def test_brentsroot_wrong_order():
if D.backend() == 'torch':
import torch
torch.set_printoptions(precision=17)
torch.autograd.set_detect_anomaly(True)
a = D.array(1.0)
b = D.array(1.0)
gt_root = -b / a
lb, ub = -b / a - 1, -b / a + 1
fun = lambda x: a * x + b
assert (D.to_numpy(D.to_float(D.abs(fun(gt_root)))) <= 32 * D.epsilon())
root, success = de.utilities.optimizer.brentsroot(fun, [ub, lb],
4 * D.epsilon(),
verbose=True)
assert (success)
assert (np.allclose(D.to_numpy(D.to_float(gt_root)),
D.to_numpy(D.to_float(root)), 32 * D.epsilon(),
32 * D.epsilon()))
def test_addcmul_within_tolerance_out(ffmt):
D.set_float_fmt(ffmt)
pi = D.to_float(D.pi)
out = D.copy(pi)
D.addcmul(pi, D.to_float(3), D.to_float(2), value=1, out=out)
assert (pi + (1 * (3 * 2)) - 2 * D.epsilon() <= out <= pi +
(1 * (3 * 2)) + 2 * D.epsilon())
def test_newtonraphson_pytorch_jacobian(ffmt, tol):
print("Set dtype to:", ffmt)
D.set_float_fmt(ffmt)
np.random.seed(21)
if tol is not None:
tol = tol * D.epsilon()
if D.backend() == 'torch':
import torch
torch.set_printoptions(precision=17)
torch.autograd.set_detect_anomaly(False)
if ffmt == 'gdual_vdouble':
pytest.skip("Root-finding is ill-conceived with vectorised gduals")
for _ in range(10):
ac_prod = D.array(np.random.uniform(0.9, 1.1))
a = D.array(np.random.uniform(-1, 1))
a = D.to_float(-1 * (a <= 0) + 1 * (a > 0))
c = ac_prod / a
b = D.sqrt(0.01 + 4 * ac_prod)
gt_root1 = -b / (2 * a) - 0.1 / (2 * a)
gt_root2 = -b / (2 * a) + 0.1 / (2 * a)
ub = -b / (2 * a) - 0.2 / (2 * a)
lb = -b / (2 * a) - 0.4 / (2 * a)
x0 = D.array(np.random.uniform(ub, lb))
fun = lambda x: a * x**2 + b * x + c
assert (D.to_numpy(D.to_float(D.abs(fun(gt_root1)))) <=
32 * D.epsilon())
assert (D.to_numpy(D.to_float(D.abs(fun(gt_root2)))) <=
32 * D.epsilon())
root, (success, num_iter,
prec) = de.utilities.optimizer.newtonraphson(fun,
x0,
tol=tol,
verbose=True)
if tol is None:
tol = D.epsilon()
conv_root1 = np.allclose(D.to_numpy(D.to_float(gt_root1)),
D.to_numpy(D.to_float(root)), 128 * tol,
32 * tol)
conv_root2 = np.allclose(D.to_numpy(D.to_float(gt_root2)),
D.to_numpy(D.to_float(root)), 128 * tol,
32 * tol)
print(conv_root1, conv_root2, root, gt_root1, gt_root2, x0,
root - gt_root1, root - gt_root2, num_iter, prec)
assert (success)
assert (conv_root1 or conv_root2)
assert (D.to_numpy(D.to_float(D.abs(fun(root)))) <= 32 * tol)
def test_frac_within_tolerance_out(ffmt):
D.set_float_fmt(ffmt)
pi = D.to_float(D.pi)
out = D.copy(pi)
D.frac(pi, out=out)
assert (0.141592653589793238 - 2 * D.epsilon() <= out <=
0.141592653589793238 + 2 * D.epsilon())
def test_float_formats_typical_shape(ffmt, integrator,
use_richardson_extrapolation, device):
if use_richardson_extrapolation and integrator.__implicit__:
pytest.skip(
"Richardson Extrapolation is too slow with implicit methods")
D.set_float_fmt(ffmt)
if D.backend() == 'torch':
import torch
torch.set_printoptions(precision=17)
torch.autograd.set_detect_anomaly(False) # Enable if a test fails
device = torch.device(device)
print("Testing {} float format".format(D.float_fmt()))
from .common import set_up_basic_system
de_mat, rhs, analytic_soln, y_init, dt, _ = set_up_basic_system(
integrator, hook_jacobian=True)
y_init = D.array([1., 0.])
if D.backend() == 'torch':
y_init = y_init.to(device)
a = de.OdeSystem(rhs,
y0=y_init,
dense_output=False,
t=(0, D.pi / 4),
dt=D.pi / 64,
rtol=D.epsilon()**0.5,
atol=D.epsilon()**0.5)
method = integrator
method_tolerance = a.atol * 10 + D.epsilon()
if use_richardson_extrapolation:
method = de.integrators.generate_richardson_integrator(method)
method_tolerance = method_tolerance * 5
with de.utilities.BlockTimer(section_label="Integrator Tests") as sttimer:
a.set_method(method)
print("Testing {} with dt = {:.4e}".format(a.integrator, a.dt))
a.integrate(eta=True)
print("Average step-size:",
D.mean(D.abs(D.array(a.t[1:]) - D.array(a.t[:-1]))))
max_diff = D.max(D.abs(analytic_soln(a.t[-1], y_init) - a.y[-1]))
if a.integrator.adaptive:
assert max_diff <= method_tolerance, "{} Failed with max_diff from analytical solution = {}".format(
a.integrator, max_diff)
if a.integrator.__implicit__:
assert rhs.analytic_jacobian_called and a.njev > 0, "Analytic jacobian was called as part of integration"
a.reset()
print("")
print("{} backend test passed successfully!".format(D.backend()))
def test_softplus_within_tolerance_out(ffmt):
D.set_float_fmt(ffmt)
pi = D.to_float(D.pi)
out = D.copy(pi)
D.softplus(pi, out=out)
assert (3.18389890758499587775 - 2 * D.epsilon() <= out <=
3.18389890758499587775 + 2 * D.epsilon())
def test_callback_called():
de_mat = D.array([[0.0, 1.0], [-1.0, 0.0]])
@de.rhs_prettifier("""[vx, -x+t]""")
def rhs(t, state, **kwargs):
return de_mat @ state + D.array([0.0, t])
y_init = D.array([1., 0.])
callback_called = False
def callback(ode_sys):
nonlocal callback_called
if not callback_called and ode_sys.t[-1] > D.pi:
callback_called = True
a = de.OdeSystem(rhs,
y0=y_init,
dense_output=True,
t=(0, 2 * D.pi),
dt=0.01,
rtol=D.epsilon()**0.5,
atol=D.epsilon()**0.5)
a.integrate(callback=callback)
assert (callback_called)
def test_keyboard_interrupt_caught():
de_mat = D.array([[0.0, 1.0], [-1.0, 0.0]])
@de.rhs_prettifier("""[vx, -x+t]""")
def rhs(t, state, **kwargs):
return de_mat @ state + D.array([0.0, t])
y_init = D.array([1., 0.])
def kb_callback(ode_sys):
if ode_sys.t[-1] > D.pi:
raise KeyboardInterrupt()
a = de.OdeSystem(rhs,
y0=y_init,
dense_output=True,
t=(0, 2 * D.pi),
dt=0.01,
rtol=D.epsilon()**0.5,
atol=D.epsilon()**0.5)
with pytest.raises(KeyboardInterrupt):
a.integrate(callback=kb_callback)
assert (a.integration_status ==
"A KeyboardInterrupt exception was raised during integration.")
def test_non_callable_rhs(ffmt):
with pytest.raises(TypeError):
D.set_float_fmt(ffmt)
if D.backend() == 'torch':
import torch
torch.set_printoptions(precision=17)
torch.autograd.set_detect_anomaly(True)
print("Testing {} float format".format(D.float_fmt()))
from . import common
(de_mat, rhs, analytic_soln, y_init, dt,
_) = common.set_up_basic_system()
a = de.OdeSystem(de_mat,
y0=y_init,
dense_output=False,
t=(0, 2 * D.pi),
dt=dt,
rtol=D.epsilon()**0.5,
atol=D.epsilon()**0.5,
constants=dict(k=1.0))
a.tf = 0.0
def test_dt_dir_fix(ffmt):
D.set_float_fmt(ffmt)
if D.backend() == 'torch':
import torch
torch.set_printoptions(precision=17)
torch.autograd.set_detect_anomaly(True)
print("Testing {} float format".format(D.float_fmt()))
de_mat = D.array([[0.0, 1.0], [-1.0, 0.0]])
@de.rhs_prettifier("""[vx, -x+t]""")
def rhs(t, state, k, **kwargs):
return de_mat @ state + D.array([0.0, t])
y_init = D.array([1., 0.])
a = de.OdeSystem(rhs,
y0=y_init,
dense_output=False,
t=(0, 2 * D.pi),
dt=-0.01,
rtol=D.epsilon()**0.5,
atol=D.epsilon()**0.5,
constants=dict(k=1.0))
def test_square_within_tolerance_out(ffmt):
D.set_float_fmt(ffmt)
pi = D.to_float(D.pi)
out = D.copy(pi)
D.square(pi, out=out)
assert (9.8696044010893586188 - 2 * D.epsilon() <= out <=
9.8696044010893586188 + 2 * D.epsilon())
def test_brentsroot():
for fmt in D.available_float_fmt():
print("Set dtype to:", fmt)
D.set_float_fmt(fmt)
for _ in range(10):
ac_prod = D.array(np.random.uniform(0.9, 1.1))
a = D.array(np.random.uniform(-1, 1))
a = D.to_float(-1 * (a <= 0) + 1 * (a > 0))
c = ac_prod / a
b = D.sqrt(0.01 + 4 * ac_prod)
gt_root = -b / (2 * a) - 0.1 / (2 * a)
ub = -b / (2 * a)
lb = -b / (2 * a) - 1.0 / (2 * a)
fun = lambda x: a * x**2 + b * x + c
assert (D.to_numpy(D.to_float(D.abs(fun(gt_root)))) <=
32 * D.epsilon())
root, success = de.utilities.optimizer.brentsroot(fun, [lb, ub],
4 * D.epsilon(),
verbose=True)
assert (success)
assert (np.allclose(D.to_numpy(D.to_float(gt_root)),
D.to_numpy(D.to_float(root)), 32 * D.epsilon(),
32 * D.epsilon()))
assert (D.to_numpy(D.to_float(D.abs(fun(root)))) <=
32 * D.epsilon())
def test_brentsroot_same_sign():
if D.backend() == 'torch':
import torch
torch.set_printoptions(precision=17)
torch.autograd.set_detect_anomaly(True)
ac_prod = D.array(np.random.uniform(0.9, 1.1))
a = D.array(1.0)
b = D.array(1.0)
gt_root = -b / a
lb, ub = -b / a - 1, -b / a - 2
fun = lambda x: a * x + b
assert (D.to_numpy(D.to_float(D.abs(fun(gt_root)))) <= 32 * D.epsilon())
root, success = de.utilities.optimizer.brentsroot(fun, [lb, ub],
4 * D.epsilon(),
verbose=True)
assert (np.isinf(root))
assert (not success)
def test_jacobian_wrapper_exact(ffmt):
D.set_float_fmt(ffmt)
rhs = lambda x: D.exp(-x)
drhs_exact = lambda x: -D.exp(-x)
jac_rhs = de.utilities.JacobianWrapper(rhs, rtol=D.epsilon() ** 0.5, atol=D.epsilon() ** 0.5)
x = D.array(0.0)
assert (D.allclose(D.to_float(drhs_exact(x)), D.to_float(jac_rhs(x)), rtol=4 * D.epsilon() ** 0.5, atol=4 * D.epsilon() ** 0.5))
def test_newtonraphson_dims(ffmt, tol, dim):
print("Set dtype to:", ffmt)
D.set_float_fmt(ffmt)
np.random.seed(30)
if tol is not None:
tol = tol * D.epsilon()
if D.backend() == 'torch':
import torch
torch.set_printoptions(precision=17)
torch.autograd.set_detect_anomaly(False)
if ffmt == 'gdual_vdouble':
pytest.skip("Root-finding is ill-conceived with vectorised gduals")
shift = D.array(np.random.uniform(1, 10, size=(dim, )))
exponent = D.array(np.random.uniform(1, 5, size=(dim, )))
gt_root1 = shift**(1 / exponent)
gt_root2 = -shift**(1 / exponent)
def fun(x):
return x**exponent - shift
def jac(x):
return D.diag(exponent * D.reshape(x, (-1, ))**(exponent - 1))
x0 = D.array(np.random.uniform(1, 3, size=(dim, )))
print(gt_root1, gt_root2)
print(x0)
print(fun(x0))
print(jac(x0))
root, (success, num_iter,
prec) = de.utilities.optimizer.newtonraphson(fun,
x0,
jac=jac,
tol=tol,
verbose=True)
if tol is None:
tol = D.epsilon()
assert (success)
conv_root1 = D.stack([
D.array(np.allclose(D.to_numpy(D.to_float(r1)),
D.to_numpy(D.to_float(r)), 128 * tol, 32 * tol),
dtype=D.bool) for r, r1 in zip(root, gt_root1)
])
conv_root2 = D.stack([
D.array(np.allclose(D.to_numpy(D.to_float(r2)),
D.to_numpy(D.to_float(r)), 128 * tol, 32 * tol),
dtype=D.bool) for r, r2 in zip(root, gt_root2)
])
assert (D.all(conv_root1 | conv_root2))
def test_integration_and_representation():
for ffmt in D.available_float_fmt():
D.set_float_fmt(ffmt)
print("Testing {} float format".format(D.float_fmt()))
de_mat = D.array([[0.0, 1.0], [-1.0, 0.0]])
@de.rhs_prettifier("""[vx, -x+t]""")
def rhs(t, state, k, **kwargs):
return de_mat @ state + D.array([0.0, t])
def analytic_soln(t, initial_conditions):
c1 = initial_conditions[0]
c2 = initial_conditions[1] - 1
return D.stack([
c2 * D.sin(D.to_float(D.asarray(t))) +
c1 * D.cos(D.to_float(D.asarray(t))) + D.asarray(t),
c2 * D.cos(D.to_float(D.asarray(t))) -
c1 * D.sin(D.to_float(D.asarray(t))) + 1
])
def kbinterrupt_cb(ode_sys):
if ode_sys[-1][0] > D.pi:
raise KeyboardInterrupt("Test Interruption and Catching")
y_init = D.array([1., 0.])
a = de.OdeSystem(rhs,
y0=y_init,
dense_output=True,
t=(0, 2 * D.pi),
dt=0.01,
rtol=D.epsilon()**0.5,
atol=D.epsilon()**0.5,
constants=dict(k=1.0))
a.integrate()
try:
print(str(a))
print(repr(a))
assert (D.max(D.abs(a.sol(a.t[0]) - y_init)) <=
8 * D.epsilon()**0.5)
assert (D.max(
D.abs(a.sol(a.t[-1]) - analytic_soln(a.t[-1], y_init))) <=
8 * D.epsilon()**0.5)
assert (D.max(D.abs(a.sol(a.t).T - analytic_soln(a.t, y_init))) <=
8 * D.epsilon()**0.5)
except:
raise
def test_not_enough_time_values():
for ffmt in D.available_float_fmt():
D.set_float_fmt(ffmt)
print("Testing {} float format".format(D.float_fmt()))
de_mat = D.array([[0.0, 1.0],[-1.0, 0.0]])
@de.rhs_prettifier("""[vx, -x+t]""")
def rhs(t, state, k, **kwargs):
return de_mat @ state + D.array([0.0, t])
def analytic_soln(t, initial_conditions):
c1 = initial_conditions[0]
c2 = initial_conditions[1] - 1
return D.stack([
c2 * D.sin(D.to_float(D.asarray(t))) + c1 * D.cos(D.to_float(D.asarray(t))) + D.asarray(t),
c2 * D.cos(D.to_float(D.asarray(t))) - c1 * D.sin(D.to_float(D.asarray(t))) + 1
])
y_init = D.array([1., 0.])
a = de.OdeSystem(rhs, y0=y_init, dense_output=False, t=(0,), dt=0.01, rtol=D.epsilon()**0.5, atol=D.epsilon()**0.5, constants=dict(k=1.0))
a.tf = 0.0
def test_matrix_inv():
A = D.array([
[-1.0, 3 / 2],
[1.0, -1.0],
], dtype=D.float64)
Ainv = D.matrix_inv(A)
assert (D.max(D.abs(D.to_float(Ainv @ A - D.eye(2)))) <= 8 * D.epsilon())
def test_integration_and_representation():
for ffmt in D.available_float_fmt():
D.set_float_fmt(ffmt)
print("Testing {} float format".format(D.float_fmt()))
de_mat = D.array([[0.0, 1.0],[-1.0, 0.0]])
@de.rhs_prettifier("""[vx, -x+t]""")
def rhs(t, state, k, **kwargs):
return de_mat @ state + D.array([0.0, t])
def analytic_soln(t, initial_conditions):
c1 = initial_conditions[0]
c2 = initial_conditions[1] - 1
return D.stack([
c2 * D.sin(D.to_float(D.asarray(t))) + c1 * D.cos(D.to_float(D.asarray(t))) + D.asarray(t),
c2 * D.cos(D.to_float(D.asarray(t))) - c1 * D.sin(D.to_float(D.asarray(t))) + 1
])
y_init = D.array([1., 0.])
a = de.OdeSystem(rhs, y0=y_init, dense_output=True, t=(0, 2*D.pi), dt=0.01, rtol=D.epsilon()**0.5, atol=D.epsilon()**0.5, constants=dict(k=1.0))
assert(a.integration_status() == "Integration has not been run.")
a.integrate()
assert(a.integration_status() == "Integration completed successfully.")
try:
print(str(a))
print(repr(a))
assert(D.max(D.abs(a.sol(a.t[0]) - y_init)) <= 8*D.epsilon()**0.5)
assert(D.max(D.abs(a.sol(a.t[-1]) - analytic_soln(a.t[-1], y_init))) <= 8*D.epsilon()**0.5)
assert(D.max(D.abs(a.sol(a.t).T - analytic_soln(a.t, y_init))) <= 8*D.epsilon()**0.5)
except:
raise
for i in a:
assert(D.max(D.abs(i.y - analytic_soln(i.t, y_init))) <= 8*D.epsilon()**0.5)
assert(len(a.y) == len(a))
assert(len(a.t) == len(a))
def test_brentsrootvec(ffmt, tol):
print("Set dtype to:", ffmt)
D.set_float_fmt(ffmt)
if tol is not None:
tol = tol * D.epsilon()
if D.backend() == 'torch':
import torch
torch.set_printoptions(precision=17)
torch.autograd.set_detect_anomaly(True)
if ffmt == 'gdual_vdouble':
pytest.skip("Root-finding is ill-conceived with vectorised gduals")
for _ in range(10):
slope_list = D.array(
np.copysign(np.random.uniform(0.9, 1.1, size=25),
np.random.uniform(-1, 1, size=25)))
intercept_list = slope_list
gt_root_list = -intercept_list / slope_list
fun_list = [(lambda m, b: lambda x: m * x + b)(m, b)
for m, b in zip(slope_list, intercept_list)]
assert (all(
map((lambda i: D.to_numpy(D.to_float(D.abs(i))) <= 32 * D.epsilon(
)), map((lambda x: x[0](x[1])), zip(fun_list, gt_root_list)))))
root_list, success = de.utilities.optimizer.brentsrootvec(
fun_list, [D.min(gt_root_list) - 1.,
D.max(gt_root_list) + 1.],
tol,
verbose=True)
assert (np.all(D.to_numpy(success)))
assert (np.allclose(D.to_numpy(D.to_float(gt_root_list)),
D.to_numpy(D.to_float(root_list)),
32 * D.epsilon(), 32 * D.epsilon()))
assert (all(
map((lambda i: D.to_numpy(D.to_float(D.abs(i))) <= 32 * D.epsilon(
)), map((lambda x: x[0](x[1])), zip(fun_list, root_list)))))
def test_contract_first_ndims_case_2(ffmt):
arr1 = D.array([[2.0, 1.0], [1.0, 0.0]])
arr2 = D.array([[1.0, 1.0], [-1.0, 1.0]])
arr4 = D.contract_first_ndims(arr1, arr2, 2)
true_arr4 = D.array(2.)
assert (D.norm(arr4 - true_arr4) <= 2 * D.epsilon())
def test_contract_first_ndims_case_1(ffmt):
arr1 = D.array([[2.0, 1.0], [1.0, 0.0]])
arr2 = D.array([[1.0, 1.0], [-1.0, 1.0]])
arr3 = D.contract_first_ndims(arr1, arr2, 1)
true_arr3 = D.array([1.0, 1.0])
assert (D.norm(arr3 - true_arr3) <= 2 * D.epsilon())
def set_up_basic_system(integrator=None, hook_jacobian=False):
de_mat = D.array([[0.0, 1.0], [-1.0, 0.0]])
@de.rhs_prettifier("""[vx, -x+t]""")
def rhs(t, state, **kwargs):
nonlocal de_mat
if D.backend() == 'torch':
de_mat = de_mat.to(state.device)
out = de_mat @ state
out[1] += t
return out
if hook_jacobian:
def rhs_jac(t, state, **kwargs):
nonlocal de_mat
rhs.analytic_jacobian_called = True
if D.backend() == 'torch':
de_mat = de_mat.to(state.device)
return de_mat
rhs.hook_jacobian_call(rhs_jac)
def analytic_soln(t, initial_conditions):
c1 = initial_conditions[0]
c2 = initial_conditions[1] - 1.0
return D.stack([
c2 * D.sin(D.to_float(D.asarray(t))) + c1 * D.cos(D.to_float(D.asarray(t))) + D.asarray(t),
c2 * D.cos(D.to_float(D.asarray(t))) - c1 * D.sin(D.to_float(D.asarray(t))) + 1
])
y_init = D.array([1., 0.])
a = de.OdeSystem(rhs, y0=y_init, dense_output=True, t=(0, 2 * D.pi), dt=0.01, rtol=D.epsilon()**0.75,
atol=D.epsilon()**0.75)
a.set_kick_vars(D.array([0,1],dtype=D.bool))
if integrator is None:
integrator = a.method
dt = (D.epsilon() ** 0.5)**(1.0/(2+integrator.order))/(2*D.pi)
a.dt = dt
return de_mat, rhs, analytic_soln, y_init, dt, a
def test_brentsroot(ffmt, tol):
print("Set dtype to:", ffmt)
D.set_float_fmt(ffmt)
if tol is not None:
tol = tol * D.epsilon()
if D.backend() == 'torch':
import torch
torch.set_printoptions(precision=17)
torch.autograd.set_detect_anomaly(True)
for _ in range(10):
ac_prod = D.array(np.random.uniform(0.9, 1.1))
a = D.array(np.random.uniform(-1, 1))
a = D.to_float(-1 * (a <= 0) + 1 * (a > 0))
c = ac_prod / a
b = D.sqrt(0.01 + 4 * ac_prod)
gt_root = -b / (2 * a) - 0.1 / (2 * a)
ub = -b / (2 * a)
lb = -b / (2 * a) - 1.0 / (2 * a)
fun = lambda x: a * x**2 + b * x + c
assert (D.to_numpy(D.to_float(D.abs(fun(gt_root)))) <=
32 * D.epsilon())
root, success = de.utilities.optimizer.brentsroot(fun, [lb, ub],
tol,
verbose=True)
assert (success)
assert (np.allclose(D.to_numpy(D.to_float(gt_root)),
D.to_numpy(D.to_float(root)), 32 * D.epsilon(),
32 * D.epsilon()))
assert (D.to_numpy(D.to_float(D.abs(fun(root)))) <= 32 * D.epsilon())
def test_event_detection():
for ffmt in D.available_float_fmt():
if ffmt == 'float16':
continue
D.set_float_fmt(ffmt)
print("Testing event detection for float format {}".format(D.float_fmt()))
de_mat = D.array([[0.0, 1.0],[-1.0, 0.0]])
@de.rhs_prettifier("""[vx, -x+t]""")
def rhs(t, state, **kwargs):
return de_mat @ state + D.array([0.0, t])
def analytic_soln(t, initial_conditions):
c1 = initial_conditions[0]
c2 = initial_conditions[1] - 1
return D.array([
c2 * D.sin(t) + c1 * D.cos(t) + t,
c2 * D.cos(t) - c1 * D.sin(t) + 1
])
y_init = D.array([1., 0.])
def time_event(t, y, **kwargs):
return t - D.pi/8
time_event.is_terminal = True
time_event.direction = 0
a = de.OdeSystem(rhs, y0=y_init, dense_output=True, t=(0, D.pi/4), dt=0.01, rtol=D.epsilon()**0.5, atol=D.epsilon()**0.5)
with de.utilities.BlockTimer(section_label="Integrator Tests") as sttimer:
for i in sorted(set(de.available_methods(False).values()), key=lambda x:x.__name__):
try:
a.set_method(i)
print("Testing {}".format(a.integrator))
a.integrate(eta=True, events=time_event)
if D.abs(a.t[-1] - D.pi/8) > 10*D.epsilon():
print("Event detection with integrator {} failed with t[-1] = {}".format(a.integrator, a.t[-1]))
raise RuntimeError("Failed to detect event for integrator {}".format(str(i)))
else:
print("Event detection with integrator {} succeeded with t[-1] = {}".format(a.integrator, a.t[-1]))
a.reset()
except Exception as e:
raise e
raise RuntimeError("Test failed for integration method: {}".format(a.integrator))
print("")
print("{} backend test passed successfully!".format(D.backend()))
def test_integration_and_nearest_float_no_dense_output(ffmt):
D.set_float_fmt(ffmt)
if D.backend() == 'torch':
import torch
torch.set_printoptions(precision=17)
torch.autograd.set_detect_anomaly(True)
print("Testing {} float format".format(D.float_fmt()))
de_mat = D.array([[0.0, 1.0], [-1.0, 0.0]])
@de.rhs_prettifier("""[vx, -x+t]""")
def rhs(t, state, k, **kwargs):
return de_mat @ state + D.array([0.0, t])
y_init = D.array([1., 0.])
a = de.OdeSystem(rhs,
y0=y_init,
dense_output=False,
t=(0, 2 * D.pi),
dt=0.01,
rtol=D.epsilon()**0.5,
atol=D.epsilon()**0.5,
constants=dict(k=1.0))
assert (a.integration_status == "Integration has not been run.")
a.integrate()
assert (a.sol is None)
assert (a.integration_status == "Integration completed successfully.")
assert (D.abs(a.t[-2] - a[2 * D.pi].t) <= D.abs(a.dt))
def test_matrix_inv_bigger():
for diag_size in range(2, 101):
np.random.seed(15)
for trial in range(3):
A = np.random.normal(size=(diag_size, diag_size))
while np.abs(np.linalg.det(D.to_float(A))) <= 1e-5:
A = np.random.normal(size=(diag_size, diag_size), std=250.0)
A = D.array(D.cast_to_float_fmt(A))
Ainv = D.matrix_inv(A)
assert (
D.max(D.abs(D.to_float(Ainv @ A - D.eye(diag_size)))) <=
4 * D.epsilon()**0.5
), "Matrix inversion failed for diagonal with size: " + str(
diag_size)
def test_brentsrootvec():
for fmt in D.available_float_fmt():
print("Set dtype to:", fmt)
D.set_float_fmt(fmt)
if fmt == 'gdual_vdouble':
continue
for _ in range(10):
slope_list = D.array(
np.copysign(np.random.uniform(0.9, 1.1, size=25),
np.random.uniform(-1, 1, size=25)))
intercept_list = slope_list
gt_root_list = -intercept_list / slope_list
fun_list = [(lambda m, b: lambda x: m * x + b)(m, b)
for m, b in zip(slope_list, intercept_list)]
assert (all(
map((lambda i: D.to_numpy(D.to_float(D.abs(i))) <= 32 * D.
epsilon()),
map((lambda x: x[0](x[1])), zip(fun_list, gt_root_list)))))
root_list, success = de.utilities.optimizer.brentsrootvec(
fun_list, [D.min(gt_root_list) - 1.,
D.max(gt_root_list) + 1.],
4 * D.epsilon(),
verbose=True)
assert (np.all(D.to_numpy(success)))
assert (np.allclose(D.to_numpy(D.to_float(gt_root_list)),
D.to_numpy(D.to_float(root_list)),
32 * D.epsilon(), 32 * D.epsilon()))
assert (all(
map((lambda i: D.to_numpy(D.to_float(D.abs(i))) <= 32 * D.
epsilon()),
map((lambda x: x[0](x[1])), zip(fun_list, root_list)))))
def test_integration_and_representation(ffmt):
D.set_float_fmt(ffmt)
if D.backend() == 'torch':
import torch
torch.set_printoptions(precision=17)
torch.autograd.set_detect_anomaly(True)
print("Testing {} float format".format(D.float_fmt()))
from . import common
(de_mat, rhs, analytic_soln, y_init, dt, a) = common.set_up_basic_system()
assert (a.integration_status == "Integration has not been run.")
a.integrate()
assert (a.integration_status == "Integration completed successfully.")
print(str(a))
print(repr(a))
assert (D.max(D.abs(a.sol(a.t[0]) - y_init)) <= 8 * D.epsilon()**0.5)
assert (D.max(D.abs(a.sol(a.t[-1]) - analytic_soln(a.t[-1], y_init))) <=
8 * D.epsilon()**0.5)
assert (D.max(D.abs(a.sol(a.t).T - analytic_soln(a.t, y_init))) <=
8 * D.epsilon()**0.5)
for i in a:
assert (D.max(D.abs(i.y - analytic_soln(i.t, y_init))) <=
8 * D.epsilon()**0.5)
assert (len(a.y) == len(a))
assert (len(a.t) == len(a))
def test_float_formats_atypical_shape(ffmt, integrator,
use_richardson_extrapolation, device):
if use_richardson_extrapolation and integrator.__implicit__:
pytest.skip(
"Richardson Extrapolation is too slow with implicit methods")
D.set_float_fmt(ffmt)
if D.backend() == 'torch':
import torch
torch.set_printoptions(precision=17)
torch.autograd.set_detect_anomaly(False) # Enable if a test fails
device = torch.device(device)
print("Testing {} float format".format(D.float_fmt()))
from .common import set_up_basic_system
de_mat, _, analytic_soln, y_init, dt, _ = set_up_basic_system(integrator)
@de.rhs_prettifier("""[vx, -x+t]""")
def rhs(t, state, **kwargs):
nonlocal de_mat
extra = D.array([0.0, t])
if D.backend() == 'torch':
de_mat = de_mat.to(state.device)
extra = extra.to(state.device)
return D.sum(de_mat[:, :, None, None, None] * state,
axis=1) + extra[:, None, None, None]
y_init = D.array([[[[1., 0.]] * 1] * 1] * 3).T
print(rhs(0.0, y_init).shape)
if D.backend() == 'torch':
y_init = y_init.contiguous().to(device)
a = de.OdeSystem(rhs,
y0=y_init,
dense_output=False,
t=(0, D.pi / 4),
dt=D.pi / 64,
rtol=D.epsilon()**0.5,
atol=D.epsilon()**0.5)
method = integrator
method_tolerance = a.atol * 10 + D.epsilon()
if use_richardson_extrapolation:
method = de.integrators.generate_richardson_integrator(method)
method_tolerance = method_tolerance * 5
with de.utilities.BlockTimer(section_label="Integrator Tests") as sttimer:
a.set_method(method)
print("Testing {} with dt = {:.4e}".format(a.integrator, a.dt))
a.integrate(eta=True)
max_diff = D.max(D.abs(analytic_soln(a.t[-1], y_init) - a.y[-1]))
if a.integrator.adaptive:
assert max_diff <= method_tolerance, "{} Failed with max_diff from analytical solution = {}".format(
a.integrator, max_diff)
a.reset()
print("")
print("{} backend test passed successfully!".format(D.backend()))
