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smap_test.py
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smap_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.mapping."""
from absl.testing import absltest
from absl.testing import parameterized
from jax.config import config as jax_config
from jax import random
import jax.numpy as np
from jax import grad
from jax import jit, vmap
from jax_md import smap, space, energy, quantity, partition, dataclasses
from jax_md.util import *
from jax_md import test_util
jax_config.parse_flags_with_absl()
test_util.update_test_tolerance(f32_tol=5e-6, f64_tol=1e-14)
PARTICLE_COUNT = 1000
STOCHASTIC_SAMPLES = 3
SPATIAL_DIMENSION = [2, 3]
NEIGHBOR_LIST_FORMAT = [partition.Dense,
partition.Sparse,
partition.OrderedSparse]
NEIGHBOR_LIST_PARTICLE_COUNT = 100
if jax_config.jax_enable_x64:
POSITION_DTYPE = [f32, f64]
else:
POSITION_DTYPE = [f32]
class SMapTest(test_util.JAXMDTestCase):
@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 POSITION_DTYPE))
def test_bond_no_type_static(self, spatial_dimension, dtype):
harmonic = lambda dr, **kwargs: (dr - f32(1)) ** f32(2)
disp, _ = space.free()
metric = space.metric(disp)
mapped = smap.bond(harmonic, metric, np.array([[0, 1], [0, 2]], i32))
key = random.PRNGKey(0)
for _ in range(STOCHASTIC_SAMPLES):
key, split = random.split(key)
R = random.uniform(
split, (PARTICLE_COUNT, spatial_dimension), dtype=dtype)
accum = harmonic(metric(R[0], R[1])) + harmonic(metric(R[0], R[2]))
self.assertAllClose(mapped(R), dtype(accum))
@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 POSITION_DTYPE))
def test_bond_no_type_dynamic(self, spatial_dimension, dtype):
harmonic = lambda dr, **kwargs: (dr - f32(1)) ** f32(2)
disp, _ = space.free()
metric = space.metric(disp)
mapped = smap.bond(harmonic, metric)
bonds = np.array([[0, 1], [0, 2]], i32)
key = random.PRNGKey(0)
for _ in range(STOCHASTIC_SAMPLES):
key, split = random.split(key)
R = random.uniform(
split, (PARTICLE_COUNT, spatial_dimension), dtype=dtype)
accum = harmonic(metric(R[0], R[1])) + harmonic(metric(R[0], R[2]))
self.assertAllClose(mapped(R, bonds), dtype(accum))
@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 POSITION_DTYPE))
def test_bond_type_static(self, spatial_dimension, dtype):
harmonic = lambda dr, sigma, **kwargs: (dr - sigma) ** f32(2)
disp, _ = space.free()
metric = space.metric(disp)
sigma = np.array([1.0, 2.0], f32)
mapped = smap.bond(
harmonic, metric,
np.array([[0, 1], [0, 2]], i32), np.array([0, 1], i32), sigma=sigma)
key = random.PRNGKey(0)
for _ in range(STOCHASTIC_SAMPLES):
key, split = random.split(key)
R = random.uniform(
split, (PARTICLE_COUNT, spatial_dimension), dtype=dtype)
accum = harmonic(metric(R[0], R[1]), 1) + harmonic(metric(R[0], R[2]), 2)
self.assertAllClose(mapped(R), dtype(accum))
@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 POSITION_DTYPE))
def test_bond_type_dynamic(self, spatial_dimension, dtype):
harmonic = lambda dr, sigma, **kwargs: (dr - sigma) ** f32(2)
disp, _ = space.free()
metric = space.metric(disp)
sigma = np.array([1.0, 2.0], f32)
mapped = smap.bond(harmonic, metric, sigma=sigma)
bonds = np.array([[0, 1], [0, 2]], i32)
bond_types = np.array([0, 1], i32)
key = random.PRNGKey(0)
for _ in range(STOCHASTIC_SAMPLES):
key, split = random.split(key)
R = random.uniform(
split, (PARTICLE_COUNT, spatial_dimension), dtype=dtype)
accum = harmonic(metric(R[0], R[1]), 1) + harmonic(metric(R[0], R[2]), 2)
self.assertAllClose(mapped(R, bonds, bond_types), dtype(accum))
@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 POSITION_DTYPE))
def test_bond_params_dynamic(self, spatial_dimension, dtype):
harmonic = lambda dr, sigma, **kwargs: (dr - sigma) ** f32(2)
disp, _ = space.free()
metric = space.metric(disp)
sigma = np.array([1.0, 2.0], f32)
mapped = smap.bond(harmonic, metric, sigma=1.0)
bonds = np.array([[0, 1], [0, 2]], i32)
key = random.PRNGKey(0)
for _ in range(STOCHASTIC_SAMPLES):
key, split = random.split(key)
R = random.uniform(
split, (PARTICLE_COUNT, spatial_dimension), dtype=dtype)
accum = harmonic(metric(R[0], R[1]), 1) + harmonic(metric(R[0], R[2]), 2)
self.assertAllClose(mapped(R, bonds, sigma=sigma), dtype(accum))
@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 POSITION_DTYPE))
def test_bond_per_bond_static(self, spatial_dimension, dtype):
harmonic = lambda dr, sigma, **kwargs: (dr - sigma) ** f32(2)
disp, _ = space.free()
metric = space.metric(disp)
sigma = np.array([1.0, 2.0], f32)
mapped = smap.bond(
harmonic, metric, np.array([[0, 1], [0, 2]], i32), sigma=sigma)
key = random.PRNGKey(0)
for _ in range(STOCHASTIC_SAMPLES):
key, split = random.split(key)
R = random.uniform(
split, (PARTICLE_COUNT, spatial_dimension), dtype=dtype)
accum = harmonic(metric(R[0], R[1]), 1) + harmonic(metric(R[0], R[2]), 2)
self.assertAllClose(mapped(R), dtype(accum))
def test_get_species_parameters(self):
species = [(0, 0), (0, 1), (1, 0), (1, 1)]
params = np.array([[2.0, 3.0], [3.0, 1.0]])
global_params = 3.0
self.assertAllClose(
smap._get_species_parameters(params, species[0]), 2.0)
self.assertAllClose(
smap._get_species_parameters(params, species[1]), 3.0)
self.assertAllClose(
smap._get_species_parameters(params, species[2]), 3.0)
self.assertAllClose(
smap._get_species_parameters(params, species[3]), 1.0)
for s in species:
self.assertAllClose(
smap._get_species_parameters(global_params, s), 3.0)
def test_get_matrix_parameters(self):
params = np.array([1.0, 2.0])
params_mat_test = np.array([[1.0, 1.5], [1.5, 2.0]])
params_mat = smap._get_matrix_parameters(params, lambda x, y: 0.5 * (x + y))
self.assertAllClose(params_mat, params_mat_test)
params_mat_direct = np.array([[1.0, 2.0], [3.0, 4.0]])
self.assertAllClose(
smap._get_matrix_parameters(params_mat_direct, None), params_mat_direct)
params_scalar = 1.0
self.assertAllClose(
smap._get_matrix_parameters(params_scalar, None), params_scalar)
@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 POSITION_DTYPE))
def test_pair_no_species_scalar(self, spatial_dimension, dtype):
square = lambda dr: dr ** 2
displacement, _ = space.free()
metric = lambda Ra, Rb, **kwargs: \
np.sum(displacement(Ra, Rb, **kwargs) ** 2, axis=-1)
mapped_square = smap.pair(square, metric)
metric = space.map_product(metric)
key = random.PRNGKey(0)
for _ in range(STOCHASTIC_SAMPLES):
key, split = random.split(key)
R = random.uniform(
split, (PARTICLE_COUNT, spatial_dimension), dtype=dtype)
self.assertAllClose(
mapped_square(R),
np.array(0.5 * np.sum(square(metric(R, R))), dtype=dtype))
@parameterized.named_parameters(test_util.cases_from_list(
{
'testcase_name': f'_dtype={dtype.__name__}',
'dtype': dtype
} for dtype in POSITION_DTYPE))
def test_pair_no_species_pytree(self, dtype):
square_scalar = lambda dr, p0, p1: p0 * dr ** 2 + p1
square_higher = lambda dr, p: p[0] * dr ** 2 + p[1]
@dataclasses.dataclass
class Parameter:
scale: Array
shift: Array
tree_fn = lambda dr, p: p.scale * dr**2 + p.shift
displacement, _ = space.free()
metric = space.metric(displacement)
p = np.array([1.0, 2.0])
M = smap.ParameterTreeMapping
mapped_scalar = smap.pair(square_scalar, metric, p0=p[0], p1=p[1])
mapped_higher = smap.pair(square_higher, metric,
p=smap.ParameterTree(p, M.Global))
p_tree = smap.ParameterTree(Parameter(scale=p[0], shift=p[1]), M.Global)
mapped_tree = smap.pair(tree_fn, metric, p=p_tree)
key = random.PRNGKey(0)
for _ in range(STOCHASTIC_SAMPLES):
key, split = random.split(key)
R = random.uniform(split, (PARTICLE_COUNT, 2), dtype=dtype)
self.assertAllClose(mapped_scalar(R), mapped_higher(R))
self.assertAllClose(mapped_scalar(R), mapped_tree(R))
@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 POSITION_DTYPE))
def test_pair_no_species_scalar_dynamic(self, spatial_dimension, dtype):
square = lambda dr, epsilon: epsilon * dr ** 2
displacement, _ = space.free()
metric = lambda Ra, Rb, **kwargs: \
np.sum(displacement(Ra, Rb, **kwargs) ** 2, axis=-1)
mapped_square = smap.pair(square, metric, epsilon=1.0)
metric = space.map_product(metric)
key = random.PRNGKey(0)
for _ in range(STOCHASTIC_SAMPLES):
key, split1, split2 = random.split(key, 3)
R = random.uniform(
split1, (PARTICLE_COUNT, spatial_dimension), dtype=dtype)
epsilon = random.uniform(split2, (PARTICLE_COUNT,), dtype=dtype)
mat_epsilon = 0.5 * (epsilon[:, np.newaxis] + epsilon[np.newaxis, :])
self.assertAllClose(
mapped_square(R, epsilon=epsilon),
np.array(0.5 * np.sum(
square(metric(R, R), mat_epsilon)), dtype=dtype))
@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 POSITION_DTYPE))
def test_pair_no_species_vector(self, spatial_dimension, dtype):
square = lambda dr: np.sum(dr ** 2, axis=2)
disp, _ = space.free()
mapped_square = smap.pair(square, disp)
disp = space.map_product(disp)
key = random.PRNGKey(0)
for _ in range(STOCHASTIC_SAMPLES):
key, split = random.split(key)
R = random.uniform(
split, (PARTICLE_COUNT, spatial_dimension), dtype=dtype)
mapped_ref = np.array(0.5 * np.sum(square(disp(R, R))), dtype=dtype)
self.assertAllClose(mapped_square(R), mapped_ref)
@parameterized.named_parameters(test_util.cases_from_list(
{
'testcase_name': f'_dtype={dtype.__name__}',
'dtype': dtype
} for dtype in POSITION_DTYPE))
def test_pair_no_species_pytree_per_particle(self, dtype):
square_scalar = lambda dr, p0, p1: p0 * dr ** 2 + p1
square_higher = lambda dr, p: p[..., 0] * dr ** 2 + p[..., 1]
@dataclasses.dataclass
class Parameter:
scale: Array
shift: Array
tree_fn = lambda dr, p: p.scale * dr**2 + p.shift
displacement, _ = space.free()
metric = space.metric(displacement)
p = random.uniform(random.PRNGKey(1), (PARTICLE_COUNT, 2))
M = smap.ParameterTreeMapping
mapped_scalar = smap.pair(square_scalar, metric, p0=p[:, 0], p1=p[:, 1])
p_higher = smap.ParameterTree(p, M.PerParticle)
mapped_higher = smap.pair(square_higher, metric, p=p_higher)
p_tree = smap.ParameterTree(Parameter(scale=p[:, 0], shift=p[:, 1]),
M.PerParticle)
mapped_tree = smap.pair(tree_fn, metric, p=p_tree)
key = random.PRNGKey(0)
for _ in range(STOCHASTIC_SAMPLES):
key, split = random.split(key)
R = random.uniform(split, (PARTICLE_COUNT, 2), dtype=dtype)
self.assertAllClose(mapped_scalar(R), mapped_higher(R))
self.assertAllClose(mapped_scalar(R), mapped_tree(R))
@parameterized.named_parameters(test_util.cases_from_list(
{
'testcase_name': f'_dtype={dtype.__name__}',
'dtype': dtype
} for dtype in POSITION_DTYPE))
def test_pair_no_species_pytree_order_per_bond(self, dtype):
square_scalar = lambda dr, p0, p1: p0 * dr ** 2 + p1
square_higher = lambda dr, p: p[..., 0] * dr ** 2 + p[..., 1]
@dataclasses.dataclass
class Parameter:
scale: Array
shift: Array
tree_fn = lambda dr, p: p.scale * dr**2 + p.shift
displacement, _ = space.free()
metric = space.metric(displacement)
p = random.uniform(random.PRNGKey(1), (PARTICLE_COUNT, PARTICLE_COUNT, 2))
M = smap.ParameterTreeMapping
mapped_scalar = smap.pair(square_scalar, metric,
p0=p[..., 0], p1=p[..., 1])
mapped_higher = smap.pair(square_higher, metric,
p=smap.ParameterTree(p, M.PerBond))
p_tree = smap.ParameterTree(Parameter(scale=p[..., 0], shift=p[..., 1]),
M.PerBond)
mapped_tree = smap.pair(tree_fn, metric, p=p_tree)
key = random.PRNGKey(0)
for _ in range(STOCHASTIC_SAMPLES):
key, split = random.split(key)
R = random.uniform(split, (PARTICLE_COUNT, 2), dtype=dtype)
self.assertAllClose(mapped_scalar(R), mapped_higher(R))
self.assertAllClose(mapped_scalar(R), mapped_tree(R))
@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 POSITION_DTYPE))
def test_pair_no_species_vector_nonadditive(self, spatial_dimension, dtype):
square = lambda dr, params: params * np.sum(dr ** 2, axis=2)
disp, _ = space.free()
mapped_square = smap.pair(square, disp, params=lambda x, y: x * y)
disp = space.map_product(disp)
key = random.PRNGKey(0)
for _ in range(STOCHASTIC_SAMPLES):
key, R_key, params_key = random.split(key, 3)
R = random.uniform(
R_key, (PARTICLE_COUNT, spatial_dimension), dtype=dtype)
params = random.uniform(
params_key, (PARTICLE_COUNT,), dtype=dtype, minval=0.1, maxval=1.5)
pp_params = params[None, :] * params[:, None]
mapped_ref = np.array(0.5 * np.sum(square(disp(R, R), pp_params)),
dtype=dtype)
self.assertAllClose(mapped_square(R, params=params), mapped_ref)
@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 POSITION_DTYPE))
def test_pair_static_species_scalar(self, spatial_dimension, dtype):
key = random.PRNGKey(0)
square = lambda dr, param=1.0: param * dr ** 2
params = f32(np.array([[1.0, 2.0], [2.0, 3.0]]))
key, split = random.split(key)
species = random.randint(split, (PARTICLE_COUNT,), 0, 2)
displacement, _ = space.free()
metric = lambda Ra, Rb, **kwargs: \
np.sum(displacement(Ra, Rb, **kwargs) ** 2, axis=-1)
mapped_square = smap.pair(
square, metric, species=species, param=params)
metric = space.map_product(metric)
for _ in range(STOCHASTIC_SAMPLES):
key, split = random.split(key)
R = random.uniform(
split, (PARTICLE_COUNT, spatial_dimension), dtype=dtype)
total = 0.0
for i in range(2):
for j in range(2):
param = params[i, j]
R_1 = R[species == i]
R_2 = R[species == j]
total = total + 0.5 * np.sum(square(metric(R_1, R_2), param))
self.assertAllClose(mapped_square(R), np.array(total, dtype=dtype))
@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 POSITION_DTYPE))
def test_pair_static_species_scalar_dynamic(self, spatial_dimension, dtype):
key = random.PRNGKey(0)
square = lambda dr, param=1.0: param * dr ** 2
key, split = random.split(key)
species = random.randint(split, (PARTICLE_COUNT,), 0, 2)
displacement, _ = space.free()
metric = lambda Ra, Rb, **kwargs: \
np.sum(displacement(Ra, Rb, **kwargs) ** 2, axis=-1)
mapped_square = smap.pair(square, metric, species=species, param=1.0)
metric = space.map_product(metric)
for _ in range(STOCHASTIC_SAMPLES):
key, split1, split2 = random.split(key, 3)
R = random.uniform(
split1, (PARTICLE_COUNT, spatial_dimension), dtype=dtype)
params = random.uniform(
split2, (2, 2), dtype=dtype)
params = f32(0.5) * (params + params.T)
total = 0.0
for i in range(2):
for j in range(2):
param = params[i, j]
R_1 = R[species == i]
R_2 = R[species == j]
total = total + 0.5 * np.sum(square(metric(R_1, R_2), param))
self.assertAllClose(
mapped_square(R, param=params), np.array(total, dtype=dtype))
@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 POSITION_DTYPE))
def test_pair_scalar_dummy_arg(
self, spatial_dimension, dtype):
key = random.PRNGKey(0)
square = lambda dr, param=f32(1.0), **unused_kwargs: param * dr ** 2
key, split = random.split(key)
R = random.normal(key, (PARTICLE_COUNT, spatial_dimension), dtype=dtype)
displacement, shift = space.free()
mapped = smap.pair(square, space.metric(displacement))
mapped(R, t=f32(0))
@parameterized.named_parameters(test_util.cases_from_list(
{
'testcase_name': f'_dtype={dtype.__name__}',
'dtype': dtype
} for dtype in POSITION_DTYPE))
def test_pair_species_pytree_global(self, dtype):
square_scalar = lambda dr, p0, p1: p0 * dr ** 2 + p1
square_higher = lambda dr, p: p[..., 0] * dr ** 2 + p[..., 1]
@dataclasses.dataclass
class Parameter:
scale: Array
shift: Array
square_tree = lambda dr, p: p.scale * dr**2 + p.shift
displacement, _ = space.free()
metric = space.metric(displacement)
p = np.array([1.0, 2.0])
M = smap.ParameterTreeMapping
species = np.where(np.arange(PARTICLE_COUNT) < PARTICLE_COUNT // 2, 0, 1)
mapped_scalar = smap.pair(square_scalar, metric, species=species,
p0=p[0], p1=p[1])
p_h = smap.ParameterTree(p, M.Global)
mapped_higher = smap.pair(square_higher, metric, species=species, p=p_h)
p_tree = smap.ParameterTree(Parameter(p[0], p[1]), M.Global)
mapped_tree = smap.pair(square_tree, metric, species=species, p=p_tree)
key = random.PRNGKey(0)
for _ in range(STOCHASTIC_SAMPLES):
key, split = random.split(key)
R = random.uniform(split, (PARTICLE_COUNT, 2), dtype=dtype)
self.assertAllClose(mapped_scalar(R), mapped_higher(R))
self.assertAllClose(mapped_scalar(R), mapped_tree(R))
@parameterized.named_parameters(test_util.cases_from_list(
{
'testcase_name': f'_dtype={dtype.__name__}',
'dtype': dtype
} for dtype in POSITION_DTYPE))
def test_pair_species_pytree_per_species(self, dtype):
square_scalar = lambda dr, p0, p1: p0 * dr ** 2 + p1
square_higher = lambda dr, p: p[..., 0] * dr ** 2 + p[..., 1]
@dataclasses.dataclass
class Parameter:
scale: Array
shift: Array
square_tree = lambda dr, p: p.scale * dr**2 + p.shift
displacement, _ = space.free()
metric = space.metric(displacement)
p = random.uniform(random.PRNGKey(1), (2, 2, 2))
p = p + np.transpose(p, (1, 0, 2))
species = np.where(np.arange(PARTICLE_COUNT) < PARTICLE_COUNT // 2, 0, 1)
mapped_scalar = smap.pair(square_scalar, metric, species=species,
p0=p[..., 0], p1=p[..., 1])
M = smap.ParameterTreeMapping
p_h = smap.ParameterTree(p, M.PerSpecies)
mapped_higher = smap.pair(square_higher, metric, species=species, p=p_h)
p_tree = smap.ParameterTree(Parameter(p[..., 0], p[..., 1]), M.PerSpecies)
mapped_tree = smap.pair(square_tree, metric, species=species, p=p_tree)
key = random.PRNGKey(0)
for _ in range(STOCHASTIC_SAMPLES):
key, split = random.split(key)
R = random.uniform(split, (PARTICLE_COUNT, 2), dtype=dtype)
self.assertAllClose(mapped_scalar(R), mapped_higher(R))
self.assertAllClose(mapped_scalar(R), mapped_tree(R))
@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 POSITION_DTYPE))
def test_pair_static_species_vector(self, spatial_dimension, dtype):
key = random.PRNGKey(0)
square = lambda dr, param=1.0: param * np.sum(dr ** 2, axis=2)
params = np.array([[1.0, 2.0], [2.0, 3.0]], dtype=f32)
key, split = random.split(key)
species = random.randint(split, (PARTICLE_COUNT,), 0, 2)
disp, _ = space.free()
mapped_square = smap.pair(square, disp, species=species, param=params)
disp = space.map_product(disp)
for _ in range(STOCHASTIC_SAMPLES):
key, split = random.split(key)
R = random.uniform(
split, (PARTICLE_COUNT, spatial_dimension), dtype=dtype)
total = 0.0
for i in range(2):
for j in range(2):
param = params[i, j]
R_1 = R[species == i]
R_2 = R[species == j]
total = total + 0.5 * np.sum(square(disp(R_1, R_2), param))
self.assertAllClose(mapped_square(R), np.array(total, dtype=dtype))
@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 POSITION_DTYPE))
def test_pair_dynamic_species_scalar(self, spatial_dimension, dtype):
key = random.PRNGKey(0)
square = lambda dr, param=1.0: param * dr ** 2
params = f32(np.array([[1.0, 2.0], [2.0, 3.0]]))
key, split = random.split(key)
species = random.randint(split, (PARTICLE_COUNT,), 0, 2)
displacement, _ = space.free()
metric = space.metric(displacement)
mapped_square = smap.pair(square, metric, species=2, param=params)
metric = space.map_product(metric)
for _ in range(STOCHASTIC_SAMPLES):
key, split = random.split(key)
R = random.uniform(
split, (PARTICLE_COUNT, spatial_dimension), dtype=dtype)
total = 0.0
for i in range(2):
for j in range(2):
param = params[i, j]
R_1 = R[species == i]
R_2 = R[species == j]
total = total + 0.5 * np.sum(square(metric(R_1, R_2), param))
self.assertAllClose(mapped_square(R, species),
np.array(total, dtype=dtype))
@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 POSITION_DTYPE))
def test_pair_dynamic_species_vector(
self, spatial_dimension, dtype):
key = random.PRNGKey(0)
square = lambda dr, param=1.0: param * np.sum(dr ** 2, axis=-1)
params = f32(np.array([[1.0, 2.0], [2.0, 3.0]]))
key, split = random.split(key)
species = random.randint(split, (PARTICLE_COUNT,), 0, 2)
disp, _ = space.free()
mapped_square = smap.pair(square, disp, species=2, param=params)
disp = vmap(vmap(disp, (0, None), 0), (None, 0), 0)
for _ in range(STOCHASTIC_SAMPLES):
key, split = random.split(key)
R = random.uniform(
split, (PARTICLE_COUNT, spatial_dimension), dtype=dtype)
total = 0.0
for i in range(2):
for j in range(2):
param = params[i, j]
R_1 = R[species == i]
R_2 = R[species == j]
total = total + 0.5 * np.sum(square(disp(R_1, R_2), param))
self.assertAllClose(mapped_square(R, species),
np.array(total, dtype=dtype))
@parameterized.named_parameters(test_util.cases_from_list(
{
'testcase_name': (f'_dim={dim}_dtype={dtype.__name__}'
f'_format={format}'),
'spatial_dimension': dim,
'dtype': dtype,
'format': format
} for dim in SPATIAL_DIMENSION
for dtype in POSITION_DTYPE
for format in NEIGHBOR_LIST_FORMAT))
def test_pair_neighbor_list_scalar(self, spatial_dimension, dtype, format):
key = random.PRNGKey(0)
def truncated_square(dr, sigma):
return np.where(dr < sigma, dr ** 2, f32(0.))
N = NEIGHBOR_LIST_PARTICLE_COUNT
box_size = 4. * N ** (1. / spatial_dimension)
key, split = random.split(key)
disp, _ = space.periodic(box_size)
d = space.metric(disp)
neighbor_square = smap.pair_neighbor_list(truncated_square, d, sigma=1.0)
neighbor_square = jit(neighbor_square)
mapped_square = jit(smap.pair(truncated_square, d, sigma=1.0))
for _ in range(STOCHASTIC_SAMPLES):
key, split = random.split(key)
R = box_size * random.uniform(
split, (N, spatial_dimension), dtype=dtype)
sigma = random.uniform(key, (), minval=0.5, maxval=2.5)
neighbor_fn = partition.neighbor_list(disp, box_size, sigma, 0.0,
format=format)
nbrs = neighbor_fn.allocate(R)
self.assertAllClose(mapped_square(R, sigma=sigma),
neighbor_square(R, nbrs, sigma=sigma))
@parameterized.named_parameters(test_util.cases_from_list(
{
'testcase_name': (f'_dtype={dtype.__name__}'
f'_format={format}'),
'dtype': dtype,
'format': format
} for dtype in POSITION_DTYPE
for format in NEIGHBOR_LIST_FORMAT))
def test_pair_neighbor_list_pytree(self, dtype, format):
key = random.PRNGKey(0)
dim = 2
def scalar_fn(dr, sigma, shift):
return np.where(dr < sigma, dr ** 2 + shift, f32(0.))
def higher_order_fn(dr, p):
sigma = random.uniform(key, (), minval=0.5, maxval=2.5)
return np.where(dr < p[..., 0], dr ** 2 + p[..., 1], f32(0.))
@dataclasses.dataclass
class Parameter:
sigma: Array
shift: Array
def tree_fn(dr, p):
return np.where(dr < p.sigma, dr ** 2 + p.shift, f32(0.))
N = NEIGHBOR_LIST_PARTICLE_COUNT
box_size = 4. * N ** (1. / dim)
key, split = random.split(key)
disp, _ = space.periodic(box_size)
d = space.metric(disp)
sigma = 1.0
shift = 2.0
M = smap.ParameterTreeMapping
neighbor_scalar = smap.pair_neighbor_list(scalar_fn, d,
sigma=sigma, shift=shift)
p = smap.ParameterTree(jnp.array([sigma, shift], dtype), M.Global)
neighbor_higher = smap.pair_neighbor_list(higher_order_fn, d, p=p)
p_tree = smap.ParameterTree(Parameter(sigma=sigma, shift=shift), M.Global)
neighbor_tree = smap.pair_neighbor_list(tree_fn, d, p=p_tree)
for _ in range(STOCHASTIC_SAMPLES):
key, split = random.split(key)
R = box_size * random.uniform(split, (N, dim), dtype=dtype)
sigma = random.uniform(key, (), minval=0.5, maxval=2.5)
neighbor_fn = partition.neighbor_list(disp, box_size, sigma, 0.0,
format=format)
nbrs = neighbor_fn.allocate(R)
self.assertAllClose(neighbor_scalar(R, nbrs),
neighbor_higher(R, nbrs))
self.assertAllClose(neighbor_scalar(R, nbrs),
neighbor_tree(R, nbrs))
@parameterized.named_parameters(test_util.cases_from_list(
{
'testcase_name': (f'_dtype={dtype.__name__}'
f'_format={str(format).split(".")[-1]}'),
'dtype': dtype,
'format': format
} for dtype in POSITION_DTYPE
for format in NEIGHBOR_LIST_FORMAT))
def test_pair_neighbor_list_per_atom_pytree(self, dtype, format):
key = random.PRNGKey(0)
dim = 2
def scalar_fn(dr, sigma, shift):
return np.where(dr < sigma, dr ** 2 + shift, f32(0.))
def higher_order_fn(dr, p):
return np.where(dr < p[..., 0], dr ** 2 + p[..., 1], f32(0.))
@dataclasses.dataclass
class Parameter:
sigma: Array
shift: Array
def tree_fn(dr, p):
return np.where(dr < p.sigma, dr ** 2 + p.shift, f32(0.))
N = NEIGHBOR_LIST_PARTICLE_COUNT
box_size = 4. * N ** (1. / dim)
key, split, split2 = random.split(key, 3)
disp, _ = space.periodic(box_size)
d = space.metric(disp)
sigma = random.uniform(split, (N,), minval=0.5, maxval=1.0, dtype=dtype)
shift = random.uniform(split2, (N,), dtype=dtype)
M = smap.ParameterTreeMapping
neighbor_scalar = smap.pair_neighbor_list(scalar_fn, d,
sigma=sigma, shift=shift)
p = smap.ParameterTree(np.concatenate([sigma[:, None], shift[:, None]],
axis=-1),
M.PerParticle)
neighbor_higher = smap.pair_neighbor_list(higher_order_fn, d, p=p)
p_tree = smap.ParameterTree(Parameter(sigma=sigma, shift=shift),
M.PerParticle)
neighbor_tree = smap.pair_neighbor_list(tree_fn, d, p=p_tree)
for _ in range(STOCHASTIC_SAMPLES):
key, split = random.split(key)
R = box_size * random.uniform(split, (N, dim), dtype=dtype)
sigma = random.uniform(key, (), minval=0.5, maxval=2.5)
neighbor_fn = partition.neighbor_list(disp, box_size, sigma, 0.0,
format=format)
nbrs = neighbor_fn.allocate(R)
self.assertAllClose(neighbor_scalar(R, nbrs),
neighbor_higher(R, nbrs))
self.assertAllClose(neighbor_scalar(R, nbrs),
neighbor_tree(R, nbrs))
@parameterized.named_parameters(test_util.cases_from_list(
{
'testcase_name': (f'_dtype={dtype.__name__}'
f'_format={str(format).split(".")[-1]}'),
'dtype': dtype,
'format': format
} for dtype in POSITION_DTYPE
for format in NEIGHBOR_LIST_FORMAT))
def test_pair_neighbor_list_per_bond_pytree(self, dtype, format):
key = random.PRNGKey(0)
dim = 2
def scalar_fn(dr, sigma, shift):
return np.where(dr < sigma, dr ** 2 + shift, f32(0.))
def higher_order_fn(dr, p):
return np.where(dr < p[..., 0], dr ** 2 + p[..., 1], f32(0.))
@dataclasses.dataclass
class Parameter:
sigma: Array
shift: Array
def tree_fn(dr, p):
return np.where(dr < p.sigma, dr ** 2 + p.shift, f32(0.))
N = NEIGHBOR_LIST_PARTICLE_COUNT
box_size = 4. * N ** (1. / dim)
key, split, split2 = random.split(key, 3)
disp, _ = space.periodic(box_size)
d = space.metric(disp)
sigma = random.uniform(split, (N, N), minval=0.5, maxval=1.0, dtype=dtype)
shift = random.uniform(split2, (N, N), dtype=dtype)
M = smap.ParameterTreeMapping
neighbor_scalar = smap.pair_neighbor_list(scalar_fn, d,
sigma=sigma, shift=shift)
p = smap.ParameterTree(np.concatenate([sigma[:, :, None],
shift[:, :, None]],
axis=-1),
M.PerBond)
neighbor_higher = smap.pair_neighbor_list(higher_order_fn, d, p=p)
p_tree = smap.ParameterTree(Parameter(sigma=sigma, shift=shift),
M.PerBond)
neighbor_tree = smap.pair_neighbor_list(tree_fn, d, p=p_tree)
for _ in range(STOCHASTIC_SAMPLES):
key, split = random.split(key)
R = box_size * random.uniform(split, (N, dim), dtype=dtype)
sigma = random.uniform(key, (), minval=0.5, maxval=2.5)
neighbor_fn = partition.neighbor_list(disp, box_size, sigma, 0.0,
format=format)
nbrs = neighbor_fn.allocate(R)
self.assertAllClose(neighbor_scalar(R, nbrs),
neighbor_higher(R, nbrs))
self.assertAllClose(neighbor_scalar(R, nbrs),
neighbor_tree(R, nbrs))
@parameterized.named_parameters(test_util.cases_from_list(
{
'testcase_name': (f'_dtype={dtype.__name__}'
f'_format={str(format).split(".")[-1]}'),
'dtype': dtype,
'format': format
} for dtype in POSITION_DTYPE
for format in NEIGHBOR_LIST_FORMAT))
def test_pair_neighbor_list_species_global_pytree(self, dtype, format):
key = random.PRNGKey(0)
dim = 2
def scalar_fn(dr, sigma, shift):
return np.where(dr < sigma, dr ** 2 + shift, f32(0.))
def higher_order_fn(dr, p):
return np.where(dr < p[0], dr ** 2 + p[1], f32(0.))
@dataclasses.dataclass
class Parameter:
sigma: Array
shift: Array
def tree_fn(dr, p):
return np.where(dr < p.sigma, dr ** 2 + p.shift, f32(0.))
N = NEIGHBOR_LIST_PARTICLE_COUNT
box_size = 4. * N ** (1. / dim)
key, split, split2 = random.split(key, 3)
disp, _ = space.periodic(box_size)
d = space.metric(disp)
sigma = f32(1.5)
shift = f32(2.0)
species = jnp.where(jnp.arange(N) < N // 2, 0, 1)
M = smap.ParameterTreeMapping
neighbor_scalar = smap.pair_neighbor_list(scalar_fn, d, species=species,
sigma=sigma, shift=shift)
p = smap.ParameterTree(np.array([sigma, shift]),
M.Global)
neighbor_higher = smap.pair_neighbor_list(higher_order_fn, d,
species=species, p=p)
p_tree = smap.ParameterTree(Parameter(sigma=sigma, shift=shift),
M.Global)
neighbor_tree = smap.pair_neighbor_list(tree_fn, d,
species=species, p=p_tree)
for _ in range(STOCHASTIC_SAMPLES):
key, split = random.split(key)
R = box_size * random.uniform(split, (N, dim), dtype=dtype)
sigma = random.uniform(key, (), minval=0.5, maxval=2.5)
neighbor_fn = partition.neighbor_list(disp, box_size, sigma, 0.0,
format=format)
nbrs = neighbor_fn.allocate(R)
self.assertAllClose(neighbor_scalar(R, nbrs),
neighbor_higher(R, nbrs))
self.assertAllClose(neighbor_scalar(R, nbrs),
neighbor_tree(R, nbrs))
@parameterized.named_parameters(test_util.cases_from_list(
{
'testcase_name': (f'_dtype={dtype.__name__}'