/
prof-gurobi.py
executable file
·142 lines (115 loc) · 4.4 KB
/
prof-gurobi.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
#!/usr/bin/env python3
import argparse
import sys
import numpy as np
import numpy.random as npr
# import qpth.solvers.pdipm.single as pdipm_s
import qpth.solvers.pdipm.batch as pdipm_b
import itertools
import time
import torch
# import gurobipy as gpy
from IPython.core import ultratb
sys.excepthook = ultratb.FormattedTB(mode='Verbose',
color_scheme='Linux', call_pdb=1)
import setproctitle
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--nTrials', type=int, default=10)
args = parser.parse_args()
setproctitle.setproctitle('bamos.optnet.prof-gurobi')
npr.seed(0)
prof(args)
def prof(args):
print('| \# Vars | \# Batch | Gurobi | single | batched |')
print('|----------+----------+--------+--------+---------|')
# for nz, nBatch in itertools.product([100,500], [1, 64, 128]):
for nz, nBatch in itertools.product([100], [1, 64, 128]):
times = []
for i in range(args.nTrials):
times.append(prof_instance(nz, nBatch))
times = np.array(times)
print(("| {:5d} " * 2 + "| ${:.5e} \pm {:.5e}$ s " * 3 + '|').format(
*([nz, nBatch] + [item for sublist in zip(times.mean(axis=0), times.std(axis=0))
for item in sublist])))
def prof_instance(nz, nBatch, cuda=True):
nineq, neq = 100, 0
assert(neq == 0)
L = npr.rand(nBatch, nz, nz)
Q = np.matmul(L, L.transpose((0, 2, 1))) + 1e-3 * np.eye(nz, nz)
G = npr.randn(nBatch, nineq, nz)
z0 = npr.randn(nBatch, nz)
s0 = npr.rand(nBatch, nineq)
p = npr.randn(nBatch, nz)
h = np.matmul(G, np.expand_dims(z0, axis=(2))).squeeze(2) + s0
A = npr.randn(nBatch, neq, nz)
b = np.matmul(A, np.expand_dims(z0, axis=(2))).squeeze(2)
# zhat_g = []
# gurobi_time = 0.0
# for i in range(nBatch):
# m = gpy.Model()
# zhat = m.addVars(nz, lb=-gpy.GRB.INFINITY, ub=gpy.GRB.INFINITY)
# obj = 0.0
# for j in range(nz):
# for k in range(nz):
# obj += 0.5 * Q[i, j, k] * zhat[j] * zhat[k]
# obj += p[i, j] * zhat[j]
# m.setObjective(obj)
# for j in range(nineq):
# con = 0
# for k in range(nz):
# con += G[i, j, k] * zhat[k]
# m.addConstr(con <= h[i, j])
# m.setParam('OutputFlag', False)
# start = time.time()
# m.optimize()
# gurobi_time += time.time() - start
# t = np.zeros(nz)
# for j in range(nz):
# t[j] = zhat[j].x
# zhat_g.append(t)
gurobi_time = -1
p, L, Q, G, z0, s0, h = [torch.Tensor(x) for x in [p, L, Q, G, z0, s0, h]]
if cuda:
p, L, Q, G, z0, s0, h = [x.cuda() for x in [p, L, Q, G, z0, s0, h]]
if neq > 0:
A = torch.Tensor(A)
b = torch.Tensor(b)
else:
A, b = [torch.Tensor()] * 2
if cuda:
A = A.cuda()
b = b.cuda()
# af = adact.AdactFunction()
# single_results = []
start = time.time()
# for i in range(nBatch):
# A_i = A[i] if neq > 0 else A
# b_i = b[i] if neq > 0 else b
# U_Q, U_S, R = pdipm_s.pre_factor_kkt(Q[i], G[i], A_i)
# single_results.append(pdipm_s.forward(p[i], Q[i], G[i], A_i, b_i, h[i],
# U_Q, U_S, R))
single_time = time.time() - start
start = time.time()
Q_LU, S_LU, R = pdipm_b.pre_factor_kkt(Q, G, A)
zhat_b, nu_b, lam_b, s_b = pdipm_b.forward(Q, p, G, h, A, b, Q_LU, S_LU, R)
batched_time = time.time() - start
# Usually between 1e-4 and 1e-5:
# print('Diff between gurobi and pdipm: ',
# np.linalg.norm(zhat_g[0]-zhat_b[0].cpu().numpy()))
# import IPython, sys; IPython.embed(); sys.exit(-1)
# import IPython, sys; IPython.embed(); sys.exit(-1)
# zhat_diff = (single_results[0][0] - zhat_b[0]).norm()
# lam_diff = (single_results[0][2] - lam_b[0]).norm()
# eps = 0.1 # Pretty relaxed.
# if zhat_diff > eps or lam_diff > eps:
# print('===========')
# print("Warning: Single and batched solutions might not match.")
# print(" + zhat_diff: {}".format(zhat_diff))
# print(" + lam_diff: {}".format(lam_diff))
# print(" + (nz, neq, nineq, nBatch) = ({}, {}, {}, {})".format(
# nz, neq, nineq, nBatch))
# print('===========')
return gurobi_time, single_time, batched_time
if __name__ == '__main__':
main()