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# 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."""
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
from jax import lax
import jax.numpy as np
from jax_md import space
from jax_md import minimize
from jax_md import quantity
from jax_md.util import *
from jax_md import test_util
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(test_util.JAXMDTestCase):
# pylint: disable=g-complex-comprehension
@parameterized.named_parameters(test_util.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(test_util.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.
step_fn = lambda i, state: opt_apply(state)
@jit
def three_steps(state):
return lax.fori_loop(0, 3, step_fn, 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()