b", "1b", "1b", "4s"],
["gpu", "1b", "2b", "1b", "1s", "1s", "1s", "1s"],
["gpu", "1b", "2b", "1b", "2s", "1s", "1s"],
["gpu", "1b", "2b", "1b", "1s", "2s", "1s"],
["gpu", "1b", "2b", "1b", "1s", "1s", "2s"],
["gpu", "1b", "2b", "1b", "2s", "2s"],
["gpu", "1b", "2b", "1b", "3s", "1s"],
["gpu", "1b", "2b", "1b", "1s", "3s"],
["gpu", "1b", "2b", "1b", "4s"],
["gpu", "1b", "1b", "2b", "1s", "1s", "1s", "1s"],
["gpu", "1b", "1b", "2b", "2s", "1s", "1s"],
["gpu", "1b", "1b", "2b", "1s", "2s", "1s"],
["gpu", "1b", "1b", "2b", "1s", "1s", "2s"],
["gpu", "1b", "1b", "2b", "2s", "2s"],
["gpu", "1b", "1b", "2b", "3s", "1s"],
["gpu", "1b", "1b", "2b", "1s", "3s"],
["gpu", "1b", "1b", "2b", "4s"],
["gpu", "2b", "2b", "1s", "1s", "1s", "1s"],
["gpu", "2b", "2b", "2s", "1s", "1s"],
["gpu", "2b", "2b", "1s", "2s", "1s"],
["gpu", "2b", "2b", "1s", "1s", "2s"],
["gpu", "2b", "2b", "2s", "2s"],
["gpu", "2b", "2b", "3s", "1s"],
["gpu", "2b", "2b", "1s", "3s"],
["gpu", "2b", "2b", "4s"],
["gpu", "3b", "1b", "1s", "1s", "1s", "1s"],
["gpu", "3b", "1b", "2s", "1s", "1s"],
["gpu", "3b", "1b", "1s", "2s", "1s"],
["gpu", "3b", "1b", "1s", "1s", "2s"],
["gpu", "3b", "1b", "2s", "2s"],
["gpu", "3b", "1b", "3s", "1s"],
["gpu", "3b", "1b", "1s", "3s"],
["gpu", "3b", "1b", "4s"],
["gpu", "1b", "3b", "1s", "1s", "1s", "1s"],
["gpu", "1b", "3b", "2s", "1s", "1s"],
["gpu", "1b", "3b", "1s", "2s", "1s"],
["gpu", "1b", "3b", "1s", "1s", "2s"],
["gpu", "1b", "3b", "2s", "2s"],
["gpu", "1b", "3b", "3s", "1s"],
["gpu", "1b", "3b", "1s", "3s"],
["gpu", "1b", "3b", "4s"],
["gpu", "4b", "1s", "1s", "1s", "1s"],
["gpu", "4b", "2s", "1s", "1s"],
["gpu", "4b", "1s", "2s", "1s"],
["gpu", "4b", "1s", "1s", "2s"],
["gpu", "4b", "2s", "2s"],
["gpu", "4b", "3s", "1s"],
["gpu", "4b", "1s", "3s"],
["gpu", "4b", "4s"]
]
def num_combinations(start, end):
list = []
if (start == end - 1):
ll = []
ll.append(start)
list.append(ll)
return list
else:
for i in range(start, end):
# print("i= ", i)
if(i == end - 1):
list.append([i])
# print("listt= ", list)
else:
tail_list = num_combinations(i+1, end)
# print("tail_list= ", tail_list)
for j in range(len(tail_list)):
l = []
l.append(i)
l.extend(tail_list[j])
list.append(l)
# print("list= ", list)
return list
def cal_time(L, P):
stage_time_list = []
first_stage_time = 0
for i in range(0, L[0]+1):
first_stage_time = first_stage_time + pd_latency[P[0]][i]
stage_time_list.append(first_stage_time)
for j in range(1, len(L)):
t = 0
for k in range(L[j-1]+1, L[j]+1):
t = t + pd_latency[P[j]][k]
stage_time_list.append(t)
return stage_time_list
def all_time(list, num_layers):
best_pipeline = []
best_combination = []
best_stage_time = []
min_maxtime = 10000
min_E = 10000
for i in range(len(pipeline)):
P = pipeline[i]
P_len = len(P)
if (P_len < num_layers + 1):
L_all = list[P_len]
for j in range(len(L_all)):
L = L_all[j]
T = cal_time(L, P)
print("L: ", L)
print("P: ", P)
print("T: ", T)
sum_time = 0
for m in range(len(T)):
sum_time = sum_time + T[m]
avg_time = sum_time / len(T)
print("avg_time: ", avg_time)
E = 0
for n in range(len(T)):
E = E + (avg_time - T[n])**2
E = E / len(T)
print("max_stage_time= ", max(T))
print("方差为： ", E)
if(min_maxtime > max(T)):
min_maxtime = max(T)
min_E = E
best_pipeline = P
best_stage_time = T
best_combination = L
elif(min_maxtime == max(T) and min_E > E):
min_maxtime = max(T)
min_E = E
best_pipeline = P
best_stage_time = T
best_combination = L
print("\n")
print("best_pipeline: ", best_pipeline)
print("best_combination: ", best_combination)
print("best_stage_time: ", best_stage_time)
print("max_time: ", min_maxtime)
print("min_E: ", min_E)
start = time.perf_counter()
a = num_combinations(0, model_layers)
print("total combinations: ", len(a))
print("list: ", a)
count = 0
useful_list = []
for i in range(0, 10):
useful_list.append([])
for k in range(len(a)):
combinations = a[k]
if(len(a[k]) < 10 and len(a[k]) > 2):
count = count + 1
useful_list[len(a[k])].append(a[k])
print("useful combinations: ", count)
print("useful list: ", useful_list)
all_time(useful_list, model_layers)
end = time.perf_counter()
print ("***************\n", end-start)