def test_warmup_adapter(jitted):
def find_reasonable_step_size(m_inv, z, rng, step_size):
return np.where(step_size < 1, step_size * 4, step_size / 4)
num_steps = 150
adaptation_schedule = build_adaptation_schedule(num_steps)
init_step_size = 1.
mass_matrix_size = 3
wa_init, wa_update = warmup_adapter(num_steps, find_reasonable_step_size)
wa_update = jit(wa_update) if jitted else wa_update
rng = random.PRNGKey(0)
z = np.ones(3)
wa_state = wa_init(z, rng, init_step_size, mass_matrix_size=mass_matrix_size)
step_size, inverse_mass_matrix, _, _, _, window_idx, _ = wa_state
assert step_size == find_reasonable_step_size(inverse_mass_matrix, z, rng, init_step_size)
assert_allclose(inverse_mass_matrix, np.ones(mass_matrix_size))
assert window_idx == 0
window = adaptation_schedule[0]
for t in range(window.start, window.end + 1):
wa_state = wa_update(t, 0.7 + 0.1 * t / (window.end - window.start), z, wa_state)
last_step_size = step_size
step_size, inverse_mass_matrix, _, _, _, window_idx, _ = wa_state
assert window_idx == 1
# step_size is decreased because accept_prob < target_accept_prob
assert step_size < last_step_size
# inverse_mass_matrix does not change at the end of the first window
assert_allclose(inverse_mass_matrix, np.ones(mass_matrix_size))
window = adaptation_schedule[1]
window_len = window.end - window.start
for t in range(window.start, window.end + 1):
wa_state = wa_update(t, 0.8 + 0.1 * (t - window.start) / window_len, 2 * z, wa_state)
last_step_size = step_size
step_size, inverse_mass_matrix, _, _, _, window_idx, _ = wa_state
assert window_idx == 2
# step_size is increased because accept_prob > target_accept_prob
assert step_size > last_step_size
# Verifies that inverse_mass_matrix changes at the end of the second window.
# Because z_flat is constant during the second window, covariance will be 0
# and only regularize_term of welford scheme is involved.
# This also verifies that z_flat terms in the first window does not affect
# the second window.
welford_regularize_term = 1e-3 * (5 / (window.end + 1 - window.start + 5))
assert_allclose(inverse_mass_matrix,
np.full((mass_matrix_size,), welford_regularize_term),
atol=1e-7)
window = adaptation_schedule[2]
for t in range(window.start, window.end + 1):
wa_state = wa_update(t, 0.8, t * z, wa_state)
last_step_size = step_size
step_size, final_inverse_mass_matrix, _, _, _, window_idx, _ = wa_state
assert window_idx == 3
# during the last window, because target_accept_prob=0.8,
# log_step_size will be equal to the constant prox_center=log(10*last_step_size)
assert_allclose(step_size, last_step_size * 10)
# Verifies that inverse_mass_matrix does not change during the last window
# despite z_flat changes w.r.t time t,
assert_allclose(final_inverse_mass_matrix, inverse_mass_matrix)
def test_build_adaptation_schedule(num_steps, expected):
adaptation_schedule = build_adaptation_schedule(num_steps)
expected_schedule = [AdaptWindow(i, j) for i, j in expected]
assert adaptation_schedule == expected_schedule