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elasticity_test.py
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elasticity_test.py
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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.elasticity."""
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
from jax import grad
import jax.numpy as np
from jax_md import space
from jax_md import energy
from jax_md import minimize
from jax_md import quantity
from jax_md import elasticity
from jax_md import test_util
from jax_md.util import *
jax_config.parse_flags_with_absl()
PARTICLE_COUNT = 64
NUM_SAMPLES = 2
SPATIAL_DIMENSION = [2, 3]
LOWPRESSURE = [True, False]
if jax_config.jax_enable_x64:
DTYPE = [f32, f64]
else:
DTYPE = [f32]
def run_minimization_while(energy_fn,
R_init,
shift,
max_grad_thresh = 1e-12,
max_num_steps=1000000,
**kwargs):
init,apply=minimize.fire_descent(jit(energy_fn), shift, **kwargs)
apply = jit(apply)
@jit
def get_maxgrad(state):
return jnp.amax(jnp.abs(state.force))
@jit
def cond_fn(val):
state, i = val
return jnp.logical_and(get_maxgrad(state) > max_grad_thresh,
i<max_num_steps)
@jit
def body_fn(val):
state, i = val
return apply(state), i+1
state = init(R_init)
state, num_iterations = lax.while_loop(cond_fn, body_fn, (state, 0))
return state.position, get_maxgrad(state), num_iterations
class ElasticityTest(test_util.JAXMDTestCase):
# pylint: disable=g-complex-comprehension
@parameterized.named_parameters(test_util.cases_from_list(
{
'testcase_name': '_dim={}_dtype={}_lowpressure={}'.format(
dim, dtype.__name__, low_pressure),
'spatial_dimension': dim,
'dtype': dtype,
'low_pressure': low_pressure
} for dim in SPATIAL_DIMENSION
for dtype in DTYPE
for low_pressure in LOWPRESSURE))
def test_EMT_from_db_basic(self, spatial_dimension, dtype, low_pressure):
if spatial_dimension == 2:
N = 64
else:
N = 128
if dtype == jnp.float32:
max_grad_thresh = 1e-5
atol = 1e-4
rtol = 1e-3
else:
max_grad_thresh = 1e-10
atol = 1e-8
rtol = 1e-5
for index in range(NUM_SAMPLES):
cijkl, R, sigma, box = test_util.load_elasticity_test_data(
spatial_dimension, low_pressure, dtype, index)
R = space.transform(box, R)
box = box[0,0]
displacement, shift = space.periodic(box)
energy_fn = energy.soft_sphere_pair(displacement, sigma=sigma)
assert( jnp.max(jnp.abs(grad(energy_fn)(R))) < max_grad_thresh )
EMT_fn = jit(elasticity.athermal_moduli(energy_fn,check_convergence=True))
C, converged = EMT_fn(R,box)
assert( C.dtype == dtype )
assert( C.shape == (spatial_dimension,spatial_dimension,
spatial_dimension,spatial_dimension) )
if converged:
self.assertAllClose(cijkl,elasticity._extract_elements(C,False),
atol=atol, rtol=rtol)
#make sure the symmetries are there
self.assertAllClose(C, jnp.einsum("ijkl->jikl", C))
self.assertAllClose(C, jnp.einsum("ijkl->ijlk", C))
self.assertAllClose(C, jnp.einsum("ijkl->lkij", C))
@parameterized.named_parameters(test_util.cases_from_list(
{
'testcase_name': '_dim={}_dtype={}_lowpressure={}'.format(dim,
dtype.__name__, low_pressure),
'spatial_dimension': dim,
'dtype': dtype,
'low_pressure': low_pressure
} for dim in SPATIAL_DIMENSION
for dtype in DTYPE
for low_pressure in LOWPRESSURE))
def test_EMT_from_db_pgeneral(self, spatial_dimension, dtype, low_pressure):
if spatial_dimension == 2:
N = 64
else:
N = 128
if dtype == jnp.float32:
max_grad_thresh = 1e-5
atol = 1e-4
rtol = 1e-3
else:
max_grad_thresh = 1e-10
atol = 1e-8
rtol = 1e-5
for index in range(NUM_SAMPLES):
cijkl, R, sigma, box = test_util.load_elasticity_test_data(
spatial_dimension, low_pressure, dtype, index)
R = space.transform(box, R)
displacement, shift = space.periodic_general(box,
fractional_coordinates=False)
energy_fn = energy.soft_sphere_pair(displacement, sigma=sigma)
assert( jnp.max(jnp.abs(grad(energy_fn)(R))) < max_grad_thresh )
EMT_fn = jit(elasticity.athermal_moduli(energy_fn,check_convergence=True))
C, converged = EMT_fn(R,box)
assert( C.dtype == dtype )
assert( C.shape == (spatial_dimension,spatial_dimension,
spatial_dimension,spatial_dimension) )
if converged:
self.assertAllClose(cijkl,elasticity._extract_elements(C,False),
atol=atol, rtol=rtol)
#make sure the symmetries are there
self.assertAllClose(C, jnp.einsum("ijkl->jikl", C))
self.assertAllClose(C, jnp.einsum("ijkl->ijlk", C))
self.assertAllClose(C, jnp.einsum("ijkl->lkij", C))
@parameterized.named_parameters(test_util.cases_from_list(
{
'testcase_name': '_dim={}_dtype={}_lowpressure={}'.format(dim,
dtype.__name__, low_pressure),
'spatial_dimension': dim,
'dtype': dtype,
'low_pressure': low_pressure
} for dim in SPATIAL_DIMENSION
for dtype in DTYPE
for low_pressure in LOWPRESSURE))
def test_EMT_from_db_fraccoord(self, spatial_dimension, dtype, low_pressure):
if spatial_dimension == 2:
N = 64
else:
N = 128
if dtype == jnp.float32:
max_grad_thresh = 1e-5
atol = 1e-4
rtol = 1e-3
else:
max_grad_thresh = 1e-10
atol = 1e-8
rtol = 1e-5
for index in range(NUM_SAMPLES):
cijkl, R, sigma, box = test_util.load_elasticity_test_data(
spatial_dimension, low_pressure, dtype, index)
displacement, shift = space.periodic_general(box,
fractional_coordinates=True)
energy_fn = energy.soft_sphere_pair(displacement, sigma=sigma)
assert( jnp.max(jnp.abs(grad(energy_fn)(R))) < max_grad_thresh )
EMT_fn = jit(elasticity.athermal_moduli(energy_fn,check_convergence=True))
C, converged = EMT_fn(R,box)
assert( C.dtype == dtype )
assert( C.shape == (spatial_dimension,spatial_dimension,
spatial_dimension,spatial_dimension) )
if converged:
self.assertAllClose(cijkl,elasticity._extract_elements(C,False),
atol=atol, rtol=rtol)
#make sure the symmetries are there
self.assertAllClose(C, jnp.einsum("ijkl->jikl", C))
self.assertAllClose(C, jnp.einsum("ijkl->ijlk", C))
self.assertAllClose(C, jnp.einsum("ijkl->lkij", C))
@parameterized.named_parameters(test_util.cases_from_list(
{
'testcase_name': '_dim={}_dtype={}_lowpressure={}'.format(dim,
dtype.__name__, low_pressure),
'spatial_dimension': dim,
'dtype': dtype,
'low_pressure': low_pressure
} for dim in SPATIAL_DIMENSION
for dtype in DTYPE
for low_pressure in LOWPRESSURE))
def test_EMT_from_db_dynamic(self, spatial_dimension, dtype, low_pressure):
if spatial_dimension == 2:
N = 64
else:
N = 128
if dtype == jnp.float32:
max_grad_thresh = 1e-5
atol = 1e-4
rtol = 1e-3
else:
max_grad_thresh = 1e-10
atol = 1e-8
rtol = 1e-5
for index in range(NUM_SAMPLES):
cijkl, R, sigma, box = test_util.load_elasticity_test_data(
spatial_dimension, low_pressure, dtype, index)
R = space.transform(box, R)
box = box[0,0]
displacement, shift = space.periodic(box)
#Below we use the wrong sigma, so we must pass it dynamically
energy_fn = energy.soft_sphere_pair(displacement, sigma=1.0)
maxgrad = jnp.max(jnp.abs(grad(energy_fn)(R, sigma=sigma)))
assert( maxgrad < max_grad_thresh )
EMT_fn = jit(elasticity.athermal_moduli(energy_fn,check_convergence=True))
C, converged = EMT_fn(R,box,sigma=sigma)
assert( C.dtype == dtype )
assert( C.shape == (spatial_dimension,spatial_dimension,
spatial_dimension,spatial_dimension) )
if converged:
self.assertAllClose(cijkl,elasticity._extract_elements(C,False),
atol=atol, rtol=rtol)
#make sure the symmetries are there
self.assertAllClose(C, jnp.einsum("ijkl->jikl", C))
self.assertAllClose(C, jnp.einsum("ijkl->ijlk", C))
self.assertAllClose(C, jnp.einsum("ijkl->lkij", C))
@parameterized.named_parameters(test_util.cases_from_list(
{
'testcase_name': '_dim={}_dtype={}_lowpressure={}'.format(dim,
dtype.__name__, low_pressure),
'spatial_dimension': dim,
'dtype': dtype,
'low_pressure': low_pressure
} for dim in SPATIAL_DIMENSION
for dtype in DTYPE
for low_pressure in LOWPRESSURE))
def test_EMT_from_db_nbrlist(self, spatial_dimension, dtype, low_pressure):
if spatial_dimension == 2:
N = 64
else:
N = 128
if dtype == jnp.float32:
max_grad_thresh = 1e-5
atol = 1e-4
rtol = 1e-3
else:
max_grad_thresh = 1e-10
atol = 1e-8
rtol = 1e-5
for index in range(NUM_SAMPLES):
cijkl, R, sigma, box = test_util.load_elasticity_test_data(
spatial_dimension, low_pressure, dtype, index)
displacement, shift = space.periodic_general(box,
fractional_coordinates=True)
neighbor_fn, energy_fn = energy.soft_sphere_neighbor_list(
displacement, box, sigma=sigma, fractional_coordinates=True)
nbrs = neighbor_fn.allocate(R)
assert( jnp.max(jnp.abs(grad(energy_fn)(R, nbrs))) < max_grad_thresh )
EMT_fn = jit(elasticity.athermal_moduli(energy_fn,check_convergence=True))
C, converged = EMT_fn(R,box,neighbor=nbrs)
assert( C.dtype == dtype )
assert( C.shape == (spatial_dimension,spatial_dimension,
spatial_dimension,spatial_dimension) )
if converged:
self.assertAllClose(cijkl,elasticity._extract_elements(C,False),
atol=atol, rtol=rtol)
#make sure the symmetries are there
self.assertAllClose(C, jnp.einsum("ijkl->jikl", C))
self.assertAllClose(C, jnp.einsum("ijkl->ijlk", C))
self.assertAllClose(C, jnp.einsum("ijkl->lkij", C))
@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_mandel(self, spatial_dimension, dtype):
key = random.PRNGKey(0)
for _ in range(NUM_SAMPLES):
key, split = random.split(key, 2)
if spatial_dimension == 2:
C = jnp.array(
[[[[ 1.13172135, 0.00230386],
[ 0.00230386, 0.55599847]],
[[ 0.00230386, 0.39732675],
[ 0.39732675, -0.01848658]]],
[[[ 0.00230386, 0.39732675],
[ 0.39732675, -0.01848658]],
[[ 0.55599847, -0.01848658],
[-0.01848658, 1.14191721]]]],
dtype = dtype)
else:
C = jnp.array(
[[[[ 6.02440072e-01, -3.81092776e-02, -5.43015909e-02],
[-3.81092776e-02, 5.29322888e-01, -2.06230517e-02],
[-5.43015909e-02, -2.06230517e-02, 2.66616858e-01]],
[[-3.81092776e-02, 1.27745712e-01, 8.22006563e-03],
[ 1.27745712e-01, 3.85350327e-02, -1.06514552e-02],
[ 8.22006563e-03, -1.06514552e-02, -3.45478902e-02]],
[[-5.43015909e-02, 8.22006563e-03, 1.12683315e-01],
[ 8.22006563e-03, 3.89981105e-02, 3.45578275e-02],
[ 1.12683315e-01, 3.45578275e-02, 1.15577485e-02]]],
[[[-3.81092776e-02, 1.27745712e-01, 8.22006563e-03],
[ 1.27745712e-01, 3.85350327e-02, -1.06514552e-02],
[ 8.22006563e-03, -1.06514552e-02, -3.45478902e-02]],
[[ 5.29322888e-01, 3.85350327e-02, 3.89981105e-02],
[ 3.85350327e-02, 6.67240460e-01, -3.73405057e-04],
[ 3.89981105e-02, -3.73405057e-04, 5.27227690e-01]],
[[-2.06230517e-02, -1.06514552e-02, 3.45578275e-02],
[-1.06514552e-02, -3.73405057e-04, 2.17405756e-01],
[ 3.45578275e-02, 2.17405756e-01, -1.62693657e-02]]],
[[[-5.43015909e-02, 8.22006563e-03, 1.12683315e-01],
[ 8.22006563e-03, 3.89981105e-02, 3.45578275e-02],
[ 1.12683315e-01, 3.45578275e-02, 1.15577485e-02]],
[[-2.06230517e-02, -1.06514552e-02, 3.45578275e-02],
[-1.06514552e-02, -3.73405057e-04, 2.17405756e-01],
[ 3.45578275e-02, 2.17405756e-01, -1.62693657e-02]],
[[ 2.66616858e-01, -3.45478902e-02, 1.15577485e-02],
[-3.45478902e-02, 5.27227690e-01, -1.62693657e-02],
[ 1.15577485e-02, -1.62693657e-02, 5.45653623e-01]]]],
dtype = dtype)
e = random.uniform(split, (spatial_dimension,spatial_dimension),
minval=-1, maxval=1, dtype=dtype)
e = (e + e.T) / 2.0
e_m = elasticity.tensor_to_mandel(e)
C_m = elasticity.tensor_to_mandel(C)
sum_m = jnp.einsum('i,ij,j->',e_m, C_m, e_m)
sum_t = jnp.einsum('ij,ijkl,kl->',e, C, e)
self.assertAllClose(sum_m, sum_t)
self.assertAllClose(e, elasticity.mandel_to_tensor(e_m))
self.assertAllClose(C, elasticity.mandel_to_tensor(C_m))
if __name__ == '__main__':
absltest.main()