Repository navigation
Expand file tree
/
Copy pathminimize_test.py
More file actions
131 lines (106 loc) · 4 KB
/
Copy pathminimize_test.py
File metadata and controls
131 lines (106 loc) · 4 KB
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
# Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for google3.third_party.py.jax_md.dynamics."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as onp
from absl.testing import absltest
from absl.testing import parameterized
from jax.config import config as jax_config
from jax import random
from jax import jit
import jax.numpy as np
from jax import test_util as jtu
from jax_md import space
from jax_md import minimize
from jax_md import quantity
from jax_md.util import *
jax_config.parse_flags_with_absl()
FLAGS = jax_config.FLAGS
PARTICLE_COUNT = 10
OPTIMIZATION_STEPS = 10
STOCHASTIC_SAMPLES = 10
SPATIAL_DIMENSION = [2, 3]
if FLAGS.jax_enable_x64:
DTYPE = [f32, f64]
else:
DTYPE = [f32]
class DynamicsTest(jtu.JaxTestCase):
# pylint: disable=g-complex-comprehension
@parameterized.named_parameters(jtu.cases_from_list(
{
'testcase_name': '_dim={}_dtype={}'.format(dim, dtype.__name__),
'spatial_dimension': dim,
'dtype': dtype
} for dim in SPATIAL_DIMENSION for dtype in DTYPE))
def test_gradient_descent(self, spatial_dimension, dtype):
key = random.PRNGKey(0)
for _ in range(STOCHASTIC_SAMPLES):
key, split, split0 = random.split(key, 3)
R = random.uniform(split, (PARTICLE_COUNT, spatial_dimension), dtype=dtype)
R0 = random.uniform(split0, (PARTICLE_COUNT, spatial_dimension), dtype=dtype)
energy = lambda R, **kwargs: np.sum((R - R0) ** 2)
_, shift_fn = space.free()
opt_init, opt_apply = minimize.gradient_descent(energy, shift_fn, f32(1e-1))
E_current = energy(R)
dr_current = np.sum((R - R0) ** 2)
for _ in range(OPTIMIZATION_STEPS):
R = opt_apply(R)
E_new = energy(R)
dr_new = np.sum((R - R0) ** 2)
assert E_new < E_current
assert E_new.dtype == dtype
assert dr_new < dr_current
assert dr_new.dtype == dtype
E_current = E_new
dr_current = dr_new
@parameterized.named_parameters(jtu.cases_from_list(
{
'testcase_name': '_dim={}_dtype={}'.format(dim, dtype.__name__),
'spatial_dimension': dim,
'dtype': dtype
} for dim in SPATIAL_DIMENSION for dtype in DTYPE))
def test_fire_descent(self, spatial_dimension, dtype):
key = random.PRNGKey(0)
for _ in range(STOCHASTIC_SAMPLES):
key, split, split0 = random.split(key, 3)
R = random.uniform(
split, (PARTICLE_COUNT, spatial_dimension), dtype=dtype)
R0 = random.uniform(
split0, (PARTICLE_COUNT, spatial_dimension), dtype=dtype)
energy = lambda R, **kwargs: np.sum((R - R0) ** 2)
_, shift_fn = space.free()
opt_init, opt_apply = minimize.fire_descent(energy, shift_fn)
opt_state = opt_init(R)
E_current = energy(R)
dr_current = np.sum((R - R0) ** 2)
# NOTE(schsam): We add this to test to make sure we can jit through the
# creation of FireDescentState namedtuple.
@jit
def three_steps(state):
return opt_apply(opt_apply(opt_apply(state)))
for _ in range(OPTIMIZATION_STEPS):
opt_state = three_steps(opt_state)
R = opt_state.position
E_new = energy(R)
dr_new = np.sum((R - R0) ** 2)
assert E_new < E_current
assert E_new.dtype == dtype
assert dr_new < dr_current
assert dr_new.dtype == dtype
E_current = E_new
dr_current = dr_new
if __name__ == '__main__':
absltest.main()