def test_gradients_simple_oscillator(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)
print("Testing {} float format".format(D.float_fmt()))
import torch
torch.set_printoptions(precision=17)
device = torch.device(device)
torch.autograd.set_detect_anomaly(False) # Enable if a test fails
def rhs(t, state, k, m, **kwargs):
return D.array([[0.0, 1.0], [-k / m, 0.0]], device=device) @ state
csts = dict(k=1.0, m=1.0)
T = 2 * D.pi * D.sqrt(D.array(csts['m'] / csts['k'])).to(device)
dt = max(0.5 * (D.epsilon()**0.5)**(1.0 / (max(2, integrator.order - 1))),
5e-2)
def true_solution_sho(t, initial_state, k, m):
w2 = D.array(k / m).to(device)
w = D.sqrt(w2)
A = D.sqrt(initial_state[0]**2 + initial_state[1]**2 / w2)
phi = D.atan2(-initial_state[1], w * initial_state[0])
return D.stack([A * D.cos(w * t + phi), -w * A * D.sin(w * t + phi)]).T
method = integrator
if use_richardson_extrapolation:
method = de.integrators.generate_richardson_integrator(method)
with de.utilities.BlockTimer(section_label="Integrator Tests"):
y_init = D.array([1., 1.], requires_grad=True).to(device)
a = de.OdeSystem(rhs,
y_init,
t=(0, T),
dt=T * dt,
rtol=D.epsilon()**0.5,
atol=D.epsilon()**0.5,
constants=csts)
a.set_method(method)
print("Testing {} with dt = {:.4e}".format(a.integrator, a.dt))
a.integrate(eta=True)
Jy = D.jacobian(a.y[-1], a.y[0])
true_Jy = D.jacobian(true_solution_sho(a.t[-1], a.y[0], **csts),
a.y[0])
print(a.integrator.adaptive,
D.mean(D.abs(D.stack(a.t[1:]) - D.stack(a.t[:-1]))),
D.norm(true_Jy - Jy),
D.epsilon()**0.5)
if a.integrator.adaptive:
assert (D.allclose(true_Jy,
Jy,
rtol=4 * a.rtol**0.75,
atol=4 * a.atol**0.75))
print("{} method test succeeded!".format(a.integrator))
print("")
print("{} backend test passed successfully!".format(D.backend()))
def test_gradients_simple_decay(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 integrator.__symplectic__:
pytest.skip(
"Exponential decay system is not in the form compatible with symplectic integrators"
)
print("Testing {} float format".format(D.float_fmt()))
import torch
torch.set_printoptions(precision=17)
device = torch.device(device)
torch.autograd.set_detect_anomaly(False) # Enable if a test fails
def rhs(t, state, k, **kwargs):
return -k * state
def rhs_jac(t, state, k, **kwargs):
return -k
rhs.jac = rhs_jac
y_init = D.array(5.0, requires_grad=True)
csts = dict(k=D.array(1.0, device=device))
dt = max(0.5 * (D.epsilon()**0.5)**(1.0 / (max(2, integrator.order - 1))),
5e-2)
def true_solution_decay(t, initial_state, k):
return initial_state * D.exp(-k * t)
method = integrator
if use_richardson_extrapolation:
method = de.integrators.generate_richardson_integrator(method)
with de.utilities.BlockTimer(section_label="Integrator Tests"):
y_init = D.ones((1, ), requires_grad=True).to(device)
y_init = y_init * D.e
a = de.OdeSystem(rhs,
y_init,
t=(0, 1.0),
dt=dt,
rtol=D.epsilon()**0.5,
atol=D.epsilon()**0.5,
constants=csts)
a.set_method(method)
print("Testing {} with dt = {:.4e}".format(a.integrator, a.dt))
a.integrate(eta=True)
Jy = D.jacobian(a.y[-1], a.y[0])
true_Jy = D.jacobian(true_solution_decay(a.t[-1], a.y[0], **csts),
a.y[0])
print(a.y[-1], true_solution_decay(a.t[-1], a.y[0], **csts),
D.abs(a.y[-1] - true_solution_decay(a.t[-1], a.y[0], **csts)))
print(a.integrator.adaptive,
D.mean(D.abs(D.stack(a.t[1:]) - D.stack(a.t[:-1]))),
D.norm(true_Jy - Jy), 32 * a.rtol)
print(a.integrator.adaptive, true_Jy, Jy)
if a.integrator.adaptive:
assert (D.allclose(true_Jy, Jy, rtol=32 * a.rtol,
atol=32 * a.atol))
print("{} method test succeeded!".format(a.integrator))
print("")
print("{} backend test passed successfully!".format(D.backend()))
def test_gradients_complex(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)
print("Testing {} float format".format(D.float_fmt()))
import torch
torch.set_printoptions(precision=17)
device = torch.device(device)
torch.autograd.set_detect_anomaly(False) # Enable if a test fails
class NNController(torch.nn.Module):
def __init__(self,
in_dim=2,
out_dim=2,
inter_dim=50,
append_time=False):
super().__init__()
self.append_time = append_time
self.net = torch.nn.Sequential(
torch.nn.Linear(in_dim + (1 if append_time else 0), inter_dim),
torch.nn.Softplus(), torch.nn.Linear(inter_dim, out_dim),
torch.nn.Sigmoid())
for idx, m in enumerate(self.net.modules()):
if isinstance(m, torch.nn.Linear):
torch.nn.init.xavier_normal_(m.weight, gain=1.0)
torch.nn.init.constant_(m.bias, 0.0)
def forward(self, t, y, dy):
if self.append_time:
return self.net(
torch.cat([y.view(-1), dy.view(-1),
t.view(-1)]))
else:
return self.net(torch.cat([y, dy]))
class SimpleODE(torch.nn.Module):
def __init__(self, inter_dim=10, k=1.0):
super().__init__()
self.nn_controller = NNController(in_dim=4,
out_dim=1,
inter_dim=inter_dim)
self.A = torch.nn.Parameter(
torch.tensor([[0.0, 1.0], [-k, -1.0]], requires_grad=False))
def forward(self, t, y, params=None):
if not isinstance(t, torch.Tensor):
torch_t = torch.tensor(t)
else:
torch_t = t
if not isinstance(y, torch.Tensor):
torch_y = torch.tensor(y)
else:
torch_y = y
if params is not None:
if not isinstance(params, torch.Tensor):
torch_params = torch.tensor(params)
else:
torch_params = params
dy = torch.matmul(self.A, torch_y)
controller_effect = self.nn_controller(
torch_t, torch_y, dy) if params is None else params
return dy + torch.cat(
[torch.tensor([0.0]).to(dy), (controller_effect * 2.0 - 1.0)])
method = integrator
if use_richardson_extrapolation:
method = de.integrators.generate_richardson_integrator(method)
with de.utilities.BlockTimer(section_label="Integrator Tests"):
yi1 = D.array([1.0, 0.0], requires_grad=True).to(device)
df = SimpleODE(k=1.0)
a = de.OdeSystem(df,
yi1,
t=(0, 0.1),
dt=1e-3,
rtol=D.epsilon()**0.5,
atol=D.epsilon()**0.5)
a.set_method(method)
print("Testing {} with dt = {:.4e}".format(a.integrator, a.dt))
a.integrate(eta=True)
dyfdyi = D.jacobian(a.y[-1], a.y[0])
dyi = D.array([0.0, 1.0]).to(device) * D.epsilon()**0.5
dyf = D.einsum("nk,k->n", dyfdyi, dyi)
yi2 = yi1 + dyi
print(a.y[-1].device)
b = de.OdeSystem(df,
yi2,
t=(0, a.t[-1]),
dt=a.dt,
rtol=a.rtol,
atol=a.atol)
b.set_method(method)
b.integrate(eta=True)
true_diff = b.y[-1] - a.y[-1]
print(D.norm(true_diff - dyf), D.epsilon()**0.5)
assert (D.allclose(true_diff, dyf, rtol=4 * a.rtol, atol=4 * a.atol))
print("{} method test succeeded!".format(a.integrator))
print("")
print("{} backend test passed successfully!".format(D.backend()))
def test_gradients_unbatched():
a = D.array([1.0, 1.0], requires_grad=True)
assert (D.all(
D.jacobian(a, a, batch_mode=False) == D.array([[1.0, 0.0], [0.0, 1.0]
])))
def test_gradients_higher_nu():
a = D.array([1.0], requires_grad=True)
b = a * a
assert (D.all(D.jacobian(b, a, nu=2, batch_mode=False) == D.array([2.0])))
def test_gradients_batched():
a = D.array([[1.0, 1.0], [1.0, 1.0]], requires_grad=True)
b = a * D.array([[1.0, 1.0], [2.0, 2.0]])
assert (D.all(
D.jacobian(b, a, batch_mode=True) == D.array(
[[[1.0, 0.0], [2.0, 0.0]], [[0.0, 1.0], [0.0, 2.0]]])))
def test_gradients_no_grad():
a = D.array([1.0, 1.0], requires_grad=False)
assert (D.all(D.jacobian(a, a) == D.array([[0.0, 0.0], [0.0, 0.0]])))
def test_gradients_nu_zero():
a = D.array([1.0, 1.0], requires_grad=True)
assert (D.all(a == D.jacobian(a, a, nu=0)))
def test_gradients_wrong_nu():
with pytest.raises(ValueError):
a = D.array([1.0, 1.0], requires_grad=True)
D.jacobian(a, a, nu=-1)
def test_gradients():
for ffmt in D.available_float_fmt():
D.set_float_fmt(ffmt)
print("Testing {} float format".format(D.float_fmt()))
import torch
torch.set_printoptions(precision=17)
torch.set_num_threads(1)
torch.autograd.set_detect_anomaly(True)
class NNController(torch.nn.Module):
def __init__(self,
in_dim=2,
out_dim=2,
inter_dim=50,
append_time=False):
super().__init__()
self.append_time = append_time
self.net = torch.nn.Sequential(
torch.nn.Linear(in_dim + (1 if append_time else 0),
inter_dim), torch.nn.Softplus(),
torch.nn.Linear(inter_dim, out_dim), torch.nn.Sigmoid())
for idx, m in enumerate(self.net.modules()):
if isinstance(m, torch.nn.Linear):
torch.nn.init.xavier_normal_(m.weight, gain=1.0)
torch.nn.init.constant_(m.bias, 0.0)
def forward(self, t, y, dy):
if self.append_time:
return self.net(
torch.cat([y.view(-1),
dy.view(-1),
t.view(-1)]))
else:
return self.net(torch.cat([y, dy]))
class SimpleODE(torch.nn.Module):
def __init__(self, inter_dim=10, k=1.0):
super().__init__()
self.nn_controller = NNController(in_dim=4,
out_dim=1,
inter_dim=inter_dim)
self.A = torch.tensor([[0.0, 1.0], [-k, -1.0]],
requires_grad=True)
def forward(self, t, y, params=None):
if not isinstance(t, torch.Tensor):
torch_t = torch.tensor(t)
else:
torch_t = t
if not isinstance(y, torch.Tensor):
torch_y = torch.tensor(y)
else:
torch_y = y
if params is not None:
if not isinstance(params, torch.Tensor):
torch_params = torch.tensor(params)
else:
torch_params = params
dy = torch.matmul(self.A, torch_y)
controller_effect = self.nn_controller(
torch_t, torch_y, dy) if params is None else params
return dy + torch.cat(
[torch.tensor([0.0]), (controller_effect * 2.0 - 1.0)])
with de.utilities.BlockTimer(section_label="Integrator Tests"):
for i in sorted(set(de.available_methods(False).values()),
key=lambda x: x.__name__):
try:
yi1 = D.array([1.0, 0.0], requires_grad=True)
df = SimpleODE(k=1.0)
a = de.OdeSystem(df,
yi1,
t=(0, 1.),
dt=0.0675,
rtol=D.epsilon()**0.5,
atol=D.epsilon()**0.5)
a.set_method(i)
a.integrate(eta=True)
dyfdyi = D.jacobian(a.y[-1], a.y[0])
dyi = D.array([0.0, 1.0]) * D.epsilon()**0.5
dyf = D.einsum("nk,k->n", dyfdyi, dyi)
yi2 = yi1 + dyi
b = de.OdeSystem(df,
yi2,
t=(0, 1.),
dt=0.0675,
rtol=D.epsilon()**0.5,
atol=D.epsilon()**0.5)
b.set_method(i)
b.integrate(eta=True)
true_diff = b.y[-1] - a.y[-1]
print(D.norm(true_diff - dyf), D.epsilon()**0.5)
assert (D.allclose(true_diff,
dyf,
rtol=4 * D.epsilon()**0.5,
atol=4 * D.epsilon()**0.5))
print("{} method test succeeded!".format(a.integrator))
except:
raise RuntimeError(
"Test failed for integration method: {}".format(
a.integrator))
print("")
print("{} backend test passed successfully!".format(D.backend()))
